chore(deps): bump langchain-community in /application

Bumps [langchain-community](https://github.com/langchain-ai/langchain) from 0.3.19 to 0.4.1.
- [Release notes](https://github.com/langchain-ai/langchain/releases)
- [Commits](https://github.com/langchain-ai/langchain/commits)

---
updated-dependencies:
- dependency-name: langchain-community
  dependency-version: 0.4.1
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

.devcontainer/Dockerfile

@@ -0,0 +1,15 @@
+FROM python:3.12-bookworm
+
+# Install Node.js 20.x
+RUN curl -fsSL https://deb.nodesource.com/setup_20.x | bash - \
+    && apt-get install -y nodejs \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install global npm packages
+RUN npm install -g husky vite
+
+# Create and activate Python virtual environment
+RUN python -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+WORKDIR /workspace

.devcontainer/devc-welcome.md

@@ -0,0 +1,49 @@
+# Welcome to DocsGPT Devcontainer
+
+Welcome to the DocsGPT development environment! This guide will help you get started quickly.
+
+## Starting Services
+
+To run DocsGPT, you need to start three main services: Flask (backend), Celery (task queue), and Vite (frontend). Here are the commands to start each service within the devcontainer:
+
+### Vite (Frontend)
+
+```bash
+cd frontend
+npm run dev -- --host
+```
+
+### Flask (Backend)
+
+```bash
+flask --app application/app.py run --host=0.0.0.0 --port=7091
+```
+
+### Celery (Task Queue)
+
+```bash
+celery -A application.app.celery worker -l INFO
+```
+
+## Github Codespaces Instructions
+
+### 1. Make Ports Public:
+
+Go to the "Ports" panel in Codespaces (usually located at the bottom of the VS Code window).
+
+For both port 5173 and 7091, right-click on the port and select "Make Public".
+
+![CleanShot 2025-02-12 at 09 46 14@2x](https://github.com/user-attachments/assets/00a34b16-a7ef-47af-9648-87a7e3008475)
+
+
+ ### 2. Update VITE_API_HOST:
+
+After making port 7091 public, copy the public URL provided by Codespaces for port 7091.
+
+Open the file frontend/.env.development.
+
+Find the line VITE_API_HOST=http://localhost:7091.
+
+Replace http://localhost:7091 with the public URL you copied from Codespaces.
+
+![CleanShot 2025-02-12 at 09 46 56@2x](https://github.com/user-attachments/assets/c472242f-1079-4cd8-bc0b-2d78db22b94c)

.devcontainer/devcontainer.json

@@ -0,0 +1,24 @@
+{
+	"name": "DocsGPT Dev Container",
+	"dockerComposeFile": ["docker-compose-dev.yaml", "docker-compose.override.yaml"],
+	"service": "dev",
+	"workspaceFolder": "/workspace",
+	"postCreateCommand": ".devcontainer/post-create-command.sh",
+	"forwardPorts": [7091, 5173, 6379, 27017],
+	"customizations": {
+	  "vscode": {
+		"extensions": [
+		  "ms-python.python",
+		  "ms-toolsai.jupyter",
+		  "esbenp.prettier-vscode",
+		  "dbaeumer.vscode-eslint"
+		]
+	  },
+	  "codespaces": {
+			"openFiles": [
+			".devcontainer/devc-welcome.md",
+			"CONTRIBUTING.md"
+			]
+		}
+	}
+  }

.devcontainer/docker-compose.override.yaml

@@ -0,0 +1,40 @@
+version: '3.8'
+
+services:
+  dev:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    volumes:
+      - ../:/workspace:cached
+    command: sleep infinity
+    depends_on:
+      redis:
+        condition: service_healthy
+      mongo:
+        condition: service_healthy
+    environment:
+      - CELERY_BROKER_URL=redis://redis:6379/0
+      - CELERY_RESULT_BACKEND=redis://redis:6379/1
+      - MONGO_URI=mongodb://mongo:27017/docsgpt
+      - CACHE_REDIS_URL=redis://redis:6379/2
+    networks:
+      - default
+
+  redis:
+    healthcheck:
+      test: ["CMD", "redis-cli", "ping"]
+      interval: 5s
+      timeout: 30s
+      retries: 5
+
+  mongo:
+    healthcheck:
+      test: ["CMD", "mongosh", "--eval", "db.adminCommand('ping')"]
+      interval: 5s
+      timeout: 30s
+      retries: 5
+
+networks:
+  default:
+    name: docsgpt-dev-network

.devcontainer/post-create-command.sh

@@ -0,0 +1,32 @@
+#!/bin/bash
+
+set -e  # Exit immediately if a command exits with a non-zero status
+
+if [ ! -f frontend/.env.development ]; then
+  cp -n .env-template frontend/.env.development || true # Assuming .env-template is in the root
+fi
+
+# Determine VITE_API_HOST based on environment
+if [ -n "$CODESPACES" ]; then
+  # Running in Codespaces
+  CODESPACE_NAME=$(echo "$CODESPACES" | cut -d'-' -f1) # Extract codespace name
+  PUBLIC_API_HOST="https://${CODESPACE_NAME}-7091.${GITHUB_CODESPACES_PORT_FORWARDING_DOMAIN}"
+  echo "Setting VITE_API_HOST for Codespaces: $PUBLIC_API_HOST in frontend/.env.development"
+  sed -i "s|VITE_API_HOST=.*|VITE_API_HOST=$PUBLIC_API_HOST|" frontend/.env.development
+else
+  # Not running in Codespaces (local devcontainer)
+  DEFAULT_API_HOST="http://localhost:7091"
+  echo "Setting VITE_API_HOST for local dev: $DEFAULT_API_HOST in frontend/.env.development"
+  sed -i "s|VITE_API_HOST=.*|VITE_API_HOST=$DEFAULT_API_HOST|" frontend/.env.development
+fi
+
+
+mkdir -p model
+if [ ! -d model/all-mpnet-base-v2 ]; then
+    wget -q https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip -O model/mpnet-base-v2.zip
+    unzip -q model/mpnet-base-v2.zip -d model
+    rm model/mpnet-base-v2.zip
+fi
+pip install -r application/requirements.txt
+cd frontend
+npm install --include=dev

.gitattributes

@@ -0,0 +1,2 @@
+# Auto detect text files and perform LF normalization
+* text=auto

.github/FUNDING.yml

@@ -0,0 +1,3 @@
+# These are supported funding model platforms
+
+github: arc53

.github/dependabot.yml

@@ -8,12 +8,12 @@ updates:
   - package-ecosystem: "pip" # See documentation for possible values
     directory: "/application" # Location of package manifests
     schedule:
-      interval: "weekly"
+      interval: "daily"
   - package-ecosystem: "npm" # See documentation for possible values
     directory: "/frontend" # Location of package manifests
     schedule:
-      interval: "weekly"
+      interval: "daily"
   - package-ecosystem: "github-actions"
     directory: "/"
     schedule:
-      interval: "weekly"
+      interval: "daily"

.github/workflows/bandit.yaml

@@ -0,0 +1,40 @@
+name: Bandit Security Scan
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    types: [opened, synchronize, reopened]
+
+jobs:
+  bandit_scan:
+    if: ${{ github.repository == 'arc53/DocsGPT' }}
+    runs-on: ubuntu-latest
+    permissions:
+      security-events: write
+      actions: read
+      contents: read
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.12'
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install bandit  # Bandit is needed for this action
+          if [ -f application/requirements.txt ]; then pip install -r application/requirements.txt; fi
+
+      - name: Run Bandit scan
+        uses: PyCQA/bandit-action@v1
+        with:
+          severity: medium
+          confidence: medium
+          targets: application/
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/ci.yml

@@ -5,20 +5,33 @@ on:
     types: [published]
 
 jobs:
-  deploy:
+  build:
     if: github.repository == 'arc53/DocsGPT'
-    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        include:
+          - platform: linux/amd64
+            runner: ubuntu-latest
+            suffix: amd64
+          - platform: linux/arm64
+            runner: ubuntu-24.04-arm
+            suffix: arm64
+    runs-on: ${{ matrix.runner }}
     permissions:
       contents: read
       packages: write
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up QEMU
+      - name: Set up QEMU  # Only needed for emulation, not for native arm64 builds
+        if: matrix.platform == 'linux/arm64'
         uses: docker/setup-qemu-action@v3
 
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
+        with:
+          driver: docker-container
+          install: true
 
       - name: Login to DockerHub
         uses: docker/login-action@v3
@@ -33,15 +46,67 @@ jobs:
           username: ${{ github.repository_owner }}
           password: ${{ secrets.GITHUB_TOKEN }}
 
-      - name: Build and push Docker images to docker.io and ghcr.io
+      - name: Build and push platform-specific images
         uses: docker/build-push-action@v6
         with:
           file: './application/Dockerfile'
-          platforms: linux/amd64
+          platforms: ${{ matrix.platform }}
           context: ./application
           push: true
           tags: |
-            ${{ secrets.DOCKER_USERNAME }}/docsgpt:${{ github.event.release.tag_name }},${{ secrets.DOCKER_USERNAME }}/docsgpt:latest
-            ghcr.io/${{ github.repository_owner }}/docsgpt:${{ github.event.release.tag_name }},ghcr.io/${{ github.repository_owner }}/docsgpt:latest
+            ${{ secrets.DOCKER_USERNAME }}/docsgpt:${{ github.event.release.tag_name }}-${{ matrix.suffix }}
+            ghcr.io/${{ github.repository_owner }}/docsgpt:${{ github.event.release.tag_name }}-${{ matrix.suffix }}
+          provenance: false
+          sbom: false
           cache-from: type=registry,ref=${{ secrets.DOCKER_USERNAME }}/docsgpt:latest
           cache-to: type=inline
+
+  manifest:
+    if: github.repository == 'arc53/DocsGPT'
+    needs: build
+    runs-on: ubuntu-latest
+    permissions:
+      packages: write
+    steps:
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+        with:
+          driver: docker-container
+          install: true
+
+      - name: Login to DockerHub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_PASSWORD }}
+
+      - name: Login to ghcr.io
+        uses: docker/login-action@v3
+        with:
+          registry: ghcr.io
+          username: ${{ github.repository_owner }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Create and push manifest for DockerHub
+        run: |
+          set -e
+          docker manifest create ${{ secrets.DOCKER_USERNAME }}/docsgpt:${{ github.event.release.tag_name }} \
+            --amend ${{ secrets.DOCKER_USERNAME }}/docsgpt:${{ github.event.release.tag_name }}-amd64 \
+            --amend ${{ secrets.DOCKER_USERNAME }}/docsgpt:${{ github.event.release.tag_name }}-arm64
+          docker manifest push ${{ secrets.DOCKER_USERNAME }}/docsgpt:${{ github.event.release.tag_name }}
+          docker manifest create ${{ secrets.DOCKER_USERNAME }}/docsgpt:latest \
+            --amend ${{ secrets.DOCKER_USERNAME }}/docsgpt:${{ github.event.release.tag_name }}-amd64 \
+            --amend ${{ secrets.DOCKER_USERNAME }}/docsgpt:${{ github.event.release.tag_name }}-arm64
+          docker manifest push ${{ secrets.DOCKER_USERNAME }}/docsgpt:latest
+
+      - name: Create and push manifest for ghcr.io
+        run: |
+          set -e
+          docker manifest create ghcr.io/${{ github.repository_owner }}/docsgpt:${{ github.event.release.tag_name }} \
+            --amend ghcr.io/${{ github.repository_owner }}/docsgpt:${{ github.event.release.tag_name }}-amd64 \
+            --amend ghcr.io/${{ github.repository_owner }}/docsgpt:${{ github.event.release.tag_name }}-arm64
+          docker manifest push ghcr.io/${{ github.repository_owner }}/docsgpt:${{ github.event.release.tag_name }}
+          docker manifest create ghcr.io/${{ github.repository_owner }}/docsgpt:latest \
+            --amend ghcr.io/${{ github.repository_owner }}/docsgpt:${{ github.event.release.tag_name }}-amd64 \
+            --amend ghcr.io/${{ github.repository_owner }}/docsgpt:${{ github.event.release.tag_name }}-arm64
+          docker manifest push ghcr.io/${{ github.repository_owner }}/docsgpt:latest

.github/workflows/cife.yml

@@ -5,20 +5,33 @@ on:
     types: [published]
 
 jobs:
-  deploy:
+  build:
     if: github.repository == 'arc53/DocsGPT'
-    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        include:
+          - platform: linux/amd64
+            runner: ubuntu-latest
+            suffix: amd64
+          - platform: linux/arm64
+            runner: ubuntu-24.04-arm
+            suffix: arm64
+    runs-on: ${{ matrix.runner }}
     permissions:
       contents: read
       packages: write
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up QEMU
+      - name: Set up QEMU # Only needed for emulation, not for native arm64 builds
+        if: matrix.platform == 'linux/arm64'
         uses: docker/setup-qemu-action@v3
 
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
+        with:
+          driver: docker-container
+          install: true
 
       - name: Login to DockerHub
         uses: docker/login-action@v3
@@ -33,16 +46,67 @@ jobs:
           username: ${{ github.repository_owner }}
           password: ${{ secrets.GITHUB_TOKEN }}
 
-      # Runs a single command using the runners shell
-      - name: Build and push Docker images to docker.io and ghcr.io
+      - name: Build and push platform-specific images
         uses: docker/build-push-action@v6
         with:
           file: './frontend/Dockerfile'
-          platforms: linux/amd64, linux/arm64
+          platforms: ${{ matrix.platform }}
           context: ./frontend
           push: true
           tags: |
-            ${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:${{ github.event.release.tag_name }},${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:latest
-            ghcr.io/${{ github.repository_owner }}/docsgpt-fe:${{ github.event.release.tag_name }},ghcr.io/${{ github.repository_owner }}/docsgpt-fe:latest
+            ${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:${{ github.event.release.tag_name }}-${{ matrix.suffix }}
+            ghcr.io/${{ github.repository_owner }}/docsgpt-fe:${{ github.event.release.tag_name }}-${{ matrix.suffix }}
+          provenance: false
+          sbom: false
           cache-from: type=registry,ref=${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:latest
           cache-to: type=inline
+
+  manifest:
+    if: github.repository == 'arc53/DocsGPT'
+    needs: build
+    runs-on: ubuntu-latest
+    permissions:
+      packages: write
+    steps:
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+        with:
+          driver: docker-container
+          install: true
+
+      - name: Login to DockerHub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_PASSWORD }}
+
+      - name: Login to ghcr.io
+        uses: docker/login-action@v3
+        with:
+          registry: ghcr.io
+          username: ${{ github.repository_owner }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Create and push manifest for DockerHub
+        run: |
+          set -e
+          docker manifest create ${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:${{ github.event.release.tag_name }} \
+            --amend ${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:${{ github.event.release.tag_name }}-amd64 \
+            --amend ${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:${{ github.event.release.tag_name }}-arm64
+          docker manifest push ${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:${{ github.event.release.tag_name }}
+          docker manifest create ${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:latest \
+            --amend ${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:${{ github.event.release.tag_name }}-amd64 \
+            --amend ${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:${{ github.event.release.tag_name }}-arm64
+          docker manifest push ${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:latest
+
+      - name: Create and push manifest for ghcr.io
+        run: |
+          set -e
+          docker manifest create ghcr.io/${{ github.repository_owner }}/docsgpt-fe:${{ github.event.release.tag_name }} \
+            --amend ghcr.io/${{ github.repository_owner }}/docsgpt-fe:${{ github.event.release.tag_name }}-amd64 \
+            --amend ghcr.io/${{ github.repository_owner }}/docsgpt-fe:${{ github.event.release.tag_name }}-arm64
+          docker manifest push ghcr.io/${{ github.repository_owner }}/docsgpt-fe:${{ github.event.release.tag_name }}
+          docker manifest create ghcr.io/${{ github.repository_owner }}/docsgpt-fe:latest \
+            --amend ghcr.io/${{ github.repository_owner }}/docsgpt-fe:${{ github.event.release.tag_name }}-amd64 \
+            --amend ghcr.io/${{ github.repository_owner }}/docsgpt-fe:${{ github.event.release.tag_name }}-arm64
+          docker manifest push ghcr.io/${{ github.repository_owner }}/docsgpt-fe:latest

.github/workflows/docker-develop-build.yml

@@ -1,4 +1,4 @@
-name: Build and push DocsGPT Docker image for development
+name: Build and push multi-arch DocsGPT Docker image
 
 on:
   workflow_dispatch:
@@ -7,27 +7,36 @@ on:
       - main
 
 jobs:
-  deploy:
+  build:
     if: github.repository == 'arc53/DocsGPT'
-    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        include:
+          - platform: linux/amd64
+            runner: ubuntu-latest
+            suffix: amd64
+          - platform: linux/arm64
+            runner: ubuntu-24.04-arm
+            suffix: arm64
+    runs-on: ${{ matrix.runner }}
     permissions:
       contents: read
       packages: write
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up QEMU
-        uses: docker/setup-qemu-action@v3
-
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
+        with:
+          driver: docker-container
+          install: true
 
       - name: Login to DockerHub
         uses: docker/login-action@v3
         with:
           username: ${{ secrets.DOCKER_USERNAME }}
           password: ${{ secrets.DOCKER_PASSWORD }}
-
+      
       - name: Login to ghcr.io
         uses: docker/login-action@v3
         with:
@@ -35,15 +44,57 @@ jobs:
           username: ${{ github.repository_owner }}
           password: ${{ secrets.GITHUB_TOKEN }}
 
-      - name: Build and push Docker images to docker.io and ghcr.io
+      - name: Build and push platform-specific images
         uses: docker/build-push-action@v6
         with:
           file: './application/Dockerfile'
-          platforms: linux/amd64
+          platforms: ${{ matrix.platform }}
           context: ./application
           push: true
           tags: |
-            ${{ secrets.DOCKER_USERNAME }}/docsgpt:develop
-            ghcr.io/${{ github.repository_owner }}/docsgpt:develop
+            ${{ secrets.DOCKER_USERNAME }}/docsgpt:develop-${{ matrix.suffix }}
+            ghcr.io/${{ github.repository_owner }}/docsgpt:develop-${{ matrix.suffix }}
+          provenance: false
+          sbom: false
           cache-from: type=registry,ref=${{ secrets.DOCKER_USERNAME }}/docsgpt:develop
           cache-to: type=inline
+
+  manifest:
+    if: github.repository == 'arc53/DocsGPT'
+    needs: build
+    runs-on: ubuntu-latest
+    permissions:
+      packages: write
+    steps:
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+        with:
+          driver: docker-container
+          install: true
+
+      - name: Login to DockerHub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_PASSWORD }}
+      
+      - name: Login to ghcr.io
+        uses: docker/login-action@v3
+        with:
+          registry: ghcr.io
+          username: ${{ github.repository_owner }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+        
+      - name: Create and push manifest for DockerHub
+        run: |
+          docker manifest create ${{ secrets.DOCKER_USERNAME }}/docsgpt:develop \
+            --amend ${{ secrets.DOCKER_USERNAME }}/docsgpt:develop-amd64 \
+            --amend ${{ secrets.DOCKER_USERNAME }}/docsgpt:develop-arm64
+          docker manifest push ${{ secrets.DOCKER_USERNAME }}/docsgpt:develop
+
+      - name: Create and push manifest for ghcr.io
+        run: |
+          docker manifest create ghcr.io/${{ github.repository_owner }}/docsgpt:develop \
+            --amend ghcr.io/${{ github.repository_owner }}/docsgpt:develop-amd64 \
+            --amend ghcr.io/${{ github.repository_owner }}/docsgpt:develop-arm64
+          docker manifest push ghcr.io/${{ github.repository_owner }}/docsgpt:develop

.github/workflows/docker-develop-fe-build.yml

@@ -7,20 +7,33 @@ on:
       - main
 
 jobs:
-  deploy:
+  build:
     if: github.repository == 'arc53/DocsGPT'
-    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        include:
+          - platform: linux/amd64
+            runner: ubuntu-latest
+            suffix: amd64
+          - platform: linux/arm64
+            runner: ubuntu-24.04-arm
+            suffix: arm64
+    runs-on: ${{ matrix.runner }}
     permissions:
       contents: read
       packages: write
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up QEMU
+      - name: Set up QEMU # Only needed for emulation, not for native arm64 builds
+        if: matrix.platform == 'linux/arm64'
         uses: docker/setup-qemu-action@v3
 
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
+        with:
+          driver: docker-container
+          install: true
 
       - name: Login to DockerHub
         uses: docker/login-action@v3
@@ -35,15 +48,57 @@ jobs:
           username: ${{ github.repository_owner }}
           password: ${{ secrets.GITHUB_TOKEN }}
 
-      - name: Build and push Docker images to docker.io and ghcr.io
+      - name: Build and push platform-specific images
         uses: docker/build-push-action@v6
         with:
           file: './frontend/Dockerfile'
-          platforms: linux/amd64
+          platforms: ${{ matrix.platform }}
           context: ./frontend
           push: true
           tags: |
-            ${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:develop
-            ghcr.io/${{ github.repository_owner }}/docsgpt-fe:develop
+            ${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:develop-${{ matrix.suffix }}
+            ghcr.io/${{ github.repository_owner }}/docsgpt-fe:develop-${{ matrix.suffix }}
+          provenance: false
+          sbom: false
           cache-from: type=registry,ref=${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:develop
           cache-to: type=inline
+
+  manifest:
+    if: github.repository == 'arc53/DocsGPT'
+    needs: build
+    runs-on: ubuntu-latest
+    permissions:
+      packages: write
+    steps:
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+        with:
+          driver: docker-container
+          install: true
+
+      - name: Login to DockerHub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_PASSWORD }}
+
+      - name: Login to ghcr.io
+        uses: docker/login-action@v3
+        with:
+          registry: ghcr.io
+          username: ${{ github.repository_owner }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Create and push manifest for DockerHub
+        run: |
+          docker manifest create ${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:develop \
+            --amend ${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:develop-amd64 \
+            --amend ${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:develop-arm64
+          docker manifest push ${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:develop
+
+      - name: Create and push manifest for ghcr.io
+        run: |
+          docker manifest create ghcr.io/${{ github.repository_owner }}/docsgpt-fe:develop \
+            --amend ghcr.io/${{ github.repository_owner }}/docsgpt-fe:develop-amd64 \
+            --amend ghcr.io/${{ github.repository_owner }}/docsgpt-fe:develop-arm64
+          docker manifest push ghcr.io/${{ github.repository_owner }}/docsgpt-fe:develop

.github/workflows/pytest.yml

@@ -6,7 +6,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.11"]
+        python-version: ["3.12"]
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
@@ -16,15 +16,15 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install pytest pytest-cov
           cd application
           if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+          cd ../tests
+          if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
       - name: Test with pytest and generate coverage report
         run: |
-          python -m pytest --cov=application --cov-report=xml
+          python -m pytest --cov=application --cov-report=xml --cov-report=term-missing
       - name: Upload coverage reports to Codecov
-        if: github.event_name == 'pull_request' && matrix.python-version == '3.11'
-        uses: codecov/codecov-action@v4
+        if: github.event_name == 'pull_request' && matrix.python-version == '3.12'
+        uses: codecov/codecov-action@v5
         env:
           CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
-

.gitignore

@@ -113,6 +113,7 @@ venv.bak/
 # Spyder project settings
 .spyderproject
 .spyproject
+.jwt_secret_key
 
 # Rope project settings
 .ropeproject

.vscode/launch.json

@@ -2,15 +2,70 @@
   "version": "0.2.0",
   "configurations": [
     {
-      "name": "Docker Debug Frontend",
+      "name": "Frontend Debug (npm)",
+      "type": "node-terminal",
       "request": "launch",
-      "type": "chrome",
-      "preLaunchTask": "docker-compose: debug:frontend",
-      "url": "http://127.0.0.1:5173",
-      "webRoot": "${workspaceFolder}/frontend",
-      "skipFiles": [
-        "<node_internals>/**"
-      ]
+      "command": "npm run dev",
+      "cwd": "${workspaceFolder}/frontend"
+    },
+    {
+        "name": "Flask Debugger",
+        "type": "debugpy",
+        "request": "launch",
+        "module": "flask",
+        "env": {
+            "FLASK_APP": "application/app.py",
+            "PYTHONPATH": "${workspaceFolder}",
+            "FLASK_ENV": "development",
+            "FLASK_DEBUG": "1",
+            "FLASK_RUN_PORT": "7091",
+            "FLASK_RUN_HOST": "0.0.0.0"
+
+        },
+        "args": [
+            "run",
+            "--no-debugger"
+        ],
+        "cwd": "${workspaceFolder}",
+    },
+    {
+      "name": "Celery Debugger",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "celery",
+      "env": {
+        "PYTHONPATH": "${workspaceFolder}",
+      },
+      "args": [
+        "-A",
+        "application.app.celery",
+        "worker",
+        "-l",
+        "INFO",
+        "--pool=solo"
+      ],
+      "cwd": "${workspaceFolder}"
+    },
+    {
+      "name": "Dev Containers (Mongo + Redis)",
+      "type": "node-terminal",
+      "request": "launch",
+      "command": "docker compose -f deployment/docker-compose-dev.yaml up --build",
+      "cwd": "${workspaceFolder}"
+    }
+  ],
+  "compounds": [
+    {
+      "name": "DocsGPT: Full Stack",
+      "configurations": [
+        "Frontend Debug (npm)",
+        "Flask Debugger",
+        "Celery Debugger"
+      ],
+      "presentation": {
+        "group": "DocsGPT",
+        "order": 1
+      }
     }
   ]
 }

.vscode/tasks.json

@@ -1,21 +0,0 @@
-{
-  "version": "2.0.0",
-  "tasks": [
-    {
-      "type": "docker-compose",
-      "label": "docker-compose: debug:frontend",
-      "dockerCompose": {
-        "up": {
-          "detached": true,
-          "services": [
-            "frontend"
-          ],
-          "build": true
-        },
-        "files": [
-          "${workspaceFolder}/docker-compose.yaml"
-        ]
-      }
-    }
-  ]
-}

CONTRIBUTING.md

@@ -27,6 +27,7 @@ Before creating issues, please check out how the latest version of our app looks
 
 ### 👨‍💻 If you're interested in contributing code, here are some important things to know:
 
+For instructions on setting up a development environment, please refer to our [Development Deployment Guide](https://docs.docsgpt.cloud/Deploying/Development-Environment).
 
 Tech Stack Overview:
 
@@ -34,19 +35,40 @@ Tech Stack Overview:
 
 - 🖥 Backend: Developed in Python 🐍
 
-### 🌐 If you are looking to contribute to frontend (⚛️React, Vite):
+### 🌐 Frontend Contributions (⚛️ React, Vite)
 
-- The current frontend is being migrated from [`/application`](https://github.com/arc53/DocsGPT/tree/main/application) to [`/frontend`](https://github.com/arc53/DocsGPT/tree/main/frontend) with a new design, so please contribute to the new one.
-- Check out this [milestone](https://github.com/arc53/DocsGPT/milestone/1) and its issues.
-- The updated Figma design can be found [here](https://www.figma.com/file/OXLtrl1EAy885to6S69554/DocsGPT?node-id=0%3A1&t=hjWVuxRg9yi5YkJ9-1).
+*   The updated Figma design can be found [here](https://www.figma.com/file/OXLtrl1EAy885to6S69554/DocsGPT?node-id=0%3A1&t=hjWVuxRg9yi5YkJ9-1).  Please try to follow the guidelines.
+*   **Coding Style:** We follow a strict coding style enforced by ESLint and Prettier. Please ensure your code adheres to the configuration provided in our repository's `fronetend/.eslintrc.js` file.  We recommend configuring your editor with ESLint and Prettier to help with this.
+* **Component Structure:** Strive for small, reusable components.  Favor functional components and hooks over class components where possible.
+* **State Management** If you need to add stores, please use Redux.
 
-Please try to follow the guidelines.
+### 🖥 Backend Contributions (🐍 Python)
 
-### 🖥 If you are looking to contribute to Backend (🐍 Python):
-
-- Review our issues and contribute to [`/application`](https://github.com/arc53/DocsGPT/tree/main/application) or [`/scripts`](https://github.com/arc53/DocsGPT/tree/main/scripts) (please disregard old [`ingest_rst.py`](https://github.com/arc53/DocsGPT/blob/main/scripts/old/ingest_rst.py) [`ingest_rst_sphinx.py`](https://github.com/arc53/DocsGPT/blob/main/scripts/old/ingest_rst_sphinx.py) files; these will be deprecated soon).
+- Review our issues and contribute to [`/application`](https://github.com/arc53/DocsGPT/tree/main/application) 
 - All new code should be covered with unit tests ([pytest](https://github.com/pytest-dev/pytest)). Please find tests under [`/tests`](https://github.com/arc53/DocsGPT/tree/main/tests) folder.
 - Before submitting your Pull Request, ensure it can be queried after ingesting some test data.
+- **Coding Style:** We adhere to the [PEP 8](https://www.python.org/dev/peps/pep-0008/) style guide for Python code. We use `ruff` as our linter and code formatter.  Please ensure your code is formatted correctly and passes `ruff` checks before submitting.
+- **Type Hinting:**  Please use type hints for all function arguments and return values. This improves code readability and helps catch errors early.  Example:
+
+    ```python
+    def my_function(name: str, count: int) -> list[str]:
+        ...
+    ```
+- **Docstrings:**  All functions and classes should have docstrings explaining their purpose, parameters, and return values.  We prefer the [Google style docstrings](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html). Example:
+
+    ```python
+    def my_function(name: str, count: int) -> list[str]:
+        """Does something with a name and a count.
+
+        Args:
+            name: The name to use.
+            count: The number of times to do it.
+
+        Returns:
+            A list of strings.
+        """
+        ...
+    ```
   
 ### Testing
 

HACKTOBERFEST.md

@@ -1,15 +1,14 @@
-# **🎉 Join the Hacktoberfest with DocsGPT and win a Free T-shirt and other prizes! 🎉**
+# **🎉 Join the Hacktoberfest with DocsGPT and win a Free T-shirt for a meaningful PR! 🎉**
 
 Welcome, contributors! We're excited to announce that DocsGPT is participating in Hacktoberfest. Get involved by submitting meaningful pull requests.
 
-All contributors with accepted PRs will receive a cool Holopin! 🤩 (Watch out for a reply in your PR to collect it).
+All Meaningful contributors with accepted PRs that were created for issues with the `hacktoberfest` label (set by our maintainer team: dartpain, siiddhantt, pabik, ManishMadan2882) will receive a cool T-shirt! 🤩.
+<img width="1331" height="678" alt="hacktoberfest-mocks-preview" src="https://github.com/user-attachments/assets/633f6377-38db-48f5-b519-a8b3855a9eb4" />
 
-### 🏆 Top 50 contributors will receive a special T-shirt
+Fill in [this form](https://forms.gle/Npaba4n9Epfyx56S8
+) after your PR was merged please 
 
-### 🏆 [LLM Document analysis by LexEU competition](https://github.com/arc53/DocsGPT/blob/main/lexeu-competition.md): 
-A separate competition is available for those who submit new retrieval / workflow method that will analyze a Document using EU laws.
-With 200$, 100$, 50$ prize for 1st, 2nd and 3rd place respectively.
-You can find more information [here](https://github.com/arc53/DocsGPT/blob/main/lexeu-competition.md)
+If you are in doubt don't hesitate to ping us on discord, ping me - Alex (dartpain).
 
 ## 📜 Here's How to Contribute:
 ```text
@@ -23,19 +22,18 @@ https://github.com/arc53/DocsGPT-cli
 
 Non-Code Contributions:
 
-📚 Wiki: Improve our documentation, create a guide or change existing documentation.
+📚 Wiki: Improve our documentation, create a guide.
 
 🖥️ Design: Improve the UI/UX or design a new feature.
-
-📝 Blogging or Content Creation: Write articles or create videos to showcase DocsGPT or highlight your contributions!
 ```
 
 ### 📝 Guidelines for Pull Requests:
 - Familiarize yourself with the current contributions and our [Roadmap](https://github.com/orgs/arc53/projects/2).
-- Before contributing we highly advise that you check existing [issues](https://github.com/arc53/DocsGPT/issues) or [create](https://github.com/arc53/DocsGPT/issues/new/choose) an issue and wait to get assigned.
-- Once you are finished with your contribution, please fill in this [form](https://airtable.com/appikMaJwdHhC1SDP/pagoblCJ9W29wf6Hf/form).
+- Before contributing check existing [issues](https://github.com/arc53/DocsGPT/issues) or [create](https://github.com/arc53/DocsGPT/issues/new/choose) an issue and wait to get assigned.
+- Once you are finished with your contribution, please fill in this [form](https://forms.gle/Npaba4n9Epfyx56S8).
 - Refer to the [Documentation](https://docs.docsgpt.cloud/).
 - Feel free to join our [Discord](https://discord.gg/n5BX8dh8rU) server. We're here to help newcomers, so don't hesitate to jump in! Join us [here](https://discord.gg/n5BX8dh8rU).
   
-Thank you very much for considering contributing to DocsGPT during Hacktoberfest! 🙏 Your contributions (not just simple typos) could earn you a stylish new t-shirt and other prizes as a token of our appreciation. 🎁 Join us, and let's code together! 🚀
+Thank you very much for considering contributing to DocsGPT during Hacktoberfest! 🙏 Your contributions (not just simple typos) could earn you a stylish new t-shirt.
 
+We will publish a t-shirt design later into the October.

README.md

@@ -3,13 +3,11 @@
 </h1>
 
 <p align="center">
-  <strong>Open-Source Documentation Assistant</strong>
+  <strong>Private AI for agents, assistants and enterprise search</strong>
 </p>
 
 <p align="left">
-  <strong><a href="https://www.docsgpt.cloud/">DocsGPT</a></strong> is a cutting-edge open-source solution that streamlines the process of finding information in the project documentation. With its integration of the powerful <strong>GPT</strong> models, developers can easily ask questions about a project and receive accurate answers.
-  
-Say goodbye to time-consuming manual searches, and let <strong><a href="https://www.docsgpt.cloud/">DocsGPT</a></strong> help you quickly find the information you need. Try it out and see how it revolutionizes your project documentation experience. Contribute to its development and be a part of the future of AI-powered assistance.
+  <strong><a href="https://www.docsgpt.cloud/">DocsGPT</a></strong> is an open-source AI platform for building intelligent agents and assistants. Features Agent Builder, deep research tools, document analysis (PDF, Office, web content), Multi-model support (choose your provider or run locally), and rich API connectivity for agents with actionable tools and integrations. Deploy anywhere with complete privacy control.
 </p>
 
 <div align="center">
@@ -17,176 +15,142 @@ Say goodbye to time-consuming manual searches, and let <strong><a href="https://
   <a href="https://github.com/arc53/DocsGPT">![link to main GitHub showing Stars number](https://img.shields.io/github/stars/arc53/docsgpt?style=social)</a>
   <a href="https://github.com/arc53/DocsGPT">![link to main GitHub showing Forks number](https://img.shields.io/github/forks/arc53/docsgpt?style=social)</a>
   <a href="https://github.com/arc53/DocsGPT/blob/main/LICENSE">![link to license file](https://img.shields.io/github/license/arc53/docsgpt)</a>
+  <a href="https://www.bestpractices.dev/projects/9907"><img src="https://www.bestpractices.dev/projects/9907/badge"></a>
   <a href="https://discord.gg/n5BX8dh8rU">![link to discord](https://img.shields.io/discord/1070046503302877216)</a>
-  <a href="https://twitter.com/docsgptai">![X (formerly Twitter) URL](https://img.shields.io/twitter/follow/docsgptai)</a>
+  <a href="https://x.com/docsgptai">![X (formerly Twitter) URL](https://img.shields.io/twitter/follow/docsgptai)</a>
+
+<a href="https://docs.docsgpt.cloud/quickstart">⚡️ Quickstart</a> • <a href="https://app.docsgpt.cloud/">☁️ Cloud Version</a> • <a href="https://discord.gg/n5BX8dh8rU">💬 Discord</a>
+<br>
+<a href="https://docs.docsgpt.cloud/">📖 Documentation</a> • <a href="https://github.com/arc53/DocsGPT/blob/main/CONTRIBUTING.md">👫 Contribute</a> • <a href="https://blog.docsgpt.cloud/">🗞 Blog</a>
+<br>
 
- 
 </div>
 
+<div align="center">
+  <br>
+🎃 <a href="https://github.com/arc53/DocsGPT/blob/main/HACKTOBERFEST.md"> Hacktoberfest Prizes, Rules & Q&A </a> 🎃
+  <br>
+  <br>
+</div>
+
+
+<div align="center">
+  <br>
+<img src="https://d3dg1063dc54p9.cloudfront.net/videos/demov7.gif" alt="video-example-of-docs-gpt" width="800" height="450">
+</div>
+<h3 align="left">
+  <strong>Key Features:</strong>
+</h3>
+<ul align="left">
+    <li><strong>🗂️ Wide Format Support:</strong> Reads PDF, DOCX, CSV, XLSX, EPUB, MD, RST, HTML, MDX, JSON, PPTX, and images.</li>
+    <li><strong>🌐 Web & Data Integration:</strong> Ingests from URLs, sitemaps, Reddit, GitHub and web crawlers.</li>
+    <li><strong>✅ Reliable Answers:</strong> Get accurate, hallucination-free responses with source citations viewable in a clean UI.</li>
+    <li><strong>🔑 Streamlined API Keys:</strong>  Generate keys linked to your settings, documents, and models, simplifying chatbot and integration setup.</li>
+    <li><strong>🔗 Actionable Tooling:</strong> Connect to APIs, tools, and other services to enable LLM actions.</li>
+    <li><strong>🧩 Pre-built Integrations:</strong> Use readily available HTML/React chat widgets, search tools, Discord/Telegram bots, and more.</li>
+    <li><strong>🔌 Flexible Deployment:</strong> Works with major LLMs (OpenAI, Google, Anthropic) and local models (Ollama, llama_cpp).</li>
+    <li><strong>🏢 Secure & Scalable:</strong> Run privately and securely with Kubernetes support, designed for enterprise-grade reliability.</li>
+</ul>
+
+## Roadmap
+
+- [x] Full GoogleAI compatibility (Jan 2025)
+- [x] Add tools (Jan 2025)
+- [x] Manually updating chunks in the app UI (Feb 2025)
+- [x] Devcontainer for easy development (Feb 2025)
+- [x] ReACT agent (March 2025)
+- [x] Chatbots menu re-design to handle tools, agent types, and more (April 2025)
+- [x] New input box in the conversation menu (April 2025)
+- [x] Add triggerable actions / tools (webhook) (April 2025)
+- [x] Agent optimisations (May 2025)
+- [x] Filesystem sources update (July 2025)
+- [x] Json Responses (August 2025)
+- [x] MCP support (August 2025)
+- [x] Google Drive integration (September 2025)
+- [x] Add OAuth 2.0 authentication for MCP (September 2025)
+- [ ] SharePoint integration (October 2025)
+- [ ] Deep Agents (October 2025)
+- [ ] Agent scheduling
+
+You can find our full roadmap [here](https://github.com/orgs/arc53/projects/2). Please don't hesitate to contribute or create issues, it helps us improve DocsGPT!
+
 ### Production Support / Help for Companies:
 
 We're eager to provide personalized assistance when deploying your DocsGPT to a live environment.
 
-<a href ="https://cal.com/arc53/docsgpt-demo-b2b">
-    <img alt="Let's chat" src="https://cal.com/book-with-cal-dark.svg" />
-</a>
+[Get a Demo :wave:](https://www.docsgpt.cloud/contact)⁠
 
-[Send Email :email:](mailto:contact@arc53.com?subject=DocsGPT%20support%2Fsolutions)
+[Send Email :email:](mailto:support@docsgpt.cloud?subject=DocsGPT%20support%2Fsolutions)
 
+## Join the Lighthouse Program 🌟
 
-<img src="https://github.com/user-attachments/assets/9a1f21de-7a15-4e42-9424-70d22ba5a913" alt="video-example-of-docs-gpt" width="1000" height="500">
+Calling all developers and GenAI innovators! The **DocsGPT Lighthouse Program** connects technical leaders actively deploying or extending DocsGPT in real-world scenarios. Collaborate directly with our team to shape the roadmap, access priority support, and build enterprise-ready solutions with exclusive community insights.
 
-## Roadmap
-
-You can find our roadmap [here](https://github.com/orgs/arc53/projects/2). Please don't hesitate to contribute or create issues, it helps us improve DocsGPT!
-
-## Our Open-Source Models Optimized for DocsGPT:
-
-| Name                                                                  | Base Model  | Requirements (or similar) |
-| --------------------------------------------------------------------- | ----------- | ------------------------- |
-| [Docsgpt-7b-mistral](https://huggingface.co/Arc53/docsgpt-7b-mistral)   | Mistral-7b   | 1xA10G gpu                |
-| [Docsgpt-14b](https://huggingface.co/Arc53/docsgpt-14b)               | llama-2-14b | 2xA10 gpu's               |
-| [Docsgpt-40b-falcon](https://huggingface.co/Arc53/docsgpt-40b-falcon) | falcon-40b  | 8xA10G gpu's              |
-
-If you don't have enough resources to run it, you can use bitsnbytes to quantize.
-
-## End to End AI Framework for Information Retrieval
-
-![Architecture chart](https://github.com/user-attachments/assets/fc6a7841-ddfc-45e6-b5a0-d05fe648cbe2)
-
-## Useful Links
-
-- :mag: :fire: [Cloud Version](https://app.docsgpt.cloud/)
-
-- :speech_balloon: :tada: [Join our Discord](https://discord.gg/n5BX8dh8rU)
-
-- :books: :sunglasses: [Guides](https://docs.docsgpt.cloud/)
-
-- :couple: [Interested in contributing?](https://github.com/arc53/DocsGPT/blob/main/CONTRIBUTING.md)
-
-- :file_folder: :rocket: [How to use any other documentation](https://docs.docsgpt.cloud/Guides/How-to-train-on-other-documentation)
-
-- :house: :closed_lock_with_key: [How to host it locally (so all data will stay on-premises)](https://docs.docsgpt.cloud/Guides/How-to-use-different-LLM)
-
-## Project Structure
-
-- Application - Flask app (main application).
-
-- Extensions - Chrome extension.
-
-- Scripts - Script that creates similarity search index for other libraries.
-
-- Frontend - Frontend uses <a href="https://vitejs.dev/">Vite</a> and <a href="https://react.dev/">React</a>.
+[Learn More & Apply →](https://docs.google.com/forms/d/1KAADiJinUJ8EMQyfTXUIGyFbqINNClNR3jBNWq7DgTE)
 
 ## QuickStart
 
 > [!Note]
 > Make sure you have [Docker](https://docs.docker.com/engine/install/) installed
 
-On Mac OS or Linux, write:
+A more detailed [Quickstart](https://docs.docsgpt.cloud/quickstart) is available in our documentation
 
-`./setup.sh`
+1. **Clone the repository:**
 
-It will install all the dependencies and allow you to download the local model, use OpenAI or use our LLM API.
-
-Otherwise, refer to this Guide for Windows:
-
-1. Download and open this repository with `git clone https://github.com/arc53/DocsGPT.git`
-2. Create a `.env` file in your root directory and set the env variables and `VITE_API_STREAMING` to true or false, depending on whether you want streaming answers or not.
-   It should look like this inside:
-
-   ```
-   LLM_NAME=[docsgpt or openai or others] 
-   VITE_API_STREAMING=true
-   API_KEY=[if LLM_NAME is openai]
+   ```bash
+   git clone https://github.com/arc53/DocsGPT.git
+   cd DocsGPT
    ```
 
-   See optional environment variables in the [/.env-template](https://github.com/arc53/DocsGPT/blob/main/.env-template) and [/application/.env_sample](https://github.com/arc53/DocsGPT/blob/main/application/.env_sample) files.
+**For macOS and Linux:**
 
-3. Run [./run-with-docker-compose.sh](https://github.com/arc53/DocsGPT/blob/main/run-with-docker-compose.sh).
-4. Navigate to http://localhost:5173/.
+2. **Run the setup script:**
 
-To stop, just run `Ctrl + C`.
+   ```bash
+   ./setup.sh
+   ```
 
-## Development Environments
+**For Windows:**
 
-### Spin up Mongo and Redis
+2. **Run the PowerShell setup script:**
 
-For development, only two containers are used from [docker-compose.yaml](https://github.com/arc53/DocsGPT/blob/main/docker-compose.yaml) (by deleting all services except for Redis and Mongo).
-See file [docker-compose-dev.yaml](./docker-compose-dev.yaml).
+   ```powershell
+   PowerShell -ExecutionPolicy Bypass -File .\setup.ps1
+   ```
 
-Run
+Either script will guide you through setting up DocsGPT. Five options available: using the public API, running locally, connecting to a local inference engine, using a cloud API provider, or build the docker image locally. Scripts will automatically configure your `.env` file and handle necessary downloads and installations based on your chosen option.
 
-```
-docker compose -f docker-compose-dev.yaml build
-docker compose -f docker-compose-dev.yaml up -d
+**Navigate to http://localhost:5173/**
+
+To stop DocsGPT, open a terminal in the `DocsGPT` directory and run:
+
+```bash
+docker compose -f deployment/docker-compose.yaml down
 ```
 
-### Run the Backend
+(or use the specific `docker compose down` command shown after running the setup script).
 
 > [!Note]
-> Make sure you have Python 3.10 or 3.11 installed.
-
-1. Export required environment variables or prepare a `.env` file in the project folder:
-   - Copy [.env-template](https://github.com/arc53/DocsGPT/blob/main/application/.env-template) and create `.env`.
-
-(check out [`application/core/settings.py`](application/core/settings.py) if you want to see more config options.)
-
-2. (optional) Create a Python virtual environment:
-   You can follow the [Python official documentation](https://docs.python.org/3/tutorial/venv.html) for virtual environments.
-
-a) On Mac OS and Linux
-
-```commandline
-python -m venv venv
-. venv/bin/activate
-```
-
-b) On Windows
-
-```commandline
-python -m venv venv
- venv/Scripts/activate
-```
-
-3. Download embedding model and save it in the `model/` folder:
-You can use the script below, or download it manually from [here](https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip), unzip it and save it in the `model/` folder.
-
-```commandline
-wget https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip
-unzip mpnet-base-v2.zip -d model
-rm mpnet-base-v2.zip
-```
-
-4. Install dependencies for the backend:
-
-```commandline
-pip install -r application/requirements.txt
-```
-
-5. Run the app using `flask --app application/app.py run --host=0.0.0.0 --port=7091`.
-6. Start worker with `celery -A application.app.celery worker -l INFO`.
-
-### Start Frontend
-
-> [!Note]
-> Make sure you have Node version 16 or higher.
-
-1. Navigate to the [/frontend](https://github.com/arc53/DocsGPT/tree/main/frontend) folder.
-2. Install the required packages `husky` and `vite` (ignore if already installed).
-
-```commandline
-npm install husky -g
-npm install vite -g
-```
-
-3. Install dependencies by running `npm install --include=dev`.
-4. Run the app using `npm run dev`.
+> For development environment setup instructions, please refer to the [Development Environment Guide](https://docs.docsgpt.cloud/Deploying/Development-Environment).
 
 ## Contributing
 
 Please refer to the [CONTRIBUTING.md](CONTRIBUTING.md) file for information about how to get involved. We welcome issues, questions, and pull requests.
 
+## Architecture
+
+![Architecture chart](https://github.com/user-attachments/assets/fc6a7841-ddfc-45e6-b5a0-d05fe648cbe2)
+
+## Project Structure
+
+- Application - Flask app (main application).
+
+- Extensions - Extensions, like react widget or discord bot.
+
+- Frontend - Frontend uses <a href="https://vitejs.dev/">Vite</a> and <a href="https://react.dev/">React</a>.
+
+- Scripts - Miscellaneous scripts.
+
 ## Code Of Conduct
 
 We as members, contributors, and leaders, pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. Please refer to the [CODE_OF_CONDUCT.md](CODE_OF_CONDUCT.md) file for more information about contributing.

application/Dockerfile

@@ -6,21 +6,20 @@ ENV DEBIAN_FRONTEND=noninteractive
 RUN apt-get update && \
     apt-get install -y software-properties-common && \
     add-apt-repository ppa:deadsnakes/ppa && \
-# Install necessary packages and Python
     apt-get update && \
-    apt-get install -y --no-install-recommends gcc wget unzip libc6-dev python3.11 python3.11-distutils python3.11-venv && \
+    apt-get install -y --no-install-recommends gcc wget unzip libc6-dev python3.12 python3.12-venv && \
     rm -rf /var/lib/apt/lists/* 
 
 # Verify Python installation and setup symlink
-RUN if [ -f /usr/bin/python3.11 ]; then \
-        ln -s /usr/bin/python3.11 /usr/bin/python; \
+RUN if [ -f /usr/bin/python3.12 ]; then \
+        ln -s /usr/bin/python3.12 /usr/bin/python; \
     else \
-        echo "Python 3.11 not found"; exit 1; \
+        echo "Python 3.12 not found"; exit 1; \
     fi
 
 # Download and unzip the model
 RUN wget https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip && \
-    unzip mpnet-base-v2.zip -d model && \
+    unzip mpnet-base-v2.zip -d models && \
     rm mpnet-base-v2.zip
 
 # Install Rust
@@ -33,7 +32,7 @@ RUN apt-get remove --purge -y wget unzip && apt-get autoremove -y && rm -rf /var
 COPY requirements.txt .
 
 # Setup Python virtual environment
-RUN python3.11 -m venv /venv
+RUN python3.12 -m venv /venv
 
 # Activate virtual environment and install Python packages
 ENV PATH="/venv/bin:$PATH"
@@ -49,9 +48,8 @@ FROM ubuntu:24.04 as final
 RUN apt-get update && \
     apt-get install -y software-properties-common && \
     add-apt-repository ppa:deadsnakes/ppa && \
-# Install Python
-    apt-get update && apt-get install -y --no-install-recommends python3.11 && \
-    ln -s /usr/bin/python3.11 /usr/bin/python && \
+    apt-get update && apt-get install -y --no-install-recommends python3.12 && \
+    ln -s /usr/bin/python3.12 /usr/bin/python && \
     rm -rf /var/lib/apt/lists/*
 
 # Set working directory
@@ -63,7 +61,8 @@ RUN groupadd -r appuser && \
 
 # Copy the virtual environment and model from the builder stage
 COPY --from=builder /venv /venv
-COPY --from=builder /model /app/model
+
+COPY --from=builder /models /app/models
 
 # Copy your application code
 COPY . /app/application
@@ -85,4 +84,4 @@ EXPOSE 7091
 USER appuser
 
 # Start Gunicorn
-CMD ["gunicorn", "-w", "2", "--timeout", "120", "--bind", "0.0.0.0:7091", "application.wsgi:app"]
+CMD ["gunicorn", "-w", "1", "--timeout", "120", "--bind", "0.0.0.0:7091", "--preload", "application.wsgi:app"]

application/agents/agent_creator.py

@@ -0,0 +1,16 @@
+from application.agents.classic_agent import ClassicAgent
+from application.agents.react_agent import ReActAgent
+
+
+class AgentCreator:
+    agents = {
+        "classic": ClassicAgent,
+        "react": ReActAgent,
+    }
+
+    @classmethod
+    def create_agent(cls, type, *args, **kwargs):
+        agent_class = cls.agents.get(type.lower())
+        if not agent_class:
+            raise ValueError(f"No agent class found for type {type}")
+        return agent_class(*args, **kwargs)

application/agents/base.py

@@ -0,0 +1,423 @@
+import logging
+import uuid
+from abc import ABC, abstractmethod
+from typing import Dict, Generator, List, Optional
+
+from bson.objectid import ObjectId
+
+from application.agents.tools.tool_action_parser import ToolActionParser
+from application.agents.tools.tool_manager import ToolManager
+from application.core.mongo_db import MongoDB
+from application.core.settings import settings
+from application.llm.handlers.handler_creator import LLMHandlerCreator
+from application.llm.llm_creator import LLMCreator
+from application.logging import build_stack_data, log_activity, LogContext
+from application.retriever.base import BaseRetriever
+
+logger = logging.getLogger(__name__)
+
+
+class BaseAgent(ABC):
+    def __init__(
+        self,
+        endpoint: str,
+        llm_name: str,
+        gpt_model: str,
+        api_key: str,
+        user_api_key: Optional[str] = None,
+        prompt: str = "",
+        chat_history: Optional[List[Dict]] = None,
+        decoded_token: Optional[Dict] = None,
+        attachments: Optional[List[Dict]] = None,
+        json_schema: Optional[Dict] = None,
+        limited_token_mode: Optional[bool] = False,
+        token_limit: Optional[int] = settings.DEFAULT_AGENT_LIMITS["token_limit"],
+        limited_request_mode: Optional[bool] = False,
+        request_limit: Optional[int] = settings.DEFAULT_AGENT_LIMITS["request_limit"],
+    ):
+        self.endpoint = endpoint
+        self.llm_name = llm_name
+        self.gpt_model = gpt_model
+        self.api_key = api_key
+        self.user_api_key = user_api_key
+        self.prompt = prompt
+        self.decoded_token = decoded_token or {}
+        self.user: str = self.decoded_token.get("sub")
+        self.tool_config: Dict = {}
+        self.tools: List[Dict] = []
+        self.tool_calls: List[Dict] = []
+        self.chat_history: List[Dict] = chat_history if chat_history is not None else []
+        self.llm = LLMCreator.create_llm(
+            llm_name,
+            api_key=api_key,
+            user_api_key=user_api_key,
+            decoded_token=decoded_token,
+        )
+        self.llm_handler = LLMHandlerCreator.create_handler(
+            llm_name if llm_name else "default"
+        )
+        self.attachments = attachments or []
+        self.json_schema = json_schema
+        self.limited_token_mode = limited_token_mode
+        self.token_limit = token_limit
+        self.limited_request_mode = limited_request_mode
+        self.request_limit = request_limit
+
+    @log_activity()
+    def gen(
+        self, query: str, retriever: BaseRetriever, log_context: LogContext = None
+    ) -> Generator[Dict, None, None]:
+        yield from self._gen_inner(query, retriever, log_context)
+
+    @abstractmethod
+    def _gen_inner(
+        self, query: str, retriever: BaseRetriever, log_context: LogContext
+    ) -> Generator[Dict, None, None]:
+        pass
+
+    def _get_tools(self, api_key: str = None) -> Dict[str, Dict]:
+        mongo = MongoDB.get_client()
+        db = mongo[settings.MONGO_DB_NAME]
+        agents_collection = db["agents"]
+        tools_collection = db["user_tools"]
+
+        agent_data = agents_collection.find_one({"key": api_key or self.user_api_key})
+        tool_ids = agent_data.get("tools", []) if agent_data else []
+
+        tools = (
+            tools_collection.find(
+                {"_id": {"$in": [ObjectId(tool_id) for tool_id in tool_ids]}}
+            )
+            if tool_ids
+            else []
+        )
+        tools = list(tools)
+        tools_by_id = {str(tool["_id"]): tool for tool in tools} if tools else {}
+
+        return tools_by_id
+
+    def _get_user_tools(self, user="local"):
+        mongo = MongoDB.get_client()
+        db = mongo[settings.MONGO_DB_NAME]
+        user_tools_collection = db["user_tools"]
+        user_tools = user_tools_collection.find({"user": user, "status": True})
+        user_tools = list(user_tools)
+
+        return {str(i): tool for i, tool in enumerate(user_tools)}
+
+    def _build_tool_parameters(self, action):
+        params = {"type": "object", "properties": {}, "required": []}
+        for param_type in ["query_params", "headers", "body", "parameters"]:
+            if param_type in action and action[param_type].get("properties"):
+                for k, v in action[param_type]["properties"].items():
+                    if v.get("filled_by_llm", True):
+                        params["properties"][k] = {
+                            key: value
+                            for key, value in v.items()
+                            if key != "filled_by_llm" and key != "value"
+                        }
+
+                        params["required"].append(k)
+        return params
+
+    def _prepare_tools(self, tools_dict):
+        self.tools = [
+            {
+                "type": "function",
+                "function": {
+                    "name": f"{action['name']}_{tool_id}",
+                    "description": action["description"],
+                    "parameters": self._build_tool_parameters(action),
+                },
+            }
+            for tool_id, tool in tools_dict.items()
+            if (
+                (tool["name"] == "api_tool" and "actions" in tool.get("config", {}))
+                or (tool["name"] != "api_tool" and "actions" in tool)
+            )
+            for action in (
+                tool["config"]["actions"].values()
+                if tool["name"] == "api_tool"
+                else tool["actions"]
+            )
+            if action.get("active", True)
+        ]
+
+    def _execute_tool_action(self, tools_dict, call):
+        parser = ToolActionParser(self.llm.__class__.__name__)
+        tool_id, action_name, call_args = parser.parse_args(call)
+
+        call_id = getattr(call, "id", None) or str(uuid.uuid4())
+
+        # Check if parsing failed
+        if tool_id is None or action_name is None:
+            error_message = f"Error: Failed to parse LLM tool call. Tool name: {getattr(call, 'name', 'unknown')}"
+            logger.error(error_message)
+
+            tool_call_data = {
+                "tool_name": "unknown",
+                "call_id": call_id,
+                "action_name": getattr(call, "name", "unknown"),
+                "arguments": call_args or {},
+                "result": f"Failed to parse tool call. Invalid tool name format: {getattr(call, 'name', 'unknown')}",
+            }
+            yield {"type": "tool_call", "data": {**tool_call_data, "status": "error"}}
+            self.tool_calls.append(tool_call_data)
+            return "Failed to parse tool call.", call_id
+
+        # Check if tool_id exists in available tools
+        if tool_id not in tools_dict:
+            error_message = f"Error: Tool ID '{tool_id}' extracted from LLM call not found in available tools_dict. Available IDs: {list(tools_dict.keys())}"
+            logger.error(error_message)
+
+            # Return error result
+            tool_call_data = {
+                "tool_name": "unknown",
+                "call_id": call_id,
+                "action_name": f"{action_name}_{tool_id}",
+                "arguments": call_args,
+                "result": f"Tool with ID {tool_id} not found. Available tools: {list(tools_dict.keys())}",
+            }
+            yield {"type": "tool_call", "data": {**tool_call_data, "status": "error"}}
+            self.tool_calls.append(tool_call_data)
+            return f"Tool with ID {tool_id} not found.", call_id
+
+        tool_call_data = {
+            "tool_name": tools_dict[tool_id]["name"],
+            "call_id": call_id,
+            "action_name": f"{action_name}_{tool_id}",
+            "arguments": call_args,
+        }
+        yield {"type": "tool_call", "data": {**tool_call_data, "status": "pending"}}
+
+        tool_data = tools_dict[tool_id]
+        action_data = (
+            tool_data["config"]["actions"][action_name]
+            if tool_data["name"] == "api_tool"
+            else next(
+                action
+                for action in tool_data["actions"]
+                if action["name"] == action_name
+            )
+        )
+
+        query_params, headers, body, parameters = {}, {}, {}, {}
+        param_types = {
+            "query_params": query_params,
+            "headers": headers,
+            "body": body,
+            "parameters": parameters,
+        }
+
+        for param_type, target_dict in param_types.items():
+            if param_type in action_data and action_data[param_type].get("properties"):
+                for param, details in action_data[param_type]["properties"].items():
+                    if param not in call_args and "value" in details:
+                        target_dict[param] = details["value"]
+        for param, value in call_args.items():
+            for param_type, target_dict in param_types.items():
+                if param_type in action_data and param in action_data[param_type].get(
+                    "properties", {}
+                ):
+                    target_dict[param] = value
+        tm = ToolManager(config={})
+
+        # Prepare tool_config and add tool_id for memory tools
+        if tool_data["name"] == "api_tool":
+            tool_config = {
+                "url": tool_data["config"]["actions"][action_name]["url"],
+                "method": tool_data["config"]["actions"][action_name]["method"],
+                "headers": headers,
+                "query_params": query_params,
+            }
+        else:
+            tool_config = tool_data["config"].copy() if tool_data["config"] else {}
+            # Add tool_id from MongoDB _id for tools that need instance isolation (like memory tool)
+            # Use MongoDB _id if available, otherwise fall back to enumerated tool_id
+            tool_config["tool_id"] = str(tool_data.get("_id", tool_id))
+
+        tool = tm.load_tool(
+            tool_data["name"],
+            tool_config=tool_config,
+            user_id=self.user,  # Pass user ID for MCP tools credential decryption
+        )
+        if tool_data["name"] == "api_tool":
+            print(
+                f"Executing api: {action_name} with query_params: {query_params}, headers: {headers}, body: {body}"
+            )
+            result = tool.execute_action(action_name, **body)
+        else:
+            print(f"Executing tool: {action_name} with args: {call_args}")
+            result = tool.execute_action(action_name, **parameters)
+        tool_call_data["result"] = (
+            f"{str(result)[:50]}..." if len(str(result)) > 50 else result
+        )
+
+        yield {"type": "tool_call", "data": {**tool_call_data, "status": "completed"}}
+        self.tool_calls.append(tool_call_data)
+
+        return result, call_id
+
+    def _get_truncated_tool_calls(self):
+        return [
+            {
+                **tool_call,
+                "result": (
+                    f"{str(tool_call['result'])[:50]}..."
+                    if len(str(tool_call["result"])) > 50
+                    else tool_call["result"]
+                ),
+                "status": "completed",
+            }
+            for tool_call in self.tool_calls
+        ]
+
+    def _build_messages(
+        self,
+        system_prompt: str,
+        query: str,
+        retrieved_data: List[Dict],
+    ) -> List[Dict]:
+        docs_with_filenames = []
+        for doc in retrieved_data:
+            filename = doc.get("filename") or doc.get("title") or doc.get("source")
+            if filename:
+                chunk_header = str(filename)
+                docs_with_filenames.append(f"{chunk_header}\n{doc['text']}")
+            else:
+                docs_with_filenames.append(doc["text"])
+        docs_together = "\n\n".join(docs_with_filenames)
+        p_chat_combine = system_prompt.replace("{summaries}", docs_together)
+        messages_combine = [{"role": "system", "content": p_chat_combine}]
+
+        for i in self.chat_history:
+            if "prompt" in i and "response" in i:
+                messages_combine.append({"role": "user", "content": i["prompt"]})
+                messages_combine.append({"role": "assistant", "content": i["response"]})
+            if "tool_calls" in i:
+                for tool_call in i["tool_calls"]:
+                    call_id = tool_call.get("call_id") or str(uuid.uuid4())
+
+                    function_call_dict = {
+                        "function_call": {
+                            "name": tool_call.get("action_name"),
+                            "args": tool_call.get("arguments"),
+                            "call_id": call_id,
+                        }
+                    }
+                    function_response_dict = {
+                        "function_response": {
+                            "name": tool_call.get("action_name"),
+                            "response": {"result": tool_call.get("result")},
+                            "call_id": call_id,
+                        }
+                    }
+
+                    messages_combine.append(
+                        {"role": "assistant", "content": [function_call_dict]}
+                    )
+                    messages_combine.append(
+                        {"role": "tool", "content": [function_response_dict]}
+                    )
+        messages_combine.append({"role": "user", "content": query})
+        return messages_combine
+
+    def _retriever_search(
+        self,
+        retriever: BaseRetriever,
+        query: str,
+        log_context: Optional[LogContext] = None,
+    ) -> List[Dict]:
+        retrieved_data = retriever.search(query)
+        if log_context:
+            data = build_stack_data(retriever, exclude_attributes=["llm"])
+            log_context.stacks.append({"component": "retriever", "data": data})
+        return retrieved_data
+
+    def _llm_gen(self, messages: List[Dict], log_context: Optional[LogContext] = None):
+        gen_kwargs = {"model": self.gpt_model, "messages": messages}
+
+        if (
+            hasattr(self.llm, "_supports_tools")
+            and self.llm._supports_tools
+            and self.tools
+        ):
+            gen_kwargs["tools"] = self.tools
+
+        if (
+            self.json_schema
+            and hasattr(self.llm, "_supports_structured_output")
+            and self.llm._supports_structured_output()
+        ):
+            structured_format = self.llm.prepare_structured_output_format(
+                self.json_schema
+            )
+            if structured_format:
+                if self.llm_name == "openai":
+                    gen_kwargs["response_format"] = structured_format
+                elif self.llm_name == "google":
+                    gen_kwargs["response_schema"] = structured_format
+
+        resp = self.llm.gen_stream(**gen_kwargs)
+
+        if log_context:
+            data = build_stack_data(self.llm, exclude_attributes=["client"])
+            log_context.stacks.append({"component": "llm", "data": data})
+        return resp
+
+    def _llm_handler(
+        self,
+        resp,
+        tools_dict: Dict,
+        messages: List[Dict],
+        log_context: Optional[LogContext] = None,
+        attachments: Optional[List[Dict]] = None,
+    ):
+        resp = self.llm_handler.process_message_flow(
+            self, resp, tools_dict, messages, attachments, True
+        )
+        if log_context:
+            data = build_stack_data(self.llm_handler, exclude_attributes=["tool_calls"])
+            log_context.stacks.append({"component": "llm_handler", "data": data})
+        return resp
+
+    def _handle_response(self, response, tools_dict, messages, log_context):
+        is_structured_output = (
+            self.json_schema is not None
+            and hasattr(self.llm, "_supports_structured_output")
+            and self.llm._supports_structured_output()
+        )
+
+        if isinstance(response, str):
+            answer_data = {"answer": response}
+            if is_structured_output:
+                answer_data["structured"] = True
+                answer_data["schema"] = self.json_schema
+            yield answer_data
+            return
+        if hasattr(response, "message") and getattr(response.message, "content", None):
+            answer_data = {"answer": response.message.content}
+            if is_structured_output:
+                answer_data["structured"] = True
+                answer_data["schema"] = self.json_schema
+            yield answer_data
+            return
+        processed_response_gen = self._llm_handler(
+            response, tools_dict, messages, log_context, self.attachments
+        )
+
+        for event in processed_response_gen:
+            if isinstance(event, str):
+                answer_data = {"answer": event}
+                if is_structured_output:
+                    answer_data["structured"] = True
+                    answer_data["schema"] = self.json_schema
+                yield answer_data
+            elif hasattr(event, "message") and getattr(event.message, "content", None):
+                answer_data = {"answer": event.message.content}
+                if is_structured_output:
+                    answer_data["structured"] = True
+                    answer_data["schema"] = self.json_schema
+                yield answer_data
+            elif isinstance(event, dict) and "type" in event:
+                yield event

application/agents/classic_agent.py

@@ -0,0 +1,53 @@
+from typing import Dict, Generator
+from application.agents.base import BaseAgent
+from application.logging import LogContext
+from application.retriever.base import BaseRetriever
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class ClassicAgent(BaseAgent):
+    """A simplified agent with clear execution flow.
+
+    Usage:
+    1. Processes a query through retrieval
+    2. Sets up available tools
+    3. Generates responses using LLM
+    4. Handles tool interactions if needed
+    5. Returns standardized outputs
+
+    Easy to extend by overriding specific steps.
+    """
+
+    def _gen_inner(
+        self, query: str, retriever: BaseRetriever, log_context: LogContext
+    ) -> Generator[Dict, None, None]:
+        # Step 1: Retrieve relevant data
+        retrieved_data = self._retriever_search(retriever, query, log_context)
+
+        # Step 2: Prepare tools
+        tools_dict = (
+            self._get_user_tools(self.user)
+            if not self.user_api_key
+            else self._get_tools(self.user_api_key)
+        )
+        self._prepare_tools(tools_dict)
+
+        # Step 3: Build and process messages
+        messages = self._build_messages(self.prompt, query, retrieved_data)
+        llm_response = self._llm_gen(messages, log_context)
+
+        # Step 4: Handle the response
+        yield from self._handle_response(
+            llm_response, tools_dict, messages, log_context
+        )
+
+        # Step 5: Return metadata
+        yield {"sources": retrieved_data}
+        yield {"tool_calls": self._get_truncated_tool_calls()}
+
+        # Log tool calls for debugging
+        log_context.stacks.append(
+            {"component": "agent", "data": {"tool_calls": self.tool_calls.copy()}}
+        )

application/agents/react_agent.py

@@ -0,0 +1,284 @@
+import os
+from typing import Dict, Generator, List, Any
+import logging
+
+from application.agents.base import BaseAgent
+from application.logging import build_stack_data, LogContext
+from application.retriever.base import BaseRetriever
+
+logger = logging.getLogger(__name__)
+
+current_dir = os.path.dirname(
+    os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+)
+with open(
+    os.path.join(current_dir, "application/prompts", "react_planning_prompt.txt"), "r"
+) as f:
+    planning_prompt_template = f.read()
+with open(
+    os.path.join(current_dir, "application/prompts", "react_final_prompt.txt"),
+    "r",
+) as f:
+    final_prompt_template = f.read()
+
+MAX_ITERATIONS_REASONING = 10
+
+
+class ReActAgent(BaseAgent):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.plan: str = ""
+        self.observations: List[str] = []
+
+    def _extract_content_from_llm_response(self, resp: Any) -> str:
+        """
+        Helper to extract string content from various LLM response types.
+        Handles strings, message objects (OpenAI-like), and streams.
+        Adapt stream handling for your specific LLM client if not OpenAI.
+        """
+        collected_content = []
+        if isinstance(resp, str):
+            collected_content.append(resp)
+        elif (  # OpenAI non-streaming or Anthropic non-streaming (older SDK style)
+            hasattr(resp, "message")
+            and hasattr(resp.message, "content")
+            and resp.message.content is not None
+        ):
+            collected_content.append(resp.message.content)
+        elif (  # OpenAI non-streaming (Pydantic model), Anthropic new SDK non-streaming
+            hasattr(resp, "choices")
+            and resp.choices
+            and hasattr(resp.choices[0], "message")
+            and hasattr(resp.choices[0].message, "content")
+            and resp.choices[0].message.content is not None
+        ):
+            collected_content.append(resp.choices[0].message.content)  # OpenAI
+        elif (  # Anthropic new SDK non-streaming content block
+            hasattr(resp, "content")
+            and isinstance(resp.content, list)
+            and resp.content
+            and hasattr(resp.content[0], "text")
+        ):
+            collected_content.append(resp.content[0].text)  # Anthropic
+        else:
+            # Assume resp is a stream if not a recognized object
+            chunk = None
+            try:
+                for (
+                    chunk
+                ) in (
+                    resp
+                ):  # This will fail if resp is not iterable (e.g. a non-streaming response object)
+                    content_piece = ""
+                    # OpenAI-like stream
+                    if (
+                        hasattr(chunk, "choices")
+                        and len(chunk.choices) > 0
+                        and hasattr(chunk.choices[0], "delta")
+                        and hasattr(chunk.choices[0].delta, "content")
+                        and chunk.choices[0].delta.content is not None
+                    ):
+                        content_piece = chunk.choices[0].delta.content
+                    # Anthropic-like stream (ContentBlockDelta)
+                    elif (
+                        hasattr(chunk, "type")
+                        and chunk.type == "content_block_delta"
+                        and hasattr(chunk, "delta")
+                        and hasattr(chunk.delta, "text")
+                    ):
+                        content_piece = chunk.delta.text
+                    elif isinstance(chunk, str):  # Simplest case: stream of strings
+                        content_piece = chunk
+
+                    if content_piece:
+                        collected_content.append(content_piece)
+            except (
+                TypeError
+            ):  # If resp is not iterable (e.g. a final response object that wasn't caught above)
+                logger.debug(
+                    f"Response type {type(resp)} could not be iterated as a stream. It might be a non-streaming object not handled by specific checks."
+                )
+            except Exception as e:
+                logger.error(
+                    f"Error processing potential stream chunk: {e}, chunk was: {getattr(chunk, '__dict__', chunk) if chunk is not None else 'N/A'}"
+                )
+
+        return "".join(collected_content)
+
+    def _gen_inner(
+        self, query: str, retriever: BaseRetriever, log_context: LogContext
+    ) -> Generator[Dict, None, None]:
+        # Reset state for this generation call
+        self.plan = ""
+        self.observations = []
+        retrieved_data = self._retriever_search(retriever, query, log_context)
+
+        if self.user_api_key:
+            tools_dict = self._get_tools(self.user_api_key)
+        else:
+            tools_dict = self._get_user_tools(self.user)
+        self._prepare_tools(tools_dict)
+
+        docs_together = "\n".join([doc["text"] for doc in retrieved_data])
+        iterating_reasoning = 0
+        while iterating_reasoning < MAX_ITERATIONS_REASONING:
+            iterating_reasoning += 1
+            # 1. Create Plan
+            logger.info("ReActAgent: Creating plan...")
+            plan_stream = self._create_plan(query, docs_together, log_context)
+            current_plan_parts = []
+            yield {"thought": f"Reasoning... (iteration {iterating_reasoning})\n\n"}
+            for line_chunk in plan_stream:
+                current_plan_parts.append(line_chunk)
+                yield {"thought": line_chunk}
+            self.plan = "".join(current_plan_parts)
+            if self.plan:
+                self.observations.append(
+                    f"Plan: {self.plan} Iteration: {iterating_reasoning}"
+                )
+
+            max_obs_len = 20000
+            obs_str = "\n".join(self.observations)
+            if len(obs_str) > max_obs_len:
+                obs_str = obs_str[:max_obs_len] + "\n...[observations truncated]"
+            execution_prompt_str = (
+                (self.prompt or "")
+                + f"\n\nFollow this plan:\n{self.plan}"
+                + f"\n\nObservations:\n{obs_str}"
+                + f"\n\nIf there is enough data to complete user query '{query}', Respond with 'SATISFIED' only. Otherwise, continue. Dont Menstion 'SATISFIED' in your response if you are not ready. "
+            )
+
+            messages = self._build_messages(execution_prompt_str, query, retrieved_data)
+
+            resp_from_llm_gen = self._llm_gen(messages, log_context)
+
+            initial_llm_thought_content = self._extract_content_from_llm_response(
+                resp_from_llm_gen
+            )
+            if initial_llm_thought_content:
+                self.observations.append(
+                    f"Initial thought/response: {initial_llm_thought_content}"
+                )
+            else:
+                logger.info(
+                    "ReActAgent: Initial LLM response (before handler) had no textual content (might be only tool calls)."
+                )
+            resp_after_handler = self._llm_handler(
+                resp_from_llm_gen, tools_dict, messages, log_context
+            )
+
+            for (
+                tool_call_info
+            ) in (
+                self.tool_calls
+            ):  # Iterate over self.tool_calls populated by _llm_handler
+                observation_string = (
+                    f"Executed Action: Tool '{tool_call_info.get('tool_name', 'N/A')}' "
+                    f"with arguments '{tool_call_info.get('arguments', '{}')}'. Result: '{str(tool_call_info.get('result', ''))[:200]}...'"
+                )
+                self.observations.append(observation_string)
+
+            content_after_handler = self._extract_content_from_llm_response(
+                resp_after_handler
+            )
+            if content_after_handler:
+                self.observations.append(
+                    f"Response after tool execution: {content_after_handler}"
+                )
+            else:
+                logger.info(
+                    "ReActAgent: LLM response after handler had no textual content."
+                )
+
+            if log_context:
+                log_context.stacks.append(
+                    {
+                        "component": "agent_tool_calls",
+                        "data": {"tool_calls": self.tool_calls.copy()},
+                    }
+                )
+
+            yield {"sources": retrieved_data}
+
+            display_tool_calls = []
+            for tc in self.tool_calls:
+                cleaned_tc = tc.copy()
+                if len(str(cleaned_tc.get("result", ""))) > 50:
+                    cleaned_tc["result"] = str(cleaned_tc["result"])[:50] + "..."
+                display_tool_calls.append(cleaned_tc)
+            if display_tool_calls:
+                yield {"tool_calls": display_tool_calls}
+
+            if "SATISFIED" in content_after_handler:
+                logger.info(
+                    "ReActAgent: LLM satisfied with the plan and data. Stopping reasoning."
+                )
+                break
+
+        # 3. Create Final Answer based on all observations
+        final_answer_stream = self._create_final_answer(
+            query, self.observations, log_context
+        )
+        for answer_chunk in final_answer_stream:
+            yield {"answer": answer_chunk}
+        logger.info("ReActAgent: Finished generating final answer.")
+
+    def _create_plan(
+        self, query: str, docs_data: str, log_context: LogContext = None
+    ) -> Generator[str, None, None]:
+        plan_prompt_filled = planning_prompt_template.replace("{query}", query)
+        if "{summaries}" in plan_prompt_filled:
+            summaries = docs_data if docs_data else "No documents retrieved."
+            plan_prompt_filled = plan_prompt_filled.replace("{summaries}", summaries)
+        plan_prompt_filled = plan_prompt_filled.replace("{prompt}", self.prompt or "")
+        plan_prompt_filled = plan_prompt_filled.replace(
+            "{observations}", "\n".join(self.observations)
+        )
+
+        messages = [{"role": "user", "content": plan_prompt_filled}]
+
+        plan_stream_from_llm = self.llm.gen_stream(
+            model=self.gpt_model,
+            messages=messages,
+            tools=getattr(self, "tools", None),  # Use self.tools
+        )
+        if log_context:
+            data = build_stack_data(self.llm)
+            log_context.stacks.append({"component": "planning_llm", "data": data})
+
+        for chunk in plan_stream_from_llm:
+            content_piece = self._extract_content_from_llm_response(chunk)
+            if content_piece:
+                yield content_piece
+
+    def _create_final_answer(
+        self, query: str, observations: List[str], log_context: LogContext = None
+    ) -> Generator[str, None, None]:
+        observation_string = "\n".join(observations)
+        max_obs_len = 10000
+        if len(observation_string) > max_obs_len:
+            observation_string = (
+                observation_string[:max_obs_len] + "\n...[observations truncated]"
+            )
+            logger.warning(
+                "ReActAgent: Truncated observations for final answer prompt due to length."
+            )
+
+        final_answer_prompt_filled = final_prompt_template.format(
+            query=query, observations=observation_string
+        )
+
+        messages = [{"role": "user", "content": final_answer_prompt_filled}]
+
+        # Final answer should synthesize, not call tools.
+        final_answer_stream_from_llm = self.llm.gen_stream(
+            model=self.gpt_model, messages=messages, tools=None
+        )
+        if log_context:
+            data = build_stack_data(self.llm)
+            log_context.stacks.append({"component": "final_answer_llm", "data": data})
+
+        for chunk in final_answer_stream_from_llm:
+            content_piece = self._extract_content_from_llm_response(chunk)
+            if content_piece:
+                yield content_piece

application/agents/tools/api_tool.py

@@ -0,0 +1,72 @@
+import json
+
+import requests
+from application.agents.tools.base import Tool
+
+
+class APITool(Tool):
+    """
+    API Tool
+    A flexible tool for performing various API actions (e.g., sending messages, retrieving data) via custom user-specified APIs
+    """
+
+    def __init__(self, config):
+        self.config = config
+        self.url = config.get("url", "")
+        self.method = config.get("method", "GET")
+        self.headers = config.get("headers", {"Content-Type": "application/json"})
+        self.query_params = config.get("query_params", {})
+
+    def execute_action(self, action_name, **kwargs):
+        return self._make_api_call(
+            self.url, self.method, self.headers, self.query_params, kwargs
+        )
+
+    def _make_api_call(self, url, method, headers, query_params, body):
+        if query_params:
+            url = f"{url}?{requests.compat.urlencode(query_params)}"
+        # if isinstance(body, dict):
+        #     body = json.dumps(body)
+        try:
+            print(f"Making API call: {method} {url} with body: {body}")
+            if body == "{}":
+                body = None
+            response = requests.request(method, url, headers=headers, data=body)
+            response.raise_for_status()
+            content_type = response.headers.get(
+                "Content-Type", "application/json"
+            ).lower()
+            if "application/json" in content_type:
+                try:
+                    data = response.json()
+                except json.JSONDecodeError as e:
+                    print(f"Error decoding JSON: {e}.  Raw response: {response.text}")
+                    return {
+                        "status_code": response.status_code,
+                        "message": f"API call returned invalid JSON.  Error: {e}",
+                        "data": response.text,
+                    }
+            elif "text/" in content_type or "application/xml" in content_type:
+                data = response.text
+            elif not response.content:
+                data = None
+            else:
+                print(f"Unsupported content type: {content_type}")
+                data = response.content
+
+            return {
+                "status_code": response.status_code,
+                "data": data,
+                "message": "API call successful.",
+            }
+        except requests.exceptions.RequestException as e:
+            return {
+                "status_code": response.status_code if response else None,
+                "message": f"API call failed: {str(e)}",
+            }
+
+    def get_actions_metadata(self):
+        return []
+
+    def get_config_requirements(self):
+        return {}

application/agents/tools/base.py

@@ -0,0 +1,21 @@
+from abc import ABC, abstractmethod
+
+
+class Tool(ABC):
+    @abstractmethod
+    def execute_action(self, action_name: str, **kwargs):
+        pass
+
+    @abstractmethod
+    def get_actions_metadata(self):
+        """
+        Returns a list of JSON objects describing the actions supported by the tool.
+        """
+        pass
+
+    @abstractmethod
+    def get_config_requirements(self):
+        """
+        Returns a dictionary describing the configuration requirements for the tool.
+        """
+        pass

application/agents/tools/brave.py

@@ -0,0 +1,182 @@
+import requests
+from application.agents.tools.base import Tool
+
+
+class BraveSearchTool(Tool):
+    """
+    Brave Search
+    A tool for performing web and image searches using the Brave Search API.
+    Requires an API key for authentication.
+    """
+
+    def __init__(self, config):
+        self.config = config
+        self.token = config.get("token", "")
+        self.base_url = "https://api.search.brave.com/res/v1"
+
+    def execute_action(self, action_name, **kwargs):
+        actions = {
+            "brave_web_search": self._web_search,
+            "brave_image_search": self._image_search,
+        }
+
+        if action_name in actions:
+            return actions[action_name](**kwargs)
+        else:
+            raise ValueError(f"Unknown action: {action_name}")
+
+    def _web_search(
+        self,
+        query,
+        country="ALL",
+        search_lang="en",
+        count=10,
+        offset=0,
+        safesearch="off",
+        freshness=None,
+        result_filter=None,
+        extra_snippets=False,
+        summary=False,
+    ):
+        """
+        Performs a web search using the Brave Search API.
+        """
+        print(f"Performing Brave web search for: {query}")
+
+        url = f"{self.base_url}/web/search"
+
+        params = {
+            "q": query,
+            "country": country,
+            "search_lang": search_lang,
+            "count": min(count, 20),
+            "offset": min(offset, 9),
+            "safesearch": safesearch,
+        }
+
+        if freshness:
+            params["freshness"] = freshness
+        if result_filter:
+            params["result_filter"] = result_filter
+        if extra_snippets:
+            params["extra_snippets"] = 1
+        if summary:
+            params["summary"] = 1
+        headers = {
+            "Accept": "application/json",
+            "Accept-Encoding": "gzip",
+            "X-Subscription-Token": self.token,
+        }
+
+        response = requests.get(url, params=params, headers=headers)
+
+        if response.status_code == 200:
+            return {
+                "status_code": response.status_code,
+                "results": response.json(),
+                "message": "Search completed successfully.",
+            }
+        else:
+            return {
+                "status_code": response.status_code,
+                "message": f"Search failed with status code: {response.status_code}.",
+            }
+
+    def _image_search(
+        self,
+        query,
+        country="ALL",
+        search_lang="en",
+        count=5,
+        safesearch="off",
+        spellcheck=False,
+    ):
+        """
+        Performs an image search using the Brave Search API.
+        """
+        print(f"Performing Brave image search for: {query}")
+
+        url = f"{self.base_url}/images/search"
+
+        params = {
+            "q": query,
+            "country": country,
+            "search_lang": search_lang,
+            "count": min(count, 100),  # API max is 100
+            "safesearch": safesearch,
+            "spellcheck": 1 if spellcheck else 0,
+        }
+
+        headers = {
+            "Accept": "application/json",
+            "Accept-Encoding": "gzip",
+            "X-Subscription-Token": self.token,
+        }
+
+        response = requests.get(url, params=params, headers=headers)
+
+        if response.status_code == 200:
+            return {
+                "status_code": response.status_code,
+                "results": response.json(),
+                "message": "Image search completed successfully.",
+            }
+        else:
+            return {
+                "status_code": response.status_code,
+                "message": f"Image search failed with status code: {response.status_code}.",
+            }
+
+    def get_actions_metadata(self):
+        return [
+            {
+                "name": "brave_web_search",
+                "description": "Perform a web search using Brave Search",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "query": {
+                            "type": "string",
+                            "description": "The search query (max 400 characters, 50 words)",
+                        },
+                        "search_lang": {
+                            "type": "string",
+                            "description": "The search language preference (default: en)",
+                        },
+                        "freshness": {
+                            "type": "string",
+                            "description": "Time filter for results (pd: last 24h, pw: last week, pm: last month, py: last year)",
+                        },
+                    },
+                    "required": ["query"],
+                    "additionalProperties": False,
+                },
+            },
+            {
+                "name": "brave_image_search",
+                "description": "Perform an image search using Brave Search",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "query": {
+                            "type": "string",
+                            "description": "The search query (max 400 characters, 50 words)",
+                        },
+                        "count": {
+                            "type": "integer",
+                            "description": "Number of results to return (max 100, default: 5)",
+                        },
+                    },
+                    "required": ["query"],
+                    "additionalProperties": False,
+                },
+            },
+        ]
+
+    def get_config_requirements(self):
+        return {
+            "token": {
+                "type": "string",
+                "description": "Brave Search API key for authentication",
+            },
+        }

application/agents/tools/cryptoprice.py

@@ -0,0 +1,76 @@
+import requests
+from application.agents.tools.base import Tool
+
+
+class CryptoPriceTool(Tool):
+    """
+    CryptoPrice
+    A tool for retrieving cryptocurrency prices using the CryptoCompare public API
+    """
+
+    def __init__(self, config):
+        self.config = config
+
+    def execute_action(self, action_name, **kwargs):
+        actions = {"cryptoprice_get": self._get_price}
+
+        if action_name in actions:
+            return actions[action_name](**kwargs)
+        else:
+            raise ValueError(f"Unknown action: {action_name}")
+
+    def _get_price(self, symbol, currency):
+        """
+        Fetches the current price of a given cryptocurrency symbol in the specified currency.
+        Example:
+            symbol = "BTC"
+            currency = "USD"
+            returns price in USD.
+        """
+        url = f"https://min-api.cryptocompare.com/data/price?fsym={symbol.upper()}&tsyms={currency.upper()}"
+        response = requests.get(url)
+        if response.status_code == 200:
+            data = response.json()
+            if currency.upper() in data:
+                return {
+                    "status_code": response.status_code,
+                    "price": data[currency.upper()],
+                    "message": f"Price of {symbol.upper()} in {currency.upper()} retrieved successfully.",
+                }
+            else:
+                return {
+                    "status_code": response.status_code,
+                    "message": f"Couldn't find price for {symbol.upper()} in {currency.upper()}.",
+                }
+        else:
+            return {
+                "status_code": response.status_code,
+                "message": "Failed to retrieve price.",
+            }
+
+    def get_actions_metadata(self):
+        return [
+            {
+                "name": "cryptoprice_get",
+                "description": "Retrieve the price of a specified cryptocurrency in a given currency",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "symbol": {
+                            "type": "string",
+                            "description": "The cryptocurrency symbol (e.g. BTC)",
+                        },
+                        "currency": {
+                            "type": "string",
+                            "description": "The currency in which you want the price (e.g. USD)",
+                        },
+                    },
+                    "required": ["symbol", "currency"],
+                    "additionalProperties": False,
+                },
+            }
+        ]
+
+    def get_config_requirements(self):
+        # No specific configuration needed for this tool as it just queries a public endpoint
+        return {}

application/agents/tools/duckduckgo.py

@@ -0,0 +1,114 @@
+from application.agents.tools.base import Tool
+from duckduckgo_search import DDGS
+
+
+class DuckDuckGoSearchTool(Tool):
+    """
+    DuckDuckGo Search
+    A tool for performing web and image searches using DuckDuckGo.
+    """
+
+    def __init__(self, config):
+        self.config = config
+
+    def execute_action(self, action_name, **kwargs):
+        actions = {
+            "ddg_web_search": self._web_search,
+            "ddg_image_search": self._image_search,
+        }
+
+        if action_name in actions:
+            return actions[action_name](**kwargs)
+        else:
+            raise ValueError(f"Unknown action: {action_name}")
+
+    def _web_search(
+        self,
+        query,
+        max_results=5,
+    ):
+        print(f"Performing DuckDuckGo web search for: {query}")
+
+        try:
+            results = DDGS().text(
+                query,
+                max_results=max_results,
+            )
+
+            return {
+                "status_code": 200,
+                "results": results,
+                "message": "Web search completed successfully.",
+            }
+        except Exception as e:
+            return {
+                "status_code": 500,
+                "message": f"Web search failed: {str(e)}",
+            }
+
+    def _image_search(
+        self,
+        query,
+        max_results=5,
+    ):
+        print(f"Performing DuckDuckGo image search for: {query}")
+
+        try:
+            results = DDGS().images(
+                keywords=query,
+                max_results=max_results,
+            )
+
+            return {
+                "status_code": 200,
+                "results": results,
+                "message": "Image search completed successfully.",
+            }
+        except Exception as e:
+            return {
+                "status_code": 500,
+                "message": f"Image search failed: {str(e)}",
+            }
+
+    def get_actions_metadata(self):
+        return [
+            {
+                "name": "ddg_web_search",
+                "description": "Perform a web search using DuckDuckGo.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "query": {
+                            "type": "string",
+                            "description": "Search query",
+                        },
+                        "max_results": {
+                            "type": "integer",
+                            "description": "Number of results to return (default: 5)",
+                        },
+                    },
+                    "required": ["query"],
+                },
+            },
+            {
+                "name": "ddg_image_search",
+                "description": "Perform an image search using DuckDuckGo.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "query": {
+                            "type": "string",
+                            "description": "Search query",
+                        },
+                        "max_results": {
+                            "type": "integer",
+                            "description": "Number of results to return (default: 5, max: 50)",
+                        },
+                    },
+                    "required": ["query"],
+                },
+            },
+        ]
+
+    def get_config_requirements(self):
+        return {}

application/agents/tools/mcp_tool.py

@@ -0,0 +1,861 @@
+import asyncio
+import base64
+import json
+import logging
+import time
+from typing import Any, Dict, List, Optional
+from urllib.parse import parse_qs, urlparse
+
+from application.agents.tools.base import Tool
+from application.api.user.tasks import mcp_oauth_status_task, mcp_oauth_task
+from application.cache import get_redis_instance
+
+from application.core.mongo_db import MongoDB
+
+from application.core.settings import settings
+
+from application.security.encryption import decrypt_credentials
+from fastmcp import Client
+from fastmcp.client.auth import BearerAuth
+from fastmcp.client.transports import (
+    SSETransport,
+    StdioTransport,
+    StreamableHttpTransport,
+)
+from mcp.client.auth import OAuthClientProvider, TokenStorage
+from mcp.shared.auth import OAuthClientInformationFull, OAuthClientMetadata, OAuthToken
+
+from pydantic import AnyHttpUrl, ValidationError
+from redis import Redis
+
+mongo = MongoDB.get_client()
+db = mongo[settings.MONGO_DB_NAME]
+
+_mcp_clients_cache = {}
+
+
+class MCPTool(Tool):
+    """
+    MCP Tool
+    Connect to remote Model Context Protocol (MCP) servers to access dynamic tools and resources.
+    """
+
+    def __init__(self, config: Dict[str, Any], user_id: Optional[str] = None):
+        """
+        Initialize the MCP Tool with configuration.
+
+        Args:
+            config: Dictionary containing MCP server configuration:
+                - server_url: URL of the remote MCP server
+                - transport_type: Transport type (auto, sse, http, stdio)
+                - auth_type: Type of authentication (bearer, oauth, api_key, basic, none)
+                - encrypted_credentials: Encrypted credentials (if available)
+                - timeout: Request timeout in seconds (default: 30)
+                - headers: Custom headers for requests
+                - command: Command for STDIO transport
+                - args: Arguments for STDIO transport
+                - oauth_scopes: OAuth scopes for oauth auth type
+                - oauth_client_name: OAuth client name for oauth auth type
+            user_id: User ID for decrypting credentials (required if encrypted_credentials exist)
+        """
+        self.config = config
+        self.user_id = user_id
+        self.server_url = config.get("server_url", "")
+        self.transport_type = config.get("transport_type", "auto")
+        self.auth_type = config.get("auth_type", "none")
+        self.timeout = config.get("timeout", 30)
+        self.custom_headers = config.get("headers", {})
+
+        self.auth_credentials = {}
+        if config.get("encrypted_credentials") and user_id:
+            self.auth_credentials = decrypt_credentials(
+                config["encrypted_credentials"], user_id
+            )
+        else:
+            self.auth_credentials = config.get("auth_credentials", {})
+        self.oauth_scopes = config.get("oauth_scopes", [])
+        self.oauth_task_id = config.get("oauth_task_id", None)
+        self.oauth_client_name = config.get("oauth_client_name", "DocsGPT-MCP")
+        self.redirect_uri = f"{settings.API_URL}/api/mcp_server/callback"
+
+        self.available_tools = []
+        self._cache_key = self._generate_cache_key()
+        self._client = None
+
+        # Only validate and setup if server_url is provided and not OAuth
+
+        if self.server_url and self.auth_type != "oauth":
+            self._setup_client()
+
+    def _generate_cache_key(self) -> str:
+        """Generate a unique cache key for this MCP server configuration."""
+        auth_key = ""
+        if self.auth_type == "oauth":
+            scopes_str = ",".join(self.oauth_scopes) if self.oauth_scopes else "none"
+            auth_key = f"oauth:{self.oauth_client_name}:{scopes_str}"
+        elif self.auth_type in ["bearer"]:
+            token = self.auth_credentials.get(
+                "bearer_token", ""
+            ) or self.auth_credentials.get("access_token", "")
+            auth_key = f"bearer:{token[:10]}..." if token else "bearer:none"
+        elif self.auth_type == "api_key":
+            api_key = self.auth_credentials.get("api_key", "")
+            auth_key = f"apikey:{api_key[:10]}..." if api_key else "apikey:none"
+        elif self.auth_type == "basic":
+            username = self.auth_credentials.get("username", "")
+            auth_key = f"basic:{username}"
+        else:
+            auth_key = "none"
+        return f"{self.server_url}#{self.transport_type}#{auth_key}"
+
+    def _setup_client(self):
+        """Setup FastMCP client with proper transport and authentication."""
+        global _mcp_clients_cache
+        if self._cache_key in _mcp_clients_cache:
+            cached_data = _mcp_clients_cache[self._cache_key]
+            if time.time() - cached_data["created_at"] < 1800:
+                self._client = cached_data["client"]
+                return
+            else:
+                del _mcp_clients_cache[self._cache_key]
+        transport = self._create_transport()
+        auth = None
+
+        if self.auth_type == "oauth":
+            redis_client = get_redis_instance()
+            auth = DocsGPTOAuth(
+                mcp_url=self.server_url,
+                scopes=self.oauth_scopes,
+                redis_client=redis_client,
+                redirect_uri=self.redirect_uri,
+                task_id=self.oauth_task_id,
+                db=db,
+                user_id=self.user_id,
+            )
+        elif self.auth_type == "bearer":
+            token = self.auth_credentials.get(
+                "bearer_token", ""
+            ) or self.auth_credentials.get("access_token", "")
+            if token:
+                auth = BearerAuth(token)
+        self._client = Client(transport, auth=auth)
+        _mcp_clients_cache[self._cache_key] = {
+            "client": self._client,
+            "created_at": time.time(),
+        }
+
+    def _create_transport(self):
+        """Create appropriate transport based on configuration."""
+        headers = {"Content-Type": "application/json", "User-Agent": "DocsGPT-MCP/1.0"}
+        headers.update(self.custom_headers)
+
+        if self.auth_type == "api_key":
+            api_key = self.auth_credentials.get("api_key", "")
+            header_name = self.auth_credentials.get("api_key_header", "X-API-Key")
+            if api_key:
+                headers[header_name] = api_key
+        elif self.auth_type == "basic":
+            username = self.auth_credentials.get("username", "")
+            password = self.auth_credentials.get("password", "")
+            if username and password:
+                credentials = base64.b64encode(
+                    f"{username}:{password}".encode()
+                ).decode()
+                headers["Authorization"] = f"Basic {credentials}"
+        if self.transport_type == "auto":
+            if "sse" in self.server_url.lower() or self.server_url.endswith("/sse"):
+                transport_type = "sse"
+            else:
+                transport_type = "http"
+        else:
+            transport_type = self.transport_type
+        if transport_type == "sse":
+            headers.update({"Accept": "text/event-stream", "Cache-Control": "no-cache"})
+            return SSETransport(url=self.server_url, headers=headers)
+        elif transport_type == "http":
+            return StreamableHttpTransport(url=self.server_url, headers=headers)
+        elif transport_type == "stdio":
+            command = self.config.get("command", "python")
+            args = self.config.get("args", [])
+            env = self.auth_credentials if self.auth_credentials else None
+            return StdioTransport(command=command, args=args, env=env)
+        else:
+            return StreamableHttpTransport(url=self.server_url, headers=headers)
+
+    def _format_tools(self, tools_response) -> List[Dict]:
+        """Format tools response to match expected format."""
+        if hasattr(tools_response, "tools"):
+            tools = tools_response.tools
+        elif isinstance(tools_response, list):
+            tools = tools_response
+        else:
+            tools = []
+        tools_dict = []
+        for tool in tools:
+            if hasattr(tool, "name"):
+                tool_dict = {
+                    "name": tool.name,
+                    "description": tool.description,
+                }
+                if hasattr(tool, "inputSchema"):
+                    tool_dict["inputSchema"] = tool.inputSchema
+                tools_dict.append(tool_dict)
+            elif isinstance(tool, dict):
+                tools_dict.append(tool)
+            else:
+                if hasattr(tool, "model_dump"):
+                    tools_dict.append(tool.model_dump())
+                else:
+                    tools_dict.append({"name": str(tool), "description": ""})
+        return tools_dict
+
+    async def _execute_with_client(self, operation: str, *args, **kwargs):
+        """Execute operation with FastMCP client."""
+        if not self._client:
+            raise Exception("FastMCP client not initialized")
+        async with self._client:
+            if operation == "ping":
+                return await self._client.ping()
+            elif operation == "list_tools":
+                tools_response = await self._client.list_tools()
+                self.available_tools = self._format_tools(tools_response)
+                return self.available_tools
+            elif operation == "call_tool":
+                tool_name = args[0]
+                tool_args = kwargs
+                return await self._client.call_tool(tool_name, tool_args)
+            elif operation == "list_resources":
+                return await self._client.list_resources()
+            elif operation == "list_prompts":
+                return await self._client.list_prompts()
+            else:
+                raise Exception(f"Unknown operation: {operation}")
+
+    def _run_async_operation(self, operation: str, *args, **kwargs):
+        """Run async operation in sync context."""
+        try:
+            try:
+                loop = asyncio.get_running_loop()
+                import concurrent.futures
+
+                def run_in_thread():
+                    new_loop = asyncio.new_event_loop()
+                    asyncio.set_event_loop(new_loop)
+                    try:
+                        return new_loop.run_until_complete(
+                            self._execute_with_client(operation, *args, **kwargs)
+                        )
+                    finally:
+                        new_loop.close()
+
+                with concurrent.futures.ThreadPoolExecutor() as executor:
+                    future = executor.submit(run_in_thread)
+                    return future.result(timeout=self.timeout)
+            except RuntimeError:
+                loop = asyncio.new_event_loop()
+                asyncio.set_event_loop(loop)
+                try:
+                    return loop.run_until_complete(
+                        self._execute_with_client(operation, *args, **kwargs)
+                    )
+                finally:
+                    loop.close()
+        except Exception as e:
+            print(f"Error occurred while running async operation: {e}")
+            raise
+
+    def discover_tools(self) -> List[Dict]:
+        """
+        Discover available tools from the MCP server using FastMCP.
+
+        Returns:
+            List of tool definitions from the server
+        """
+        if not self.server_url:
+            return []
+        if not self._client:
+            self._setup_client()
+        try:
+            tools = self._run_async_operation("list_tools")
+            self.available_tools = tools
+            return self.available_tools
+        except Exception as e:
+            raise Exception(f"Failed to discover tools from MCP server: {str(e)}")
+
+    def execute_action(self, action_name: str, **kwargs) -> Any:
+        """
+        Execute an action on the remote MCP server using FastMCP.
+
+        Args:
+            action_name: Name of the action to execute
+            **kwargs: Parameters for the action
+
+        Returns:
+            Result from the MCP server
+        """
+        if not self.server_url:
+            raise Exception("No MCP server configured")
+        if not self._client:
+            self._setup_client()
+        cleaned_kwargs = {}
+        for key, value in kwargs.items():
+            if value == "" or value is None:
+                continue
+            cleaned_kwargs[key] = value
+        try:
+            result = self._run_async_operation(
+                "call_tool", action_name, **cleaned_kwargs
+            )
+            return self._format_result(result)
+        except Exception as e:
+            raise Exception(f"Failed to execute action '{action_name}': {str(e)}")
+
+    def _format_result(self, result) -> Dict:
+        """Format FastMCP result to match expected format."""
+        if hasattr(result, "content"):
+            content_list = []
+            for content_item in result.content:
+                if hasattr(content_item, "text"):
+                    content_list.append({"type": "text", "text": content_item.text})
+                elif hasattr(content_item, "data"):
+                    content_list.append({"type": "data", "data": content_item.data})
+                else:
+                    content_list.append(
+                        {"type": "unknown", "content": str(content_item)}
+                    )
+            return {
+                "content": content_list,
+                "isError": getattr(result, "isError", False),
+            }
+        else:
+            return result
+
+    def test_connection(self) -> Dict:
+        """
+        Test the connection to the MCP server and validate functionality.
+
+        Returns:
+            Dictionary with connection test results including tool count
+        """
+        if not self.server_url:
+            return {
+                "success": False,
+                "message": "No MCP server URL configured",
+                "tools_count": 0,
+                "transport_type": self.transport_type,
+                "auth_type": self.auth_type,
+                "error_type": "ConfigurationError",
+            }
+        if not self._client:
+            self._setup_client()
+        try:
+            if self.auth_type == "oauth":
+                return self._test_oauth_connection()
+            else:
+                return self._test_regular_connection()
+        except Exception as e:
+            return {
+                "success": False,
+                "message": f"Connection failed: {str(e)}",
+                "tools_count": 0,
+                "transport_type": self.transport_type,
+                "auth_type": self.auth_type,
+                "error_type": type(e).__name__,
+            }
+
+    def _test_regular_connection(self) -> Dict:
+        """Test connection for non-OAuth auth types."""
+        try:
+            self._run_async_operation("ping")
+            ping_success = True
+        except Exception:
+            ping_success = False
+        tools = self.discover_tools()
+
+        message = f"Successfully connected to MCP server. Found {len(tools)} tools."
+        if not ping_success:
+            message += " (Ping not supported, but tool discovery worked)"
+        return {
+            "success": True,
+            "message": message,
+            "tools_count": len(tools),
+            "transport_type": self.transport_type,
+            "auth_type": self.auth_type,
+            "ping_supported": ping_success,
+            "tools": [tool.get("name", "unknown") for tool in tools],
+        }
+
+    def _test_oauth_connection(self) -> Dict:
+        """Test connection for OAuth auth type with proper async handling."""
+        try:
+            task = mcp_oauth_task.delay(config=self.config, user=self.user_id)
+            if not task:
+                raise Exception("Failed to start OAuth authentication")
+            return {
+                "success": True,
+                "requires_oauth": True,
+                "task_id": task.id,
+                "status": "pending",
+                "message": "OAuth flow started",
+            }
+        except Exception as e:
+            return {
+                "success": False,
+                "message": f"OAuth connection failed: {str(e)}",
+                "tools_count": 0,
+                "transport_type": self.transport_type,
+                "auth_type": self.auth_type,
+                "error_type": type(e).__name__,
+            }
+
+    def get_actions_metadata(self) -> List[Dict]:
+        """
+        Get metadata for all available actions.
+
+        Returns:
+            List of action metadata dictionaries
+        """
+        actions = []
+        for tool in self.available_tools:
+            input_schema = (
+                tool.get("inputSchema")
+                or tool.get("input_schema")
+                or tool.get("schema")
+                or tool.get("parameters")
+            )
+
+            parameters_schema = {
+                "type": "object",
+                "properties": {},
+                "required": [],
+            }
+
+            if input_schema:
+                if isinstance(input_schema, dict):
+                    if "properties" in input_schema:
+                        parameters_schema = {
+                            "type": input_schema.get("type", "object"),
+                            "properties": input_schema.get("properties", {}),
+                            "required": input_schema.get("required", []),
+                        }
+
+                        for key in ["additionalProperties", "description"]:
+                            if key in input_schema:
+                                parameters_schema[key] = input_schema[key]
+                    else:
+                        parameters_schema["properties"] = input_schema
+            action = {
+                "name": tool.get("name", ""),
+                "description": tool.get("description", ""),
+                "parameters": parameters_schema,
+            }
+            actions.append(action)
+        return actions
+
+    def get_config_requirements(self) -> Dict:
+        """Get configuration requirements for the MCP tool."""
+        return {
+            "server_url": {
+                "type": "string",
+                "description": "URL of the remote MCP server (e.g., https://api.example.com/mcp or https://docs.mcp.cloudflare.com/sse)",
+                "required": True,
+            },
+            "transport_type": {
+                "type": "string",
+                "description": "Transport type for connection",
+                "enum": ["auto", "sse", "http", "stdio"],
+                "default": "auto",
+                "required": False,
+                "help": {
+                    "auto": "Automatically detect best transport",
+                    "sse": "Server-Sent Events (for real-time streaming)",
+                    "http": "HTTP streaming (recommended for production)",
+                    "stdio": "Standard I/O (for local servers)",
+                },
+            },
+            "auth_type": {
+                "type": "string",
+                "description": "Authentication type",
+                "enum": ["none", "bearer", "oauth", "api_key", "basic"],
+                "default": "none",
+                "required": True,
+                "help": {
+                    "none": "No authentication",
+                    "bearer": "Bearer token authentication",
+                    "oauth": "OAuth 2.1 authentication (with frontend integration)",
+                    "api_key": "API key authentication",
+                    "basic": "Basic authentication",
+                },
+            },
+            "auth_credentials": {
+                "type": "object",
+                "description": "Authentication credentials (varies by auth_type)",
+                "required": False,
+                "properties": {
+                    "bearer_token": {
+                        "type": "string",
+                        "description": "Bearer token for bearer auth",
+                    },
+                    "access_token": {
+                        "type": "string",
+                        "description": "Access token for OAuth (if pre-obtained)",
+                    },
+                    "api_key": {
+                        "type": "string",
+                        "description": "API key for api_key auth",
+                    },
+                    "api_key_header": {
+                        "type": "string",
+                        "description": "Header name for API key (default: X-API-Key)",
+                    },
+                    "username": {
+                        "type": "string",
+                        "description": "Username for basic auth",
+                    },
+                    "password": {
+                        "type": "string",
+                        "description": "Password for basic auth",
+                    },
+                },
+            },
+            "oauth_scopes": {
+                "type": "array",
+                "description": "OAuth scopes to request (for oauth auth_type)",
+                "items": {"type": "string"},
+                "required": False,
+                "default": [],
+            },
+            "oauth_client_name": {
+                "type": "string",
+                "description": "Client name for OAuth registration (for oauth auth_type)",
+                "default": "DocsGPT-MCP",
+                "required": False,
+            },
+            "headers": {
+                "type": "object",
+                "description": "Custom headers to send with requests",
+                "required": False,
+            },
+            "timeout": {
+                "type": "integer",
+                "description": "Request timeout in seconds",
+                "default": 30,
+                "minimum": 1,
+                "maximum": 300,
+                "required": False,
+            },
+            "command": {
+                "type": "string",
+                "description": "Command to run for STDIO transport (e.g., 'python')",
+                "required": False,
+            },
+            "args": {
+                "type": "array",
+                "description": "Arguments for STDIO command",
+                "items": {"type": "string"},
+                "required": False,
+            },
+        }
+
+
+class DocsGPTOAuth(OAuthClientProvider):
+    """
+    Custom OAuth handler for DocsGPT that uses frontend redirect instead of browser.
+    """
+
+    def __init__(
+        self,
+        mcp_url: str,
+        redirect_uri: str,
+        redis_client: Redis | None = None,
+        redis_prefix: str = "mcp_oauth:",
+        task_id: str = None,
+        scopes: str | list[str] | None = None,
+        client_name: str = "DocsGPT-MCP",
+        user_id=None,
+        db=None,
+        additional_client_metadata: dict[str, Any] | None = None,
+    ):
+        """
+        Initialize custom OAuth client provider for DocsGPT.
+
+        Args:
+            mcp_url: Full URL to the MCP endpoint
+            redirect_uri: Custom redirect URI for DocsGPT frontend
+            redis_client: Redis client for storing auth state
+            redis_prefix: Prefix for Redis keys
+            task_id: Task ID for tracking auth status
+            scopes: OAuth scopes to request
+            client_name: Name for this client during registration
+            user_id: User ID for token storage
+            db: Database instance for token storage
+            additional_client_metadata: Extra fields for OAuthClientMetadata
+        """
+
+        self.redirect_uri = redirect_uri
+        self.redis_client = redis_client
+        self.redis_prefix = redis_prefix
+        self.task_id = task_id
+        self.user_id = user_id
+        self.db = db
+
+        parsed_url = urlparse(mcp_url)
+        self.server_base_url = f"{parsed_url.scheme}://{parsed_url.netloc}"
+
+        if isinstance(scopes, list):
+            scopes = " ".join(scopes)
+        client_metadata = OAuthClientMetadata(
+            client_name=client_name,
+            redirect_uris=[AnyHttpUrl(redirect_uri)],
+            grant_types=["authorization_code", "refresh_token"],
+            response_types=["code"],
+            scope=scopes,
+            **(additional_client_metadata or {}),
+        )
+
+        storage = DBTokenStorage(
+            server_url=self.server_base_url, user_id=self.user_id, db_client=self.db
+        )
+
+        super().__init__(
+            server_url=self.server_base_url,
+            client_metadata=client_metadata,
+            storage=storage,
+            redirect_handler=self.redirect_handler,
+            callback_handler=self.callback_handler,
+        )
+
+        self.auth_url = None
+        self.extracted_state = None
+
+    def _process_auth_url(self, authorization_url: str) -> tuple[str, str]:
+        """Process authorization URL to extract state"""
+        try:
+            parsed_url = urlparse(authorization_url)
+            query_params = parse_qs(parsed_url.query)
+
+            state_params = query_params.get("state", [])
+            if state_params:
+                state = state_params[0]
+            else:
+                raise ValueError("No state in auth URL")
+            return authorization_url, state
+        except Exception as e:
+            raise Exception(f"Failed to process auth URL: {e}")
+
+    async def redirect_handler(self, authorization_url: str) -> None:
+        """Store auth URL and state in Redis for frontend to use."""
+        auth_url, state = self._process_auth_url(authorization_url)
+        logging.info(
+            "[DocsGPTOAuth] Processed auth_url: %s, state: %s", auth_url, state
+        )
+        self.auth_url = auth_url
+        self.extracted_state = state
+
+        if self.redis_client and self.extracted_state:
+            key = f"{self.redis_prefix}auth_url:{self.extracted_state}"
+            self.redis_client.setex(key, 600, auth_url)
+            logging.info("[DocsGPTOAuth] Stored auth_url in Redis: %s", key)
+
+            if self.task_id:
+                status_key = f"mcp_oauth_status:{self.task_id}"
+                status_data = {
+                    "status": "requires_redirect",
+                    "message": "OAuth authorization required",
+                    "authorization_url": self.auth_url,
+                    "state": self.extracted_state,
+                    "requires_oauth": True,
+                    "task_id": self.task_id,
+                }
+                self.redis_client.setex(status_key, 600, json.dumps(status_data))
+
+    async def callback_handler(self) -> tuple[str, str | None]:
+        """Wait for auth code from Redis using the state value."""
+        if not self.redis_client or not self.extracted_state:
+            raise Exception("Redis client or state not configured for OAuth")
+        poll_interval = 1
+        max_wait_time = 300
+        code_key = f"{self.redis_prefix}code:{self.extracted_state}"
+
+        if self.task_id:
+            status_key = f"mcp_oauth_status:{self.task_id}"
+            status_data = {
+                "status": "awaiting_callback",
+                "message": "Waiting for OAuth callback...",
+                "authorization_url": self.auth_url,
+                "state": self.extracted_state,
+                "requires_oauth": True,
+                "task_id": self.task_id,
+            }
+            self.redis_client.setex(status_key, 600, json.dumps(status_data))
+        start_time = time.time()
+        while time.time() - start_time < max_wait_time:
+            code_data = self.redis_client.get(code_key)
+            if code_data:
+                code = code_data.decode()
+                returned_state = self.extracted_state
+
+                self.redis_client.delete(code_key)
+                self.redis_client.delete(
+                    f"{self.redis_prefix}auth_url:{self.extracted_state}"
+                )
+                self.redis_client.delete(
+                    f"{self.redis_prefix}state:{self.extracted_state}"
+                )
+
+                if self.task_id:
+                    status_data = {
+                        "status": "callback_received",
+                        "message": "OAuth callback received, completing authentication...",
+                        "task_id": self.task_id,
+                    }
+                    self.redis_client.setex(status_key, 600, json.dumps(status_data))
+                return code, returned_state
+            error_key = f"{self.redis_prefix}error:{self.extracted_state}"
+            error_data = self.redis_client.get(error_key)
+            if error_data:
+                error_msg = error_data.decode()
+                self.redis_client.delete(error_key)
+                self.redis_client.delete(
+                    f"{self.redis_prefix}auth_url:{self.extracted_state}"
+                )
+                self.redis_client.delete(
+                    f"{self.redis_prefix}state:{self.extracted_state}"
+                )
+                raise Exception(f"OAuth error: {error_msg}")
+            await asyncio.sleep(poll_interval)
+        self.redis_client.delete(f"{self.redis_prefix}auth_url:{self.extracted_state}")
+        self.redis_client.delete(f"{self.redis_prefix}state:{self.extracted_state}")
+        raise Exception("OAuth callback timeout: no code received within 5 minutes")
+
+
+class DBTokenStorage(TokenStorage):
+    def __init__(self, server_url: str, user_id: str, db_client):
+        self.server_url = server_url
+        self.user_id = user_id
+        self.db_client = db_client
+        self.collection = db_client["connector_sessions"]
+
+    @staticmethod
+    def get_base_url(url: str) -> str:
+        parsed = urlparse(url)
+        return f"{parsed.scheme}://{parsed.netloc}"
+
+    def get_db_key(self) -> dict:
+        return {
+            "server_url": self.get_base_url(self.server_url),
+            "user_id": self.user_id,
+        }
+
+    async def get_tokens(self) -> OAuthToken | None:
+        doc = await asyncio.to_thread(self.collection.find_one, self.get_db_key())
+        if not doc or "tokens" not in doc:
+            return None
+        try:
+            tokens = OAuthToken.model_validate(doc["tokens"])
+            return tokens
+        except ValidationError as e:
+            logging.error(f"Could not load tokens: {e}")
+            return None
+
+    async def set_tokens(self, tokens: OAuthToken) -> None:
+        await asyncio.to_thread(
+            self.collection.update_one,
+            self.get_db_key(),
+            {"$set": {"tokens": tokens.model_dump()}},
+            True,
+        )
+        logging.info(f"Saved tokens for {self.get_base_url(self.server_url)}")
+
+    async def get_client_info(self) -> OAuthClientInformationFull | None:
+        doc = await asyncio.to_thread(self.collection.find_one, self.get_db_key())
+        if not doc or "client_info" not in doc:
+            return None
+        try:
+            client_info = OAuthClientInformationFull.model_validate(doc["client_info"])
+            tokens = await self.get_tokens()
+            if tokens is None:
+                logging.debug(
+                    "No tokens found, clearing client info to force fresh registration."
+                )
+                await asyncio.to_thread(
+                    self.collection.update_one,
+                    self.get_db_key(),
+                    {"$unset": {"client_info": ""}},
+                )
+                return None
+            return client_info
+        except ValidationError as e:
+            logging.error(f"Could not load client info: {e}")
+            return None
+
+    def _serialize_client_info(self, info: dict) -> dict:
+        if "redirect_uris" in info and isinstance(info["redirect_uris"], list):
+            info["redirect_uris"] = [str(u) for u in info["redirect_uris"]]
+        return info
+
+    async def set_client_info(self, client_info: OAuthClientInformationFull) -> None:
+        serialized_info = self._serialize_client_info(client_info.model_dump())
+        await asyncio.to_thread(
+            self.collection.update_one,
+            self.get_db_key(),
+            {"$set": {"client_info": serialized_info}},
+            True,
+        )
+        logging.info(f"Saved client info for {self.get_base_url(self.server_url)}")
+
+    async def clear(self) -> None:
+        await asyncio.to_thread(self.collection.delete_one, self.get_db_key())
+        logging.info(f"Cleared OAuth cache for {self.get_base_url(self.server_url)}")
+
+    @classmethod
+    async def clear_all(cls, db_client) -> None:
+        collection = db_client["connector_sessions"]
+        await asyncio.to_thread(collection.delete_many, {})
+        logging.info("Cleared all OAuth client cache data.")
+
+
+class MCPOAuthManager:
+    """Manager for handling MCP OAuth callbacks."""
+
+    def __init__(self, redis_client: Redis | None, redis_prefix: str = "mcp_oauth:"):
+        self.redis_client = redis_client
+        self.redis_prefix = redis_prefix
+
+    def handle_oauth_callback(
+        self, state: str, code: str, error: Optional[str] = None
+    ) -> bool:
+        """
+        Handle OAuth callback from provider.
+
+        Args:
+            state: The state parameter from OAuth callback
+            code: The authorization code from OAuth callback
+            error: Error message if OAuth failed
+
+        Returns:
+            True if successful, False otherwise
+        """
+        try:
+            if not self.redis_client or not state:
+                raise Exception("Redis client or state not provided")
+            if error:
+                error_key = f"{self.redis_prefix}error:{state}"
+                self.redis_client.setex(error_key, 300, error)
+                raise Exception(f"OAuth error received: {error}")
+            code_key = f"{self.redis_prefix}code:{state}"
+            self.redis_client.setex(code_key, 300, code)
+
+            state_key = f"{self.redis_prefix}state:{state}"
+            self.redis_client.setex(state_key, 300, "completed")
+
+            return True
+        except Exception as e:
+            logging.error(f"Error handling OAuth callback: {e}")
+            return False
+
+    def get_oauth_status(self, task_id: str) -> Dict[str, Any]:
+        """Get current status of OAuth flow using provided task_id."""
+        if not task_id:
+            return {"status": "not_started", "message": "OAuth flow not started"}
+        return mcp_oauth_status_task(task_id)

application/agents/tools/memory.py

@@ -0,0 +1,546 @@
+from datetime import datetime
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+import re
+import uuid
+
+from .base import Tool
+from application.core.mongo_db import MongoDB
+from application.core.settings import settings
+
+
+class MemoryTool(Tool):
+    """Memory
+
+    Stores and retrieves information across conversations through a memory file directory.
+    """
+
+    def __init__(self, tool_config: Optional[Dict[str, Any]] = None, user_id: Optional[str] = None) -> None:
+        """Initialize the tool.
+
+        Args:
+            tool_config: Optional tool configuration. Should include:
+                - tool_id: Unique identifier for this memory tool instance (from user_tools._id)
+                           This ensures each user's tool configuration has isolated memories
+            user_id: The authenticated user's id (should come from decoded_token["sub"]).
+        """
+        self.user_id: Optional[str] = user_id
+
+        # Get tool_id from configuration (passed from user_tools._id in production)
+        # In production, tool_id is the MongoDB ObjectId string from user_tools collection
+        if tool_config and "tool_id" in tool_config:
+            self.tool_id = tool_config["tool_id"]
+        elif user_id:
+            # Fallback for backward compatibility or testing
+            self.tool_id = f"default_{user_id}"
+        else:
+            # Last resort fallback (shouldn't happen in normal use)
+            self.tool_id = str(uuid.uuid4())
+
+        db = MongoDB.get_client()[settings.MONGO_DB_NAME]
+        self.collection = db["memories"]
+
+    # -----------------------------
+    # Action implementations
+    # -----------------------------
+    def execute_action(self, action_name: str, **kwargs: Any) -> str:
+        """Execute an action by name.
+
+        Args:
+            action_name: One of view, create, str_replace, insert, delete, rename.
+            **kwargs: Parameters for the action.
+
+        Returns:
+            A human-readable string result.
+        """
+        if not self.user_id:
+            return "Error: MemoryTool requires a valid user_id."
+
+        if action_name == "view":
+            return self._view(
+                kwargs.get("path", "/"),
+                kwargs.get("view_range")
+            )
+
+        if action_name == "create":
+            return self._create(
+                kwargs.get("path", ""),
+                kwargs.get("file_text", "")
+            )
+
+        if action_name == "str_replace":
+            return self._str_replace(
+                kwargs.get("path", ""),
+                kwargs.get("old_str", ""),
+                kwargs.get("new_str", "")
+            )
+
+        if action_name == "insert":
+            return self._insert(
+                kwargs.get("path", ""),
+                kwargs.get("insert_line", 1),
+                kwargs.get("insert_text", "")
+            )
+
+        if action_name == "delete":
+            return self._delete(kwargs.get("path", ""))
+
+        if action_name == "rename":
+            return self._rename(
+                kwargs.get("old_path", ""),
+                kwargs.get("new_path", "")
+            )
+
+        return f"Unknown action: {action_name}"
+
+    def get_actions_metadata(self) -> List[Dict[str, Any]]:
+        """Return JSON metadata describing supported actions for tool schemas."""
+        return [
+            {
+                "name": "view",
+                "description": "Shows directory contents or file contents with optional line ranges.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "path": {
+                            "type": "string",
+                            "description": "Path to file or directory (e.g., /notes.txt or /project/ or /)."
+                        },
+                        "view_range": {
+                            "type": "array",
+                            "items": {"type": "integer"},
+                            "description": "Optional [start_line, end_line] to view specific lines (1-indexed)."
+                        }
+                    },
+                    "required": ["path"]
+                },
+            },
+            {
+                "name": "create",
+                "description": "Create or overwrite a file.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "path": {
+                            "type": "string",
+                            "description": "File path to create (e.g., /notes.txt or /project/task.txt)."
+                        },
+                        "file_text": {
+                            "type": "string",
+                            "description": "Content to write to the file."
+                        }
+                    },
+                    "required": ["path", "file_text"]
+                },
+            },
+            {
+                "name": "str_replace",
+                "description": "Replace text in a file.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "path": {
+                            "type": "string",
+                            "description": "File path (e.g., /notes.txt)."
+                        },
+                        "old_str": {
+                            "type": "string",
+                            "description": "String to find."
+                        },
+                        "new_str": {
+                            "type": "string",
+                            "description": "String to replace with."
+                        }
+                    },
+                    "required": ["path", "old_str", "new_str"]
+                },
+            },
+            {
+                "name": "insert",
+                "description": "Insert text at a specific line in a file.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "path": {
+                            "type": "string",
+                            "description": "File path (e.g., /notes.txt)."
+                        },
+                        "insert_line": {
+                            "type": "integer",
+                            "description": "Line number to insert at (1-indexed)."
+                        },
+                        "insert_text": {
+                            "type": "string",
+                            "description": "Text to insert."
+                        }
+                    },
+                    "required": ["path", "insert_line", "insert_text"]
+                },
+            },
+            {
+                "name": "delete",
+                "description": "Delete a file or directory.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "path": {
+                            "type": "string",
+                            "description": "Path to delete (e.g., /notes.txt or /project/)."
+                        }
+                    },
+                    "required": ["path"]
+                },
+            },
+            {
+                "name": "rename",
+                "description": "Rename or move a file/directory.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "old_path": {
+                            "type": "string",
+                            "description": "Current path (e.g., /old.txt)."
+                        },
+                        "new_path": {
+                            "type": "string",
+                            "description": "New path (e.g., /new.txt)."
+                        }
+                    },
+                    "required": ["old_path", "new_path"]
+                },
+            },
+        ]
+
+    def get_config_requirements(self) -> Dict[str, Any]:
+        """Return configuration requirements."""
+        return {}
+
+    # -----------------------------
+    # Path validation
+    # -----------------------------
+    def _validate_path(self, path: str) -> Optional[str]:
+        """Validate and normalize path.
+
+        Args:
+            path: User-provided path.
+
+        Returns:
+            Normalized path or None if invalid.
+        """
+        if not path:
+            return None
+
+        # Remove any leading/trailing whitespace
+        path = path.strip()
+
+        # Preserve whether path ends with / (indicates directory)
+        is_directory = path.endswith("/")
+
+        # Ensure path starts with / for consistency
+        if not path.startswith("/"):
+            path = "/" + path
+
+        # Check for directory traversal patterns
+        if ".." in path or path.count("//") > 0:
+            return None
+
+        # Normalize the path
+        try:
+            # Convert to Path object and resolve to canonical form
+            normalized = str(Path(path).as_posix())
+
+            # Ensure it still starts with /
+            if not normalized.startswith("/"):
+                return None
+
+            # Preserve trailing slash for directories
+            if is_directory and not normalized.endswith("/") and normalized != "/":
+                normalized = normalized + "/"
+
+            return normalized
+        except Exception:
+            return None
+
+    # -----------------------------
+    # Internal helpers
+    # -----------------------------
+    def _view(self, path: str, view_range: Optional[List[int]] = None) -> str:
+        """View directory contents or file contents."""
+        validated_path = self._validate_path(path)
+        if not validated_path:
+            return "Error: Invalid path."
+
+        # Check if viewing directory (ends with / or is root)
+        if validated_path == "/" or validated_path.endswith("/"):
+            return self._view_directory(validated_path)
+
+        # Otherwise view file
+        return self._view_file(validated_path, view_range)
+
+    def _view_directory(self, path: str) -> str:
+        """List files in a directory."""
+        # Ensure path ends with / for proper prefix matching
+        search_path = path if path.endswith("/") else path + "/"
+
+        # Find all files that start with this directory path
+        query = {
+            "user_id": self.user_id,
+            "tool_id": self.tool_id,
+            "path": {"$regex": f"^{re.escape(search_path)}"}
+        }
+
+        docs = list(self.collection.find(query, {"path": 1}))
+
+        if not docs:
+            return f"Directory: {path}\n(empty)"
+
+        # Extract filenames relative to the directory
+        files = []
+        for doc in docs:
+            file_path = doc["path"]
+            # Remove the directory prefix
+            if file_path.startswith(search_path):
+                relative = file_path[len(search_path):]
+                if relative:
+                    files.append(relative)
+
+        files.sort()
+        file_list = "\n".join(f"- {f}" for f in files)
+        return f"Directory: {path}\n{file_list}"
+
+    def _view_file(self, path: str, view_range: Optional[List[int]] = None) -> str:
+        """View file contents with optional line range."""
+        doc = self.collection.find_one({"user_id": self.user_id, "tool_id": self.tool_id, "path": path})
+
+        if not doc or not doc.get("content"):
+            return f"Error: File not found: {path}"
+
+        content = str(doc["content"])
+
+        # Apply view_range if specified
+        if view_range and len(view_range) == 2:
+            lines = content.split("\n")
+            start, end = view_range
+            # Convert to 0-indexed
+            start_idx = max(0, start - 1)
+            end_idx = min(len(lines), end)
+
+            if start_idx >= len(lines):
+                return f"Error: Line range out of bounds. File has {len(lines)} lines."
+
+            selected_lines = lines[start_idx:end_idx]
+            # Add line numbers (enumerate with 1-based start)
+            numbered_lines = [f"{i}: {line}" for i, line in enumerate(selected_lines, start=start)]
+            return "\n".join(numbered_lines)
+
+        return content
+
+    def _create(self, path: str, file_text: str) -> str:
+        """Create or overwrite a file."""
+        validated_path = self._validate_path(path)
+        if not validated_path:
+            return "Error: Invalid path."
+
+        if validated_path == "/" or validated_path.endswith("/"):
+            return "Error: Cannot create a file at directory path."
+
+        self.collection.update_one(
+            {"user_id": self.user_id, "tool_id": self.tool_id, "path": validated_path},
+            {
+                "$set": {
+                    "content": file_text,
+                    "updated_at": datetime.now()
+                }
+            },
+            upsert=True
+        )
+
+        return f"File created: {validated_path}"
+
+    def _str_replace(self, path: str, old_str: str, new_str: str) -> str:
+        """Replace text in a file."""
+        validated_path = self._validate_path(path)
+        if not validated_path:
+            return "Error: Invalid path."
+
+        if not old_str:
+            return "Error: old_str is required."
+
+        doc = self.collection.find_one({"user_id": self.user_id, "tool_id": self.tool_id, "path": validated_path})
+
+        if not doc or not doc.get("content"):
+            return f"Error: File not found: {validated_path}"
+
+        current_content = str(doc["content"])
+
+        # Check if old_str exists (case-insensitive)
+        if old_str.lower() not in current_content.lower():
+            return f"Error: String '{old_str}' not found in file."
+
+        # Replace the string (case-insensitive)
+        import re as regex_module
+        updated_content = regex_module.sub(regex_module.escape(old_str), new_str, current_content, flags=regex_module.IGNORECASE)
+
+        self.collection.update_one(
+            {"user_id": self.user_id, "tool_id": self.tool_id, "path": validated_path},
+            {
+                "$set": {
+                    "content": updated_content,
+                    "updated_at": datetime.now()
+                }
+            }
+        )
+
+        return f"File updated: {validated_path}"
+
+    def _insert(self, path: str, insert_line: int, insert_text: str) -> str:
+        """Insert text at a specific line."""
+        validated_path = self._validate_path(path)
+        if not validated_path:
+            return "Error: Invalid path."
+
+        if not insert_text:
+            return "Error: insert_text is required."
+
+        doc = self.collection.find_one({"user_id": self.user_id, "tool_id": self.tool_id, "path": validated_path})
+
+        if not doc or not doc.get("content"):
+            return f"Error: File not found: {validated_path}"
+
+        current_content = str(doc["content"])
+        lines = current_content.split("\n")
+
+        # Convert to 0-indexed
+        index = insert_line - 1
+        if index < 0 or index > len(lines):
+            return f"Error: Invalid line number. File has {len(lines)} lines."
+
+        lines.insert(index, insert_text)
+        updated_content = "\n".join(lines)
+
+        self.collection.update_one(
+            {"user_id": self.user_id, "tool_id": self.tool_id, "path": validated_path},
+            {
+                "$set": {
+                    "content": updated_content,
+                    "updated_at": datetime.now()
+                }
+            }
+        )
+
+        return f"Text inserted at line {insert_line} in {validated_path}"
+
+    def _delete(self, path: str) -> str:
+        """Delete a file or directory."""
+        validated_path = self._validate_path(path)
+        if not validated_path:
+            return "Error: Invalid path."
+
+        if validated_path == "/":
+            # Delete all files for this user and tool
+            result = self.collection.delete_many({"user_id": self.user_id, "tool_id": self.tool_id})
+            return f"Deleted {result.deleted_count} file(s) from memory."
+
+        # Check if it's a directory (ends with /)
+        if validated_path.endswith("/"):
+            # Delete all files in directory
+            result = self.collection.delete_many({
+                "user_id": self.user_id,
+                "tool_id": self.tool_id,
+                "path": {"$regex": f"^{re.escape(validated_path)}"}
+            })
+            return f"Deleted directory and {result.deleted_count} file(s)."
+
+        # Try to delete as directory first (without trailing slash)
+        # Check if any files start with this path + /
+        search_path = validated_path + "/"
+        directory_result = self.collection.delete_many({
+            "user_id": self.user_id,
+            "tool_id": self.tool_id,
+            "path": {"$regex": f"^{re.escape(search_path)}"}
+        })
+
+        if directory_result.deleted_count > 0:
+            return f"Deleted directory and {directory_result.deleted_count} file(s)."
+
+        # Delete single file
+        result = self.collection.delete_one({
+            "user_id": self.user_id,
+            "tool_id": self.tool_id,
+            "path": validated_path
+        })
+
+        if result.deleted_count:
+            return f"Deleted: {validated_path}"
+        return f"Error: File not found: {validated_path}"
+
+    def _rename(self, old_path: str, new_path: str) -> str:
+        """Rename or move a file/directory."""
+        validated_old = self._validate_path(old_path)
+        validated_new = self._validate_path(new_path)
+
+        if not validated_old or not validated_new:
+            return "Error: Invalid path."
+
+        if validated_old == "/" or validated_new == "/":
+            return "Error: Cannot rename root directory."
+
+        # Check if renaming a directory
+        if validated_old.endswith("/"):
+            # Ensure validated_new also ends with / for proper path replacement
+            if not validated_new.endswith("/"):
+                validated_new = validated_new + "/"
+
+            # Find all files in the old directory
+            docs = list(self.collection.find({
+                "user_id": self.user_id,
+                "tool_id": self.tool_id,
+                "path": {"$regex": f"^{re.escape(validated_old)}"}
+            }))
+
+            if not docs:
+                return f"Error: Directory not found: {validated_old}"
+
+            # Update paths for all files
+            for doc in docs:
+                old_file_path = doc["path"]
+                new_file_path = old_file_path.replace(validated_old, validated_new, 1)
+
+                self.collection.update_one(
+                    {"_id": doc["_id"]},
+                    {"$set": {"path": new_file_path, "updated_at": datetime.now()}}
+                )
+
+            return f"Renamed directory: {validated_old} -> {validated_new} ({len(docs)} files)"
+
+        # Rename single file
+        doc = self.collection.find_one({
+            "user_id": self.user_id,
+            "tool_id": self.tool_id,
+            "path": validated_old
+        })
+
+        if not doc:
+            return f"Error: File not found: {validated_old}"
+
+        # Check if new path already exists
+        existing = self.collection.find_one({
+            "user_id": self.user_id,
+            "tool_id": self.tool_id,
+            "path": validated_new
+        })
+
+        if existing:
+            return f"Error: File already exists at {validated_new}"
+
+        # Delete the old document and create a new one with the new path
+        self.collection.delete_one({"user_id": self.user_id, "tool_id": self.tool_id, "path": validated_old})
+        self.collection.insert_one({
+            "user_id": self.user_id,
+            "tool_id": self.tool_id,
+            "path": validated_new,
+            "content": doc.get("content", ""),
+            "updated_at": datetime.now()
+        })
+
+        return f"Renamed: {validated_old} -> {validated_new}"

application/agents/tools/notes.py

@@ -0,0 +1,199 @@
+from datetime import datetime
+from typing import Any, Dict, List, Optional
+import uuid
+
+from .base import Tool
+from application.core.mongo_db import MongoDB
+from application.core.settings import settings
+
+
+class NotesTool(Tool):
+    """Notepad
+
+    Single note. Supports viewing, overwriting, string replacement.
+    """
+
+    def __init__(self, tool_config: Optional[Dict[str, Any]] = None, user_id: Optional[str] = None) -> None:
+        """Initialize the tool.
+
+        Args:
+            tool_config: Optional tool configuration. Should include:
+                - tool_id: Unique identifier for this notes tool instance (from user_tools._id)
+                           This ensures each user's tool configuration has isolated notes
+            user_id: The authenticated user's id (should come from decoded_token["sub"]).
+        """
+        self.user_id: Optional[str] = user_id
+
+        # Get tool_id from configuration (passed from user_tools._id in production)
+        # In production, tool_id is the MongoDB ObjectId string from user_tools collection
+        if tool_config and "tool_id" in tool_config:
+            self.tool_id = tool_config["tool_id"]
+        elif user_id:
+            # Fallback for backward compatibility or testing
+            self.tool_id = f"default_{user_id}"
+        else:
+            # Last resort fallback (shouldn't happen in normal use)
+            self.tool_id = str(uuid.uuid4())
+
+        db = MongoDB.get_client()[settings.MONGO_DB_NAME]
+        self.collection = db["notes"]
+
+    # -----------------------------
+    # Action implementations
+    # -----------------------------
+    def execute_action(self, action_name: str, **kwargs: Any) -> str:
+        """Execute an action by name.
+
+        Args:
+            action_name: One of view, overwrite, str_replace, insert, delete.
+            **kwargs: Parameters for the action.
+
+        Returns:
+            A human-readable string result.
+        """
+        if not self.user_id:
+             return "Error: NotesTool requires a valid user_id."
+
+        if action_name == "view":
+            return self._get_note()
+
+        if action_name == "overwrite":
+            return self._overwrite_note(kwargs.get("text", ""))
+
+        if action_name == "str_replace":
+            return self._str_replace(kwargs.get("old_str", ""), kwargs.get("new_str", ""))
+
+        if action_name == "insert":
+            return self._insert(kwargs.get("line_number", 1), kwargs.get("text", ""))
+
+        if action_name == "delete":
+            return self._delete_note()
+
+        return f"Unknown action: {action_name}"
+
+    def get_actions_metadata(self) -> List[Dict[str, Any]]:
+        """Return JSON metadata describing supported actions for tool schemas."""
+        return [
+            {
+                "name": "view",
+                "description": "Retrieve the user's note.",
+                "parameters": {"type": "object", "properties": {}},
+            },
+            {
+                "name": "overwrite",
+                "description": "Replace the entire note content (creates if doesn't exist).",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "text": {"type": "string", "description": "New note content."}
+                    },
+                    "required": ["text"],
+                },
+            },
+            {
+                "name": "str_replace",
+                "description": "Replace occurrences of old_str with new_str in the note.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "old_str": {"type": "string", "description": "String to find."},
+                        "new_str": {"type": "string", "description": "String to replace with."}
+                    },
+                    "required": ["old_str", "new_str"],
+                },
+            },
+            {
+                "name": "insert",
+                "description": "Insert text at the specified line number (1-indexed).",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "line_number": {"type": "integer", "description": "Line number to insert at (1-indexed)."},
+                        "text": {"type": "string", "description": "Text to insert."}
+                    },
+                    "required": ["line_number", "text"],
+                },
+            },
+            {
+                "name": "delete",
+                "description": "Delete the user's note.",
+                "parameters": {"type": "object", "properties": {}},
+            },
+        ]
+
+    def get_config_requirements(self) -> Dict[str, Any]:
+        """Return configuration requirements (none for now)."""
+        return {}
+
+    # -----------------------------
+    # Internal helpers (single-note)
+    # -----------------------------
+    def _get_note(self) -> str:
+        doc = self.collection.find_one({"user_id": self.user_id, "tool_id": self.tool_id})
+        if not doc or not doc.get("note"):
+            return "No note found."
+        return str(doc["note"])
+
+    def _overwrite_note(self, content: str) -> str:
+        content = (content or "").strip()
+        if not content:
+            return "Note content required."
+        self.collection.update_one(
+            {"user_id": self.user_id, "tool_id": self.tool_id},
+            {"$set": {"note": content, "updated_at": datetime.utcnow()}},
+            upsert=True,  # ✅ create if missing
+        )
+        return "Note saved."
+
+    def _str_replace(self, old_str: str, new_str: str) -> str:
+        if not old_str:
+            return "old_str is required."
+
+        doc = self.collection.find_one({"user_id": self.user_id, "tool_id": self.tool_id})
+        if not doc or not doc.get("note"):
+            return "No note found."
+
+        current_note = str(doc["note"])
+
+        # Case-insensitive search
+        if old_str.lower() not in current_note.lower():
+            return f"String '{old_str}' not found in note."
+
+        # Case-insensitive replacement
+        import re
+        updated_note = re.sub(re.escape(old_str), new_str, current_note, flags=re.IGNORECASE)
+
+        self.collection.update_one(
+            {"user_id": self.user_id, "tool_id": self.tool_id},
+            {"$set": {"note": updated_note, "updated_at": datetime.utcnow()}},
+        )
+        return "Note updated."
+
+    def _insert(self, line_number: int, text: str) -> str:
+        if not text:
+            return "Text is required."
+
+        doc = self.collection.find_one({"user_id": self.user_id, "tool_id": self.tool_id})
+        if not doc or not doc.get("note"):
+            return "No note found."
+
+        current_note = str(doc["note"])
+        lines = current_note.split("\n")
+
+        # Convert to 0-indexed and validate
+        index = line_number - 1
+        if index < 0 or index > len(lines):
+            return f"Invalid line number. Note has {len(lines)} lines."
+
+        lines.insert(index, text)
+        updated_note = "\n".join(lines)
+
+        self.collection.update_one(
+            {"user_id": self.user_id, "tool_id": self.tool_id},
+            {"$set": {"note": updated_note, "updated_at": datetime.utcnow()}},
+        )
+        return "Text inserted."
+
+    def _delete_note(self) -> str:
+        res = self.collection.delete_one({"user_id": self.user_id, "tool_id": self.tool_id})
+        return "Note deleted." if res.deleted_count else "No note found to delete."

application/agents/tools/ntfy.py

@@ -0,0 +1,127 @@
+import requests
+from application.agents.tools.base import Tool
+
+class NtfyTool(Tool):
+    """
+    Ntfy Tool
+    A tool for sending notifications to ntfy topics on a specified server.
+    """
+
+    def __init__(self, config):
+        """
+        Initialize the NtfyTool with configuration.
+
+        Args:
+            config (dict): Configuration dictionary containing the access token.
+        """
+        self.config = config
+        self.token = config.get("token", "")
+
+    def execute_action(self, action_name, **kwargs):
+        """
+        Execute the specified action with given parameters.
+
+        Args:
+            action_name (str): Name of the action to execute.
+            **kwargs: Parameters for the action, including server_url.
+
+        Returns:
+            dict: Result of the action with status code and message.
+
+        Raises:
+            ValueError: If the action name is unknown.
+        """
+        actions = {
+            "ntfy_send_message": self._send_message,
+        }
+        if action_name in actions:
+            return actions[action_name](**kwargs)
+        else:
+            raise ValueError(f"Unknown action: {action_name}")
+
+    def _send_message(self, server_url, message, topic, title=None, priority=None):
+        """
+        Send a message to an ntfy topic on the specified server.
+
+        Args:
+            server_url (str): Base URL of the ntfy server (e.g., https://ntfy.sh).
+            message (str): The message text to send.
+            topic (str): The topic to send the message to.
+            title (str, optional): Title of the notification.
+            priority (int, optional): Priority of the notification (1-5).
+
+        Returns:
+            dict: Response with status code and a confirmation message.
+
+        Raises:
+            ValueError: If priority is not an integer between 1 and 5.
+        """
+        url = f"{server_url.rstrip('/')}/{topic}"
+        headers = {}
+        if title:
+            headers["X-Title"] = title
+        if priority:
+            try:
+                priority = int(priority)
+            except (ValueError, TypeError):
+                raise ValueError("Priority must be convertible to an integer")
+            if priority < 1 or priority > 5:
+                raise ValueError("Priority must be an integer between 1 and 5")
+            headers["X-Priority"] = str(priority)
+        if self.token:
+            headers["Authorization"] = f"Basic {self.token}"
+        data = message.encode("utf-8")
+        response = requests.post(url, headers=headers, data=data)
+        return {"status_code": response.status_code, "message": "Message sent"}
+
+    def get_actions_metadata(self):
+        """
+        Provide metadata about available actions.
+
+        Returns:
+            list: List of dictionaries describing each action.
+        """
+        return [
+            {
+                "name": "ntfy_send_message",
+                "description": "Send a notification to an ntfy topic",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "server_url": {
+                            "type": "string",
+                            "description": "Base URL of the ntfy server",
+                        },
+                        "message": {
+                            "type": "string",
+                            "description": "Text to send in the notification",
+                        },
+                        "topic": {
+                            "type": "string",
+                            "description": "Topic to send the notification to",
+                        },
+                        "title": {
+                            "type": "string",
+                            "description": "Title of the notification (optional)",
+                        },
+                        "priority": {
+                            "type": "integer",
+                            "description": "Priority of the notification (1-5, optional)",
+                        },
+                    },
+                    "required": ["server_url", "message", "topic"],
+                    "additionalProperties": False,
+                },
+            },
+        ]
+
+    def get_config_requirements(self):
+        """
+        Specify the configuration requirements.
+
+        Returns:
+            dict: Dictionary describing required config parameters.
+        """
+        return {
+            "token": {"type": "string", "description": "Access token for authentication"},
+        }

application/agents/tools/postgres.py

@@ -0,0 +1,163 @@
+import psycopg2
+from application.agents.tools.base import Tool
+
+class PostgresTool(Tool):
+    """
+    PostgreSQL Database Tool
+    A tool for connecting to a PostgreSQL database using a connection string,
+    executing SQL queries, and retrieving schema information.
+    """
+
+    def __init__(self, config):
+        self.config = config
+        self.connection_string = config.get("token", "")
+
+    def execute_action(self, action_name, **kwargs):
+        actions = {
+            "postgres_execute_sql": self._execute_sql,
+            "postgres_get_schema": self._get_schema,
+        }
+
+        if action_name in actions:
+            return actions[action_name](**kwargs)
+        else:
+            raise ValueError(f"Unknown action: {action_name}")
+
+    def _execute_sql(self, sql_query):
+        """
+        Executes an SQL query against the PostgreSQL database using a connection string.
+        """
+        conn = None  # Initialize conn to None for error handling
+        try:
+            conn = psycopg2.connect(self.connection_string)
+            cur = conn.cursor()
+            cur.execute(sql_query)
+            conn.commit()
+
+            if sql_query.strip().lower().startswith("select"):
+                column_names = [desc[0] for desc in cur.description] if cur.description else []
+                results = []
+                rows = cur.fetchall()
+                for row in rows:
+                    results.append(dict(zip(column_names, row)))
+                response_data = {"data": results, "column_names": column_names}
+            else:
+                row_count = cur.rowcount
+                response_data = {"message": f"Query executed successfully, {row_count} rows affected."}
+
+            cur.close()
+            return {
+                "status_code": 200,
+                "message": "SQL query executed successfully.",
+                "response_data": response_data,
+            }
+
+        except psycopg2.Error as e:
+            error_message = f"Database error: {e}"
+            print(f"Database error: {e}")
+            return {
+                "status_code": 500,
+                "message": "Failed to execute SQL query.",
+                "error": error_message,
+            }
+        finally:
+            if conn:  # Ensure connection is closed even if errors occur
+                conn.close()
+
+    def _get_schema(self, db_name):
+        """
+        Retrieves the schema of the PostgreSQL database using a connection string.
+        """
+        conn = None # Initialize conn to None for error handling
+        try:
+            conn = psycopg2.connect(self.connection_string)
+            cur = conn.cursor()
+
+            cur.execute("""
+                SELECT
+                    table_name,
+                    column_name,
+                    data_type,
+                    column_default,
+                    is_nullable
+                FROM
+                    information_schema.columns
+                WHERE
+                    table_schema = 'public'
+                ORDER BY
+                    table_name,
+                    ordinal_position;
+            """)
+
+            schema_data = {}
+            for row in cur.fetchall():
+                table_name, column_name, data_type, column_default, is_nullable = row
+                if table_name not in schema_data:
+                    schema_data[table_name] = []
+                schema_data[table_name].append({
+                    "column_name": column_name,
+                    "data_type": data_type,
+                    "column_default": column_default,
+                    "is_nullable": is_nullable
+                })
+
+            cur.close()
+            return {
+                "status_code": 200,
+                "message": "Database schema retrieved successfully.",
+                "schema": schema_data,
+            }
+
+        except psycopg2.Error as e:
+            error_message = f"Database error: {e}"
+            print(f"Database error: {e}")
+            return {
+                "status_code": 500,
+                "message": "Failed to retrieve database schema.",
+                "error": error_message,
+            }
+        finally:
+            if conn: # Ensure connection is closed even if errors occur
+                conn.close()
+
+    def get_actions_metadata(self):
+        return [
+            {
+                "name": "postgres_execute_sql",
+                "description": "Execute an SQL query against the PostgreSQL database and return the results. Use this tool to interact with the database, e.g., retrieve specific data or perform updates. Only SELECT queries will return data, other queries will return execution status.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "sql_query": {
+                            "type": "string",
+                            "description": "The SQL query to execute.",
+                        },
+                    },
+                    "required": ["sql_query"],
+                    "additionalProperties": False,
+                },
+            },
+            {
+                "name": "postgres_get_schema",
+                "description": "Retrieve the schema of the PostgreSQL database, including tables and their columns. Use this to understand the database structure before executing queries. db_name is 'default' if not provided.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "db_name": {
+                            "type": "string",
+                            "description": "The name of the database to retrieve the schema for.",
+                        },
+                    },
+                    "required": ["db_name"],
+                    "additionalProperties": False,
+                },
+            },
+        ]
+
+    def get_config_requirements(self):
+        return {
+            "token": {
+                "type": "string",
+                "description": "PostgreSQL database connection string (e.g., 'postgresql://user:password@host:port/dbname')",
+            },
+        }

application/agents/tools/read_webpage.py

@@ -0,0 +1,83 @@
+import requests
+from markdownify import markdownify
+from application.agents.tools.base import Tool
+from urllib.parse import urlparse
+
+class ReadWebpageTool(Tool):
+    """
+    Read Webpage (browser)
+    A tool to fetch the HTML content of a URL and convert it to Markdown.
+    """
+
+    def __init__(self, config=None):
+        """
+        Initializes the tool.
+        :param config: Optional configuration dictionary. Not used by this tool.
+        """
+        self.config = config
+
+    def execute_action(self, action_name: str, **kwargs) -> str:
+        """
+        Executes the specified action. For this tool, the only action is 'read_webpage'.
+
+        :param action_name: The name of the action to execute. Should be 'read_webpage'.
+        :param kwargs: Keyword arguments, must include 'url'.
+        :return: The Markdown content of the webpage or an error message.
+        """
+        if action_name != "read_webpage":
+            return f"Error: Unknown action '{action_name}'. This tool only supports 'read_webpage'."
+
+        url = kwargs.get("url")
+        if not url:
+            return "Error: URL parameter is missing."
+
+        # Ensure the URL has a scheme (if not, default to http)
+        parsed_url = urlparse(url)
+        if not parsed_url.scheme:
+            url = "http://" + url
+        
+        try:
+            response = requests.get(url, timeout=10, headers={'User-Agent': 'DocsGPT-Agent/1.0'})
+            response.raise_for_status()  # Raise an exception for HTTP errors (4xx or 5xx)
+            
+            html_content = response.text
+            #soup = BeautifulSoup(html_content, 'html.parser')
+            
+            
+            markdown_content = markdownify(html_content, heading_style="ATX", newline_style="BACKSLASH")
+            
+            return markdown_content
+
+        except requests.exceptions.RequestException as e:
+            return f"Error fetching URL {url}: {e}"
+        except Exception as e:
+            return f"Error processing URL {url}: {e}"
+
+    def get_actions_metadata(self):
+        """
+        Returns metadata for the actions supported by this tool.
+        """
+        return [
+            {
+                "name": "read_webpage",
+                "description": "Fetches the HTML content of a given URL and returns it as clean Markdown text. Input must be a valid URL.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "url": {
+                            "type": "string",
+                            "description": "The fully qualified URL of the webpage to read (e.g., 'https://www.example.com').",
+                        }
+                    },
+                    "required": ["url"],
+                    "additionalProperties": False,
+                },
+            }
+        ]
+
+    def get_config_requirements(self):
+        """
+        Returns a dictionary describing the configuration requirements for the tool.
+        This tool does not require any specific configuration.
+        """
+        return {}

application/agents/tools/telegram.py

@@ -0,0 +1,86 @@
+import requests
+from application.agents.tools.base import Tool
+
+
+class TelegramTool(Tool):
+    """
+    Telegram Bot
+    A flexible Telegram tool for performing various actions (e.g., sending messages, images).
+    Requires a bot token and chat ID for configuration
+    """
+
+    def __init__(self, config):
+        self.config = config
+        self.token = config.get("token", "")
+
+    def execute_action(self, action_name, **kwargs):
+        actions = {
+            "telegram_send_message": self._send_message,
+            "telegram_send_image": self._send_image,
+        }
+
+        if action_name in actions:
+            return actions[action_name](**kwargs)
+        else:
+            raise ValueError(f"Unknown action: {action_name}")
+
+    def _send_message(self, text, chat_id):
+        print(f"Sending message: {text}")
+        url = f"https://api.telegram.org/bot{self.token}/sendMessage"
+        payload = {"chat_id": chat_id, "text": text}
+        response = requests.post(url, data=payload)
+        return {"status_code": response.status_code, "message": "Message sent"}
+
+    def _send_image(self, image_url, chat_id):
+        print(f"Sending image: {image_url}")
+        url = f"https://api.telegram.org/bot{self.token}/sendPhoto"
+        payload = {"chat_id": chat_id, "photo": image_url}
+        response = requests.post(url, data=payload)
+        return {"status_code": response.status_code, "message": "Image sent"}
+
+    def get_actions_metadata(self):
+        return [
+            {
+                "name": "telegram_send_message",
+                "description": "Send a notification to Telegram chat",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "text": {
+                            "type": "string",
+                            "description": "Text to send in the notification",
+                        },
+                        "chat_id": {
+                            "type": "string",
+                            "description": "Chat ID to send the notification to",
+                        },
+                    },
+                    "required": ["text"],
+                    "additionalProperties": False,
+                },
+            },
+            {
+                "name": "telegram_send_image",
+                "description": "Send an image to the Telegram chat",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "image_url": {
+                            "type": "string",
+                            "description": "URL of the image to send",
+                        },
+                        "chat_id": {
+                            "type": "string",
+                            "description": "Chat ID to send the image to",
+                        },
+                    },
+                    "required": ["image_url"],
+                    "additionalProperties": False,
+                },
+            },
+        ]
+
+    def get_config_requirements(self):
+        return {
+            "token": {"type": "string", "description": "Bot token for authentication"},
+        }

application/agents/tools/todo_list.py

@@ -0,0 +1,321 @@
+from datetime import datetime
+from typing import Any, Dict, List, Optional
+import uuid
+
+from .base import Tool
+from application.core.mongo_db import MongoDB
+from application.core.settings import settings
+
+
+class TodoListTool(Tool):
+    """Todo List
+
+    Manages todo items for users. Supports creating, viewing, updating, and deleting todos.
+    """
+
+    def __init__(self, tool_config: Optional[Dict[str, Any]] = None, user_id: Optional[str] = None) -> None:
+        """Initialize the tool.
+
+        Args:
+            tool_config: Optional tool configuration. Should include:
+                - tool_id: Unique identifier for this todo list tool instance (from user_tools._id)
+                           This ensures each user's tool configuration has isolated todos
+            user_id: The authenticated user's id (should come from decoded_token["sub"]).
+        """
+        self.user_id: Optional[str] = user_id
+
+        # Get tool_id from configuration (passed from user_tools._id in production)
+        # In production, tool_id is the MongoDB ObjectId string from user_tools collection
+        if tool_config and "tool_id" in tool_config:
+            self.tool_id = tool_config["tool_id"]
+        elif user_id:
+            # Fallback for backward compatibility or testing
+            self.tool_id = f"default_{user_id}"
+        else:
+            # Last resort fallback (shouldn't happen in normal use)
+            self.tool_id = str(uuid.uuid4())
+
+        db = MongoDB.get_client()[settings.MONGO_DB_NAME]
+        self.collection = db["todos"]
+
+    # -----------------------------
+    # Action implementations
+    # -----------------------------
+    def execute_action(self, action_name: str, **kwargs: Any) -> str:
+        """Execute an action by name.
+
+        Args:
+            action_name: One of list, create, get, update, complete, delete.
+            **kwargs: Parameters for the action.
+
+        Returns:
+            A human-readable string result.
+        """
+        if not self.user_id:
+            return "Error: TodoListTool requires a valid user_id."
+
+        if action_name == "list":
+            return self._list()
+
+        if action_name == "create":
+            return self._create(kwargs.get("title", ""))
+
+        if action_name == "get":
+            return self._get(kwargs.get("todo_id"))
+
+        if action_name == "update":
+            return self._update(
+                kwargs.get("todo_id"),
+                kwargs.get("title", "")
+            )
+
+        if action_name == "complete":
+            return self._complete(kwargs.get("todo_id"))
+
+        if action_name == "delete":
+            return self._delete(kwargs.get("todo_id"))
+
+        return f"Unknown action: {action_name}"
+
+    def get_actions_metadata(self) -> List[Dict[str, Any]]:
+        """Return JSON metadata describing supported actions for tool schemas."""
+        return [
+            {
+                "name": "list",
+                "description": "List all todos for the user.",
+                "parameters": {"type": "object", "properties": {}},
+            },
+            {
+                "name": "create",
+                "description": "Create a new todo item.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "title": {
+                            "type": "string",
+                            "description": "Title of the todo item."
+                        }
+                    },
+                    "required": ["title"],
+                },
+            },
+            {
+                "name": "get",
+                "description": "Get a specific todo by ID.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "todo_id": {
+                            "type": "integer",
+                            "description": "The ID of the todo to retrieve."
+                        }
+                    },
+                    "required": ["todo_id"],
+                },
+            },
+            {
+                "name": "update",
+                "description": "Update a todo's title by ID.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "todo_id": {
+                            "type": "integer",
+                            "description": "The ID of the todo to update."
+                        },
+                        "title": {
+                            "type": "string",
+                            "description": "The new title for the todo."
+                        }
+                    },
+                    "required": ["todo_id", "title"],
+                },
+            },
+            {
+                "name": "complete",
+                "description": "Mark a todo as completed.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "todo_id": {
+                            "type": "integer",
+                            "description": "The ID of the todo to mark as completed."
+                        }
+                    },
+                    "required": ["todo_id"],
+                },
+            },
+            {
+                "name": "delete",
+                "description": "Delete a specific todo by ID.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "todo_id": {
+                            "type": "integer",
+                            "description": "The ID of the todo to delete."
+                        }
+                    },
+                    "required": ["todo_id"],
+                },
+            },
+        ]
+
+    def get_config_requirements(self) -> Dict[str, Any]:
+        """Return configuration requirements."""
+        return {}
+
+    # -----------------------------
+    # Internal helpers
+    # -----------------------------
+    def _coerce_todo_id(self, value: Optional[Any]) -> Optional[int]:
+        """Convert todo identifiers to sequential integers."""
+        if value is None:
+            return None
+
+        if isinstance(value, int):
+            return value if value > 0 else None
+
+        if isinstance(value, str):
+            stripped = value.strip()
+            if stripped.isdigit():
+                numeric_value = int(stripped)
+                return numeric_value if numeric_value > 0 else None
+
+        return None
+
+    def _get_next_todo_id(self) -> int:
+        """Get the next sequential todo_id for this user and tool.
+
+        Returns a simple integer (1, 2, 3, ...) scoped to this user/tool.
+        With 5-10 todos max, scanning is negligible.
+        """
+        # Find all todos for this user/tool and get their IDs
+        todos = list(self.collection.find(
+            {"user_id": self.user_id, "tool_id": self.tool_id},
+            {"todo_id": 1}
+        ))
+
+        # Find the maximum todo_id
+        max_id = 0
+        for todo in todos:
+            todo_id = self._coerce_todo_id(todo.get("todo_id"))
+            if todo_id is not None:
+                max_id = max(max_id, todo_id)
+
+        return max_id + 1
+
+    def _list(self) -> str:
+        """List all todos for the user."""
+        cursor = self.collection.find({"user_id": self.user_id, "tool_id": self.tool_id})
+        todos = list(cursor)
+
+        if not todos:
+            return "No todos found."
+
+        result_lines = ["Todos:"]
+        for doc in todos:
+            todo_id = doc.get("todo_id")
+            title = doc.get("title", "Untitled")
+            status = doc.get("status", "open")
+
+            line = f"[{todo_id}] {title} ({status})"
+            result_lines.append(line)
+
+        return "\n".join(result_lines)
+
+    def _create(self, title: str) -> str:
+        """Create a new todo item."""
+        title = (title or "").strip()
+        if not title:
+            return "Error: Title is required."
+
+        now = datetime.now()
+        todo_id = self._get_next_todo_id()
+
+        doc = {
+            "todo_id": todo_id,
+            "user_id": self.user_id,
+            "tool_id": self.tool_id,
+            "title": title,
+            "status": "open",
+            "created_at": now,
+            "updated_at": now,
+        }
+        self.collection.insert_one(doc)
+        return f"Todo created with ID {todo_id}: {title}"
+
+    def _get(self, todo_id: Optional[Any]) -> str:
+        """Get a specific todo by ID."""
+        parsed_todo_id = self._coerce_todo_id(todo_id)
+        if parsed_todo_id is None:
+            return "Error: todo_id must be a positive integer."
+
+        doc = self.collection.find_one({
+            "user_id": self.user_id,
+            "tool_id": self.tool_id,
+            "todo_id": parsed_todo_id
+        })
+
+        if not doc:
+            return f"Error: Todo with ID {parsed_todo_id} not found."
+
+        title = doc.get("title", "Untitled")
+        status = doc.get("status", "open")
+
+        result = f"Todo [{parsed_todo_id}]:\nTitle: {title}\nStatus: {status}"
+
+        return result
+
+    def _update(self, todo_id: Optional[Any], title: str) -> str:
+        """Update a todo's title by ID."""
+        parsed_todo_id = self._coerce_todo_id(todo_id)
+        if parsed_todo_id is None:
+            return "Error: todo_id must be a positive integer."
+
+        title = (title or "").strip()
+        if not title:
+            return "Error: Title is required."
+
+        result = self.collection.update_one(
+            {"user_id": self.user_id, "tool_id": self.tool_id, "todo_id": parsed_todo_id},
+            {"$set": {"title": title, "updated_at": datetime.now()}}
+        )
+
+        if result.matched_count == 0:
+            return f"Error: Todo with ID {parsed_todo_id} not found."
+
+        return f"Todo {parsed_todo_id} updated to: {title}"
+
+    def _complete(self, todo_id: Optional[Any]) -> str:
+        """Mark a todo as completed."""
+        parsed_todo_id = self._coerce_todo_id(todo_id)
+        if parsed_todo_id is None:
+            return "Error: todo_id must be a positive integer."
+
+        result = self.collection.update_one(
+            {"user_id": self.user_id, "tool_id": self.tool_id, "todo_id": parsed_todo_id},
+            {"$set": {"status": "completed", "updated_at": datetime.now()}}
+        )
+
+        if result.matched_count == 0:
+            return f"Error: Todo with ID {parsed_todo_id} not found."
+
+        return f"Todo {parsed_todo_id} marked as completed."
+
+    def _delete(self, todo_id: Optional[Any]) -> str:
+        """Delete a specific todo by ID."""
+        parsed_todo_id = self._coerce_todo_id(todo_id)
+        if parsed_todo_id is None:
+            return "Error: todo_id must be a positive integer."
+
+        result = self.collection.delete_one({
+            "user_id": self.user_id,
+            "tool_id": self.tool_id,
+            "todo_id": parsed_todo_id
+        })
+
+        if result.deleted_count == 0:
+            return f"Error: Todo with ID {parsed_todo_id} not found."
+
+        return f"Todo {parsed_todo_id} deleted."

application/agents/tools/tool_action_parser.py

@@ -0,0 +1,69 @@
+import json
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class ToolActionParser:
+    def __init__(self, llm_type):
+        self.llm_type = llm_type
+        self.parsers = {
+            "OpenAILLM": self._parse_openai_llm,
+            "GoogleLLM": self._parse_google_llm,
+        }
+
+    def parse_args(self, call):
+        parser = self.parsers.get(self.llm_type, self._parse_openai_llm)
+        return parser(call)
+
+    def _parse_openai_llm(self, call):
+        try:
+            call_args = json.loads(call.arguments)
+            tool_parts = call.name.split("_")
+
+            # If the tool name doesn't contain an underscore, it's likely a hallucinated tool
+            if len(tool_parts) < 2:
+                logger.warning(
+                    f"Invalid tool name format: {call.name}. Expected format: action_name_tool_id"
+                )
+                return None, None, None
+
+            tool_id = tool_parts[-1]
+            action_name = "_".join(tool_parts[:-1])
+
+            # Validate that tool_id looks like a numerical ID
+            if not tool_id.isdigit():
+                logger.warning(
+                    f"Tool ID '{tool_id}' is not numerical. This might be a hallucinated tool call."
+                )
+
+        except (AttributeError, TypeError, json.JSONDecodeError) as e:
+            logger.error(f"Error parsing OpenAI LLM call: {e}")
+            return None, None, None
+        return tool_id, action_name, call_args
+
+    def _parse_google_llm(self, call):
+        try:
+            call_args = call.arguments
+            tool_parts = call.name.split("_")
+
+            # If the tool name doesn't contain an underscore, it's likely a hallucinated tool
+            if len(tool_parts) < 2:
+                logger.warning(
+                    f"Invalid tool name format: {call.name}. Expected format: action_name_tool_id"
+                )
+                return None, None, None
+
+            tool_id = tool_parts[-1]
+            action_name = "_".join(tool_parts[:-1])
+
+            # Validate that tool_id looks like a numerical ID
+            if not tool_id.isdigit():
+                logger.warning(
+                    f"Tool ID '{tool_id}' is not numerical. This might be a hallucinated tool call."
+                )
+
+        except (AttributeError, TypeError) as e:
+            logger.error(f"Error parsing Google LLM call: {e}")
+            return None, None, None
+        return tool_id, action_name, call_args

application/agents/tools/tool_manager.py

@@ -0,0 +1,49 @@
+import importlib
+import inspect
+import os
+import pkgutil
+
+from application.agents.tools.base import Tool
+
+
+class ToolManager:
+    def __init__(self, config):
+        self.config = config
+        self.tools = {}
+        self.load_tools()
+
+    def load_tools(self):
+        tools_dir = os.path.join(os.path.dirname(__file__))
+        for finder, name, ispkg in pkgutil.iter_modules([tools_dir]):
+            if name == "base" or name.startswith("__"):
+                continue
+            module = importlib.import_module(f"application.agents.tools.{name}")
+            for member_name, obj in inspect.getmembers(module, inspect.isclass):
+                if issubclass(obj, Tool) and obj is not Tool:
+                    tool_config = self.config.get(name, {})
+                    self.tools[name] = obj(tool_config)
+
+    def load_tool(self, tool_name, tool_config, user_id=None):
+        self.config[tool_name] = tool_config
+        module = importlib.import_module(f"application.agents.tools.{tool_name}")
+        for member_name, obj in inspect.getmembers(module, inspect.isclass):
+            if issubclass(obj, Tool) and obj is not Tool:
+                if tool_name in {"mcp_tool", "notes", "memory", "todo_list"} and user_id:
+                    return obj(tool_config, user_id)
+                else:
+                    return obj(tool_config)
+
+    def execute_action(self, tool_name, action_name, user_id=None, **kwargs):
+        if tool_name not in self.tools:
+            raise ValueError(f"Tool '{tool_name}' not loaded")
+        if tool_name in {"mcp_tool", "memory", "todo_list"} and user_id:
+            tool_config = self.config.get(tool_name, {})
+            tool = self.load_tool(tool_name, tool_config, user_id)
+            return tool.execute_action(action_name, **kwargs)
+        return self.tools[tool_name].execute_action(action_name, **kwargs)
+
+    def get_all_actions_metadata(self):
+        metadata = []
+        for tool in self.tools.values():
+            metadata.extend(tool.get_actions_metadata())
+        return metadata

application/api/__init__.py

@@ -0,0 +1,7 @@
+from flask_restx import Api
+
+api = Api(
+    version="1.0",
+    title="DocsGPT API",
+    description="API for DocsGPT",
+)

application/api/answer/__init__.py

@@ -0,0 +1,19 @@
+from flask import Blueprint
+
+from application.api import api
+from application.api.answer.routes.answer import AnswerResource
+from application.api.answer.routes.base import answer_ns
+from application.api.answer.routes.stream import StreamResource
+
+
+answer = Blueprint("answer", __name__)
+
+api.add_namespace(answer_ns)
+
+
+def init_answer_routes():
+    api.add_resource(StreamResource, "/stream")
+    api.add_resource(AnswerResource, "/api/answer")
+
+
+init_answer_routes()

application/api/answer/routes.py

@@ -1,618 +0,0 @@
-import asyncio
-import datetime
-import json
-import logging
-import os
-import sys
-import traceback
-
-from bson.dbref import DBRef
-from bson.objectid import ObjectId
-from flask import Blueprint, current_app, make_response, request, Response
-from flask_restx import fields, Namespace, Resource
-
-
-from application.core.mongo_db import MongoDB
-from application.core.settings import settings
-from application.error import bad_request
-from application.extensions import api
-from application.llm.llm_creator import LLMCreator
-from application.retriever.retriever_creator import RetrieverCreator
-from application.utils import check_required_fields
-
-logger = logging.getLogger(__name__)
-
-mongo = MongoDB.get_client()
-db = mongo["docsgpt"]
-conversations_collection = db["conversations"]
-sources_collection = db["sources"]
-prompts_collection = db["prompts"]
-api_key_collection = db["api_keys"]
-user_logs_collection = db["user_logs"]
-
-answer = Blueprint("answer", __name__)
-answer_ns = Namespace("answer", description="Answer related operations", path="/")
-api.add_namespace(answer_ns)
-
-gpt_model = ""
-# to have some kind of default behaviour
-if settings.LLM_NAME == "openai":
-    gpt_model = "gpt-3.5-turbo"
-elif settings.LLM_NAME == "anthropic":
-    gpt_model = "claude-2"
-elif settings.LLM_NAME == "groq":
-    gpt_model = "llama3-8b-8192"
-
-if settings.MODEL_NAME:  # in case there is particular model name configured
-    gpt_model = settings.MODEL_NAME
-
-# load the prompts
-current_dir = os.path.dirname(
-    os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-)
-with open(os.path.join(current_dir, "prompts", "chat_combine_default.txt"), "r") as f:
-    chat_combine_template = f.read()
-
-with open(os.path.join(current_dir, "prompts", "chat_reduce_prompt.txt"), "r") as f:
-    chat_reduce_template = f.read()
-
-with open(os.path.join(current_dir, "prompts", "chat_combine_creative.txt"), "r") as f:
-    chat_combine_creative = f.read()
-
-with open(os.path.join(current_dir, "prompts", "chat_combine_strict.txt"), "r") as f:
-    chat_combine_strict = f.read()
-
-api_key_set = settings.API_KEY is not None
-embeddings_key_set = settings.EMBEDDINGS_KEY is not None
-
-
-async def async_generate(chain, question, chat_history):
-    result = await chain.arun({"question": question, "chat_history": chat_history})
-    return result
-
-
-def run_async_chain(chain, question, chat_history):
-    loop = asyncio.new_event_loop()
-    asyncio.set_event_loop(loop)
-    result = {}
-    try:
-        answer = loop.run_until_complete(async_generate(chain, question, chat_history))
-    finally:
-        loop.close()
-    result["answer"] = answer
-    return result
-
-
-def get_data_from_api_key(api_key):
-    data = api_key_collection.find_one({"key": api_key})
-    # # Raise custom exception if the API key is not found
-    if data is None:
-        raise Exception("Invalid API Key, please generate new key", 401)
-
-    if "retriever" not in data:
-        data["retriever"] = None
-
-    if "source" in data and isinstance(data["source"], DBRef):
-        source_doc = db.dereference(data["source"])
-        data["source"] = str(source_doc["_id"])
-        if "retriever" in source_doc:
-            data["retriever"] = source_doc["retriever"]
-    else:
-        data["source"] = {}
-    return data
-
-
-def get_retriever(source_id: str):
-    doc = sources_collection.find_one({"_id": ObjectId(source_id)})
-    if doc is None:
-        raise Exception("Source document does not exist", 404)
-    retriever_name = None if "retriever" not in doc else doc["retriever"]
-    return retriever_name
-
-
-def is_azure_configured():
-    return (
-        settings.OPENAI_API_BASE
-        and settings.OPENAI_API_VERSION
-        and settings.AZURE_DEPLOYMENT_NAME
-    )
-
-
-def save_conversation(conversation_id, question, response, source_log_docs, llm):
-    if conversation_id is not None and conversation_id != "None":
-        conversations_collection.update_one(
-            {"_id": ObjectId(conversation_id)},
-            {
-                "$push": {
-                    "queries": {
-                        "prompt": question,
-                        "response": response,
-                        "sources": source_log_docs,
-                    }
-                }
-            },
-        )
-
-    else:
-        # create new conversation
-        # generate summary
-        messages_summary = [
-            {
-                "role": "assistant",
-                "content": "Summarise following conversation in no more than 3 "
-                "words, respond ONLY with the summary, use the same "
-                "language as the system \n\nUser: "
-                + question
-                + "\n\n"
-                + "AI: "
-                + response,
-            },
-            {
-                "role": "user",
-                "content": "Summarise following conversation in no more than 3 words, "
-                "respond ONLY with the summary, use the same language as the "
-                "system",
-            },
-        ]
-
-        completion = llm.gen(model=gpt_model, messages=messages_summary, max_tokens=30)
-        conversation_id = conversations_collection.insert_one(
-            {
-                "user": "local",
-                "date": datetime.datetime.utcnow(),
-                "name": completion,
-                "queries": [
-                    {
-                        "prompt": question,
-                        "response": response,
-                        "sources": source_log_docs,
-                    }
-                ],
-            }
-        ).inserted_id
-    return conversation_id
-
-
-def get_prompt(prompt_id):
-    if prompt_id == "default":
-        prompt = chat_combine_template
-    elif prompt_id == "creative":
-        prompt = chat_combine_creative
-    elif prompt_id == "strict":
-        prompt = chat_combine_strict
-    else:
-        prompt = prompts_collection.find_one({"_id": ObjectId(prompt_id)})["content"]
-    return prompt
-
-
-def complete_stream(
-    question, retriever, conversation_id, user_api_key, isNoneDoc=False
-):
-
-    try:
-        response_full = ""
-        source_log_docs = []
-        answer = retriever.gen()
-        sources = retriever.search()
-        for source in sources:
-            if "text" in source:
-                source["text"] = source["text"][:100].strip() + "..."
-        if len(sources) > 0:
-            data = json.dumps({"type": "source", "source": sources})
-            yield f"data: {data}\n\n"
-        for line in answer:
-            if "answer" in line:
-                response_full += str(line["answer"])
-                data = json.dumps(line)
-                yield f"data: {data}\n\n"
-            elif "source" in line:
-                source_log_docs.append(line["source"])
-
-        if isNoneDoc:
-            for doc in source_log_docs:
-                doc["source"] = "None"
-
-        llm = LLMCreator.create_llm(
-            settings.LLM_NAME, api_key=settings.API_KEY, user_api_key=user_api_key
-        )
-        if user_api_key is None:
-            conversation_id = save_conversation(
-                conversation_id, question, response_full, source_log_docs, llm
-            )
-            # send data.type = "end" to indicate that the stream has ended as json
-            data = json.dumps({"type": "id", "id": str(conversation_id)})
-            yield f"data: {data}\n\n"
-
-        retriever_params = retriever.get_params()
-        user_logs_collection.insert_one(
-            {
-                "action": "stream_answer",
-                "level": "info",
-                "user": "local",
-                "api_key": user_api_key,
-                "question": question,
-                "response": response_full,
-                "sources": source_log_docs,
-                "retriever_params": retriever_params,
-                "timestamp": datetime.datetime.now(datetime.timezone.utc),
-            }
-        )
-        data = json.dumps({"type": "end"})
-        yield f"data: {data}\n\n"
-    except Exception as e:
-        print("\033[91merr", str(e), file=sys.stderr)
-        data = json.dumps(
-            {
-                "type": "error",
-                "error": "Please try again later. We apologize for any inconvenience.",
-                "error_exception": str(e),
-            }
-        )
-        yield f"data: {data}\n\n"
-        return
-
-
-@answer_ns.route("/stream")
-class Stream(Resource):
-    stream_model = api.model(
-        "StreamModel",
-        {
-            "question": fields.String(
-                required=True, description="Question to be asked"
-            ),
-            "history": fields.List(
-                fields.String, required=False, description="Chat history"
-            ),
-            "conversation_id": fields.String(
-                required=False, description="Conversation ID"
-            ),
-            "prompt_id": fields.String(
-                required=False, default="default", description="Prompt ID"
-            ),
-            "chunks": fields.Integer(
-                required=False, default=2, description="Number of chunks"
-            ),
-            "token_limit": fields.Integer(required=False, description="Token limit"),
-            "retriever": fields.String(required=False, description="Retriever type"),
-            "api_key": fields.String(required=False, description="API key"),
-            "active_docs": fields.String(
-                required=False, description="Active documents"
-            ),
-            "isNoneDoc": fields.Boolean(
-                required=False, description="Flag indicating if no document is used"
-            ),
-        },
-    )
-
-    @api.expect(stream_model)
-    @api.doc(description="Stream a response based on the question and retriever")
-    def post(self):
-        data = request.get_json()
-        required_fields = ["question"]
-
-        missing_fields = check_required_fields(data, required_fields)
-        if missing_fields:
-            return missing_fields
-
-        try:
-            question = data["question"]
-            history = data.get("history", [])
-            history = json.loads(history)
-            conversation_id = data.get("conversation_id")
-            prompt_id = data.get("prompt_id", "default")
-            
-
-            chunks = int(data.get("chunks", 2))
-            token_limit = data.get("token_limit", settings.DEFAULT_MAX_HISTORY)
-            retriever_name = data.get("retriever", "classic")
-
-            if "api_key" in data:
-                data_key = get_data_from_api_key(data["api_key"])
-                chunks = int(data_key.get("chunks", 2))
-                prompt_id = data_key.get("prompt_id", "default")
-                source = {"active_docs": data_key.get("source")}
-                retriever_name = data_key.get("retriever", retriever_name)
-                user_api_key = data["api_key"]
-
-            elif "active_docs" in data:
-                source = {"active_docs": data["active_docs"]}
-                retriever_name = get_retriever(data["active_docs"]) or retriever_name
-                user_api_key = None
-
-            else:
-                source = {}
-                user_api_key = None
-
-            current_app.logger.info(
-                f"/stream - request_data: {data}, source: {source}",
-                extra={"data": json.dumps({"request_data": data, "source": source})},
-            )
-
-            prompt = get_prompt(prompt_id)
-            if "isNoneDoc" in data and data["isNoneDoc"] is True:
-                chunks = 0
-            retriever = RetrieverCreator.create_retriever(
-                retriever_name,
-                question=question,
-                source=source,
-                chat_history=history,
-                prompt=prompt,
-                chunks=chunks,
-                token_limit=token_limit,
-                gpt_model=gpt_model,
-                user_api_key=user_api_key,
-            )
-
-            return Response(
-                complete_stream(
-                    question=question,
-                    retriever=retriever,
-                    conversation_id=conversation_id,
-                    user_api_key=user_api_key,
-                    isNoneDoc=data.get("isNoneDoc"),
-                ),
-                mimetype="text/event-stream",
-            )
-
-        except ValueError:
-            message = "Malformed request body"
-            print("\033[91merr", str(message), file=sys.stderr)
-            return Response(
-                error_stream_generate(message),
-                status=400,
-                mimetype="text/event-stream",
-            )
-        except Exception as e:
-            current_app.logger.error(
-                f"/stream - error: {str(e)} - traceback: {traceback.format_exc()}",
-                extra={"error": str(e), "traceback": traceback.format_exc()},
-            )
-            message = e.args[0]
-            status_code = 400
-            # Custom exceptions with two arguments, index 1 as status code
-            if len(e.args) >= 2:
-                status_code = e.args[1]
-            return Response(
-                error_stream_generate(message),
-                status=status_code,
-                mimetype="text/event-stream",
-            )
-
-
-def error_stream_generate(err_response):
-    data = json.dumps({"type": "error", "error": err_response})
-    yield f"data: {data}\n\n"
-
-
-@answer_ns.route("/api/answer")
-class Answer(Resource):
-    answer_model = api.model(
-        "AnswerModel",
-        {
-            "question": fields.String(
-                required=True, description="The question to answer"
-            ),
-            "history": fields.List(
-                fields.String, required=False, description="Conversation history"
-            ),
-            "conversation_id": fields.String(
-                required=False, description="Conversation ID"
-            ),
-            "prompt_id": fields.String(
-                required=False, default="default", description="Prompt ID"
-            ),
-            "chunks": fields.Integer(
-                required=False, default=2, description="Number of chunks"
-            ),
-            "token_limit": fields.Integer(required=False, description="Token limit"),
-            "retriever": fields.String(required=False, description="Retriever type"),
-            "api_key": fields.String(required=False, description="API key"),
-            "active_docs": fields.String(
-                required=False, description="Active documents"
-            ),
-            "isNoneDoc": fields.Boolean(
-                required=False, description="Flag indicating if no document is used"
-            ),
-        },
-    )
-
-    @api.expect(answer_model)
-    @api.doc(description="Provide an answer based on the question and retriever")
-    def post(self):
-        data = request.get_json()
-        required_fields = ["question"]       
-        missing_fields = check_required_fields(data, required_fields)
-        if missing_fields:
-            return missing_fields
-
-        try:
-            question = data["question"]
-            history = data.get("history", [])
-            conversation_id = data.get("conversation_id")
-            prompt_id = data.get("prompt_id", "default")
-            chunks = int(data.get("chunks", 2))
-            token_limit = data.get("token_limit", settings.DEFAULT_MAX_HISTORY)
-            retriever_name = data.get("retriever", "classic")
-
-            if "api_key" in data:
-                data_key = get_data_from_api_key(data["api_key"])
-                chunks = int(data_key.get("chunks", 2))
-                prompt_id = data_key.get("prompt_id", "default")
-                source = {"active_docs": data_key.get("source")}
-                retriever_name = data_key.get("retriever", retriever_name)
-                user_api_key = data["api_key"]
-            elif "active_docs" in data:
-                source = {"active_docs": data["active_docs"]}
-                retriever_name = get_retriever(data["active_docs"]) or retriever_name
-                user_api_key = None
-            else:
-                source = {}
-                user_api_key = None
-
-            prompt = get_prompt(prompt_id)
-
-            current_app.logger.info(
-                f"/api/answer - request_data: {data}, source: {source}",
-                extra={"data": json.dumps({"request_data": data, "source": source})},
-            )
-
-            retriever = RetrieverCreator.create_retriever(
-                retriever_name,
-                question=question,
-                source=source,
-                chat_history=history,
-                prompt=prompt,
-                chunks=chunks,
-                token_limit=token_limit,
-                gpt_model=gpt_model,
-                user_api_key=user_api_key,
-            )
-
-            source_log_docs = []
-            response_full = ""
-            for line in retriever.gen():
-                if "source" in line:
-                    source_log_docs.append(line["source"])
-                elif "answer" in line:
-                    response_full += line["answer"]
-
-            if data.get("isNoneDoc"):
-                for doc in source_log_docs:
-                    doc["source"] = "None"
-
-            llm = LLMCreator.create_llm(
-                settings.LLM_NAME, api_key=settings.API_KEY, user_api_key=user_api_key
-            )
-
-            result = {"answer": response_full, "sources": source_log_docs}
-            result["conversation_id"] = str(
-                save_conversation(
-                    conversation_id, question, response_full, source_log_docs, llm
-                )
-            )
-            retriever_params = retriever.get_params()
-            user_logs_collection.insert_one(
-                {
-                    "action": "api_answer",
-                    "level": "info",
-                    "user": "local",
-                    "api_key": user_api_key,
-                    "question": question,
-                    "response": response_full,
-                    "sources": source_log_docs,
-                    "retriever_params": retriever_params,
-                    "timestamp": datetime.datetime.now(datetime.timezone.utc),
-                }
-            )
-
-        except Exception as e:
-            current_app.logger.error(
-                f"/api/answer - error: {str(e)} - traceback: {traceback.format_exc()}",
-                extra={"error": str(e), "traceback": traceback.format_exc()},
-            )
-            return bad_request(500, str(e))
-
-        return make_response(result, 200)
-
-
-@answer_ns.route("/api/search")
-class Search(Resource):
-    search_model = api.model(
-        "SearchModel",
-        {
-            "question": fields.String(
-                required=True, description="The question to search"
-            ),
-            "chunks": fields.Integer(
-                required=False, default=2, description="Number of chunks"
-            ),
-            "api_key": fields.String(
-                required=False, description="API key for authentication"
-            ),
-            "active_docs": fields.String(
-                required=False, description="Active documents for retrieval"
-            ),
-            "retriever": fields.String(required=False, description="Retriever type"),
-            "token_limit": fields.Integer(
-                required=False, description="Limit for tokens"
-            ),
-            "isNoneDoc": fields.Boolean(
-                required=False, description="Flag indicating if no document is used"
-            ),
-        },
-    )
-
-    @api.expect(search_model)
-    @api.doc(
-        description="Search for relevant documents based on the question and retriever"
-    )
-    def post(self):
-        data = request.get_json()
-        required_fields = ["question"]
-        missing_fields = check_required_fields(data, required_fields)
-        if missing_fields:
-            return missing_fields
-
-        try:
-            question = data["question"]
-            chunks = int(data.get("chunks", 2))
-            token_limit = data.get("token_limit", settings.DEFAULT_MAX_HISTORY)
-            retriever_name = data.get("retriever", "classic")
-
-            if "api_key" in data:
-                data_key = get_data_from_api_key(data["api_key"])
-                chunks = int(data_key.get("chunks", 2))
-                source = {"active_docs": data_key.get("source")}
-                user_api_key = data["api_key"]
-            elif "active_docs" in data:
-                source = {"active_docs": data["active_docs"]}
-                user_api_key = None
-            else:
-                source = {}
-                user_api_key = None
-
-            current_app.logger.info(
-                f"/api/answer - request_data: {data}, source: {source}",
-                extra={"data": json.dumps({"request_data": data, "source": source})},
-            )
-
-            retriever = RetrieverCreator.create_retriever(
-                retriever_name,
-                question=question,
-                source=source,
-                chat_history=[],
-                prompt="default",
-                chunks=chunks,
-                token_limit=token_limit,
-                gpt_model=gpt_model,
-                user_api_key=user_api_key,
-            )
-
-            docs = retriever.search()
-            retriever_params = retriever.get_params()
-
-            user_logs_collection.insert_one(
-                {
-                    "action": "api_search",
-                    "level": "info",
-                    "user": "local",
-                    "api_key": user_api_key,
-                    "question": question,
-                    "sources": docs,
-                    "retriever_params": retriever_params,
-                    "timestamp": datetime.datetime.now(datetime.timezone.utc),
-                }
-            )
-
-            if data.get("isNoneDoc"):
-                for doc in docs:
-                    doc["source"] = "None"
-
-        except Exception as e:
-            current_app.logger.error(
-                f"/api/search - error: {str(e)} - traceback: {traceback.format_exc()}",
-                extra={"error": str(e), "traceback": traceback.format_exc()},
-            )
-            return bad_request(500, str(e))
-
-        return make_response(docs, 200)

application/api/answer/routes/answer.py

@@ -0,0 +1,125 @@
+import logging
+import traceback
+
+from flask import make_response, request
+from flask_restx import fields, Resource
+
+from application.api import api
+
+from application.api.answer.routes.base import answer_ns, BaseAnswerResource
+
+from application.api.answer.services.stream_processor import StreamProcessor
+
+logger = logging.getLogger(__name__)
+
+
+@answer_ns.route("/api/answer")
+class AnswerResource(Resource, BaseAnswerResource):
+    def __init__(self, *args, **kwargs):
+        Resource.__init__(self, *args, **kwargs)
+        BaseAnswerResource.__init__(self)
+
+    answer_model = answer_ns.model(
+        "AnswerModel",
+        {
+            "question": fields.String(
+                required=True, description="Question to be asked"
+            ),
+            "history": fields.List(
+                fields.String,
+                required=False,
+                description="Conversation history (only for new conversations)",
+            ),
+            "conversation_id": fields.String(
+                required=False,
+                description="Existing conversation ID (loads history)",
+            ),
+            "prompt_id": fields.String(
+                required=False, default="default", description="Prompt ID"
+            ),
+            "chunks": fields.Integer(
+                required=False, default=2, description="Number of chunks"
+            ),
+            "token_limit": fields.Integer(required=False, description="Token limit"),
+            "retriever": fields.String(required=False, description="Retriever type"),
+            "api_key": fields.String(required=False, description="API key"),
+            "active_docs": fields.String(
+                required=False, description="Active documents"
+            ),
+            "isNoneDoc": fields.Boolean(
+                required=False, description="Flag indicating if no document is used"
+            ),
+            "save_conversation": fields.Boolean(
+                required=False,
+                default=True,
+                description="Whether to save the conversation",
+            ),
+        },
+    )
+
+    @api.expect(answer_model)
+    @api.doc(description="Provide a response based on the question and retriever")
+    def post(self):
+        data = request.get_json()
+        if error := self.validate_request(data):
+            return error
+        decoded_token = getattr(request, "decoded_token", None)
+        processor = StreamProcessor(data, decoded_token)
+        try:
+            processor.initialize()
+            if not processor.decoded_token:
+                return make_response({"error": "Unauthorized"}, 401)
+            agent = processor.create_agent()
+            retriever = processor.create_retriever()
+
+            if error := self.check_usage(processor.agent_config):
+                return error
+
+            stream = self.complete_stream(
+                question=data["question"],
+                agent=agent,
+                retriever=retriever,
+                conversation_id=processor.conversation_id,
+                user_api_key=processor.agent_config.get("user_api_key"),
+                decoded_token=processor.decoded_token,
+                isNoneDoc=data.get("isNoneDoc"),
+                index=None,
+                should_save_conversation=data.get("save_conversation", True),
+            )
+            stream_result = self.process_response_stream(stream)
+
+            if len(stream_result) == 7:
+                (
+                    conversation_id,
+                    response,
+                    sources,
+                    tool_calls,
+                    thought,
+                    error,
+                    structured_info,
+                ) = stream_result
+            else:
+                conversation_id, response, sources, tool_calls, thought, error = (
+                    stream_result
+                )
+                structured_info = None
+
+            if error:
+                return make_response({"error": error}, 400)
+            result = {
+                "conversation_id": conversation_id,
+                "answer": response,
+                "sources": sources,
+                "tool_calls": tool_calls,
+                "thought": thought,
+            }
+
+            if structured_info:
+                result.update(structured_info)
+        except Exception as e:
+            logger.error(
+                f"/api/answer - error: {str(e)} - traceback: {traceback.format_exc()}",
+                extra={"error": str(e), "traceback": traceback.format_exc()},
+            )
+            return make_response({"error": str(e)}, 500)
+        return make_response(result, 200)

application/api/answer/routes/base.py

@@ -0,0 +1,385 @@
+import datetime
+import json
+import logging
+from typing import Any, Dict, Generator, List, Optional
+
+from flask import Response, make_response, jsonify
+from flask_restx import Namespace
+
+from application.api.answer.services.conversation_service import ConversationService
+
+from application.core.mongo_db import MongoDB
+from application.core.settings import settings
+from application.llm.llm_creator import LLMCreator
+from application.utils import check_required_fields, get_gpt_model
+
+logger = logging.getLogger(__name__)
+
+
+answer_ns = Namespace("answer", description="Answer related operations", path="/")
+
+
+class BaseAnswerResource:
+    """Shared base class for answer endpoints"""
+
+    def __init__(self):
+        mongo = MongoDB.get_client()
+        db = mongo[settings.MONGO_DB_NAME]
+        self.db = db
+        self.user_logs_collection = db["user_logs"]
+        self.gpt_model = get_gpt_model()
+        self.conversation_service = ConversationService()
+
+    def validate_request(
+        self, data: Dict[str, Any], require_conversation_id: bool = False
+    ) -> Optional[Response]:
+        """Common request validation"""
+        required_fields = ["question"]
+        if require_conversation_id:
+            required_fields.append("conversation_id")
+        if missing_fields := check_required_fields(data, required_fields):
+            return missing_fields
+        return None
+
+    def check_usage(
+            self, agent_config: Dict
+    ) -> Optional[Response]:
+        """Check if there is a usage limit and if it is exceeded
+
+        Args:
+            agent_config: The config dict of agent instance
+
+        Returns:
+            None or Response if either of limits exceeded.
+        
+        """
+        api_key = agent_config.get("user_api_key")
+        if not api_key:
+            return None
+        
+        agents_collection = self.db["agents"]
+        agent = agents_collection.find_one({"key": api_key})
+
+        if not agent:
+            return make_response(
+                jsonify(
+                    {
+                        "success": False,
+                        "message": "Invalid API key."
+                    }
+                ),
+                401
+            )
+
+        limited_token_mode = agent.get("limited_token_mode", False)
+        limited_request_mode = agent.get("limited_request_mode", False)
+        token_limit = int(agent.get("token_limit", settings.DEFAULT_AGENT_LIMITS["token_limit"]))
+        request_limit = int(agent.get("request_limit", settings.DEFAULT_AGENT_LIMITS["request_limit"]))
+
+        token_usage_collection = self.db["token_usage"]
+
+        end_date = datetime.datetime.now()
+        start_date = end_date - datetime.timedelta(hours=24)
+
+        match_query = {
+            "timestamp": {"$gte": start_date, "$lte": end_date},
+            "api_key": api_key
+        }
+        
+        if limited_token_mode:
+            token_pipeline = [
+                {"$match": match_query},
+                {
+                    "$group": {
+                        "_id": None,
+                        "total_tokens": {"$sum": {"$add": ["$prompt_tokens", "$generated_tokens"]}}
+                    }
+                }
+            ]
+            token_result = list(token_usage_collection.aggregate(token_pipeline))
+            daily_token_usage = token_result[0]["total_tokens"] if token_result else 0
+        else:
+            daily_token_usage = 0
+
+        if limited_request_mode:
+            daily_request_usage = token_usage_collection.count_documents(match_query)
+        else:
+            daily_request_usage = 0
+
+        if not limited_token_mode and not limited_request_mode:
+            return None
+        elif limited_token_mode and token_limit > daily_token_usage:
+            return None
+        elif limited_request_mode and request_limit > daily_request_usage:
+            return None
+
+        return make_response(
+            jsonify(
+                {
+                    "success": False,
+                    "message": "Exceeding usage limit, please try again later."
+                }
+            ),
+            429, # too many requests
+        )
+
+    def complete_stream(
+        self,
+        question: str,
+        agent: Any,
+        retriever: Any,
+        conversation_id: Optional[str],
+        user_api_key: Optional[str],
+        decoded_token: Dict[str, Any],
+        isNoneDoc: bool = False,
+        index: Optional[int] = None,
+        should_save_conversation: bool = True,
+        attachment_ids: Optional[List[str]] = None,
+        agent_id: Optional[str] = None,
+        is_shared_usage: bool = False,
+        shared_token: Optional[str] = None,
+    ) -> Generator[str, None, None]:
+        """
+        Generator function that streams the complete conversation response.
+
+        Args:
+            question: The user's question
+            agent: The agent instance
+            retriever: The retriever instance
+            conversation_id: Existing conversation ID
+            user_api_key: User's API key if any
+            decoded_token: Decoded JWT token
+            isNoneDoc: Flag for document-less responses
+            index: Index of message to update
+            should_save_conversation: Whether to persist the conversation
+            attachment_ids: List of attachment IDs
+            agent_id: ID of agent used
+            is_shared_usage: Flag for shared agent usage
+            shared_token: Token for shared agent
+
+        Yields:
+            Server-sent event strings
+        """
+        try:
+            response_full, thought, source_log_docs, tool_calls = "", "", [], []
+            is_structured = False
+            schema_info = None
+            structured_chunks = []
+
+            for line in agent.gen(query=question, retriever=retriever):
+                if "answer" in line:
+                    response_full += str(line["answer"])
+                    if line.get("structured"):
+                        is_structured = True
+                        schema_info = line.get("schema")
+                        structured_chunks.append(line["answer"])
+                    else:
+                        data = json.dumps({"type": "answer", "answer": line["answer"]})
+                        yield f"data: {data}\n\n"
+                elif "sources" in line:
+                    truncated_sources = []
+                    source_log_docs = line["sources"]
+                    for source in line["sources"]:
+                        truncated_source = source.copy()
+                        if "text" in truncated_source:
+                            truncated_source["text"] = (
+                                truncated_source["text"][:100].strip() + "..."
+                            )
+                        truncated_sources.append(truncated_source)
+                    if truncated_sources:
+                        data = json.dumps(
+                            {"type": "source", "source": truncated_sources}
+                        )
+                        yield f"data: {data}\n\n"
+                elif "tool_calls" in line:
+                    tool_calls = line["tool_calls"]
+                    data = json.dumps({"type": "tool_calls", "tool_calls": tool_calls})
+                    yield f"data: {data}\n\n"
+                elif "thought" in line:
+                    thought += line["thought"]
+                    data = json.dumps({"type": "thought", "thought": line["thought"]})
+                    yield f"data: {data}\n\n"
+                elif "type" in line:
+                    data = json.dumps(line)
+                    yield f"data: {data}\n\n"
+
+            if is_structured and structured_chunks:
+                structured_data = {
+                    "type": "structured_answer",
+                    "answer": response_full,
+                    "structured": True,
+                    "schema": schema_info,
+                }
+                data = json.dumps(structured_data)
+                yield f"data: {data}\n\n"
+
+            if isNoneDoc:
+                for doc in source_log_docs:
+                    doc["source"] = "None"
+            llm = LLMCreator.create_llm(
+                settings.LLM_PROVIDER,
+                api_key=settings.API_KEY,
+                user_api_key=user_api_key,
+                decoded_token=decoded_token,
+            )
+
+            if should_save_conversation:
+                conversation_id = self.conversation_service.save_conversation(
+                    conversation_id,
+                    question,
+                    response_full,
+                    thought,
+                    source_log_docs,
+                    tool_calls,
+                    llm,
+                    self.gpt_model,
+                    decoded_token,
+                    index=index,
+                    api_key=user_api_key,
+                    agent_id=agent_id,
+                    is_shared_usage=is_shared_usage,
+                    shared_token=shared_token,
+                    attachment_ids=attachment_ids,
+                )
+            else:
+                conversation_id = None
+            id_data = {"type": "id", "id": str(conversation_id)}
+            data = json.dumps(id_data)
+            yield f"data: {data}\n\n"
+
+            retriever_params = retriever.get_params()
+            log_data = {
+                "action": "stream_answer",
+                "level": "info",
+                "user": decoded_token.get("sub"),
+                "api_key": user_api_key,
+                "question": question,
+                "response": response_full,
+                "sources": source_log_docs,
+                "retriever_params": retriever_params,
+                "attachments": attachment_ids,
+                "timestamp": datetime.datetime.now(datetime.timezone.utc),
+            }
+            if is_structured:
+                log_data["structured_output"] = True
+                if schema_info:
+                    log_data["schema"] = schema_info
+  
+            # clean up text fields to be no longer than 10000 characters
+            for key, value in log_data.items():
+                if isinstance(value, str) and len(value) > 10000:
+                    log_data[key] = value[:10000]
+            
+            self.user_logs_collection.insert_one(log_data)
+
+            # End of stream
+
+            data = json.dumps({"type": "end"})
+            yield f"data: {data}\n\n"
+        except GeneratorExit:
+            # Client aborted the connection
+            logger.info(
+                f"Stream aborted by client for question: {question[:50]}... "
+            )
+            # Save partial response to database before exiting
+            if should_save_conversation and response_full:
+                try:
+                    if isNoneDoc:
+                        for doc in source_log_docs:
+                            doc["source"] = "None"
+                    llm = LLMCreator.create_llm(
+                        settings.LLM_PROVIDER,
+                        api_key=settings.API_KEY,
+                        user_api_key=user_api_key,
+                        decoded_token=decoded_token,
+                    )
+                    self.conversation_service.save_conversation(
+                        conversation_id,
+                        question,
+                        response_full,
+                        thought,
+                        source_log_docs,
+                        tool_calls,
+                        llm,
+                        self.gpt_model,
+                        decoded_token,
+                        index=index,
+                        api_key=user_api_key,
+                        agent_id=agent_id,
+                        is_shared_usage=is_shared_usage,
+                        shared_token=shared_token,
+                        attachment_ids=attachment_ids,
+                    )
+                except Exception as e:
+                    logger.error(f"Error saving partial response: {str(e)}", exc_info=True)
+            raise
+        except Exception as e:
+            logger.error(f"Error in stream: {str(e)}", exc_info=True)
+            data = json.dumps(
+                {
+                    "type": "error",
+                    "error": "Please try again later. We apologize for any inconvenience.",
+                }
+            )
+            yield f"data: {data}\n\n"
+            return
+
+    def process_response_stream(self, stream):
+        """Process the stream response for non-streaming endpoint"""
+        conversation_id = ""
+        response_full = ""
+        source_log_docs = []
+        tool_calls = []
+        thought = ""
+        stream_ended = False
+        is_structured = False
+        schema_info = None
+
+        for line in stream:
+            try:
+                event_data = line.replace("data: ", "").strip()
+                event = json.loads(event_data)
+
+                if event["type"] == "id":
+                    conversation_id = event["id"]
+                elif event["type"] == "answer":
+                    response_full += event["answer"]
+                elif event["type"] == "structured_answer":
+                    response_full = event["answer"]
+                    is_structured = True
+                    schema_info = event.get("schema")
+                elif event["type"] == "source":
+                    source_log_docs = event["source"]
+                elif event["type"] == "tool_calls":
+                    tool_calls = event["tool_calls"]
+                elif event["type"] == "thought":
+                    thought = event["thought"]
+                elif event["type"] == "error":
+                    logger.error(f"Error from stream: {event['error']}")
+                    return None, None, None, None, event["error"]
+                elif event["type"] == "end":
+                    stream_ended = True
+            except (json.JSONDecodeError, KeyError) as e:
+                logger.warning(f"Error parsing stream event: {e}, line: {line}")
+                continue
+        if not stream_ended:
+            logger.error("Stream ended unexpectedly without an 'end' event.")
+            return None, None, None, None, "Stream ended unexpectedly"
+
+        result = (
+            conversation_id,
+            response_full,
+            source_log_docs,
+            tool_calls,
+            thought,
+            None,
+        )
+
+        if is_structured:
+            result = result + ({"structured": True, "schema": schema_info},)
+
+        return result
+
+    def error_stream_generate(self, err_response):
+        data = json.dumps({"type": "error", "error": err_response})
+        yield f"data: {data}\n\n"

application/api/answer/routes/stream.py

@@ -0,0 +1,120 @@
+import logging
+import traceback
+
+from flask import request, Response
+from flask_restx import fields, Resource
+
+from application.api import api
+
+from application.api.answer.routes.base import answer_ns, BaseAnswerResource
+
+from application.api.answer.services.stream_processor import StreamProcessor
+
+logger = logging.getLogger(__name__)
+
+
+@answer_ns.route("/stream")
+class StreamResource(Resource, BaseAnswerResource):
+    def __init__(self, *args, **kwargs):
+        Resource.__init__(self, *args, **kwargs)
+        BaseAnswerResource.__init__(self)
+
+    stream_model = answer_ns.model(
+        "StreamModel",
+        {
+            "question": fields.String(
+                required=True, description="Question to be asked"
+            ),
+            "history": fields.List(
+                fields.String,
+                required=False,
+                description="Conversation history (only for new conversations)",
+            ),
+            "conversation_id": fields.String(
+                required=False,
+                description="Existing conversation ID (loads history)",
+            ),
+            "prompt_id": fields.String(
+                required=False, default="default", description="Prompt ID"
+            ),
+            "chunks": fields.Integer(
+                required=False, default=2, description="Number of chunks"
+            ),
+            "token_limit": fields.Integer(required=False, description="Token limit"),
+            "retriever": fields.String(required=False, description="Retriever type"),
+            "api_key": fields.String(required=False, description="API key"),
+            "active_docs": fields.String(
+                required=False, description="Active documents"
+            ),
+            "isNoneDoc": fields.Boolean(
+                required=False, description="Flag indicating if no document is used"
+            ),
+            "index": fields.Integer(
+                required=False, description="Index of the query to update"
+            ),
+            "save_conversation": fields.Boolean(
+                required=False,
+                default=True,
+                description="Whether to save the conversation",
+            ),
+            "attachments": fields.List(
+                fields.String, required=False, description="List of attachment IDs"
+            ),
+        },
+    )
+
+    @api.expect(stream_model)
+    @api.doc(description="Stream a response based on the question and retriever")
+    def post(self):
+        data = request.get_json()
+        if error := self.validate_request(data, "index" in data):
+            return error
+        decoded_token = getattr(request, "decoded_token", None)
+        processor = StreamProcessor(data, decoded_token)
+        try:
+            processor.initialize()
+            agent = processor.create_agent()
+            retriever = processor.create_retriever()
+
+            if error := self.check_usage(processor.agent_config):
+                return error
+
+            return Response(
+                self.complete_stream(
+                    question=data["question"],
+                    agent=agent,
+                    retriever=retriever,
+                    conversation_id=processor.conversation_id,
+                    user_api_key=processor.agent_config.get("user_api_key"),
+                    decoded_token=processor.decoded_token,
+                    isNoneDoc=data.get("isNoneDoc"),
+                    index=data.get("index"),
+                    should_save_conversation=data.get("save_conversation", True),
+                    attachment_ids=data.get("attachments", []),
+                    agent_id=data.get("agent_id"),
+                    is_shared_usage=processor.is_shared_usage,
+                    shared_token=processor.shared_token,
+                ),
+                mimetype="text/event-stream",
+            )
+        except ValueError as e:
+            message = "Malformed request body"
+            logger.error(
+                f"/stream - error: {message} - specific error: {str(e)} - traceback: {traceback.format_exc()}",
+                extra={"error": str(e), "traceback": traceback.format_exc()},
+            )
+            return Response(
+                self.error_stream_generate(message),
+                status=400,
+                mimetype="text/event-stream",
+            )
+        except Exception as e:
+            logger.error(
+                f"/stream - error: {str(e)} - traceback: {traceback.format_exc()}",
+                extra={"error": str(e), "traceback": traceback.format_exc()},
+            )
+            return Response(
+                self.error_stream_generate("Unknown error occurred"),
+                status=400,
+                mimetype="text/event-stream",
+            )

application/api/answer/services/conversation_service.py

@@ -0,0 +1,180 @@
+import logging
+from datetime import datetime, timezone
+from typing import Any, Dict, List, Optional
+
+from application.core.mongo_db import MongoDB
+
+from application.core.settings import settings
+from bson import ObjectId
+
+
+logger = logging.getLogger(__name__)
+
+
+class ConversationService:
+    def __init__(self):
+        mongo = MongoDB.get_client()
+        db = mongo[settings.MONGO_DB_NAME]
+        self.conversations_collection = db["conversations"]
+        self.agents_collection = db["agents"]
+
+    def get_conversation(
+        self, conversation_id: str, user_id: str
+    ) -> Optional[Dict[str, Any]]:
+        """Retrieve a conversation with proper access control"""
+        if not conversation_id or not user_id:
+            return None
+        try:
+            conversation = self.conversations_collection.find_one(
+                {
+                    "_id": ObjectId(conversation_id),
+                    "$or": [{"user": user_id}, {"shared_with": user_id}],
+                }
+            )
+
+            if not conversation:
+                logger.warning(
+                    f"Conversation not found or unauthorized - ID: {conversation_id}, User: {user_id}"
+                )
+                return None
+            conversation["_id"] = str(conversation["_id"])
+            return conversation
+        except Exception as e:
+            logger.error(f"Error fetching conversation: {str(e)}", exc_info=True)
+            return None
+
+    def save_conversation(
+        self,
+        conversation_id: Optional[str],
+        question: str,
+        response: str,
+        thought: str,
+        sources: List[Dict[str, Any]],
+        tool_calls: List[Dict[str, Any]],
+        llm: Any,
+        gpt_model: str,
+        decoded_token: Dict[str, Any],
+        index: Optional[int] = None,
+        api_key: Optional[str] = None,
+        agent_id: Optional[str] = None,
+        is_shared_usage: bool = False,
+        shared_token: Optional[str] = None,
+        attachment_ids: Optional[List[str]] = None,
+    ) -> str:
+        """Save or update a conversation in the database"""
+        user_id = decoded_token.get("sub")
+        if not user_id:
+            raise ValueError("User ID not found in token")
+        current_time = datetime.now(timezone.utc)
+        
+        # clean up in sources array such that we save max 1k characters for text part
+        for source in sources:
+            if "text" in source and isinstance(source["text"], str):
+                source["text"] = source["text"][:1000]
+
+        if conversation_id is not None and index is not None:
+            # Update existing conversation with new query
+
+            result = self.conversations_collection.update_one(
+                {
+                    "_id": ObjectId(conversation_id),
+                    "user": user_id,
+                    f"queries.{index}": {"$exists": True},
+                },
+                {
+                    "$set": {
+                        f"queries.{index}.prompt": question,
+                        f"queries.{index}.response": response,
+                        f"queries.{index}.thought": thought,
+                        f"queries.{index}.sources": sources,
+                        f"queries.{index}.tool_calls": tool_calls,
+                        f"queries.{index}.timestamp": current_time,
+                        f"queries.{index}.attachments": attachment_ids,
+                    }
+                },
+            )
+
+            if result.matched_count == 0:
+                raise ValueError("Conversation not found or unauthorized")
+            self.conversations_collection.update_one(
+                {
+                    "_id": ObjectId(conversation_id),
+                    "user": user_id,
+                    f"queries.{index}": {"$exists": True},
+                },
+                {"$push": {"queries": {"$each": [], "$slice": index + 1}}},
+            )
+            return conversation_id
+        elif conversation_id:
+            # Append new message to existing conversation
+
+            result = self.conversations_collection.update_one(
+                {"_id": ObjectId(conversation_id), "user": user_id},
+                {
+                    "$push": {
+                        "queries": {
+                            "prompt": question,
+                            "response": response,
+                            "thought": thought,
+                            "sources": sources,
+                            "tool_calls": tool_calls,
+                            "timestamp": current_time,
+                            "attachments": attachment_ids,
+                        }
+                    }
+                },
+            )
+
+            if result.matched_count == 0:
+                raise ValueError("Conversation not found or unauthorized")
+            return conversation_id
+        else:
+            # Create new conversation
+
+            messages_summary = [
+                {
+                    "role": "assistant",
+                    "content": "Summarise following conversation in no more than 3 "
+                    "words, respond ONLY with the summary, use the same "
+                    "language as the user query",
+                },
+                {
+                    "role": "user",
+                    "content": "Summarise following conversation in no more than 3 words, "
+                    "respond ONLY with the summary, use the same language as the "
+                    "user query \n\nUser: " + question + "\n\n" + "AI: " + response,
+                },
+            ]
+
+            completion = llm.gen(
+                model=gpt_model, messages=messages_summary, max_tokens=30
+            )
+
+            conversation_data = {
+                "user": user_id,
+                "date": current_time,
+                "name": completion,
+                "queries": [
+                    {
+                        "prompt": question,
+                        "response": response,
+                        "thought": thought,
+                        "sources": sources,
+                        "tool_calls": tool_calls,
+                        "timestamp": current_time,
+                        "attachments": attachment_ids,
+                    }
+                ],
+            }
+
+            if api_key:
+                if agent_id:
+                    conversation_data["agent_id"] = agent_id
+                    if is_shared_usage:
+                        conversation_data["is_shared_usage"] = is_shared_usage
+                        conversation_data["shared_token"] = shared_token
+                agent = self.agents_collection.find_one({"key": api_key})
+                if agent:
+                    conversation_data["api_key"] = agent["key"]
+            result = self.conversations_collection.insert_one(conversation_data)
+            return str(result.inserted_id)

application/api/answer/services/stream_processor.py

@@ -0,0 +1,353 @@
+import datetime
+import json
+import logging
+import os
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+from bson.dbref import DBRef
+
+from bson.objectid import ObjectId
+
+from application.agents.agent_creator import AgentCreator
+from application.api.answer.services.conversation_service import ConversationService
+from application.core.mongo_db import MongoDB
+from application.core.settings import settings
+from application.retriever.retriever_creator import RetrieverCreator
+from application.utils import get_gpt_model, limit_chat_history
+
+logger = logging.getLogger(__name__)
+
+
+def get_prompt(prompt_id: str, prompts_collection=None) -> str:
+    """
+    Get a prompt by preset name or MongoDB ID
+    """
+    current_dir = Path(__file__).resolve().parents[3]
+    prompts_dir = current_dir / "prompts"
+
+    preset_mapping = {
+        "default": "chat_combine_default.txt",
+        "creative": "chat_combine_creative.txt",
+        "strict": "chat_combine_strict.txt",
+        "reduce": "chat_reduce_prompt.txt",
+    }
+
+    if prompt_id in preset_mapping:
+        file_path = os.path.join(prompts_dir, preset_mapping[prompt_id])
+        try:
+            with open(file_path, "r") as f:
+                return f.read()
+        except FileNotFoundError:
+            raise FileNotFoundError(f"Prompt file not found: {file_path}")
+    try:
+        if prompts_collection is None:
+            mongo = MongoDB.get_client()
+            db = mongo[settings.MONGO_DB_NAME]
+            prompts_collection = db["prompts"]
+        prompt_doc = prompts_collection.find_one({"_id": ObjectId(prompt_id)})
+        if not prompt_doc:
+            raise ValueError(f"Prompt with ID {prompt_id} not found")
+        return prompt_doc["content"]
+    except Exception as e:
+        raise ValueError(f"Invalid prompt ID: {prompt_id}") from e
+
+
+class StreamProcessor:
+    def __init__(
+        self, request_data: Dict[str, Any], decoded_token: Optional[Dict[str, Any]]
+    ):
+        mongo = MongoDB.get_client()
+        self.db = mongo[settings.MONGO_DB_NAME]
+        self.agents_collection = self.db["agents"]
+        self.attachments_collection = self.db["attachments"]
+        self.prompts_collection = self.db["prompts"]
+
+        self.data = request_data
+        self.decoded_token = decoded_token
+        self.initial_user_id = (
+            self.decoded_token.get("sub") if self.decoded_token is not None else None
+        )
+        self.conversation_id = self.data.get("conversation_id")
+        self.source = {}
+        self.all_sources = []
+        self.attachments = []
+        self.history = []
+        self.agent_config = {}
+        self.retriever_config = {}
+        self.is_shared_usage = False
+        self.shared_token = None
+        self.gpt_model = get_gpt_model()
+        self.conversation_service = ConversationService()
+
+    def initialize(self):
+        """Initialize all required components for processing"""
+        self._configure_agent()
+        self._configure_source()
+        self._configure_retriever()
+        self._configure_agent()
+        self._load_conversation_history()
+        self._process_attachments()
+
+    def _load_conversation_history(self):
+        """Load conversation history either from DB or request"""
+        if self.conversation_id and self.initial_user_id:
+            conversation = self.conversation_service.get_conversation(
+                self.conversation_id, self.initial_user_id
+            )
+            if not conversation:
+                raise ValueError("Conversation not found or unauthorized")
+            self.history = [
+                {"prompt": query["prompt"], "response": query["response"]}
+                for query in conversation.get("queries", [])
+            ]
+        else:
+            self.history = limit_chat_history(
+                json.loads(self.data.get("history", "[]")), gpt_model=self.gpt_model
+            )
+
+    def _process_attachments(self):
+        """Process any attachments in the request"""
+        attachment_ids = self.data.get("attachments", [])
+        self.attachments = self._get_attachments_content(
+            attachment_ids, self.initial_user_id
+        )
+
+    def _get_attachments_content(self, attachment_ids, user_id):
+        """
+        Retrieve content from attachment documents based on their IDs.
+        """
+        if not attachment_ids:
+            return []
+        attachments = []
+        for attachment_id in attachment_ids:
+            try:
+                attachment_doc = self.attachments_collection.find_one(
+                    {"_id": ObjectId(attachment_id), "user": user_id}
+                )
+
+                if attachment_doc:
+                    attachments.append(attachment_doc)
+            except Exception as e:
+                logger.error(
+                    f"Error retrieving attachment {attachment_id}: {e}", exc_info=True
+                )
+        return attachments
+
+    def _get_agent_key(self, agent_id: Optional[str], user_id: Optional[str]) -> tuple:
+        """Get API key for agent with access control"""
+        if not agent_id:
+            return None, False, None
+        try:
+            agent = self.agents_collection.find_one({"_id": ObjectId(agent_id)})
+            if agent is None:
+                raise Exception("Agent not found")
+            is_owner = agent.get("user") == user_id
+            is_shared_with_user = agent.get(
+                "shared_publicly", False
+            ) or user_id in agent.get("shared_with", [])
+
+            if not (is_owner or is_shared_with_user):
+                raise Exception("Unauthorized access to the agent")
+            if is_owner:
+                self.agents_collection.update_one(
+                    {"_id": ObjectId(agent_id)},
+                    {
+                        "$set": {
+                            "lastUsedAt": datetime.datetime.now(datetime.timezone.utc)
+                        }
+                    },
+                )
+            return str(agent["key"]), not is_owner, agent.get("shared_token")
+        except Exception as e:
+            logger.error(f"Error in get_agent_key: {str(e)}", exc_info=True)
+            raise
+
+    def _get_data_from_api_key(self, api_key: str) -> Dict[str, Any]:
+        data = self.agents_collection.find_one({"key": api_key})
+        if not data:
+            raise Exception("Invalid API Key, please generate a new key", 401)
+        source = data.get("source")
+        if isinstance(source, DBRef):
+            source_doc = self.db.dereference(source)
+            if source_doc:
+                data["source"] = str(source_doc["_id"])
+                data["retriever"] = source_doc.get("retriever", data.get("retriever"))
+                data["chunks"] = source_doc.get("chunks", data.get("chunks"))
+            else:
+                data["source"] = None
+        elif source == "default":
+            data["source"] = "default"
+        else:
+            data["source"] = None
+        # Handle multiple sources
+
+        sources = data.get("sources", [])
+        if sources and isinstance(sources, list):
+            sources_list = []
+            for i, source_ref in enumerate(sources):
+                if source_ref == "default":
+                    processed_source = {
+                        "id": "default",
+                        "retriever": "classic",
+                        "chunks": data.get("chunks", "2"),
+                    }
+                    sources_list.append(processed_source)
+                elif isinstance(source_ref, DBRef):
+                    source_doc = self.db.dereference(source_ref)
+                    if source_doc:
+                        processed_source = {
+                            "id": str(source_doc["_id"]),
+                            "retriever": source_doc.get("retriever", "classic"),
+                            "chunks": source_doc.get("chunks", data.get("chunks", "2")),
+                        }
+                        sources_list.append(processed_source)
+            data["sources"] = sources_list
+        else:
+            data["sources"] = []
+        return data
+
+    def _configure_source(self):
+        """Configure the source based on agent data"""
+        api_key = self.data.get("api_key") or self.agent_key
+
+        if api_key:
+            agent_data = self._get_data_from_api_key(api_key)
+
+            if agent_data.get("sources") and len(agent_data["sources"]) > 0:
+                source_ids = [
+                    source["id"] for source in agent_data["sources"] if source.get("id")
+                ]
+                if source_ids:
+                    self.source = {"active_docs": source_ids}
+                else:
+                    self.source = {}
+                self.all_sources = agent_data["sources"]
+            elif agent_data.get("source"):
+                self.source = {"active_docs": agent_data["source"]}
+                self.all_sources = [
+                    {
+                        "id": agent_data["source"],
+                        "retriever": agent_data.get("retriever", "classic"),
+                    }
+                ]
+            else:
+                self.source = {}
+                self.all_sources = []
+            return
+        if "active_docs" in self.data:
+            self.source = {"active_docs": self.data["active_docs"]}
+            return
+        self.source = {}
+        self.all_sources = []
+
+    def _configure_agent(self):
+        """Configure the agent based on request data"""
+        agent_id = self.data.get("agent_id")
+        self.agent_key, self.is_shared_usage, self.shared_token = self._get_agent_key(
+            agent_id, self.initial_user_id
+        )
+
+        api_key = self.data.get("api_key")
+        if api_key:
+            data_key = self._get_data_from_api_key(api_key)
+            self.agent_config.update(
+                {
+                    "prompt_id": data_key.get("prompt_id", "default"),
+                    "agent_type": data_key.get("agent_type", settings.AGENT_NAME),
+                    "user_api_key": api_key,
+                    "json_schema": data_key.get("json_schema"),
+                }
+            )
+            self.initial_user_id = data_key.get("user")
+            self.decoded_token = {"sub": data_key.get("user")}
+            if data_key.get("source"):
+                self.source = {"active_docs": data_key["source"]}
+            if data_key.get("retriever"):
+                self.retriever_config["retriever_name"] = data_key["retriever"]
+            if data_key.get("chunks") is not None:
+                try:
+                    self.retriever_config["chunks"] = int(data_key["chunks"])
+                except (ValueError, TypeError):
+                    logger.warning(
+                        f"Invalid chunks value: {data_key['chunks']}, using default value 2"
+                    )
+                    self.retriever_config["chunks"] = 2
+        elif self.agent_key:
+            data_key = self._get_data_from_api_key(self.agent_key)
+            self.agent_config.update(
+                {
+                    "prompt_id": data_key.get("prompt_id", "default"),
+                    "agent_type": data_key.get("agent_type", settings.AGENT_NAME),
+                    "user_api_key": self.agent_key,
+                    "json_schema": data_key.get("json_schema"),
+                }
+            )
+            self.decoded_token = (
+                self.decoded_token
+                if self.is_shared_usage
+                else {"sub": data_key.get("user")}
+            )
+            if data_key.get("source"):
+                self.source = {"active_docs": data_key["source"]}
+            if data_key.get("retriever"):
+                self.retriever_config["retriever_name"] = data_key["retriever"]
+            if data_key.get("chunks") is not None:
+                try:
+                    self.retriever_config["chunks"] = int(data_key["chunks"])
+                except (ValueError, TypeError):
+                    logger.warning(
+                        f"Invalid chunks value: {data_key['chunks']}, using default value 2"
+                    )
+                    self.retriever_config["chunks"] = 2
+        else:
+            self.agent_config.update(
+                {
+                    "prompt_id": self.data.get("prompt_id", "default"),
+                    "agent_type": settings.AGENT_NAME,
+                    "user_api_key": None,
+                    "json_schema": None,
+                }
+            )
+
+    def _configure_retriever(self):
+        """Configure the retriever based on request data"""
+        self.retriever_config = {
+            "retriever_name": self.data.get("retriever", "classic"),
+            "chunks": int(self.data.get("chunks", 2)),
+            "token_limit": self.data.get("token_limit", settings.DEFAULT_MAX_HISTORY),
+        }
+
+        api_key = self.data.get("api_key") or self.agent_key
+        if not api_key and "isNoneDoc" in self.data and self.data["isNoneDoc"]:
+            self.retriever_config["chunks"] = 0
+
+    def create_agent(self):
+        """Create and return the configured agent"""
+        return AgentCreator.create_agent(
+            self.agent_config["agent_type"],
+            endpoint="stream",
+            llm_name=settings.LLM_PROVIDER,
+            gpt_model=self.gpt_model,
+            api_key=settings.API_KEY,
+            user_api_key=self.agent_config["user_api_key"],
+            prompt=get_prompt(self.agent_config["prompt_id"], self.prompts_collection),
+            chat_history=self.history,
+            decoded_token=self.decoded_token,
+            attachments=self.attachments,
+            json_schema=self.agent_config.get("json_schema"),
+        )
+
+    def create_retriever(self):
+        """Create and return the configured retriever"""
+        return RetrieverCreator.create_retriever(
+            self.retriever_config["retriever_name"],
+            source=self.source,
+            chat_history=self.history,
+            prompt=get_prompt(self.agent_config["prompt_id"], self.prompts_collection),
+            chunks=self.retriever_config["chunks"],
+            token_limit=self.retriever_config["token_limit"],
+            gpt_model=self.gpt_model,
+            user_api_key=self.agent_config["user_api_key"],
+            decoded_token=self.decoded_token,
+        )

application/api/connector/routes.py

@@ -0,0 +1,489 @@
+import base64
+import datetime
+import json
+import uuid
+
+
+from bson.objectid import ObjectId
+from flask import (
+    Blueprint,
+    current_app,
+    jsonify,
+    make_response,
+    request
+)
+from flask_restx import fields, Namespace, Resource
+
+
+from application.api.user.tasks import (
+    ingest_connector_task,
+)
+from application.core.mongo_db import MongoDB
+from application.core.settings import settings
+from application.api import api
+
+
+from application.parser.connectors.connector_creator import ConnectorCreator
+
+
+mongo = MongoDB.get_client()
+db = mongo[settings.MONGO_DB_NAME]
+sources_collection = db["sources"]
+sessions_collection = db["connector_sessions"]
+
+connector = Blueprint("connector", __name__)
+connectors_ns = Namespace("connectors", description="Connector operations", path="/")
+api.add_namespace(connectors_ns)
+
+
+
+@connectors_ns.route("/api/connectors/auth")
+class ConnectorAuth(Resource):
+    @api.doc(description="Get connector OAuth authorization URL", params={"provider": "Connector provider (e.g., google_drive)"})
+    def get(self):
+        try:
+            provider = request.args.get('provider') or request.args.get('source')
+            if not provider:
+                return make_response(jsonify({"success": False, "error": "Missing provider"}), 400)
+
+            if not ConnectorCreator.is_supported(provider):
+                return make_response(jsonify({"success": False, "error": f"Unsupported provider: {provider}"}), 400)
+
+            decoded_token = request.decoded_token
+            if not decoded_token:
+                return make_response(jsonify({"success": False, "error": "Unauthorized"}), 401)
+            user_id = decoded_token.get('sub')
+
+            now = datetime.datetime.now(datetime.timezone.utc)
+            result = sessions_collection.insert_one({
+                "provider": provider,
+                "user": user_id,
+                "status": "pending",
+                "created_at": now
+            })
+            state_dict = {
+                "provider": provider,
+                "object_id": str(result.inserted_id)
+            }
+            state = base64.urlsafe_b64encode(json.dumps(state_dict).encode()).decode()
+
+            auth = ConnectorCreator.create_auth(provider)
+            authorization_url = auth.get_authorization_url(state=state)
+            return make_response(jsonify({
+                "success": True,
+                "authorization_url": authorization_url,
+                "state": state
+            }), 200)
+        except Exception as e:
+            current_app.logger.error(f"Error generating connector auth URL: {e}")
+            return make_response(jsonify({"success": False, "error": str(e)}), 500)
+
+
+@connectors_ns.route("/api/connectors/callback")
+class ConnectorsCallback(Resource):
+    @api.doc(description="Handle OAuth callback for external connectors")
+    def get(self):
+        """Handle OAuth callback for external connectors"""
+        try:
+            from application.parser.connectors.connector_creator import ConnectorCreator
+            from flask import request, redirect
+
+            authorization_code = request.args.get('code')
+            state = request.args.get('state')
+            error = request.args.get('error')
+
+            state_dict = json.loads(base64.urlsafe_b64decode(state.encode()).decode())
+            provider = state_dict["provider"]
+            state_object_id = state_dict["object_id"]
+
+            if error:
+                if error == "access_denied":
+                    return redirect(f"/api/connectors/callback-status?status=cancelled&message=Authentication+was+cancelled.+You+can+try+again+if+you'd+like+to+connect+your+account.&provider={provider}")
+                else:
+                    current_app.logger.warning(f"OAuth error in callback: {error}")
+                    return redirect(f"/api/connectors/callback-status?status=error&message=Authentication+failed.+Please+try+again+and+make+sure+to+grant+all+requested+permissions.&provider={provider}")
+
+            if not authorization_code:
+                return redirect(f"/api/connectors/callback-status?status=error&message=Authentication+failed.+Please+try+again+and+make+sure+to+grant+all+requested+permissions.&provider={provider}")
+
+            try:
+                auth = ConnectorCreator.create_auth(provider)
+                token_info = auth.exchange_code_for_tokens(authorization_code)
+
+                session_token = str(uuid.uuid4())
+
+                try:
+                    credentials = auth.create_credentials_from_token_info(token_info)
+                    service = auth.build_drive_service(credentials)
+                    user_info = service.about().get(fields="user").execute()
+                    user_email = user_info.get('user', {}).get('emailAddress', 'Connected User')
+                except Exception as e:
+                    current_app.logger.warning(f"Could not get user info: {e}")
+                    user_email = 'Connected User'
+
+                sanitized_token_info = {
+                    "access_token": token_info.get("access_token"),
+                    "refresh_token": token_info.get("refresh_token"),
+                    "token_uri": token_info.get("token_uri"),
+                    "expiry": token_info.get("expiry")
+                }
+
+                sessions_collection.find_one_and_update(
+                    {"_id": ObjectId(state_object_id), "provider": provider},
+                    {
+                        "$set": {
+                            "session_token": session_token,
+                            "token_info": sanitized_token_info,
+                            "user_email": user_email,
+                            "status": "authorized"
+                        }
+                    }
+                )
+
+                # Redirect to success page with session token and user email
+                return redirect(f"/api/connectors/callback-status?status=success&message=Authentication+successful&provider={provider}&session_token={session_token}&user_email={user_email}")
+
+            except Exception as e:
+                current_app.logger.error(f"Error exchanging code for tokens: {str(e)}", exc_info=True)
+                return redirect(f"/api/connectors/callback-status?status=error&message=Authentication+failed.+Please+try+again+and+make+sure+to+grant+all+requested+permissions.&provider={provider}")
+
+        except Exception as e:
+            current_app.logger.error(f"Error handling connector callback: {e}")
+            return redirect("/api/connectors/callback-status?status=error&message=Authentication+failed.+Please+try+again+and+make+sure+to+grant+all+requested+permissions.")
+
+
+@connectors_ns.route("/api/connectors/files")
+class ConnectorFiles(Resource):
+    @api.expect(api.model("ConnectorFilesModel", {
+        "provider": fields.String(required=True), 
+        "session_token": fields.String(required=True), 
+        "folder_id": fields.String(required=False), 
+        "limit": fields.Integer(required=False), 
+        "page_token": fields.String(required=False),
+        "search_query": fields.String(required=False)
+    }))
+    @api.doc(description="List files from a connector provider (supports pagination and search)")
+    def post(self):
+        try:
+            data = request.get_json()
+            provider = data.get('provider')
+            session_token = data.get('session_token')
+            folder_id = data.get('folder_id')
+            limit = data.get('limit', 10)
+            page_token = data.get('page_token')
+            search_query = data.get('search_query')
+            
+            if not provider or not session_token:
+                return make_response(jsonify({"success": False, "error": "provider and session_token are required"}), 400)
+
+            decoded_token = request.decoded_token
+            if not decoded_token:
+                return make_response(jsonify({"success": False, "error": "Unauthorized"}), 401)
+            user = decoded_token.get('sub')
+            session = sessions_collection.find_one({"session_token": session_token, "user": user})
+            if not session:
+                return make_response(jsonify({"success": False, "error": "Invalid or unauthorized session"}), 401)
+
+            loader = ConnectorCreator.create_connector(provider, session_token)
+            input_config = {
+                'limit': limit,
+                'list_only': True,
+                'session_token': session_token,
+                'folder_id': folder_id,
+                'page_token': page_token
+            }
+            if search_query:
+                input_config['search_query'] = search_query
+                
+            documents = loader.load_data(input_config)
+
+            files = []
+            for doc in documents[:limit]:
+                metadata = doc.extra_info
+                modified_time = metadata.get('modified_time')
+                if modified_time:
+                    date_part = modified_time.split('T')[0]
+                    time_part = modified_time.split('T')[1].split('.')[0].split('Z')[0]
+                    formatted_time = f"{date_part} {time_part}"
+                else:
+                    formatted_time = None
+
+                files.append({
+                    'id': doc.doc_id,
+                    'name': metadata.get('file_name', 'Unknown File'),
+                    'type': metadata.get('mime_type', 'unknown'),
+                    'size': metadata.get('size', None),
+                    'modifiedTime': formatted_time,
+                    'isFolder': metadata.get('is_folder', False)
+                })
+
+            next_token = getattr(loader, 'next_page_token', None)
+            has_more = bool(next_token)
+
+            return make_response(jsonify({
+                "success": True, 
+                "files": files, 
+                "total": len(files), 
+                "next_page_token": next_token, 
+                "has_more": has_more
+            }), 200)
+        except Exception as e:
+            current_app.logger.error(f"Error loading connector files: {e}")
+            return make_response(jsonify({"success": False, "error": f"Failed to load files: {str(e)}"}), 500)
+
+
+@connectors_ns.route("/api/connectors/validate-session")
+class ConnectorValidateSession(Resource):
+    @api.expect(api.model("ConnectorValidateSessionModel", {"provider": fields.String(required=True), "session_token": fields.String(required=True)}))
+    @api.doc(description="Validate connector session token and return user info and access token")
+    def post(self):
+        try:
+            data = request.get_json()
+            provider = data.get('provider')
+            session_token = data.get('session_token')
+            if not provider or not session_token:
+                return make_response(jsonify({"success": False, "error": "provider and session_token are required"}), 400)
+
+            decoded_token = request.decoded_token
+            if not decoded_token:
+                return make_response(jsonify({"success": False, "error": "Unauthorized"}), 401)
+            user = decoded_token.get('sub')
+
+            session = sessions_collection.find_one({"session_token": session_token, "user": user})
+            if not session or "token_info" not in session:
+                return make_response(jsonify({"success": False, "error": "Invalid or expired session"}), 401)
+
+            token_info = session["token_info"]
+            auth = ConnectorCreator.create_auth(provider)
+            is_expired = auth.is_token_expired(token_info)
+
+            if is_expired and token_info.get('refresh_token'):
+                try:
+                    refreshed_token_info = auth.refresh_access_token(token_info.get('refresh_token'))
+                    sanitized_token_info = {
+                    "access_token": refreshed_token_info.get("access_token"),
+                    "refresh_token": refreshed_token_info.get("refresh_token"),
+                    "token_uri": refreshed_token_info.get("token_uri"),
+                    "expiry": refreshed_token_info.get("expiry")
+                }    
+                    sessions_collection.update_one(
+                        {"session_token": session_token},
+                        {"$set": {"token_info": sanitized_token_info}}
+                    )
+                    token_info = sanitized_token_info
+                    is_expired = False
+                except Exception as refresh_error:
+                    current_app.logger.error(f"Failed to refresh token: {refresh_error}")
+            
+            if is_expired:
+                return make_response(jsonify({
+                    "success": False,
+                    "expired": True,
+                    "error": "Session token has expired. Please reconnect."
+                }), 401)
+
+            return make_response(jsonify({
+                "success": True,
+                "expired": False,
+                "user_email": session.get('user_email', 'Connected User'),
+                "access_token": token_info.get('access_token')
+            }), 200)
+        except Exception as e:
+            current_app.logger.error(f"Error validating connector session: {e}")
+            return make_response(jsonify({"success": False, "error": str(e)}), 500)
+
+
+@connectors_ns.route("/api/connectors/disconnect")
+class ConnectorDisconnect(Resource):
+    @api.expect(api.model("ConnectorDisconnectModel", {"provider": fields.String(required=True), "session_token": fields.String(required=False)}))
+    @api.doc(description="Disconnect a connector session")
+    def post(self):
+        try:
+            data = request.get_json()
+            provider = data.get('provider')
+            session_token = data.get('session_token')
+            if not provider:
+                return make_response(jsonify({"success": False, "error": "provider is required"}), 400)
+
+
+            if session_token:
+                sessions_collection.delete_one({"session_token": session_token})
+            
+            return make_response(jsonify({"success": True}), 200)
+        except Exception as e:
+            current_app.logger.error(f"Error disconnecting connector session: {e}")
+            return make_response(jsonify({"success": False, "error": str(e)}), 500)
+
+
+@connectors_ns.route("/api/connectors/sync")
+class ConnectorSync(Resource):
+    @api.expect(
+        api.model(
+            "ConnectorSyncModel",
+            {
+                "source_id": fields.String(required=True, description="Source ID to sync"),
+                "session_token": fields.String(required=True, description="Authentication token")
+            },
+        )
+    )
+    @api.doc(description="Sync connector source to check for modifications")
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+
+        try:
+            data = request.get_json()
+            source_id = data.get('source_id')
+            session_token = data.get('session_token')
+
+            if not all([source_id, session_token]):
+                return make_response(
+                    jsonify({
+                        "success": False,
+                        "error": "source_id and session_token are required"
+                    }), 
+                    400
+                )
+            source = sources_collection.find_one({"_id": ObjectId(source_id)})
+            if not source:
+                return make_response(
+                    jsonify({
+                        "success": False,
+                        "error": "Source not found"
+                    }), 
+                    404
+                )
+
+            if source.get('user') != decoded_token.get('sub'):
+                return make_response(
+                    jsonify({
+                        "success": False,
+                        "error": "Unauthorized access to source"
+                    }), 
+                    403
+                )
+
+            remote_data = {}
+            try:
+                if source.get('remote_data'):
+                    remote_data = json.loads(source.get('remote_data'))
+            except json.JSONDecodeError:
+                current_app.logger.error(f"Invalid remote_data format for source {source_id}")
+                remote_data = {}
+
+            source_type = remote_data.get('provider')
+            if not source_type:
+                return make_response(
+                    jsonify({
+                        "success": False,
+                        "error": "Source provider not found in remote_data"
+                    }), 
+                    400
+                )
+
+            # Extract configuration from remote_data
+            file_ids = remote_data.get('file_ids', [])
+            folder_ids = remote_data.get('folder_ids', [])
+            recursive = remote_data.get('recursive', True)
+
+            # Start the sync task
+            task = ingest_connector_task.delay(
+                job_name=source.get('name'),
+                user=decoded_token.get('sub'),
+                source_type=source_type,
+                session_token=session_token,
+                file_ids=file_ids,
+                folder_ids=folder_ids,
+                recursive=recursive,
+                retriever=source.get('retriever', 'classic'),
+                operation_mode="sync",
+                doc_id=source_id,
+                sync_frequency=source.get('sync_frequency', 'never')
+            )
+
+            return make_response(
+                jsonify({
+                    "success": True,
+                    "task_id": task.id
+                }), 
+                200
+            )
+
+        except Exception as err:
+            current_app.logger.error(
+                f"Error syncing connector source: {err}",
+                exc_info=True
+            )
+            return make_response(
+                jsonify({
+                    "success": False,
+                    "error": str(err)
+                }), 
+                400
+            )
+
+
+@connectors_ns.route("/api/connectors/callback-status")
+class ConnectorCallbackStatus(Resource):
+    @api.doc(description="Return HTML page with connector authentication status")
+    def get(self):
+        """Return HTML page with connector authentication status"""
+        try:
+            status = request.args.get('status', 'error')
+            message = request.args.get('message', '')
+            provider = request.args.get('provider', 'connector')
+            session_token = request.args.get('session_token', '')
+            user_email = request.args.get('user_email', '')
+            
+            html_content = f"""
+            <!DOCTYPE html>
+            <html>
+            <head>
+                <title>{provider.replace('_', ' ').title()} Authentication</title>
+                <style>
+                    body {{ font-family: Arial, sans-serif; text-align: center; padding: 40px; }}
+                    .container {{ max-width: 600px; margin: 0 auto; }}
+                    .success {{ color: #4CAF50; }}
+                    .error {{ color: #F44336; }}
+                    .cancelled {{ color: #FF9800; }}
+                </style>
+                <script>
+                    window.onload = function() {{
+                        const status = "{status}";
+                        const sessionToken = "{session_token}";
+                        const userEmail = "{user_email}";
+
+                        if (status === "success" && window.opener) {{
+                            window.opener.postMessage({{
+                                type: '{provider}_auth_success',
+                                session_token: sessionToken,
+                                user_email: userEmail
+                            }}, '*');
+
+                            setTimeout(() => window.close(), 3000);
+                        }} else if (status === "cancelled" || status === "error") {{
+                            setTimeout(() => window.close(), 3000);
+                        }}
+                    }};
+                </script>
+            </head>
+            <body>
+                <div class="container">
+                    <h2>{provider.replace('_', ' ').title()} Authentication</h2>
+                    <div class="{status}">
+                        <p>{message}</p>
+                        {f'<p>Connected as: {user_email}</p>' if status == 'success' else ''}
+                    </div>
+                    <p><small>You can close this window. {f"Your {provider.replace('_', ' ').title()} is now connected and ready to use." if status == 'success' else "Feel free to close this window."}</small></p>
+                </div>
+            </body>
+            </html>
+            """
+            
+            return make_response(html_content, 200, {'Content-Type': 'text/html'})
+        except Exception as e:
+            current_app.logger.error(f"Error rendering callback status page: {e}")
+            return make_response("Authentication error occurred", 500, {'Content-Type': 'text/html'})
+
+

application/api/internal/routes.py

@@ -1,14 +1,18 @@
 import os
 import datetime
+import json
 from flask import Blueprint, request, send_from_directory
 from werkzeug.utils import secure_filename
 from bson.objectid import ObjectId
-
+import logging
 from application.core.mongo_db import MongoDB
 from application.core.settings import settings
+from application.storage.storage_creator import StorageCreator
 
+
+logger = logging.getLogger(__name__)
 mongo = MongoDB.get_client()
-db = mongo["docsgpt"]
+db = mongo[settings.MONGO_DB_NAME]
 conversations_collection = db["conversations"]
 sources_collection = db["sources"]
 
@@ -34,37 +38,52 @@ def upload_index_files():
     """Upload two files(index.faiss, index.pkl) to the user's folder."""
     if "user" not in request.form:
         return {"status": "no user"}
-    user = secure_filename(request.form["user"])
+    user = request.form["user"] 
     if "name" not in request.form:
         return {"status": "no name"}
-    job_name = secure_filename(request.form["name"])
-    tokens = secure_filename(request.form["tokens"])
-    retriever = secure_filename(request.form["retriever"])
-    id = secure_filename(request.form["id"])
-    type = secure_filename(request.form["type"])
+    job_name = request.form["name"]
+    tokens = request.form["tokens"]
+    retriever = request.form["retriever"]
+    id = request.form["id"]
+    type = request.form["type"]
     remote_data = request.form["remote_data"] if "remote_data" in request.form else None
-    sync_frequency = secure_filename(request.form["sync_frequency"]) if "sync_frequency" in request.form else None
+    sync_frequency = request.form["sync_frequency"] if "sync_frequency" in request.form else None
+    
+    file_path = request.form.get("file_path")
+    directory_structure = request.form.get("directory_structure")
+    
+    if directory_structure:
+        try:
+            directory_structure = json.loads(directory_structure)
+        except Exception:
+            logger.error("Error parsing directory_structure")
+            directory_structure = {}
+    else:
+        directory_structure = {}
 
-    save_dir = os.path.join(current_dir, "indexes", str(id))
+    storage = StorageCreator.get_storage()
+    index_base_path = f"indexes/{id}"
+    
     if settings.VECTOR_STORE == "faiss":
         if "file_faiss" not in request.files:
-            print("No file part")
+            logger.error("No file_faiss part")
             return {"status": "no file"}
         file_faiss = request.files["file_faiss"]
         if file_faiss.filename == "":
             return {"status": "no file name"}
         if "file_pkl" not in request.files:
-            print("No file part")
+            logger.error("No file_pkl part")
             return {"status": "no file"}
         file_pkl = request.files["file_pkl"]
         if file_pkl.filename == "":
             return {"status": "no file name"}
-        # saves index files
 
-        if not os.path.exists(save_dir):
-            os.makedirs(save_dir)
-        file_faiss.save(os.path.join(save_dir, "index.faiss"))
-        file_pkl.save(os.path.join(save_dir, "index.pkl"))
+        # Save index files to storage
+        faiss_storage_path = f"{index_base_path}/index.faiss"
+        pkl_storage_path = f"{index_base_path}/index.pkl"
+        storage.save_file(file_faiss, faiss_storage_path)
+        storage.save_file(file_pkl, pkl_storage_path)
+
 
     existing_entry = sources_collection.find_one({"_id": ObjectId(id)})
     if existing_entry:
@@ -82,6 +101,8 @@ def upload_index_files():
                     "retriever": retriever,
                     "remote_data": remote_data,
                     "sync_frequency": sync_frequency,
+                    "file_path": file_path,
+                    "directory_structure": directory_structure,
                 }
             },
         )
@@ -99,6 +120,8 @@ def upload_index_files():
                 "retriever": retriever,
                 "remote_data": remote_data,
                 "sync_frequency": sync_frequency,
+                "file_path": file_path,
+                "directory_structure": directory_structure,
             }
         )
     return {"status": "ok"}

application/api/user/__init__.py

@@ -0,0 +1,5 @@
+"""User API module - provides all user-related API endpoints"""
+
+from .routes import user
+
+__all__ = ["user"]

application/api/user/agents/__init__.py

@@ -0,0 +1,7 @@
+"""Agents module."""
+
+from .routes import agents_ns
+from .sharing import agents_sharing_ns
+from .webhooks import agents_webhooks_ns
+
+__all__ = ["agents_ns", "agents_sharing_ns", "agents_webhooks_ns"]

application/api/user/agents/sharing.py

@@ -0,0 +1,263 @@
+"""Agent management sharing functionality."""
+
+import datetime
+import secrets
+
+from bson import DBRef
+from bson.objectid import ObjectId
+from flask import current_app, jsonify, make_response, request
+from flask_restx import fields, Namespace, Resource
+
+from application.api import api
+from application.core.settings import settings
+from application.api.user.base import (
+    agents_collection,
+    db,
+    ensure_user_doc,
+    resolve_tool_details,
+    user_tools_collection,
+    users_collection,
+)
+from application.utils import generate_image_url
+
+agents_sharing_ns = Namespace(
+    "agents", description="Agent management operations", path="/api"
+)
+
+
+@agents_sharing_ns.route("/shared_agent")
+class SharedAgent(Resource):
+    @api.doc(
+        params={
+            "token": "Shared token of the agent",
+        },
+        description="Get a shared agent by token or ID",
+    )
+    def get(self):
+        shared_token = request.args.get("token")
+
+        if not shared_token:
+            return make_response(
+                jsonify({"success": False, "message": "Token or ID is required"}), 400
+            )
+        try:
+            query = {
+                "shared_publicly": True,
+                "shared_token": shared_token,
+            }
+            shared_agent = agents_collection.find_one(query)
+            if not shared_agent:
+                return make_response(
+                    jsonify({"success": False, "message": "Shared agent not found"}),
+                    404,
+                )
+            agent_id = str(shared_agent["_id"])
+            data = {
+                "id": agent_id,
+                "user": shared_agent.get("user", ""),
+                "name": shared_agent.get("name", ""),
+                "image": (
+                    generate_image_url(shared_agent["image"])
+                    if shared_agent.get("image")
+                    else ""
+                ),
+                "description": shared_agent.get("description", ""),
+                "source": (
+                    str(source_doc["_id"])
+                    if isinstance(shared_agent.get("source"), DBRef)
+                    and (source_doc := db.dereference(shared_agent.get("source")))
+                    else ""
+                ),
+                "chunks": shared_agent.get("chunks", "0"),
+                "retriever": shared_agent.get("retriever", "classic"),
+                "prompt_id": shared_agent.get("prompt_id", "default"),
+                "tools": shared_agent.get("tools", []),
+                "tool_details": resolve_tool_details(shared_agent.get("tools", [])),
+                "agent_type": shared_agent.get("agent_type", ""),
+                "status": shared_agent.get("status", ""),
+                "json_schema": shared_agent.get("json_schema"),
+                "limited_token_mode": shared_agent.get("limited_token_mode", False),
+                "token_limit": shared_agent.get("token_limit", settings.DEFAULT_AGENT_LIMITS["token_limit"]),
+                "limited_request_mode": shared_agent.get("limited_request_mode", False),
+                "request_limit": shared_agent.get("request_limit", settings.DEFAULT_AGENT_LIMITS["request_limit"]),
+                "created_at": shared_agent.get("createdAt", ""),
+                "updated_at": shared_agent.get("updatedAt", ""),
+                "shared": shared_agent.get("shared_publicly", False),
+                "shared_token": shared_agent.get("shared_token", ""),
+                "shared_metadata": shared_agent.get("shared_metadata", {}),
+            }
+
+            if data["tools"]:
+                enriched_tools = []
+                for tool in data["tools"]:
+                    tool_data = user_tools_collection.find_one({"_id": ObjectId(tool)})
+                    if tool_data:
+                        enriched_tools.append(tool_data.get("name", ""))
+                data["tools"] = enriched_tools
+            decoded_token = getattr(request, "decoded_token", None)
+            if decoded_token:
+                user_id = decoded_token.get("sub")
+                owner_id = shared_agent.get("user")
+
+                if user_id != owner_id:
+                    ensure_user_doc(user_id)
+                    users_collection.update_one(
+                        {"user_id": user_id},
+                        {"$addToSet": {"agent_preferences.shared_with_me": agent_id}},
+                    )
+            return make_response(jsonify(data), 200)
+        except Exception as err:
+            current_app.logger.error(f"Error retrieving shared agent: {err}")
+            return make_response(jsonify({"success": False}), 400)
+
+
+@agents_sharing_ns.route("/shared_agents")
+class SharedAgents(Resource):
+    @api.doc(description="Get shared agents explicitly shared with the user")
+    def get(self):
+        try:
+            decoded_token = request.decoded_token
+            if not decoded_token:
+                return make_response(jsonify({"success": False}), 401)
+            user_id = decoded_token.get("sub")
+
+            user_doc = ensure_user_doc(user_id)
+            shared_with_ids = user_doc.get("agent_preferences", {}).get(
+                "shared_with_me", []
+            )
+            shared_object_ids = [ObjectId(id) for id in shared_with_ids]
+
+            shared_agents_cursor = agents_collection.find(
+                {"_id": {"$in": shared_object_ids}, "shared_publicly": True}
+            )
+            shared_agents = list(shared_agents_cursor)
+
+            found_ids_set = {str(agent["_id"]) for agent in shared_agents}
+            stale_ids = [id for id in shared_with_ids if id not in found_ids_set]
+            if stale_ids:
+                users_collection.update_one(
+                    {"user_id": user_id},
+                    {"$pullAll": {"agent_preferences.shared_with_me": stale_ids}},
+                )
+            pinned_ids = set(user_doc.get("agent_preferences", {}).get("pinned", []))
+
+            list_shared_agents = [
+                {
+                    "id": str(agent["_id"]),
+                    "name": agent.get("name", ""),
+                    "description": agent.get("description", ""),
+                    "image": (
+                        generate_image_url(agent["image"]) if agent.get("image") else ""
+                    ),
+                    "tools": agent.get("tools", []),
+                    "tool_details": resolve_tool_details(agent.get("tools", [])),
+                    "agent_type": agent.get("agent_type", ""),
+                    "status": agent.get("status", ""),
+                    "json_schema": agent.get("json_schema"),
+                    "limited_token_mode": agent.get("limited_token_mode", False),
+                    "token_limit": agent.get("token_limit", settings.DEFAULT_AGENT_LIMITS["token_limit"]),
+                    "limited_request_mode": agent.get("limited_request_mode", False),
+                    "request_limit": agent.get("request_limit", settings.DEFAULT_AGENT_LIMITS["request_limit"]),
+                    "created_at": agent.get("createdAt", ""),
+                    "updated_at": agent.get("updatedAt", ""),
+                    "pinned": str(agent["_id"]) in pinned_ids,
+                    "shared": agent.get("shared_publicly", False),
+                    "shared_token": agent.get("shared_token", ""),
+                    "shared_metadata": agent.get("shared_metadata", {}),
+                }
+                for agent in shared_agents
+            ]
+
+            return make_response(jsonify(list_shared_agents), 200)
+        except Exception as err:
+            current_app.logger.error(f"Error retrieving shared agents: {err}")
+            return make_response(jsonify({"success": False}), 400)
+
+
+@agents_sharing_ns.route("/share_agent")
+class ShareAgent(Resource):
+    @api.expect(
+        api.model(
+            "ShareAgentModel",
+            {
+                "id": fields.String(required=True, description="ID of the agent"),
+                "shared": fields.Boolean(
+                    required=True, description="Share or unshare the agent"
+                ),
+                "username": fields.String(
+                    required=False, description="Name of the user"
+                ),
+            },
+        )
+    )
+    @api.doc(description="Share or unshare an agent")
+    def put(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+
+        data = request.get_json()
+        if not data:
+            return make_response(
+                jsonify({"success": False, "message": "Missing JSON body"}), 400
+            )
+        agent_id = data.get("id")
+        shared = data.get("shared")
+        username = data.get("username", "")
+
+        if not agent_id:
+            return make_response(
+                jsonify({"success": False, "message": "ID is required"}), 400
+            )
+        if shared is None:
+            return make_response(
+                jsonify(
+                    {
+                        "success": False,
+                        "message": "Shared parameter is required and must be true or false",
+                    }
+                ),
+                400,
+            )
+        try:
+            try:
+                agent_oid = ObjectId(agent_id)
+            except Exception:
+                return make_response(
+                    jsonify({"success": False, "message": "Invalid agent ID"}), 400
+                )
+            agent = agents_collection.find_one({"_id": agent_oid, "user": user})
+            if not agent:
+                return make_response(
+                    jsonify({"success": False, "message": "Agent not found"}), 404
+                )
+            if shared:
+                shared_metadata = {
+                    "shared_by": username,
+                    "shared_at": datetime.datetime.now(datetime.timezone.utc),
+                }
+                shared_token = secrets.token_urlsafe(32)
+                agents_collection.update_one(
+                    {"_id": agent_oid, "user": user},
+                    {
+                        "$set": {
+                            "shared_publicly": shared,
+                            "shared_metadata": shared_metadata,
+                            "shared_token": shared_token,
+                        }
+                    },
+                )
+            else:
+                agents_collection.update_one(
+                    {"_id": agent_oid, "user": user},
+                    {"$set": {"shared_publicly": shared, "shared_token": None}},
+                    {"$unset": {"shared_metadata": ""}},
+                )
+        except Exception as err:
+            current_app.logger.error(f"Error sharing/unsharing agent: {err}")
+            return make_response(jsonify({"success": False, "error": str(err)}), 400)
+        shared_token = shared_token if shared else None
+        return make_response(
+            jsonify({"success": True, "shared_token": shared_token}), 200
+        )

application/api/user/agents/webhooks.py

@@ -0,0 +1,119 @@
+"""Agent management webhook handlers."""
+
+import secrets
+
+from bson.objectid import ObjectId
+from flask import current_app, jsonify, make_response, request
+from flask_restx import Namespace, Resource
+
+from application.api import api
+from application.api.user.base import agents_collection, require_agent
+from application.api.user.tasks import process_agent_webhook
+from application.core.settings import settings
+
+
+agents_webhooks_ns = Namespace(
+    "agents", description="Agent management operations", path="/api"
+)
+
+
+@agents_webhooks_ns.route("/agent_webhook")
+class AgentWebhook(Resource):
+    @api.doc(
+        params={"id": "ID of the agent"},
+        description="Generate webhook URL for the agent",
+    )
+    def get(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        agent_id = request.args.get("id")
+        if not agent_id:
+            return make_response(
+                jsonify({"success": False, "message": "ID is required"}), 400
+            )
+        try:
+            agent = agents_collection.find_one(
+                {"_id": ObjectId(agent_id), "user": user}
+            )
+            if not agent:
+                return make_response(
+                    jsonify({"success": False, "message": "Agent not found"}), 404
+                )
+            webhook_token = agent.get("incoming_webhook_token")
+            if not webhook_token:
+                webhook_token = secrets.token_urlsafe(32)
+                agents_collection.update_one(
+                    {"_id": ObjectId(agent_id), "user": user},
+                    {"$set": {"incoming_webhook_token": webhook_token}},
+                )
+            base_url = settings.API_URL.rstrip("/")
+            full_webhook_url = f"{base_url}/api/webhooks/agents/{webhook_token}"
+        except Exception as err:
+            current_app.logger.error(
+                f"Error generating webhook URL: {err}", exc_info=True
+            )
+            return make_response(
+                jsonify({"success": False, "message": "Error generating webhook URL"}),
+                400,
+            )
+        return make_response(
+            jsonify({"success": True, "webhook_url": full_webhook_url}), 200
+        )
+
+
+@agents_webhooks_ns.route("/webhooks/agents/<string:webhook_token>")
+class AgentWebhookListener(Resource):
+    method_decorators = [require_agent]
+
+    def _enqueue_webhook_task(self, agent_id_str, payload, source_method):
+        if not payload:
+            current_app.logger.warning(
+                f"Webhook ({source_method}) received for agent {agent_id_str} with empty payload."
+            )
+        current_app.logger.info(
+            f"Incoming {source_method} webhook for agent {agent_id_str}. Enqueuing task with payload: {payload}"
+        )
+
+        try:
+            task = process_agent_webhook.delay(
+                agent_id=agent_id_str,
+                payload=payload,
+            )
+            current_app.logger.info(
+                f"Task {task.id} enqueued for agent {agent_id_str} ({source_method})."
+            )
+            return make_response(jsonify({"success": True, "task_id": task.id}), 200)
+        except Exception as err:
+            current_app.logger.error(
+                f"Error enqueuing webhook task ({source_method}) for agent {agent_id_str}: {err}",
+                exc_info=True,
+            )
+            return make_response(
+                jsonify({"success": False, "message": "Error processing webhook"}), 500
+            )
+
+    @api.doc(
+        description="Webhook listener for agent events (POST). Expects JSON payload, which is used to trigger processing.",
+    )
+    def post(self, webhook_token, agent, agent_id_str):
+        payload = request.get_json()
+        if payload is None:
+            return make_response(
+                jsonify(
+                    {
+                        "success": False,
+                        "message": "Invalid or missing JSON data in request body",
+                    }
+                ),
+                400,
+            )
+        return self._enqueue_webhook_task(agent_id_str, payload, source_method="POST")
+
+    @api.doc(
+        description="Webhook listener for agent events (GET). Uses URL query parameters as payload to trigger processing.",
+    )
+    def get(self, webhook_token, agent, agent_id_str):
+        payload = request.args.to_dict(flat=True)
+        return self._enqueue_webhook_task(agent_id_str, payload, source_method="GET")

application/api/user/analytics/__init__.py

@@ -0,0 +1,5 @@
+"""Analytics module."""
+
+from .routes import analytics_ns
+
+__all__ = ["analytics_ns"]

application/api/user/analytics/routes.py

@@ -0,0 +1,540 @@
+"""Analytics and reporting routes."""
+
+import datetime
+
+from bson.objectid import ObjectId
+from flask import current_app, jsonify, make_response, request
+from flask_restx import fields, Namespace, Resource
+
+from application.api import api
+from application.api.user.base import (
+    agents_collection,
+    conversations_collection,
+    generate_date_range,
+    generate_hourly_range,
+    generate_minute_range,
+    token_usage_collection,
+    user_logs_collection,
+)
+
+analytics_ns = Namespace(
+    "analytics", description="Analytics and reporting operations", path="/api"
+)
+
+
+@analytics_ns.route("/get_message_analytics")
+class GetMessageAnalytics(Resource):
+    get_message_analytics_model = api.model(
+        "GetMessageAnalyticsModel",
+        {
+            "api_key_id": fields.String(required=False, description="API Key ID"),
+            "filter_option": fields.String(
+                required=False,
+                description="Filter option for analytics",
+                default="last_30_days",
+                enum=[
+                    "last_hour",
+                    "last_24_hour",
+                    "last_7_days",
+                    "last_15_days",
+                    "last_30_days",
+                ],
+            ),
+        },
+    )
+
+    @api.expect(get_message_analytics_model)
+    @api.doc(description="Get message analytics based on filter option")
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        data = request.get_json()
+        api_key_id = data.get("api_key_id")
+        filter_option = data.get("filter_option", "last_30_days")
+
+        try:
+            api_key = (
+                agents_collection.find_one({"_id": ObjectId(api_key_id), "user": user})[
+                    "key"
+                ]
+                if api_key_id
+                else None
+            )
+        except Exception as err:
+            current_app.logger.error(f"Error getting API key: {err}", exc_info=True)
+            return make_response(jsonify({"success": False}), 400)
+        end_date = datetime.datetime.now(datetime.timezone.utc)
+
+        if filter_option == "last_hour":
+            start_date = end_date - datetime.timedelta(hours=1)
+            group_format = "%Y-%m-%d %H:%M:00"
+        elif filter_option == "last_24_hour":
+            start_date = end_date - datetime.timedelta(hours=24)
+            group_format = "%Y-%m-%d %H:00"
+        else:
+            if filter_option in ["last_7_days", "last_15_days", "last_30_days"]:
+                filter_days = (
+                    6
+                    if filter_option == "last_7_days"
+                    else 14 if filter_option == "last_15_days" else 29
+                )
+            else:
+                return make_response(
+                    jsonify({"success": False, "message": "Invalid option"}), 400
+                )
+            start_date = end_date - datetime.timedelta(days=filter_days)
+            start_date = start_date.replace(hour=0, minute=0, second=0, microsecond=0)
+            end_date = end_date.replace(
+                hour=23, minute=59, second=59, microsecond=999999
+            )
+            group_format = "%Y-%m-%d"
+        try:
+            match_stage = {
+                "$match": {
+                    "user": user,
+                }
+            }
+            if api_key:
+                match_stage["$match"]["api_key"] = api_key
+            pipeline = [
+                match_stage,
+                {"$unwind": "$queries"},
+                {
+                    "$match": {
+                        "queries.timestamp": {"$gte": start_date, "$lte": end_date}
+                    }
+                },
+                {
+                    "$group": {
+                        "_id": {
+                            "$dateToString": {
+                                "format": group_format,
+                                "date": "$queries.timestamp",
+                            }
+                        },
+                        "count": {"$sum": 1},
+                    }
+                },
+                {"$sort": {"_id": 1}},
+            ]
+
+            message_data = conversations_collection.aggregate(pipeline)
+
+            if filter_option == "last_hour":
+                intervals = generate_minute_range(start_date, end_date)
+            elif filter_option == "last_24_hour":
+                intervals = generate_hourly_range(start_date, end_date)
+            else:
+                intervals = generate_date_range(start_date, end_date)
+            daily_messages = {interval: 0 for interval in intervals}
+
+            for entry in message_data:
+                daily_messages[entry["_id"]] = entry["count"]
+        except Exception as err:
+            current_app.logger.error(
+                f"Error getting message analytics: {err}", exc_info=True
+            )
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(
+            jsonify({"success": True, "messages": daily_messages}), 200
+        )
+
+
+@analytics_ns.route("/get_token_analytics")
+class GetTokenAnalytics(Resource):
+    get_token_analytics_model = api.model(
+        "GetTokenAnalyticsModel",
+        {
+            "api_key_id": fields.String(required=False, description="API Key ID"),
+            "filter_option": fields.String(
+                required=False,
+                description="Filter option for analytics",
+                default="last_30_days",
+                enum=[
+                    "last_hour",
+                    "last_24_hour",
+                    "last_7_days",
+                    "last_15_days",
+                    "last_30_days",
+                ],
+            ),
+        },
+    )
+
+    @api.expect(get_token_analytics_model)
+    @api.doc(description="Get token analytics data")
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        data = request.get_json()
+        api_key_id = data.get("api_key_id")
+        filter_option = data.get("filter_option", "last_30_days")
+
+        try:
+            api_key = (
+                agents_collection.find_one({"_id": ObjectId(api_key_id), "user": user})[
+                    "key"
+                ]
+                if api_key_id
+                else None
+            )
+        except Exception as err:
+            current_app.logger.error(f"Error getting API key: {err}", exc_info=True)
+            return make_response(jsonify({"success": False}), 400)
+        end_date = datetime.datetime.now(datetime.timezone.utc)
+
+        if filter_option == "last_hour":
+            start_date = end_date - datetime.timedelta(hours=1)
+            group_format = "%Y-%m-%d %H:%M:00"
+            group_stage = {
+                "$group": {
+                    "_id": {
+                        "minute": {
+                            "$dateToString": {
+                                "format": group_format,
+                                "date": "$timestamp",
+                            }
+                        }
+                    },
+                    "total_tokens": {
+                        "$sum": {"$add": ["$prompt_tokens", "$generated_tokens"]}
+                    },
+                }
+            }
+        elif filter_option == "last_24_hour":
+            start_date = end_date - datetime.timedelta(hours=24)
+            group_format = "%Y-%m-%d %H:00"
+            group_stage = {
+                "$group": {
+                    "_id": {
+                        "hour": {
+                            "$dateToString": {
+                                "format": group_format,
+                                "date": "$timestamp",
+                            }
+                        }
+                    },
+                    "total_tokens": {
+                        "$sum": {"$add": ["$prompt_tokens", "$generated_tokens"]}
+                    },
+                }
+            }
+        else:
+            if filter_option in ["last_7_days", "last_15_days", "last_30_days"]:
+                filter_days = (
+                    6
+                    if filter_option == "last_7_days"
+                    else (14 if filter_option == "last_15_days" else 29)
+                )
+            else:
+                return make_response(
+                    jsonify({"success": False, "message": "Invalid option"}), 400
+                )
+            start_date = end_date - datetime.timedelta(days=filter_days)
+            start_date = start_date.replace(hour=0, minute=0, second=0, microsecond=0)
+            end_date = end_date.replace(
+                hour=23, minute=59, second=59, microsecond=999999
+            )
+            group_format = "%Y-%m-%d"
+            group_stage = {
+                "$group": {
+                    "_id": {
+                        "day": {
+                            "$dateToString": {
+                                "format": group_format,
+                                "date": "$timestamp",
+                            }
+                        }
+                    },
+                    "total_tokens": {
+                        "$sum": {"$add": ["$prompt_tokens", "$generated_tokens"]}
+                    },
+                }
+            }
+        try:
+            match_stage = {
+                "$match": {
+                    "user_id": user,
+                    "timestamp": {"$gte": start_date, "$lte": end_date},
+                }
+            }
+            if api_key:
+                match_stage["$match"]["api_key"] = api_key
+            token_usage_data = token_usage_collection.aggregate(
+                [
+                    match_stage,
+                    group_stage,
+                    {"$sort": {"_id": 1}},
+                ]
+            )
+
+            if filter_option == "last_hour":
+                intervals = generate_minute_range(start_date, end_date)
+            elif filter_option == "last_24_hour":
+                intervals = generate_hourly_range(start_date, end_date)
+            else:
+                intervals = generate_date_range(start_date, end_date)
+            daily_token_usage = {interval: 0 for interval in intervals}
+
+            for entry in token_usage_data:
+                if filter_option == "last_hour":
+                    daily_token_usage[entry["_id"]["minute"]] = entry["total_tokens"]
+                elif filter_option == "last_24_hour":
+                    daily_token_usage[entry["_id"]["hour"]] = entry["total_tokens"]
+                else:
+                    daily_token_usage[entry["_id"]["day"]] = entry["total_tokens"]
+        except Exception as err:
+            current_app.logger.error(
+                f"Error getting token analytics: {err}", exc_info=True
+            )
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(
+            jsonify({"success": True, "token_usage": daily_token_usage}), 200
+        )
+
+
+@analytics_ns.route("/get_feedback_analytics")
+class GetFeedbackAnalytics(Resource):
+    get_feedback_analytics_model = api.model(
+        "GetFeedbackAnalyticsModel",
+        {
+            "api_key_id": fields.String(required=False, description="API Key ID"),
+            "filter_option": fields.String(
+                required=False,
+                description="Filter option for analytics",
+                default="last_30_days",
+                enum=[
+                    "last_hour",
+                    "last_24_hour",
+                    "last_7_days",
+                    "last_15_days",
+                    "last_30_days",
+                ],
+            ),
+        },
+    )
+
+    @api.expect(get_feedback_analytics_model)
+    @api.doc(description="Get feedback analytics data")
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        data = request.get_json()
+        api_key_id = data.get("api_key_id")
+        filter_option = data.get("filter_option", "last_30_days")
+
+        try:
+            api_key = (
+                agents_collection.find_one({"_id": ObjectId(api_key_id), "user": user})[
+                    "key"
+                ]
+                if api_key_id
+                else None
+            )
+        except Exception as err:
+            current_app.logger.error(f"Error getting API key: {err}", exc_info=True)
+            return make_response(jsonify({"success": False}), 400)
+        end_date = datetime.datetime.now(datetime.timezone.utc)
+
+        if filter_option == "last_hour":
+            start_date = end_date - datetime.timedelta(hours=1)
+            group_format = "%Y-%m-%d %H:%M:00"
+            date_field = {
+                "$dateToString": {
+                    "format": group_format,
+                    "date": "$queries.feedback_timestamp",
+                }
+            }
+        elif filter_option == "last_24_hour":
+            start_date = end_date - datetime.timedelta(hours=24)
+            group_format = "%Y-%m-%d %H:00"
+            date_field = {
+                "$dateToString": {
+                    "format": group_format,
+                    "date": "$queries.feedback_timestamp",
+                }
+            }
+        else:
+            if filter_option in ["last_7_days", "last_15_days", "last_30_days"]:
+                filter_days = (
+                    6
+                    if filter_option == "last_7_days"
+                    else (14 if filter_option == "last_15_days" else 29)
+                )
+            else:
+                return make_response(
+                    jsonify({"success": False, "message": "Invalid option"}), 400
+                )
+            start_date = end_date - datetime.timedelta(days=filter_days)
+            start_date = start_date.replace(hour=0, minute=0, second=0, microsecond=0)
+            end_date = end_date.replace(
+                hour=23, minute=59, second=59, microsecond=999999
+            )
+            group_format = "%Y-%m-%d"
+            date_field = {
+                "$dateToString": {
+                    "format": group_format,
+                    "date": "$queries.feedback_timestamp",
+                }
+            }
+        try:
+            match_stage = {
+                "$match": {
+                    "queries.feedback_timestamp": {
+                        "$gte": start_date,
+                        "$lte": end_date,
+                    },
+                    "queries.feedback": {"$exists": True},
+                }
+            }
+            if api_key:
+                match_stage["$match"]["api_key"] = api_key
+            pipeline = [
+                match_stage,
+                {"$unwind": "$queries"},
+                {"$match": {"queries.feedback": {"$exists": True}}},
+                {
+                    "$group": {
+                        "_id": {"time": date_field, "feedback": "$queries.feedback"},
+                        "count": {"$sum": 1},
+                    }
+                },
+                {
+                    "$group": {
+                        "_id": "$_id.time",
+                        "positive": {
+                            "$sum": {
+                                "$cond": [
+                                    {"$eq": ["$_id.feedback", "LIKE"]},
+                                    "$count",
+                                    0,
+                                ]
+                            }
+                        },
+                        "negative": {
+                            "$sum": {
+                                "$cond": [
+                                    {"$eq": ["$_id.feedback", "DISLIKE"]},
+                                    "$count",
+                                    0,
+                                ]
+                            }
+                        },
+                    }
+                },
+                {"$sort": {"_id": 1}},
+            ]
+
+            feedback_data = conversations_collection.aggregate(pipeline)
+
+            if filter_option == "last_hour":
+                intervals = generate_minute_range(start_date, end_date)
+            elif filter_option == "last_24_hour":
+                intervals = generate_hourly_range(start_date, end_date)
+            else:
+                intervals = generate_date_range(start_date, end_date)
+            daily_feedback = {
+                interval: {"positive": 0, "negative": 0} for interval in intervals
+            }
+
+            for entry in feedback_data:
+                daily_feedback[entry["_id"]] = {
+                    "positive": entry["positive"],
+                    "negative": entry["negative"],
+                }
+        except Exception as err:
+            current_app.logger.error(
+                f"Error getting feedback analytics: {err}", exc_info=True
+            )
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(
+            jsonify({"success": True, "feedback": daily_feedback}), 200
+        )
+
+
+@analytics_ns.route("/get_user_logs")
+class GetUserLogs(Resource):
+    get_user_logs_model = api.model(
+        "GetUserLogsModel",
+        {
+            "page": fields.Integer(
+                required=False,
+                description="Page number for pagination",
+                default=1,
+            ),
+            "api_key_id": fields.String(required=False, description="API Key ID"),
+            "page_size": fields.Integer(
+                required=False,
+                description="Number of logs per page",
+                default=10,
+            ),
+        },
+    )
+
+    @api.expect(get_user_logs_model)
+    @api.doc(description="Get user logs with pagination")
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        data = request.get_json()
+        page = int(data.get("page", 1))
+        api_key_id = data.get("api_key_id")
+        page_size = int(data.get("page_size", 10))
+        skip = (page - 1) * page_size
+
+        try:
+            api_key = (
+                agents_collection.find_one({"_id": ObjectId(api_key_id)})["key"]
+                if api_key_id
+                else None
+            )
+        except Exception as err:
+            current_app.logger.error(f"Error getting API key: {err}", exc_info=True)
+            return make_response(jsonify({"success": False}), 400)
+        query = {"user": user}
+        if api_key:
+            query = {"api_key": api_key}
+        items_cursor = (
+            user_logs_collection.find(query)
+            .sort("timestamp", -1)
+            .skip(skip)
+            .limit(page_size + 1)
+        )
+        items = list(items_cursor)
+
+        results = [
+            {
+                "id": str(item.get("_id")),
+                "action": item.get("action"),
+                "level": item.get("level"),
+                "user": item.get("user"),
+                "question": item.get("question"),
+                "sources": item.get("sources"),
+                "retriever_params": item.get("retriever_params"),
+                "timestamp": item.get("timestamp"),
+            }
+            for item in items[:page_size]
+        ]
+
+        has_more = len(items) > page_size
+
+        return make_response(
+            jsonify(
+                {
+                    "success": True,
+                    "logs": results,
+                    "page": page,
+                    "page_size": page_size,
+                    "has_more": has_more,
+                }
+            ),
+            200,
+        )

application/api/user/attachments/__init__.py

@@ -0,0 +1,5 @@
+"""Attachments module."""
+
+from .routes import attachments_ns
+
+__all__ = ["attachments_ns"]

application/api/user/attachments/routes.py

@@ -0,0 +1,150 @@
+"""File attachments and media routes."""
+
+import os
+
+from bson.objectid import ObjectId
+from flask import current_app, jsonify, make_response, request
+from flask_restx import fields, Namespace, Resource
+
+from application.api import api
+from application.api.user.base import agents_collection, storage
+from application.api.user.tasks import store_attachment
+from application.core.settings import settings
+from application.tts.tts_creator import TTSCreator
+from application.utils import safe_filename
+
+
+attachments_ns = Namespace(
+    "attachments", description="File attachments and media operations", path="/api"
+)
+
+
+@attachments_ns.route("/store_attachment")
+class StoreAttachment(Resource):
+    @api.expect(
+        api.model(
+            "AttachmentModel",
+            {
+                "file": fields.Raw(required=True, description="File to upload"),
+                "api_key": fields.String(
+                    required=False, description="API key (optional)"
+                ),
+            },
+        )
+    )
+    @api.doc(
+        description="Stores a single attachment without vectorization or training. Supports user or API key authentication."
+    )
+    def post(self):
+        decoded_token = getattr(request, "decoded_token", None)
+        api_key = request.form.get("api_key") or request.args.get("api_key")
+        file = request.files.get("file")
+
+        if not file or file.filename == "":
+            return make_response(
+                jsonify({"status": "error", "message": "Missing file"}),
+                400,
+            )
+        user = None
+        if decoded_token:
+            user = safe_filename(decoded_token.get("sub"))
+        elif api_key:
+            agent = agents_collection.find_one({"key": api_key})
+            if not agent:
+                return make_response(
+                    jsonify({"success": False, "message": "Invalid API key"}), 401
+                )
+            user = safe_filename(agent.get("user"))
+        else:
+            return make_response(
+                jsonify({"success": False, "message": "Authentication required"}), 401
+            )
+        try:
+            attachment_id = ObjectId()
+            original_filename = safe_filename(os.path.basename(file.filename))
+            relative_path = f"{settings.UPLOAD_FOLDER}/{user}/attachments/{str(attachment_id)}/{original_filename}"
+
+            metadata = storage.save_file(file, relative_path)
+
+            file_info = {
+                "filename": original_filename,
+                "attachment_id": str(attachment_id),
+                "path": relative_path,
+                "metadata": metadata,
+            }
+
+            task = store_attachment.delay(file_info, user)
+
+            return make_response(
+                jsonify(
+                    {
+                        "success": True,
+                        "task_id": task.id,
+                        "message": "File uploaded successfully. Processing started.",
+                    }
+                ),
+                200,
+            )
+        except Exception as err:
+            current_app.logger.error(f"Error storing attachment: {err}", exc_info=True)
+            return make_response(jsonify({"success": False, "error": str(err)}), 400)
+
+
+@attachments_ns.route("/images/<path:image_path>")
+class ServeImage(Resource):
+    @api.doc(description="Serve an image from storage")
+    def get(self, image_path):
+        try:
+            file_obj = storage.get_file(image_path)
+            extension = image_path.split(".")[-1].lower()
+            content_type = f"image/{extension}"
+            if extension == "jpg":
+                content_type = "image/jpeg"
+            response = make_response(file_obj.read())
+            response.headers.set("Content-Type", content_type)
+            response.headers.set("Cache-Control", "max-age=86400")
+
+            return response
+        except FileNotFoundError:
+            return make_response(
+                jsonify({"success": False, "message": "Image not found"}), 404
+            )
+        except Exception as e:
+            current_app.logger.error(f"Error serving image: {e}")
+            return make_response(
+                jsonify({"success": False, "message": "Error retrieving image"}), 500
+            )
+
+
+@attachments_ns.route("/tts")
+class TextToSpeech(Resource):
+    tts_model = api.model(
+        "TextToSpeechModel",
+        {
+            "text": fields.String(
+                required=True, description="Text to be synthesized as audio"
+            ),
+        },
+    )
+
+    @api.expect(tts_model)
+    @api.doc(description="Synthesize audio speech from text")
+    def post(self):
+        data = request.get_json()
+        text = data["text"]
+        try:
+            tts_instance = TTSCreator.create_tts(settings.TTS_PROVIDER)
+            audio_base64, detected_language = tts_instance.text_to_speech(text)
+            return make_response(
+                jsonify(
+                    {
+                        "success": True,
+                        "audio_base64": audio_base64,
+                        "lang": detected_language,
+                    }
+                ),
+                200,
+            )
+        except Exception as err:
+            current_app.logger.error(f"Error synthesizing audio: {err}", exc_info=True)
+            return make_response(jsonify({"success": False}), 400)

application/api/user/base.py

@@ -0,0 +1,222 @@
+"""
+Shared utilities, database connections, and helper functions for user API routes.
+"""
+
+import datetime
+import os
+import uuid
+from functools import wraps
+from typing import Optional, Tuple
+
+from bson.objectid import ObjectId
+from flask import current_app, jsonify, make_response, Response
+from pymongo import ReturnDocument
+from werkzeug.utils import secure_filename
+
+from application.core.mongo_db import MongoDB
+from application.core.settings import settings
+from application.storage.storage_creator import StorageCreator
+from application.vectorstore.vector_creator import VectorCreator
+
+
+storage = StorageCreator.get_storage()
+
+
+mongo = MongoDB.get_client()
+db = mongo[settings.MONGO_DB_NAME]
+
+
+conversations_collection = db["conversations"]
+sources_collection = db["sources"]
+prompts_collection = db["prompts"]
+feedback_collection = db["feedback"]
+agents_collection = db["agents"]
+token_usage_collection = db["token_usage"]
+shared_conversations_collections = db["shared_conversations"]
+users_collection = db["users"]
+user_logs_collection = db["user_logs"]
+user_tools_collection = db["user_tools"]
+attachments_collection = db["attachments"]
+
+
+try:
+    agents_collection.create_index(
+        [("shared", 1)],
+        name="shared_index",
+        background=True,
+    )
+    users_collection.create_index("user_id", unique=True)
+except Exception as e:
+    print("Error creating indexes:", e)
+current_dir = os.path.dirname(
+    os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+)
+
+
+def generate_minute_range(start_date, end_date):
+    """Generate a dictionary with minute-level time ranges."""
+    return {
+        (start_date + datetime.timedelta(minutes=i)).strftime("%Y-%m-%d %H:%M:00"): 0
+        for i in range(int((end_date - start_date).total_seconds() // 60) + 1)
+    }
+
+
+def generate_hourly_range(start_date, end_date):
+    """Generate a dictionary with hourly time ranges."""
+    return {
+        (start_date + datetime.timedelta(hours=i)).strftime("%Y-%m-%d %H:00"): 0
+        for i in range(int((end_date - start_date).total_seconds() // 3600) + 1)
+    }
+
+
+def generate_date_range(start_date, end_date):
+    """Generate a dictionary with daily date ranges."""
+    return {
+        (start_date + datetime.timedelta(days=i)).strftime("%Y-%m-%d"): 0
+        for i in range((end_date - start_date).days + 1)
+    }
+
+
+def ensure_user_doc(user_id):
+    """
+    Ensure user document exists with proper agent preferences structure.
+
+    Args:
+        user_id: The user ID to ensure
+
+    Returns:
+        The user document
+    """
+    default_prefs = {
+        "pinned": [],
+        "shared_with_me": [],
+    }
+
+    user_doc = users_collection.find_one_and_update(
+        {"user_id": user_id},
+        {"$setOnInsert": {"agent_preferences": default_prefs}},
+        upsert=True,
+        return_document=ReturnDocument.AFTER,
+    )
+
+    prefs = user_doc.get("agent_preferences", {})
+    updates = {}
+    if "pinned" not in prefs:
+        updates["agent_preferences.pinned"] = []
+    if "shared_with_me" not in prefs:
+        updates["agent_preferences.shared_with_me"] = []
+    if updates:
+        users_collection.update_one({"user_id": user_id}, {"$set": updates})
+        user_doc = users_collection.find_one({"user_id": user_id})
+    return user_doc
+
+
+def resolve_tool_details(tool_ids):
+    """
+    Resolve tool IDs to their details.
+
+    Args:
+        tool_ids: List of tool IDs
+
+    Returns:
+        List of tool details with id, name, and display_name
+    """
+    tools = user_tools_collection.find(
+        {"_id": {"$in": [ObjectId(tid) for tid in tool_ids]}}
+    )
+    return [
+        {
+            "id": str(tool["_id"]),
+            "name": tool.get("name", ""),
+            "display_name": tool.get("displayName", tool.get("name", "")),
+        }
+        for tool in tools
+    ]
+
+
+def get_vector_store(source_id):
+    """
+    Get the Vector Store for a given source ID.
+
+    Args:
+        source_id (str): source id of the document
+
+    Returns:
+        Vector store instance
+    """
+    store = VectorCreator.create_vectorstore(
+        settings.VECTOR_STORE,
+        source_id=source_id,
+        embeddings_key=os.getenv("EMBEDDINGS_KEY"),
+    )
+    return store
+
+
+def handle_image_upload(
+    request, existing_url: str, user: str, storage, base_path: str = "attachments/"
+) -> Tuple[str, Optional[Response]]:
+    """
+    Handle image file upload from request.
+
+    Args:
+        request: Flask request object
+        existing_url: Existing image URL (fallback)
+        user: User ID
+        storage: Storage instance
+        base_path: Base path for upload
+
+    Returns:
+        Tuple of (image_url, error_response)
+    """
+    image_url = existing_url
+
+    if "image" in request.files:
+        file = request.files["image"]
+        if file.filename != "":
+            filename = secure_filename(file.filename)
+            upload_path = f"{settings.UPLOAD_FOLDER.rstrip('/')}/{user}/{base_path.rstrip('/')}/{uuid.uuid4()}_{filename}"
+            try:
+                storage.save_file(file, upload_path, storage_class="STANDARD")
+                image_url = upload_path
+            except Exception as e:
+                current_app.logger.error(f"Error uploading image: {e}")
+                return None, make_response(
+                    jsonify({"success": False, "message": "Image upload failed"}),
+                    400,
+                )
+    return image_url, None
+
+
+def require_agent(func):
+    """
+    Decorator to require valid agent webhook token.
+
+    Args:
+        func: Function to decorate
+
+    Returns:
+        Wrapped function
+    """
+
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        webhook_token = kwargs.get("webhook_token")
+        if not webhook_token:
+            return make_response(
+                jsonify({"success": False, "message": "Webhook token missing"}), 400
+            )
+        agent = agents_collection.find_one(
+            {"incoming_webhook_token": webhook_token}, {"_id": 1}
+        )
+        if not agent:
+            current_app.logger.warning(
+                f"Webhook attempt with invalid token: {webhook_token}"
+            )
+            return make_response(
+                jsonify({"success": False, "message": "Agent not found"}), 404
+            )
+        kwargs["agent"] = agent
+        kwargs["agent_id_str"] = str(agent["_id"])
+        return func(*args, **kwargs)
+
+    return wrapper

application/api/user/conversations/__init__.py

@@ -0,0 +1,5 @@
+"""Conversation management module."""
+
+from .routes import conversations_ns
+
+__all__ = ["conversations_ns"]

application/api/user/conversations/routes.py

@@ -0,0 +1,280 @@
+"""Conversation management routes."""
+
+import datetime
+
+from bson.objectid import ObjectId
+from flask import current_app, jsonify, make_response, request
+from flask_restx import fields, Namespace, Resource
+
+from application.api import api
+from application.api.user.base import attachments_collection, conversations_collection
+from application.utils import check_required_fields
+
+conversations_ns = Namespace(
+    "conversations", description="Conversation management operations", path="/api"
+)
+
+
+@conversations_ns.route("/delete_conversation")
+class DeleteConversation(Resource):
+    @api.doc(
+        description="Deletes a conversation by ID",
+        params={"id": "The ID of the conversation to delete"},
+    )
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        conversation_id = request.args.get("id")
+        if not conversation_id:
+            return make_response(
+                jsonify({"success": False, "message": "ID is required"}), 400
+            )
+        try:
+            conversations_collection.delete_one(
+                {"_id": ObjectId(conversation_id), "user": decoded_token["sub"]}
+            )
+        except Exception as err:
+            current_app.logger.error(
+                f"Error deleting conversation: {err}", exc_info=True
+            )
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify({"success": True}), 200)
+
+
+@conversations_ns.route("/delete_all_conversations")
+class DeleteAllConversations(Resource):
+    @api.doc(
+        description="Deletes all conversations for a specific user",
+    )
+    def get(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user_id = decoded_token.get("sub")
+        try:
+            conversations_collection.delete_many({"user": user_id})
+        except Exception as err:
+            current_app.logger.error(
+                f"Error deleting all conversations: {err}", exc_info=True
+            )
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify({"success": True}), 200)
+
+
+@conversations_ns.route("/get_conversations")
+class GetConversations(Resource):
+    @api.doc(
+        description="Retrieve a list of the latest 30 conversations (excluding API key conversations)",
+    )
+    def get(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        try:
+            conversations = (
+                conversations_collection.find(
+                    {
+                        "$or": [
+                            {"api_key": {"$exists": False}},
+                            {"agent_id": {"$exists": True}},
+                        ],
+                        "user": decoded_token.get("sub"),
+                    }
+                )
+                .sort("date", -1)
+                .limit(30)
+            )
+
+            list_conversations = [
+                {
+                    "id": str(conversation["_id"]),
+                    "name": conversation["name"],
+                    "agent_id": conversation.get("agent_id", None),
+                    "is_shared_usage": conversation.get("is_shared_usage", False),
+                    "shared_token": conversation.get("shared_token", None),
+                }
+                for conversation in conversations
+            ]
+        except Exception as err:
+            current_app.logger.error(
+                f"Error retrieving conversations: {err}", exc_info=True
+            )
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify(list_conversations), 200)
+
+
+@conversations_ns.route("/get_single_conversation")
+class GetSingleConversation(Resource):
+    @api.doc(
+        description="Retrieve a single conversation by ID",
+        params={"id": "The conversation ID"},
+    )
+    def get(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        conversation_id = request.args.get("id")
+        if not conversation_id:
+            return make_response(
+                jsonify({"success": False, "message": "ID is required"}), 400
+            )
+        try:
+            conversation = conversations_collection.find_one(
+                {"_id": ObjectId(conversation_id), "user": decoded_token.get("sub")}
+            )
+            if not conversation:
+                return make_response(jsonify({"status": "not found"}), 404)
+            # Process queries to include attachment names
+
+            queries = conversation["queries"]
+            for query in queries:
+                if "attachments" in query and query["attachments"]:
+                    attachment_details = []
+                    for attachment_id in query["attachments"]:
+                        try:
+                            attachment = attachments_collection.find_one(
+                                {"_id": ObjectId(attachment_id)}
+                            )
+                            if attachment:
+                                attachment_details.append(
+                                    {
+                                        "id": str(attachment["_id"]),
+                                        "fileName": attachment.get(
+                                            "filename", "Unknown file"
+                                        ),
+                                    }
+                                )
+                        except Exception as e:
+                            current_app.logger.error(
+                                f"Error retrieving attachment {attachment_id}: {e}",
+                                exc_info=True,
+                            )
+                    query["attachments"] = attachment_details
+        except Exception as err:
+            current_app.logger.error(
+                f"Error retrieving conversation: {err}", exc_info=True
+            )
+            return make_response(jsonify({"success": False}), 400)
+        data = {
+            "queries": queries,
+            "agent_id": conversation.get("agent_id"),
+            "is_shared_usage": conversation.get("is_shared_usage", False),
+            "shared_token": conversation.get("shared_token", None),
+        }
+        return make_response(jsonify(data), 200)
+
+
+@conversations_ns.route("/update_conversation_name")
+class UpdateConversationName(Resource):
+    @api.expect(
+        api.model(
+            "UpdateConversationModel",
+            {
+                "id": fields.String(required=True, description="Conversation ID"),
+                "name": fields.String(
+                    required=True, description="New name of the conversation"
+                ),
+            },
+        )
+    )
+    @api.doc(
+        description="Updates the name of a conversation",
+    )
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        data = request.get_json()
+        required_fields = ["id", "name"]
+        missing_fields = check_required_fields(data, required_fields)
+        if missing_fields:
+            return missing_fields
+        try:
+            conversations_collection.update_one(
+                {"_id": ObjectId(data["id"]), "user": decoded_token.get("sub")},
+                {"$set": {"name": data["name"]}},
+            )
+        except Exception as err:
+            current_app.logger.error(
+                f"Error updating conversation name: {err}", exc_info=True
+            )
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify({"success": True}), 200)
+
+
+@conversations_ns.route("/feedback")
+class SubmitFeedback(Resource):
+    @api.expect(
+        api.model(
+            "FeedbackModel",
+            {
+                "question": fields.String(
+                    required=False, description="The user question"
+                ),
+                "answer": fields.String(required=False, description="The AI answer"),
+                "feedback": fields.String(required=True, description="User feedback"),
+                "question_index": fields.Integer(
+                    required=True,
+                    description="The question number in that particular conversation",
+                ),
+                "conversation_id": fields.String(
+                    required=True, description="id of the particular conversation"
+                ),
+                "api_key": fields.String(description="Optional API key"),
+            },
+        )
+    )
+    @api.doc(
+        description="Submit feedback for a conversation",
+    )
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        data = request.get_json()
+        required_fields = ["feedback", "conversation_id", "question_index"]
+        missing_fields = check_required_fields(data, required_fields)
+        if missing_fields:
+            return missing_fields
+        try:
+            if data["feedback"] is None:
+                # Remove feedback and feedback_timestamp if feedback is null
+
+                conversations_collection.update_one(
+                    {
+                        "_id": ObjectId(data["conversation_id"]),
+                        "user": decoded_token.get("sub"),
+                        f"queries.{data['question_index']}": {"$exists": True},
+                    },
+                    {
+                        "$unset": {
+                            f"queries.{data['question_index']}.feedback": "",
+                            f"queries.{data['question_index']}.feedback_timestamp": "",
+                        }
+                    },
+                )
+            else:
+                # Set feedback and feedback_timestamp if feedback has a value
+
+                conversations_collection.update_one(
+                    {
+                        "_id": ObjectId(data["conversation_id"]),
+                        "user": decoded_token.get("sub"),
+                        f"queries.{data['question_index']}": {"$exists": True},
+                    },
+                    {
+                        "$set": {
+                            f"queries.{data['question_index']}.feedback": data[
+                                "feedback"
+                            ],
+                            f"queries.{data['question_index']}.feedback_timestamp": datetime.datetime.now(
+                                datetime.timezone.utc
+                            ),
+                        }
+                    },
+                )
+        except Exception as err:
+            current_app.logger.error(f"Error submitting feedback: {err}", exc_info=True)
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify({"success": True}), 200)

application/api/user/prompts/__init__.py

@@ -0,0 +1,5 @@
+"""Prompts module."""
+
+from .routes import prompts_ns
+
+__all__ = ["prompts_ns"]

application/api/user/prompts/routes.py

@@ -0,0 +1,191 @@
+"""Prompt management routes."""
+
+import os
+
+from bson.objectid import ObjectId
+from flask import current_app, jsonify, make_response, request
+from flask_restx import fields, Namespace, Resource
+
+from application.api import api
+from application.api.user.base import current_dir, prompts_collection
+from application.utils import check_required_fields
+
+prompts_ns = Namespace(
+    "prompts", description="Prompt management operations", path="/api"
+)
+
+
+@prompts_ns.route("/create_prompt")
+class CreatePrompt(Resource):
+    create_prompt_model = api.model(
+        "CreatePromptModel",
+        {
+            "content": fields.String(
+                required=True, description="Content of the prompt"
+            ),
+            "name": fields.String(required=True, description="Name of the prompt"),
+        },
+    )
+
+    @api.expect(create_prompt_model)
+    @api.doc(description="Create a new prompt")
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        data = request.get_json()
+        required_fields = ["content", "name"]
+        missing_fields = check_required_fields(data, required_fields)
+        if missing_fields:
+            return missing_fields
+        user = decoded_token.get("sub")
+        try:
+
+            resp = prompts_collection.insert_one(
+                {
+                    "name": data["name"],
+                    "content": data["content"],
+                    "user": user,
+                }
+            )
+            new_id = str(resp.inserted_id)
+        except Exception as err:
+            current_app.logger.error(f"Error creating prompt: {err}", exc_info=True)
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify({"id": new_id}), 200)
+
+
+@prompts_ns.route("/get_prompts")
+class GetPrompts(Resource):
+    @api.doc(description="Get all prompts for the user")
+    def get(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        try:
+            prompts = prompts_collection.find({"user": user})
+            list_prompts = [
+                {"id": "default", "name": "default", "type": "public"},
+                {"id": "creative", "name": "creative", "type": "public"},
+                {"id": "strict", "name": "strict", "type": "public"},
+            ]
+
+            for prompt in prompts:
+                list_prompts.append(
+                    {
+                        "id": str(prompt["_id"]),
+                        "name": prompt["name"],
+                        "type": "private",
+                    }
+                )
+        except Exception as err:
+            current_app.logger.error(f"Error retrieving prompts: {err}", exc_info=True)
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify(list_prompts), 200)
+
+
+@prompts_ns.route("/get_single_prompt")
+class GetSinglePrompt(Resource):
+    @api.doc(params={"id": "ID of the prompt"}, description="Get a single prompt by ID")
+    def get(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        prompt_id = request.args.get("id")
+        if not prompt_id:
+            return make_response(
+                jsonify({"success": False, "message": "ID is required"}), 400
+            )
+        try:
+            if prompt_id == "default":
+                with open(
+                    os.path.join(current_dir, "prompts", "chat_combine_default.txt"),
+                    "r",
+                ) as f:
+                    chat_combine_template = f.read()
+                return make_response(jsonify({"content": chat_combine_template}), 200)
+            elif prompt_id == "creative":
+                with open(
+                    os.path.join(current_dir, "prompts", "chat_combine_creative.txt"),
+                    "r",
+                ) as f:
+                    chat_reduce_creative = f.read()
+                return make_response(jsonify({"content": chat_reduce_creative}), 200)
+            elif prompt_id == "strict":
+                with open(
+                    os.path.join(current_dir, "prompts", "chat_combine_strict.txt"), "r"
+                ) as f:
+                    chat_reduce_strict = f.read()
+                return make_response(jsonify({"content": chat_reduce_strict}), 200)
+            prompt = prompts_collection.find_one(
+                {"_id": ObjectId(prompt_id), "user": user}
+            )
+        except Exception as err:
+            current_app.logger.error(f"Error retrieving prompt: {err}", exc_info=True)
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify({"content": prompt["content"]}), 200)
+
+
+@prompts_ns.route("/delete_prompt")
+class DeletePrompt(Resource):
+    delete_prompt_model = api.model(
+        "DeletePromptModel",
+        {"id": fields.String(required=True, description="Prompt ID to delete")},
+    )
+
+    @api.expect(delete_prompt_model)
+    @api.doc(description="Delete a prompt by ID")
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        data = request.get_json()
+        required_fields = ["id"]
+        missing_fields = check_required_fields(data, required_fields)
+        if missing_fields:
+            return missing_fields
+        try:
+            prompts_collection.delete_one({"_id": ObjectId(data["id"]), "user": user})
+        except Exception as err:
+            current_app.logger.error(f"Error deleting prompt: {err}", exc_info=True)
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify({"success": True}), 200)
+
+
+@prompts_ns.route("/update_prompt")
+class UpdatePrompt(Resource):
+    update_prompt_model = api.model(
+        "UpdatePromptModel",
+        {
+            "id": fields.String(required=True, description="Prompt ID to update"),
+            "name": fields.String(required=True, description="New name of the prompt"),
+            "content": fields.String(
+                required=True, description="New content of the prompt"
+            ),
+        },
+    )
+
+    @api.expect(update_prompt_model)
+    @api.doc(description="Update an existing prompt")
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        data = request.get_json()
+        required_fields = ["id", "name", "content"]
+        missing_fields = check_required_fields(data, required_fields)
+        if missing_fields:
+            return missing_fields
+        try:
+            prompts_collection.update_one(
+                {"_id": ObjectId(data["id"]), "user": user},
+                {"$set": {"name": data["name"], "content": data["content"]}},
+            )
+        except Exception as err:
+            current_app.logger.error(f"Error updating prompt: {err}", exc_info=True)
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify({"success": True}), 200)

application/api/user/sharing/__init__.py

@@ -0,0 +1,5 @@
+"""Sharing module."""
+
+from .routes import sharing_ns
+
+__all__ = ["sharing_ns"]

application/api/user/sharing/routes.py

@@ -0,0 +1,301 @@
+"""Conversation sharing routes."""
+
+import uuid
+
+from bson.binary import Binary, UuidRepresentation
+from bson.dbref import DBRef
+from bson.objectid import ObjectId
+from flask import current_app, jsonify, make_response, request
+from flask_restx import fields, inputs, Namespace, Resource
+
+from application.api import api
+from application.api.user.base import (
+    agents_collection,
+    attachments_collection,
+    conversations_collection,
+    db,
+    shared_conversations_collections,
+)
+from application.utils import check_required_fields
+
+sharing_ns = Namespace(
+    "sharing", description="Conversation sharing operations", path="/api"
+)
+
+
+@sharing_ns.route("/share")
+class ShareConversation(Resource):
+    share_conversation_model = api.model(
+        "ShareConversationModel",
+        {
+            "conversation_id": fields.String(
+                required=True, description="Conversation ID"
+            ),
+            "user": fields.String(description="User ID (optional)"),
+            "prompt_id": fields.String(description="Prompt ID (optional)"),
+            "chunks": fields.Integer(description="Chunks count (optional)"),
+        },
+    )
+
+    @api.expect(share_conversation_model)
+    @api.doc(description="Share a conversation")
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        data = request.get_json()
+        required_fields = ["conversation_id"]
+        missing_fields = check_required_fields(data, required_fields)
+        if missing_fields:
+            return missing_fields
+        is_promptable = request.args.get("isPromptable", type=inputs.boolean)
+        if is_promptable is None:
+            return make_response(
+                jsonify({"success": False, "message": "isPromptable is required"}), 400
+            )
+        conversation_id = data["conversation_id"]
+
+        try:
+            conversation = conversations_collection.find_one(
+                {"_id": ObjectId(conversation_id)}
+            )
+            if conversation is None:
+                return make_response(
+                    jsonify(
+                        {
+                            "status": "error",
+                            "message": "Conversation does not exist",
+                        }
+                    ),
+                    404,
+                )
+            current_n_queries = len(conversation["queries"])
+            explicit_binary = Binary.from_uuid(
+                uuid.uuid4(), UuidRepresentation.STANDARD
+            )
+
+            if is_promptable:
+                prompt_id = data.get("prompt_id", "default")
+                chunks = data.get("chunks", "2")
+
+                name = conversation["name"] + "(shared)"
+                new_api_key_data = {
+                    "prompt_id": prompt_id,
+                    "chunks": chunks,
+                    "user": user,
+                }
+
+                if "source" in data and ObjectId.is_valid(data["source"]):
+                    new_api_key_data["source"] = DBRef(
+                        "sources", ObjectId(data["source"])
+                    )
+                if "retriever" in data:
+                    new_api_key_data["retriever"] = data["retriever"]
+                pre_existing_api_document = agents_collection.find_one(new_api_key_data)
+                if pre_existing_api_document:
+                    api_uuid = pre_existing_api_document["key"]
+                    pre_existing = shared_conversations_collections.find_one(
+                        {
+                            "conversation_id": DBRef(
+                                "conversations", ObjectId(conversation_id)
+                            ),
+                            "isPromptable": is_promptable,
+                            "first_n_queries": current_n_queries,
+                            "user": user,
+                            "api_key": api_uuid,
+                        }
+                    )
+                    if pre_existing is not None:
+                        return make_response(
+                            jsonify(
+                                {
+                                    "success": True,
+                                    "identifier": str(pre_existing["uuid"].as_uuid()),
+                                }
+                            ),
+                            200,
+                        )
+                    else:
+                        shared_conversations_collections.insert_one(
+                            {
+                                "uuid": explicit_binary,
+                                "conversation_id": {
+                                    "$ref": "conversations",
+                                    "$id": ObjectId(conversation_id),
+                                },
+                                "isPromptable": is_promptable,
+                                "first_n_queries": current_n_queries,
+                                "user": user,
+                                "api_key": api_uuid,
+                            }
+                        )
+                        return make_response(
+                            jsonify(
+                                {
+                                    "success": True,
+                                    "identifier": str(explicit_binary.as_uuid()),
+                                }
+                            ),
+                            201,
+                        )
+                else:
+                    api_uuid = str(uuid.uuid4())
+                    new_api_key_data["key"] = api_uuid
+                    new_api_key_data["name"] = name
+
+                    if "source" in data and ObjectId.is_valid(data["source"]):
+                        new_api_key_data["source"] = DBRef(
+                            "sources", ObjectId(data["source"])
+                        )
+                    if "retriever" in data:
+                        new_api_key_data["retriever"] = data["retriever"]
+                    agents_collection.insert_one(new_api_key_data)
+                    shared_conversations_collections.insert_one(
+                        {
+                            "uuid": explicit_binary,
+                            "conversation_id": {
+                                "$ref": "conversations",
+                                "$id": ObjectId(conversation_id),
+                            },
+                            "isPromptable": is_promptable,
+                            "first_n_queries": current_n_queries,
+                            "user": user,
+                            "api_key": api_uuid,
+                        }
+                    )
+                    return make_response(
+                        jsonify(
+                            {
+                                "success": True,
+                                "identifier": str(explicit_binary.as_uuid()),
+                            }
+                        ),
+                        201,
+                    )
+            pre_existing = shared_conversations_collections.find_one(
+                {
+                    "conversation_id": DBRef(
+                        "conversations", ObjectId(conversation_id)
+                    ),
+                    "isPromptable": is_promptable,
+                    "first_n_queries": current_n_queries,
+                    "user": user,
+                }
+            )
+            if pre_existing is not None:
+                return make_response(
+                    jsonify(
+                        {
+                            "success": True,
+                            "identifier": str(pre_existing["uuid"].as_uuid()),
+                        }
+                    ),
+                    200,
+                )
+            else:
+                shared_conversations_collections.insert_one(
+                    {
+                        "uuid": explicit_binary,
+                        "conversation_id": {
+                            "$ref": "conversations",
+                            "$id": ObjectId(conversation_id),
+                        },
+                        "isPromptable": is_promptable,
+                        "first_n_queries": current_n_queries,
+                        "user": user,
+                    }
+                )
+                return make_response(
+                    jsonify(
+                        {"success": True, "identifier": str(explicit_binary.as_uuid())}
+                    ),
+                    201,
+                )
+        except Exception as err:
+            current_app.logger.error(
+                f"Error sharing conversation: {err}", exc_info=True
+            )
+            return make_response(jsonify({"success": False}), 400)
+
+
+@sharing_ns.route("/shared_conversation/<string:identifier>")
+class GetPubliclySharedConversations(Resource):
+    @api.doc(description="Get publicly shared conversations by identifier")
+    def get(self, identifier: str):
+        try:
+            query_uuid = Binary.from_uuid(
+                uuid.UUID(identifier), UuidRepresentation.STANDARD
+            )
+            shared = shared_conversations_collections.find_one({"uuid": query_uuid})
+            conversation_queries = []
+
+            if (
+                shared
+                and "conversation_id" in shared
+                and isinstance(shared["conversation_id"], DBRef)
+            ):
+                conversation_ref = shared["conversation_id"]
+                conversation = db.dereference(conversation_ref)
+                if conversation is None:
+                    return make_response(
+                        jsonify(
+                            {
+                                "success": False,
+                                "error": "might have broken url or the conversation does not exist",
+                            }
+                        ),
+                        404,
+                    )
+                conversation_queries = conversation["queries"][
+                    : (shared["first_n_queries"])
+                ]
+
+                for query in conversation_queries:
+                    if "attachments" in query and query["attachments"]:
+                        attachment_details = []
+                        for attachment_id in query["attachments"]:
+                            try:
+                                attachment = attachments_collection.find_one(
+                                    {"_id": ObjectId(attachment_id)}
+                                )
+                                if attachment:
+                                    attachment_details.append(
+                                        {
+                                            "id": str(attachment["_id"]),
+                                            "fileName": attachment.get(
+                                                "filename", "Unknown file"
+                                            ),
+                                        }
+                                    )
+                            except Exception as e:
+                                current_app.logger.error(
+                                    f"Error retrieving attachment {attachment_id}: {e}",
+                                    exc_info=True,
+                                )
+                        query["attachments"] = attachment_details
+            else:
+                return make_response(
+                    jsonify(
+                        {
+                            "success": False,
+                            "error": "might have broken url or the conversation does not exist",
+                        }
+                    ),
+                    404,
+                )
+            date = conversation["_id"].generation_time.isoformat()
+            res = {
+                "success": True,
+                "queries": conversation_queries,
+                "title": conversation["name"],
+                "timestamp": date,
+            }
+            if shared["isPromptable"] and "api_key" in shared:
+                res["api_key"] = shared["api_key"]
+            return make_response(jsonify(res), 200)
+        except Exception as err:
+            current_app.logger.error(
+                f"Error getting shared conversation: {err}", exc_info=True
+            )
+            return make_response(jsonify({"success": False}), 400)

application/api/user/sources/__init__.py

@@ -0,0 +1,7 @@
+"""Sources module."""
+
+from .chunks import sources_chunks_ns
+from .routes import sources_ns
+from .upload import sources_upload_ns
+
+__all__ = ["sources_ns", "sources_chunks_ns", "sources_upload_ns"]

application/api/user/sources/chunks.py

@@ -0,0 +1,278 @@
+"""Source document management chunk management."""
+
+from bson.objectid import ObjectId
+from flask import current_app, jsonify, make_response, request
+from flask_restx import fields, Namespace, Resource
+
+from application.api import api
+from application.api.user.base import get_vector_store, sources_collection
+from application.utils import check_required_fields, num_tokens_from_string
+
+sources_chunks_ns = Namespace(
+    "sources", description="Source document management operations", path="/api"
+)
+
+
+@sources_chunks_ns.route("/get_chunks")
+class GetChunks(Resource):
+    @api.doc(
+        description="Retrieves chunks from a document, optionally filtered by file path and search term",
+        params={
+            "id": "The document ID",
+            "page": "Page number for pagination",
+            "per_page": "Number of chunks per page",
+            "path": "Optional: Filter chunks by relative file path",
+            "search": "Optional: Search term to filter chunks by title or content",
+        },
+    )
+    def get(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        doc_id = request.args.get("id")
+        page = int(request.args.get("page", 1))
+        per_page = int(request.args.get("per_page", 10))
+        path = request.args.get("path")
+        search_term = request.args.get("search", "").strip().lower()
+
+        if not ObjectId.is_valid(doc_id):
+            return make_response(jsonify({"error": "Invalid doc_id"}), 400)
+        doc = sources_collection.find_one({"_id": ObjectId(doc_id), "user": user})
+        if not doc:
+            return make_response(
+                jsonify({"error": "Document not found or access denied"}), 404
+            )
+        try:
+            store = get_vector_store(doc_id)
+            chunks = store.get_chunks()
+
+            filtered_chunks = []
+            for chunk in chunks:
+                metadata = chunk.get("metadata", {})
+
+                # Filter by path if provided
+
+                if path:
+                    chunk_source = metadata.get("source", "")
+                    # Check if the chunk's source matches the requested path
+
+                    if not chunk_source or not chunk_source.endswith(path):
+                        continue
+                # Filter by search term if provided
+
+                if search_term:
+                    text_match = search_term in chunk.get("text", "").lower()
+                    title_match = search_term in metadata.get("title", "").lower()
+
+                    if not (text_match or title_match):
+                        continue
+                filtered_chunks.append(chunk)
+            chunks = filtered_chunks
+
+            total_chunks = len(chunks)
+            start = (page - 1) * per_page
+            end = start + per_page
+            paginated_chunks = chunks[start:end]
+
+            return make_response(
+                jsonify(
+                    {
+                        "page": page,
+                        "per_page": per_page,
+                        "total": total_chunks,
+                        "chunks": paginated_chunks,
+                        "path": path if path else None,
+                        "search": search_term if search_term else None,
+                    }
+                ),
+                200,
+            )
+        except Exception as e:
+            current_app.logger.error(f"Error getting chunks: {e}", exc_info=True)
+            return make_response(jsonify({"success": False}), 500)
+
+
+@sources_chunks_ns.route("/add_chunk")
+class AddChunk(Resource):
+    @api.expect(
+        api.model(
+            "AddChunkModel",
+            {
+                "id": fields.String(required=True, description="Document ID"),
+                "text": fields.String(required=True, description="Text of the chunk"),
+                "metadata": fields.Raw(
+                    required=False,
+                    description="Metadata associated with the chunk",
+                ),
+            },
+        )
+    )
+    @api.doc(
+        description="Adds a new chunk to the document",
+    )
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        data = request.get_json()
+        required_fields = ["id", "text"]
+        missing_fields = check_required_fields(data, required_fields)
+        if missing_fields:
+            return missing_fields
+        doc_id = data.get("id")
+        text = data.get("text")
+        metadata = data.get("metadata", {})
+        token_count = num_tokens_from_string(text)
+        metadata["token_count"] = token_count
+
+        if not ObjectId.is_valid(doc_id):
+            return make_response(jsonify({"error": "Invalid doc_id"}), 400)
+        doc = sources_collection.find_one({"_id": ObjectId(doc_id), "user": user})
+        if not doc:
+            return make_response(
+                jsonify({"error": "Document not found or access denied"}), 404
+            )
+        try:
+            store = get_vector_store(doc_id)
+            chunk_id = store.add_chunk(text, metadata)
+            return make_response(
+                jsonify({"message": "Chunk added successfully", "chunk_id": chunk_id}),
+                201,
+            )
+        except Exception as e:
+            current_app.logger.error(f"Error adding chunk: {e}", exc_info=True)
+            return make_response(jsonify({"success": False}), 500)
+
+
+@sources_chunks_ns.route("/delete_chunk")
+class DeleteChunk(Resource):
+    @api.doc(
+        description="Deletes a specific chunk from the document.",
+        params={"id": "The document ID", "chunk_id": "The ID of the chunk to delete"},
+    )
+    def delete(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        doc_id = request.args.get("id")
+        chunk_id = request.args.get("chunk_id")
+
+        if not ObjectId.is_valid(doc_id):
+            return make_response(jsonify({"error": "Invalid doc_id"}), 400)
+        doc = sources_collection.find_one({"_id": ObjectId(doc_id), "user": user})
+        if not doc:
+            return make_response(
+                jsonify({"error": "Document not found or access denied"}), 404
+            )
+        try:
+            store = get_vector_store(doc_id)
+            deleted = store.delete_chunk(chunk_id)
+            if deleted:
+                return make_response(
+                    jsonify({"message": "Chunk deleted successfully"}), 200
+                )
+            else:
+                return make_response(
+                    jsonify({"message": "Chunk not found or could not be deleted"}),
+                    404,
+                )
+        except Exception as e:
+            current_app.logger.error(f"Error deleting chunk: {e}", exc_info=True)
+            return make_response(jsonify({"success": False}), 500)
+
+
+@sources_chunks_ns.route("/update_chunk")
+class UpdateChunk(Resource):
+    @api.expect(
+        api.model(
+            "UpdateChunkModel",
+            {
+                "id": fields.String(required=True, description="Document ID"),
+                "chunk_id": fields.String(
+                    required=True, description="Chunk ID to update"
+                ),
+                "text": fields.String(
+                    required=False, description="New text of the chunk"
+                ),
+                "metadata": fields.Raw(
+                    required=False,
+                    description="Updated metadata associated with the chunk",
+                ),
+            },
+        )
+    )
+    @api.doc(
+        description="Updates an existing chunk in the document.",
+    )
+    def put(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        data = request.get_json()
+        required_fields = ["id", "chunk_id"]
+        missing_fields = check_required_fields(data, required_fields)
+        if missing_fields:
+            return missing_fields
+        doc_id = data.get("id")
+        chunk_id = data.get("chunk_id")
+        text = data.get("text")
+        metadata = data.get("metadata")
+
+        if text is not None:
+            token_count = num_tokens_from_string(text)
+            if metadata is None:
+                metadata = {}
+            metadata["token_count"] = token_count
+        if not ObjectId.is_valid(doc_id):
+            return make_response(jsonify({"error": "Invalid doc_id"}), 400)
+        doc = sources_collection.find_one({"_id": ObjectId(doc_id), "user": user})
+        if not doc:
+            return make_response(
+                jsonify({"error": "Document not found or access denied"}), 404
+            )
+        try:
+            store = get_vector_store(doc_id)
+
+            chunks = store.get_chunks()
+            existing_chunk = next((c for c in chunks if c["doc_id"] == chunk_id), None)
+            if not existing_chunk:
+                return make_response(jsonify({"error": "Chunk not found"}), 404)
+            new_text = text if text is not None else existing_chunk["text"]
+
+            if metadata is not None:
+                new_metadata = existing_chunk["metadata"].copy()
+                new_metadata.update(metadata)
+            else:
+                new_metadata = existing_chunk["metadata"].copy()
+            if text is not None:
+                new_metadata["token_count"] = num_tokens_from_string(new_text)
+            try:
+                new_chunk_id = store.add_chunk(new_text, new_metadata)
+
+                deleted = store.delete_chunk(chunk_id)
+                if not deleted:
+                    current_app.logger.warning(
+                        f"Failed to delete old chunk {chunk_id}, but new chunk {new_chunk_id} was created"
+                    )
+                return make_response(
+                    jsonify(
+                        {
+                            "message": "Chunk updated successfully",
+                            "chunk_id": new_chunk_id,
+                            "original_chunk_id": chunk_id,
+                        }
+                    ),
+                    200,
+                )
+            except Exception as add_error:
+                current_app.logger.error(f"Failed to add updated chunk: {add_error}")
+                return make_response(
+                    jsonify({"error": "Failed to update chunk - addition failed"}), 500
+                )
+        except Exception as e:
+            current_app.logger.error(f"Error updating chunk: {e}", exc_info=True)
+            return make_response(jsonify({"success": False}), 500)

application/api/user/sources/routes.py

@@ -0,0 +1,323 @@
+"""Source document management routes."""
+
+import json
+import math
+
+from bson.objectid import ObjectId
+from flask import current_app, jsonify, make_response, redirect, request
+from flask_restx import fields, Namespace, Resource
+
+from application.api import api
+from application.api.user.base import sources_collection
+from application.core.settings import settings
+from application.storage.storage_creator import StorageCreator
+from application.utils import check_required_fields
+from application.vectorstore.vector_creator import VectorCreator
+
+
+sources_ns = Namespace(
+    "sources", description="Source document management operations", path="/api"
+)
+
+
+@sources_ns.route("/sources")
+class CombinedJson(Resource):
+    @api.doc(description="Provide JSON file with combined available indexes")
+    def get(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        data = [
+            {
+                "name": "Default",
+                "date": "default",
+                "model": settings.EMBEDDINGS_NAME,
+                "location": "remote",
+                "tokens": "",
+                "retriever": "classic",
+            }
+        ]
+
+        try:
+            for index in sources_collection.find({"user": user}).sort("date", -1):
+                data.append(
+                    {
+                        "id": str(index["_id"]),
+                        "name": index.get("name"),
+                        "date": index.get("date"),
+                        "model": settings.EMBEDDINGS_NAME,
+                        "location": "local",
+                        "tokens": index.get("tokens", ""),
+                        "retriever": index.get("retriever", "classic"),
+                        "syncFrequency": index.get("sync_frequency", ""),
+                        "is_nested": bool(index.get("directory_structure")),
+                        "type": index.get(
+                            "type", "file"
+                        ),  # Add type field with default "file"
+                    }
+                )
+        except Exception as err:
+            current_app.logger.error(f"Error retrieving sources: {err}", exc_info=True)
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify(data), 200)
+
+
+@sources_ns.route("/sources/paginated")
+class PaginatedSources(Resource):
+    @api.doc(description="Get document with pagination, sorting and filtering")
+    def get(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        sort_field = request.args.get("sort", "date")  # Default to 'date'
+        sort_order = request.args.get("order", "desc")  # Default to 'desc'
+        page = int(request.args.get("page", 1))  # Default to 1
+        rows_per_page = int(request.args.get("rows", 10))  # Default to 10
+        # add .strip() to remove leading and trailing whitespaces
+
+        search_term = request.args.get(
+            "search", ""
+        ).strip()  # add search for filter documents
+
+        # Prepare query for filtering
+
+        query = {"user": user}
+        if search_term:
+            query["name"] = {
+                "$regex": search_term,
+                "$options": "i",  # using case-insensitive search
+            }
+        total_documents = sources_collection.count_documents(query)
+        total_pages = max(1, math.ceil(total_documents / rows_per_page))
+        page = min(
+            max(1, page), total_pages
+        )  # add this to make sure page inbound is within the range
+        sort_order = 1 if sort_order == "asc" else -1
+        skip = (page - 1) * rows_per_page
+
+        try:
+            documents = (
+                sources_collection.find(query)
+                .sort(sort_field, sort_order)
+                .skip(skip)
+                .limit(rows_per_page)
+            )
+
+            paginated_docs = []
+            for doc in documents:
+                doc_data = {
+                    "id": str(doc["_id"]),
+                    "name": doc.get("name", ""),
+                    "date": doc.get("date", ""),
+                    "model": settings.EMBEDDINGS_NAME,
+                    "location": "local",
+                    "tokens": doc.get("tokens", ""),
+                    "retriever": doc.get("retriever", "classic"),
+                    "syncFrequency": doc.get("sync_frequency", ""),
+                    "isNested": bool(doc.get("directory_structure")),
+                    "type": doc.get("type", "file"),
+                }
+                paginated_docs.append(doc_data)
+            response = {
+                "total": total_documents,
+                "totalPages": total_pages,
+                "currentPage": page,
+                "paginated": paginated_docs,
+            }
+            return make_response(jsonify(response), 200)
+        except Exception as err:
+            current_app.logger.error(
+                f"Error retrieving paginated sources: {err}", exc_info=True
+            )
+            return make_response(jsonify({"success": False}), 400)
+
+
+@sources_ns.route("/delete_by_ids")
+class DeleteByIds(Resource):
+    @api.doc(
+        description="Deletes documents from the vector store by IDs",
+        params={"path": "Comma-separated list of IDs"},
+    )
+    def get(self):
+        ids = request.args.get("path")
+        if not ids:
+            return make_response(
+                jsonify({"success": False, "message": "Missing required fields"}), 400
+            )
+        try:
+            result = sources_collection.delete_index(ids=ids)
+            if result:
+                return make_response(jsonify({"success": True}), 200)
+        except Exception as err:
+            current_app.logger.error(f"Error deleting indexes: {err}", exc_info=True)
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify({"success": False}), 400)
+
+
+@sources_ns.route("/delete_old")
+class DeleteOldIndexes(Resource):
+    @api.doc(
+        description="Deletes old indexes and associated files",
+        params={"source_id": "The source ID to delete"},
+    )
+    def get(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        source_id = request.args.get("source_id")
+        if not source_id:
+            return make_response(
+                jsonify({"success": False, "message": "Missing required fields"}), 400
+            )
+        doc = sources_collection.find_one(
+            {"_id": ObjectId(source_id), "user": decoded_token.get("sub")}
+        )
+        if not doc:
+            return make_response(jsonify({"status": "not found"}), 404)
+        storage = StorageCreator.get_storage()
+
+        try:
+            # Delete vector index
+
+            if settings.VECTOR_STORE == "faiss":
+                index_path = f"indexes/{str(doc['_id'])}"
+                if storage.file_exists(f"{index_path}/index.faiss"):
+                    storage.delete_file(f"{index_path}/index.faiss")
+                if storage.file_exists(f"{index_path}/index.pkl"):
+                    storage.delete_file(f"{index_path}/index.pkl")
+            else:
+                vectorstore = VectorCreator.create_vectorstore(
+                    settings.VECTOR_STORE, source_id=str(doc["_id"])
+                )
+                vectorstore.delete_index()
+            if "file_path" in doc and doc["file_path"]:
+                file_path = doc["file_path"]
+                if storage.is_directory(file_path):
+                    files = storage.list_files(file_path)
+                    for f in files:
+                        storage.delete_file(f)
+                else:
+                    storage.delete_file(file_path)
+        except FileNotFoundError:
+            pass
+        except Exception as err:
+            current_app.logger.error(
+                f"Error deleting files and indexes: {err}", exc_info=True
+            )
+            return make_response(jsonify({"success": False}), 400)
+        sources_collection.delete_one({"_id": ObjectId(source_id)})
+        return make_response(jsonify({"success": True}), 200)
+
+
+@sources_ns.route("/combine")
+class RedirectToSources(Resource):
+    @api.doc(
+        description="Redirects /api/combine to /api/sources for backward compatibility"
+    )
+    def get(self):
+        return redirect("/api/sources", code=301)
+
+
+@sources_ns.route("/manage_sync")
+class ManageSync(Resource):
+    manage_sync_model = api.model(
+        "ManageSyncModel",
+        {
+            "source_id": fields.String(required=True, description="Source ID"),
+            "sync_frequency": fields.String(
+                required=True,
+                description="Sync frequency (never, daily, weekly, monthly)",
+            ),
+        },
+    )
+
+    @api.expect(manage_sync_model)
+    @api.doc(description="Manage sync frequency for sources")
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        data = request.get_json()
+        required_fields = ["source_id", "sync_frequency"]
+        missing_fields = check_required_fields(data, required_fields)
+        if missing_fields:
+            return missing_fields
+        source_id = data["source_id"]
+        sync_frequency = data["sync_frequency"]
+
+        if sync_frequency not in ["never", "daily", "weekly", "monthly"]:
+            return make_response(
+                jsonify({"success": False, "message": "Invalid frequency"}), 400
+            )
+        update_data = {"$set": {"sync_frequency": sync_frequency}}
+        try:
+            sources_collection.update_one(
+                {
+                    "_id": ObjectId(source_id),
+                    "user": user,
+                },
+                update_data,
+            )
+        except Exception as err:
+            current_app.logger.error(
+                f"Error updating sync frequency: {err}", exc_info=True
+            )
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify({"success": True}), 200)
+
+
+@sources_ns.route("/directory_structure")
+class DirectoryStructure(Resource):
+    @api.doc(
+        description="Get the directory structure for a document",
+        params={"id": "The document ID"},
+    )
+    def get(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        doc_id = request.args.get("id")
+
+        if not doc_id:
+            return make_response(jsonify({"error": "Document ID is required"}), 400)
+        if not ObjectId.is_valid(doc_id):
+            return make_response(jsonify({"error": "Invalid document ID"}), 400)
+        try:
+            doc = sources_collection.find_one({"_id": ObjectId(doc_id), "user": user})
+            if not doc:
+                return make_response(
+                    jsonify({"error": "Document not found or access denied"}), 404
+                )
+            directory_structure = doc.get("directory_structure", {})
+            base_path = doc.get("file_path", "")
+
+            provider = None
+            remote_data = doc.get("remote_data")
+            try:
+                if isinstance(remote_data, str) and remote_data:
+                    remote_data_obj = json.loads(remote_data)
+                    provider = remote_data_obj.get("provider")
+            except Exception as e:
+                current_app.logger.warning(
+                    f"Failed to parse remote_data for doc {doc_id}: {e}"
+                )
+            return make_response(
+                jsonify(
+                    {
+                        "success": True,
+                        "directory_structure": directory_structure,
+                        "base_path": base_path,
+                        "provider": provider,
+                    }
+                ),
+                200,
+            )
+        except Exception as e:
+            current_app.logger.error(
+                f"Error retrieving directory structure: {e}", exc_info=True
+            )
+            return make_response(jsonify({"success": False, "error": str(e)}), 500)

application/api/user/sources/upload.py

@@ -0,0 +1,583 @@
+"""Source document management upload functionality."""
+
+import json
+import os
+import tempfile
+import zipfile
+
+from bson.objectid import ObjectId
+from flask import current_app, jsonify, make_response, request
+from flask_restx import fields, Namespace, Resource
+
+from application.api import api
+from application.api.user.base import sources_collection
+from application.api.user.tasks import ingest, ingest_connector_task, ingest_remote
+from application.core.settings import settings
+from application.parser.connectors.connector_creator import ConnectorCreator
+from application.storage.storage_creator import StorageCreator
+from application.utils import check_required_fields, safe_filename
+
+
+sources_upload_ns = Namespace(
+    "sources", description="Source document management operations", path="/api"
+)
+
+
+@sources_upload_ns.route("/upload")
+class UploadFile(Resource):
+    @api.expect(
+        api.model(
+            "UploadModel",
+            {
+                "user": fields.String(required=True, description="User ID"),
+                "name": fields.String(required=True, description="Job name"),
+                "file": fields.Raw(required=True, description="File(s) to upload"),
+            },
+        )
+    )
+    @api.doc(
+        description="Uploads a file to be vectorized and indexed",
+    )
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        data = request.form
+        files = request.files.getlist("file")
+        required_fields = ["user", "name"]
+        missing_fields = check_required_fields(data, required_fields)
+        if missing_fields or not files or all(file.filename == "" for file in files):
+            return make_response(
+                jsonify(
+                    {
+                        "status": "error",
+                        "message": "Missing required fields or files",
+                    }
+                ),
+                400,
+            )
+        user = decoded_token.get("sub")
+        job_name = request.form["name"]
+
+        # Create safe versions for filesystem operations
+
+        safe_user = safe_filename(user)
+        dir_name = safe_filename(job_name)
+        base_path = f"{settings.UPLOAD_FOLDER}/{safe_user}/{dir_name}"
+
+        try:
+            storage = StorageCreator.get_storage()
+
+            for file in files:
+                original_filename = file.filename
+                safe_file = safe_filename(original_filename)
+
+                with tempfile.TemporaryDirectory() as temp_dir:
+                    temp_file_path = os.path.join(temp_dir, safe_file)
+                    file.save(temp_file_path)
+
+                    if zipfile.is_zipfile(temp_file_path):
+                        try:
+                            with zipfile.ZipFile(temp_file_path, "r") as zip_ref:
+                                zip_ref.extractall(path=temp_dir)
+
+                                # Walk through extracted files and upload them
+
+                                for root, _, files in os.walk(temp_dir):
+                                    for extracted_file in files:
+                                        if (
+                                            os.path.join(root, extracted_file)
+                                            == temp_file_path
+                                        ):
+                                            continue
+                                        rel_path = os.path.relpath(
+                                            os.path.join(root, extracted_file), temp_dir
+                                        )
+                                        storage_path = f"{base_path}/{rel_path}"
+
+                                        with open(
+                                            os.path.join(root, extracted_file), "rb"
+                                        ) as f:
+                                            storage.save_file(f, storage_path)
+                        except Exception as e:
+                            current_app.logger.error(
+                                f"Error extracting zip: {e}", exc_info=True
+                            )
+                            # If zip extraction fails, save the original zip file
+
+                            file_path = f"{base_path}/{safe_file}"
+                            with open(temp_file_path, "rb") as f:
+                                storage.save_file(f, file_path)
+                    else:
+                        # For non-zip files, save directly
+
+                        file_path = f"{base_path}/{safe_file}"
+                        with open(temp_file_path, "rb") as f:
+                            storage.save_file(f, file_path)
+            task = ingest.delay(
+                settings.UPLOAD_FOLDER,
+                [
+                    ".rst",
+                    ".md",
+                    ".pdf",
+                    ".txt",
+                    ".docx",
+                    ".csv",
+                    ".epub",
+                    ".html",
+                    ".mdx",
+                    ".json",
+                    ".xlsx",
+                    ".pptx",
+                    ".png",
+                    ".jpg",
+                    ".jpeg",
+                ],
+                job_name,
+                user,
+                file_path=base_path,
+                filename=dir_name,
+            )
+        except Exception as err:
+            current_app.logger.error(f"Error uploading file: {err}", exc_info=True)
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify({"success": True, "task_id": task.id}), 200)
+
+
+@sources_upload_ns.route("/remote")
+class UploadRemote(Resource):
+    @api.expect(
+        api.model(
+            "RemoteUploadModel",
+            {
+                "user": fields.String(required=True, description="User ID"),
+                "source": fields.String(
+                    required=True, description="Source of the data"
+                ),
+                "name": fields.String(required=True, description="Job name"),
+                "data": fields.String(required=True, description="Data to process"),
+                "repo_url": fields.String(description="GitHub repository URL"),
+            },
+        )
+    )
+    @api.doc(
+        description="Uploads remote source for vectorization",
+    )
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        data = request.form
+        required_fields = ["user", "source", "name", "data"]
+        missing_fields = check_required_fields(data, required_fields)
+        if missing_fields:
+            return missing_fields
+        try:
+            config = json.loads(data["data"])
+            source_data = None
+
+            if data["source"] == "github":
+                source_data = config.get("repo_url")
+            elif data["source"] in ["crawler", "url"]:
+                source_data = config.get("url")
+            elif data["source"] == "reddit":
+                source_data = config
+            elif data["source"] in ConnectorCreator.get_supported_connectors():
+                session_token = config.get("session_token")
+                if not session_token:
+                    return make_response(
+                        jsonify(
+                            {
+                                "success": False,
+                                "error": f"Missing session_token in {data['source']} configuration",
+                            }
+                        ),
+                        400,
+                    )
+                # Process file_ids
+
+                file_ids = config.get("file_ids", [])
+                if isinstance(file_ids, str):
+                    file_ids = [id.strip() for id in file_ids.split(",") if id.strip()]
+                elif not isinstance(file_ids, list):
+                    file_ids = []
+                # Process folder_ids
+
+                folder_ids = config.get("folder_ids", [])
+                if isinstance(folder_ids, str):
+                    folder_ids = [
+                        id.strip() for id in folder_ids.split(",") if id.strip()
+                    ]
+                elif not isinstance(folder_ids, list):
+                    folder_ids = []
+                config["file_ids"] = file_ids
+                config["folder_ids"] = folder_ids
+
+                task = ingest_connector_task.delay(
+                    job_name=data["name"],
+                    user=decoded_token.get("sub"),
+                    source_type=data["source"],
+                    session_token=session_token,
+                    file_ids=file_ids,
+                    folder_ids=folder_ids,
+                    recursive=config.get("recursive", False),
+                    retriever=config.get("retriever", "classic"),
+                )
+                return make_response(
+                    jsonify({"success": True, "task_id": task.id}), 200
+                )
+            task = ingest_remote.delay(
+                source_data=source_data,
+                job_name=data["name"],
+                user=decoded_token.get("sub"),
+                loader=data["source"],
+            )
+        except Exception as err:
+            current_app.logger.error(
+                f"Error uploading remote source: {err}", exc_info=True
+            )
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify({"success": True, "task_id": task.id}), 200)
+
+
+@sources_upload_ns.route("/manage_source_files")
+class ManageSourceFiles(Resource):
+    @api.expect(
+        api.model(
+            "ManageSourceFilesModel",
+            {
+                "source_id": fields.String(
+                    required=True, description="Source ID to modify"
+                ),
+                "operation": fields.String(
+                    required=True,
+                    description="Operation: 'add', 'remove', or 'remove_directory'",
+                ),
+                "file_paths": fields.List(
+                    fields.String,
+                    required=False,
+                    description="File paths to remove (for remove operation)",
+                ),
+                "directory_path": fields.String(
+                    required=False,
+                    description="Directory path to remove (for remove_directory operation)",
+                ),
+                "file": fields.Raw(
+                    required=False, description="Files to add (for add operation)"
+                ),
+                "parent_dir": fields.String(
+                    required=False,
+                    description="Parent directory path relative to source root",
+                ),
+            },
+        )
+    )
+    @api.doc(
+        description="Add files, remove files, or remove directories from an existing source",
+    )
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(
+                jsonify({"success": False, "message": "Unauthorized"}), 401
+            )
+        user = decoded_token.get("sub")
+        source_id = request.form.get("source_id")
+        operation = request.form.get("operation")
+
+        if not source_id or not operation:
+            return make_response(
+                jsonify(
+                    {
+                        "success": False,
+                        "message": "source_id and operation are required",
+                    }
+                ),
+                400,
+            )
+        if operation not in ["add", "remove", "remove_directory"]:
+            return make_response(
+                jsonify(
+                    {
+                        "success": False,
+                        "message": "operation must be 'add', 'remove', or 'remove_directory'",
+                    }
+                ),
+                400,
+            )
+        try:
+            ObjectId(source_id)
+        except Exception:
+            return make_response(
+                jsonify({"success": False, "message": "Invalid source ID format"}), 400
+            )
+        try:
+            source = sources_collection.find_one(
+                {"_id": ObjectId(source_id), "user": user}
+            )
+            if not source:
+                return make_response(
+                    jsonify(
+                        {
+                            "success": False,
+                            "message": "Source not found or access denied",
+                        }
+                    ),
+                    404,
+                )
+        except Exception as err:
+            current_app.logger.error(f"Error finding source: {err}", exc_info=True)
+            return make_response(
+                jsonify({"success": False, "message": "Database error"}), 500
+            )
+        try:
+            storage = StorageCreator.get_storage()
+            source_file_path = source.get("file_path", "")
+            parent_dir = request.form.get("parent_dir", "")
+
+            if parent_dir and (parent_dir.startswith("/") or ".." in parent_dir):
+                return make_response(
+                    jsonify(
+                        {"success": False, "message": "Invalid parent directory path"}
+                    ),
+                    400,
+                )
+            if operation == "add":
+                files = request.files.getlist("file")
+                if not files or all(file.filename == "" for file in files):
+                    return make_response(
+                        jsonify(
+                            {
+                                "success": False,
+                                "message": "No files provided for add operation",
+                            }
+                        ),
+                        400,
+                    )
+                added_files = []
+
+                target_dir = source_file_path
+                if parent_dir:
+                    target_dir = f"{source_file_path}/{parent_dir}"
+                for file in files:
+                    if file.filename:
+                        safe_filename_str = safe_filename(file.filename)
+                        file_path = f"{target_dir}/{safe_filename_str}"
+
+                        # Save file to storage
+
+                        storage.save_file(file, file_path)
+                        added_files.append(safe_filename_str)
+                # Trigger re-ingestion pipeline
+
+                from application.api.user.tasks import reingest_source_task
+
+                task = reingest_source_task.delay(source_id=source_id, user=user)
+
+                return make_response(
+                    jsonify(
+                        {
+                            "success": True,
+                            "message": f"Added {len(added_files)} files",
+                            "added_files": added_files,
+                            "parent_dir": parent_dir,
+                            "reingest_task_id": task.id,
+                        }
+                    ),
+                    200,
+                )
+            elif operation == "remove":
+                file_paths_str = request.form.get("file_paths")
+                if not file_paths_str:
+                    return make_response(
+                        jsonify(
+                            {
+                                "success": False,
+                                "message": "file_paths required for remove operation",
+                            }
+                        ),
+                        400,
+                    )
+                try:
+                    file_paths = (
+                        json.loads(file_paths_str)
+                        if isinstance(file_paths_str, str)
+                        else file_paths_str
+                    )
+                except Exception:
+                    return make_response(
+                        jsonify(
+                            {"success": False, "message": "Invalid file_paths format"}
+                        ),
+                        400,
+                    )
+                # Remove files from storage and directory structure
+
+                removed_files = []
+                for file_path in file_paths:
+                    full_path = f"{source_file_path}/{file_path}"
+
+                    # Remove from storage
+
+                    if storage.file_exists(full_path):
+                        storage.delete_file(full_path)
+                        removed_files.append(file_path)
+                # Trigger re-ingestion pipeline
+
+                from application.api.user.tasks import reingest_source_task
+
+                task = reingest_source_task.delay(source_id=source_id, user=user)
+
+                return make_response(
+                    jsonify(
+                        {
+                            "success": True,
+                            "message": f"Removed {len(removed_files)} files",
+                            "removed_files": removed_files,
+                            "reingest_task_id": task.id,
+                        }
+                    ),
+                    200,
+                )
+            elif operation == "remove_directory":
+                directory_path = request.form.get("directory_path")
+                if not directory_path:
+                    return make_response(
+                        jsonify(
+                            {
+                                "success": False,
+                                "message": "directory_path required for remove_directory operation",
+                            }
+                        ),
+                        400,
+                    )
+                # Validate directory path (prevent path traversal)
+
+                if directory_path.startswith("/") or ".." in directory_path:
+                    current_app.logger.warning(
+                        f"Invalid directory path attempted for removal. "
+                        f"User: {user}, Source ID: {source_id}, Directory path: {directory_path}"
+                    )
+                    return make_response(
+                        jsonify(
+                            {"success": False, "message": "Invalid directory path"}
+                        ),
+                        400,
+                    )
+                full_directory_path = (
+                    f"{source_file_path}/{directory_path}"
+                    if directory_path
+                    else source_file_path
+                )
+
+                if not storage.is_directory(full_directory_path):
+                    current_app.logger.warning(
+                        f"Directory not found or is not a directory for removal. "
+                        f"User: {user}, Source ID: {source_id}, Directory path: {directory_path}, "
+                        f"Full path: {full_directory_path}"
+                    )
+                    return make_response(
+                        jsonify(
+                            {
+                                "success": False,
+                                "message": "Directory not found or is not a directory",
+                            }
+                        ),
+                        404,
+                    )
+                success = storage.remove_directory(full_directory_path)
+
+                if not success:
+                    current_app.logger.error(
+                        f"Failed to remove directory from storage. "
+                        f"User: {user}, Source ID: {source_id}, Directory path: {directory_path}, "
+                        f"Full path: {full_directory_path}"
+                    )
+                    return make_response(
+                        jsonify(
+                            {"success": False, "message": "Failed to remove directory"}
+                        ),
+                        500,
+                    )
+                current_app.logger.info(
+                    f"Successfully removed directory. "
+                    f"User: {user}, Source ID: {source_id}, Directory path: {directory_path}, "
+                    f"Full path: {full_directory_path}"
+                )
+
+                # Trigger re-ingestion pipeline
+
+                from application.api.user.tasks import reingest_source_task
+
+                task = reingest_source_task.delay(source_id=source_id, user=user)
+
+                return make_response(
+                    jsonify(
+                        {
+                            "success": True,
+                            "message": f"Successfully removed directory: {directory_path}",
+                            "removed_directory": directory_path,
+                            "reingest_task_id": task.id,
+                        }
+                    ),
+                    200,
+                )
+        except Exception as err:
+            error_context = f"operation={operation}, user={user}, source_id={source_id}"
+            if operation == "remove_directory":
+                directory_path = request.form.get("directory_path", "")
+                error_context += f", directory_path={directory_path}"
+            elif operation == "remove":
+                file_paths_str = request.form.get("file_paths", "")
+                error_context += f", file_paths={file_paths_str}"
+            elif operation == "add":
+                parent_dir = request.form.get("parent_dir", "")
+                error_context += f", parent_dir={parent_dir}"
+            current_app.logger.error(
+                f"Error managing source files: {err} ({error_context})", exc_info=True
+            )
+            return make_response(
+                jsonify({"success": False, "message": "Operation failed"}), 500
+            )
+
+
+@sources_upload_ns.route("/task_status")
+class TaskStatus(Resource):
+    task_status_model = api.model(
+        "TaskStatusModel",
+        {"task_id": fields.String(required=True, description="Task ID")},
+    )
+
+    @api.expect(task_status_model)
+    @api.doc(description="Get celery job status")
+    def get(self):
+        task_id = request.args.get("task_id")
+        if not task_id:
+            return make_response(
+                jsonify({"success": False, "message": "Task ID is required"}), 400
+            )
+        try:
+            from application.celery_init import celery
+
+            task = celery.AsyncResult(task_id)
+            task_meta = task.info
+            print(f"Task status: {task.status}")
+
+            if task.status == "PENDING":
+                inspect = celery.control.inspect()
+                active_workers = inspect.ping()
+                if not active_workers:
+                    raise ConnectionError("Service unavailable")
+
+            if not isinstance(
+                task_meta, (dict, list, str, int, float, bool, type(None))
+            ):
+                task_meta = str(task_meta)  # Convert to a string representation
+        except ConnectionError as err:
+            return make_response(
+                jsonify({"success": False, "message": str(err)}), 503
+            )
+        except Exception as err:
+            current_app.logger.error(f"Error getting task status: {err}", exc_info=True)
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify({"status": task.status, "result": task_meta}), 200)

application/api/user/tasks.py

@@ -1,12 +1,20 @@
 from datetime import timedelta
 
 from application.celery_init import celery
-from application.worker import ingest_worker, remote_worker, sync_worker
+from application.worker import (
+    agent_webhook_worker,
+    attachment_worker,
+    ingest_worker,
+    mcp_oauth,
+    mcp_oauth_status,
+    remote_worker,
+    sync_worker,
+)
 
 
 @celery.task(bind=True)
-def ingest(self, directory, formats, name_job, filename, user):
-    resp = ingest_worker(self, directory, formats, name_job, filename, user)
+def ingest(self, directory, formats, job_name, user, file_path, filename):
+    resp = ingest_worker(self, directory, formats, job_name, file_path, filename, user)
     return resp
 
 
@@ -16,12 +24,66 @@ def ingest_remote(self, source_data, job_name, user, loader):
     return resp
 
 
+@celery.task(bind=True)
+def reingest_source_task(self, source_id, user):
+    from application.worker import reingest_source_worker
+
+    resp = reingest_source_worker(self, source_id, user)
+    return resp
+
+
 @celery.task(bind=True)
 def schedule_syncs(self, frequency):
     resp = sync_worker(self, frequency)
     return resp
 
 
+@celery.task(bind=True)
+def store_attachment(self, file_info, user):
+    resp = attachment_worker(self, file_info, user)
+    return resp
+
+
+@celery.task(bind=True)
+def process_agent_webhook(self, agent_id, payload):
+    resp = agent_webhook_worker(self, agent_id, payload)
+    return resp
+
+
+@celery.task(bind=True)
+def ingest_connector_task(
+    self,
+    job_name,
+    user,
+    source_type,
+    session_token=None,
+    file_ids=None,
+    folder_ids=None,
+    recursive=True,
+    retriever="classic",
+    operation_mode="upload",
+    doc_id=None,
+    sync_frequency="never",
+):
+    from application.worker import ingest_connector
+
+    resp = ingest_connector(
+        self,
+        job_name,
+        user,
+        source_type,
+        session_token=session_token,
+        file_ids=file_ids,
+        folder_ids=folder_ids,
+        recursive=recursive,
+        retriever=retriever,
+        operation_mode=operation_mode,
+        doc_id=doc_id,
+        sync_frequency=sync_frequency,
+    )
+    return resp
+
+
 @celery.on_after_configure.connect
 def setup_periodic_tasks(sender, **kwargs):
     sender.add_periodic_task(
@@ -36,3 +98,15 @@ def setup_periodic_tasks(sender, **kwargs):
         timedelta(days=30),
         schedule_syncs.s("monthly"),
     )
+
+
+@celery.task(bind=True)
+def mcp_oauth_task(self, config, user):
+    resp = mcp_oauth(self, config, user)
+    return resp
+
+
+@celery.task(bind=True)
+def mcp_oauth_status_task(self, task_id):
+    resp = mcp_oauth_status(self, task_id)
+    return resp

application/api/user/tools/__init__.py

@@ -0,0 +1,6 @@
+"""Tools module."""
+
+from .mcp import tools_mcp_ns
+from .routes import tools_ns
+
+__all__ = ["tools_ns", "tools_mcp_ns"]

application/api/user/tools/mcp.py

@@ -0,0 +1,333 @@
+"""Tool management MCP server integration."""
+
+import json
+from email.quoprimime import unquote
+
+from bson.objectid import ObjectId
+from flask import current_app, jsonify, make_response, redirect, request
+from flask_restx import fields, Namespace, Resource
+
+from application.agents.tools.mcp_tool import MCPOAuthManager, MCPTool
+from application.api import api
+from application.api.user.base import user_tools_collection
+from application.cache import get_redis_instance
+from application.security.encryption import encrypt_credentials
+from application.utils import check_required_fields
+
+tools_mcp_ns = Namespace("tools", description="Tool management operations", path="/api")
+
+
+@tools_mcp_ns.route("/mcp_server/test")
+class TestMCPServerConfig(Resource):
+    @api.expect(
+        api.model(
+            "MCPServerTestModel",
+            {
+                "config": fields.Raw(
+                    required=True, description="MCP server configuration to test"
+                ),
+            },
+        )
+    )
+    @api.doc(description="Test MCP server connection with provided configuration")
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        data = request.get_json()
+
+        required_fields = ["config"]
+        missing_fields = check_required_fields(data, required_fields)
+        if missing_fields:
+            return missing_fields
+        try:
+            config = data["config"]
+
+            auth_credentials = {}
+            auth_type = config.get("auth_type", "none")
+
+            if auth_type == "api_key" and "api_key" in config:
+                auth_credentials["api_key"] = config["api_key"]
+                if "api_key_header" in config:
+                    auth_credentials["api_key_header"] = config["api_key_header"]
+            elif auth_type == "bearer" and "bearer_token" in config:
+                auth_credentials["bearer_token"] = config["bearer_token"]
+            elif auth_type == "basic":
+                if "username" in config:
+                    auth_credentials["username"] = config["username"]
+                if "password" in config:
+                    auth_credentials["password"] = config["password"]
+            test_config = config.copy()
+            test_config["auth_credentials"] = auth_credentials
+
+            mcp_tool = MCPTool(config=test_config, user_id=user)
+            result = mcp_tool.test_connection()
+
+            return make_response(jsonify(result), 200)
+        except Exception as e:
+            current_app.logger.error(f"Error testing MCP server: {e}", exc_info=True)
+            return make_response(
+                jsonify(
+                    {"success": False, "error": f"Connection test failed: {str(e)}"}
+                ),
+                500,
+            )
+
+
+@tools_mcp_ns.route("/mcp_server/save")
+class MCPServerSave(Resource):
+    @api.expect(
+        api.model(
+            "MCPServerSaveModel",
+            {
+                "id": fields.String(
+                    required=False, description="Tool ID for updates (optional)"
+                ),
+                "displayName": fields.String(
+                    required=True, description="Display name for the MCP server"
+                ),
+                "config": fields.Raw(
+                    required=True, description="MCP server configuration"
+                ),
+                "status": fields.Boolean(
+                    required=False, default=True, description="Tool status"
+                ),
+            },
+        )
+    )
+    @api.doc(description="Create or update MCP server with automatic tool discovery")
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        data = request.get_json()
+
+        required_fields = ["displayName", "config"]
+        missing_fields = check_required_fields(data, required_fields)
+        if missing_fields:
+            return missing_fields
+        try:
+            config = data["config"]
+
+            auth_credentials = {}
+            auth_type = config.get("auth_type", "none")
+            if auth_type == "api_key":
+                if "api_key" in config and config["api_key"]:
+                    auth_credentials["api_key"] = config["api_key"]
+                if "api_key_header" in config:
+                    auth_credentials["api_key_header"] = config["api_key_header"]
+            elif auth_type == "bearer":
+                if "bearer_token" in config and config["bearer_token"]:
+                    auth_credentials["bearer_token"] = config["bearer_token"]
+            elif auth_type == "basic":
+                if "username" in config and config["username"]:
+                    auth_credentials["username"] = config["username"]
+                if "password" in config and config["password"]:
+                    auth_credentials["password"] = config["password"]
+            mcp_config = config.copy()
+            mcp_config["auth_credentials"] = auth_credentials
+
+            if auth_type == "oauth":
+                if not config.get("oauth_task_id"):
+                    return make_response(
+                        jsonify(
+                            {
+                                "success": False,
+                                "error": "Connection not authorized. Please complete the OAuth authorization first.",
+                            }
+                        ),
+                        400,
+                    )
+                redis_client = get_redis_instance()
+                manager = MCPOAuthManager(redis_client)
+                result = manager.get_oauth_status(config["oauth_task_id"])
+                if not result.get("status") == "completed":
+                    return make_response(
+                        jsonify(
+                            {
+                                "success": False,
+                                "error": "OAuth failed or not completed. Please try authorizing again.",
+                            }
+                        ),
+                        400,
+                    )
+                actions_metadata = result.get("tools", [])
+            elif auth_type == "none" or auth_credentials:
+                mcp_tool = MCPTool(config=mcp_config, user_id=user)
+                mcp_tool.discover_tools()
+                actions_metadata = mcp_tool.get_actions_metadata()
+            else:
+                raise Exception(
+                    "No valid credentials provided for the selected authentication type"
+                )
+            storage_config = config.copy()
+            if auth_credentials:
+                encrypted_credentials_string = encrypt_credentials(
+                    auth_credentials, user
+                )
+                storage_config["encrypted_credentials"] = encrypted_credentials_string
+            for field in [
+                "api_key",
+                "bearer_token",
+                "username",
+                "password",
+                "api_key_header",
+            ]:
+                storage_config.pop(field, None)
+            transformed_actions = []
+            for action in actions_metadata:
+                action["active"] = True
+                if "parameters" in action:
+                    if "properties" in action["parameters"]:
+                        for param_name, param_details in action["parameters"][
+                            "properties"
+                        ].items():
+                            param_details["filled_by_llm"] = True
+                            param_details["value"] = ""
+                transformed_actions.append(action)
+            tool_data = {
+                "name": "mcp_tool",
+                "displayName": data["displayName"],
+                "customName": data["displayName"],
+                "description": f"MCP Server: {storage_config.get('server_url', 'Unknown')}",
+                "config": storage_config,
+                "actions": transformed_actions,
+                "status": data.get("status", True),
+                "user": user,
+            }
+
+            tool_id = data.get("id")
+            if tool_id:
+                result = user_tools_collection.update_one(
+                    {"_id": ObjectId(tool_id), "user": user, "name": "mcp_tool"},
+                    {"$set": {k: v for k, v in tool_data.items() if k != "user"}},
+                )
+                if result.matched_count == 0:
+                    return make_response(
+                        jsonify(
+                            {
+                                "success": False,
+                                "error": "Tool not found or access denied",
+                            }
+                        ),
+                        404,
+                    )
+                response_data = {
+                    "success": True,
+                    "id": tool_id,
+                    "message": f"MCP server updated successfully! Discovered {len(transformed_actions)} tools.",
+                    "tools_count": len(transformed_actions),
+                }
+            else:
+                result = user_tools_collection.insert_one(tool_data)
+                tool_id = str(result.inserted_id)
+                response_data = {
+                    "success": True,
+                    "id": tool_id,
+                    "message": f"MCP server created successfully! Discovered {len(transformed_actions)} tools.",
+                    "tools_count": len(transformed_actions),
+                }
+            return make_response(jsonify(response_data), 200)
+        except Exception as e:
+            current_app.logger.error(f"Error saving MCP server: {e}", exc_info=True)
+            return make_response(
+                jsonify(
+                    {"success": False, "error": f"Failed to save MCP server: {str(e)}"}
+                ),
+                500,
+            )
+
+
+@tools_mcp_ns.route("/mcp_server/callback")
+class MCPOAuthCallback(Resource):
+    @api.expect(
+        api.model(
+            "MCPServerCallbackModel",
+            {
+                "code": fields.String(required=True, description="Authorization code"),
+                "state": fields.String(required=True, description="State parameter"),
+                "error": fields.String(
+                    required=False, description="Error message (if any)"
+                ),
+            },
+        )
+    )
+    @api.doc(
+        description="Handle OAuth callback by providing the authorization code and state"
+    )
+    def get(self):
+        code = request.args.get("code")
+        state = request.args.get("state")
+        error = request.args.get("error")
+
+        if error:
+            return redirect(
+                f"/api/connectors/callback-status?status=error&message=OAuth+error:+{error}.+Please+try+again+and+make+sure+to+grant+all+requested+permissions,+including+offline+access.&provider=mcp_tool"
+            )
+        if not code or not state:
+            return redirect(
+                "/api/connectors/callback-status?status=error&message=Authorization+code+or+state+not+provided.+Please+complete+the+authorization+process+and+make+sure+to+grant+offline+access.&provider=mcp_tool"
+            )
+        try:
+            redis_client = get_redis_instance()
+            if not redis_client:
+                return redirect(
+                    "/api/connectors/callback-status?status=error&message=Internal+server+error:+Redis+not+available.&provider=mcp_tool"
+                )
+            code = unquote(code)
+            manager = MCPOAuthManager(redis_client)
+            success = manager.handle_oauth_callback(state, code, error)
+            if success:
+                return redirect(
+                    "/api/connectors/callback-status?status=success&message=Authorization+code+received+successfully.+You+can+close+this+window.&provider=mcp_tool"
+                )
+            else:
+                return redirect(
+                    "/api/connectors/callback-status?status=error&message=OAuth+callback+failed.&provider=mcp_tool"
+                )
+        except Exception as e:
+            current_app.logger.error(
+                f"Error handling MCP OAuth callback: {str(e)}", exc_info=True
+            )
+            return redirect(
+                f"/api/connectors/callback-status?status=error&message=Internal+server+error:+{str(e)}.&provider=mcp_tool"
+            )
+
+
+@tools_mcp_ns.route("/mcp_server/oauth_status/<string:task_id>")
+class MCPOAuthStatus(Resource):
+    def get(self, task_id):
+        """
+        Get current status of OAuth flow.
+        Frontend should poll this endpoint periodically.
+        """
+        try:
+            redis_client = get_redis_instance()
+            status_key = f"mcp_oauth_status:{task_id}"
+            status_data = redis_client.get(status_key)
+
+            if status_data:
+                status = json.loads(status_data)
+                return make_response(
+                    jsonify({"success": True, "task_id": task_id, **status})
+                )
+            else:
+                return make_response(
+                    jsonify(
+                        {
+                            "success": False,
+                            "error": "Task not found or expired",
+                            "task_id": task_id,
+                        }
+                    ),
+                    404,
+                )
+        except Exception as e:
+            current_app.logger.error(
+                f"Error getting OAuth status for task {task_id}: {str(e)}"
+            )
+            return make_response(
+                jsonify({"success": False, "error": str(e), "task_id": task_id}), 500
+            )

application/api/user/tools/routes.py

@@ -0,0 +1,415 @@
+"""Tool management routes."""
+
+from bson.objectid import ObjectId
+from flask import current_app, jsonify, make_response, request
+from flask_restx import fields, Namespace, Resource
+
+from application.agents.tools.tool_manager import ToolManager
+from application.api import api
+from application.api.user.base import user_tools_collection
+from application.security.encryption import decrypt_credentials, encrypt_credentials
+from application.utils import check_required_fields, validate_function_name
+
+tool_config = {}
+tool_manager = ToolManager(config=tool_config)
+
+
+tools_ns = Namespace("tools", description="Tool management operations", path="/api")
+
+
+@tools_ns.route("/available_tools")
+class AvailableTools(Resource):
+    @api.doc(description="Get available tools for a user")
+    def get(self):
+        try:
+            tools_metadata = []
+            for tool_name, tool_instance in tool_manager.tools.items():
+                doc = tool_instance.__doc__.strip()
+                lines = doc.split("\n", 1)
+                name = lines[0].strip()
+                description = lines[1].strip() if len(lines) > 1 else ""
+                tools_metadata.append(
+                    {
+                        "name": tool_name,
+                        "displayName": name,
+                        "description": description,
+                        "configRequirements": tool_instance.get_config_requirements(),
+                    }
+                )
+        except Exception as err:
+            current_app.logger.error(
+                f"Error getting available tools: {err}", exc_info=True
+            )
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify({"success": True, "data": tools_metadata}), 200)
+
+
+@tools_ns.route("/get_tools")
+class GetTools(Resource):
+    @api.doc(description="Get tools created by a user")
+    def get(self):
+        try:
+            decoded_token = request.decoded_token
+            if not decoded_token:
+                return make_response(jsonify({"success": False}), 401)
+            user = decoded_token.get("sub")
+            tools = user_tools_collection.find({"user": user})
+            user_tools = []
+            for tool in tools:
+                tool["id"] = str(tool["_id"])
+                tool.pop("_id")
+                user_tools.append(tool)
+        except Exception as err:
+            current_app.logger.error(f"Error getting user tools: {err}", exc_info=True)
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify({"success": True, "tools": user_tools}), 200)
+
+
+@tools_ns.route("/create_tool")
+class CreateTool(Resource):
+    @api.expect(
+        api.model(
+            "CreateToolModel",
+            {
+                "name": fields.String(required=True, description="Name of the tool"),
+                "displayName": fields.String(
+                    required=True, description="Display name for the tool"
+                ),
+                "description": fields.String(
+                    required=True, description="Tool description"
+                ),
+                "config": fields.Raw(
+                    required=True, description="Configuration of the tool"
+                ),
+                "customName": fields.String(
+                    required=False, description="Custom name for the tool"
+                ),
+                "status": fields.Boolean(
+                    required=True, description="Status of the tool"
+                ),
+            },
+        )
+    )
+    @api.doc(description="Create a new tool")
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        data = request.get_json()
+        required_fields = [
+            "name",
+            "displayName",
+            "description",
+            "config",
+            "status",
+        ]
+        missing_fields = check_required_fields(data, required_fields)
+        if missing_fields:
+            return missing_fields
+        try:
+            tool_instance = tool_manager.tools.get(data["name"])
+            if not tool_instance:
+                return make_response(
+                    jsonify({"success": False, "message": "Tool not found"}), 404
+                )
+            actions_metadata = tool_instance.get_actions_metadata()
+            transformed_actions = []
+            for action in actions_metadata:
+                action["active"] = True
+                if "parameters" in action:
+                    if "properties" in action["parameters"]:
+                        for param_name, param_details in action["parameters"][
+                            "properties"
+                        ].items():
+                            param_details["filled_by_llm"] = True
+                            param_details["value"] = ""
+                transformed_actions.append(action)
+        except Exception as err:
+            current_app.logger.error(
+                f"Error getting tool actions: {err}", exc_info=True
+            )
+            return make_response(jsonify({"success": False}), 400)
+        try:
+            new_tool = {
+                "user": user,
+                "name": data["name"],
+                "displayName": data["displayName"],
+                "description": data["description"],
+                "customName": data.get("customName", ""),
+                "actions": transformed_actions,
+                "config": data["config"],
+                "status": data["status"],
+            }
+            resp = user_tools_collection.insert_one(new_tool)
+            new_id = str(resp.inserted_id)
+        except Exception as err:
+            current_app.logger.error(f"Error creating tool: {err}", exc_info=True)
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify({"id": new_id}), 200)
+
+
+@tools_ns.route("/update_tool")
+class UpdateTool(Resource):
+    @api.expect(
+        api.model(
+            "UpdateToolModel",
+            {
+                "id": fields.String(required=True, description="Tool ID"),
+                "name": fields.String(description="Name of the tool"),
+                "displayName": fields.String(description="Display name for the tool"),
+                "customName": fields.String(description="Custom name for the tool"),
+                "description": fields.String(description="Tool description"),
+                "config": fields.Raw(description="Configuration of the tool"),
+                "actions": fields.List(
+                    fields.Raw, description="Actions the tool can perform"
+                ),
+                "status": fields.Boolean(description="Status of the tool"),
+            },
+        )
+    )
+    @api.doc(description="Update a tool by ID")
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        data = request.get_json()
+        required_fields = ["id"]
+        missing_fields = check_required_fields(data, required_fields)
+        if missing_fields:
+            return missing_fields
+        try:
+            update_data = {}
+            if "name" in data:
+                update_data["name"] = data["name"]
+            if "displayName" in data:
+                update_data["displayName"] = data["displayName"]
+            if "customName" in data:
+                update_data["customName"] = data["customName"]
+            if "description" in data:
+                update_data["description"] = data["description"]
+            if "actions" in data:
+                update_data["actions"] = data["actions"]
+            if "config" in data:
+                if "actions" in data["config"]:
+                    for action_name in list(data["config"]["actions"].keys()):
+                        if not validate_function_name(action_name):
+                            return make_response(
+                                jsonify(
+                                    {
+                                        "success": False,
+                                        "message": f"Invalid function name '{action_name}'. Function names must match pattern '^[a-zA-Z0-9_-]+$'.",
+                                        "param": "tools[].function.name",
+                                    }
+                                ),
+                                400,
+                            )
+                tool_doc = user_tools_collection.find_one(
+                    {"_id": ObjectId(data["id"]), "user": user}
+                )
+                if tool_doc and tool_doc.get("name") == "mcp_tool":
+                    config = data["config"]
+                    existing_config = tool_doc.get("config", {})
+                    storage_config = existing_config.copy()
+
+                    storage_config.update(config)
+                    existing_credentials = {}
+                    if "encrypted_credentials" in existing_config:
+                        existing_credentials = decrypt_credentials(
+                            existing_config["encrypted_credentials"], user
+                        )
+                    auth_credentials = existing_credentials.copy()
+                    auth_type = storage_config.get("auth_type", "none")
+                    if auth_type == "api_key":
+                        if "api_key" in config and config["api_key"]:
+                            auth_credentials["api_key"] = config["api_key"]
+                        if "api_key_header" in config:
+                            auth_credentials["api_key_header"] = config[
+                                "api_key_header"
+                            ]
+                    elif auth_type == "bearer":
+                        if "bearer_token" in config and config["bearer_token"]:
+                            auth_credentials["bearer_token"] = config["bearer_token"]
+                        elif "encrypted_token" in config and config["encrypted_token"]:
+                            auth_credentials["bearer_token"] = config["encrypted_token"]
+                    elif auth_type == "basic":
+                        if "username" in config and config["username"]:
+                            auth_credentials["username"] = config["username"]
+                        if "password" in config and config["password"]:
+                            auth_credentials["password"] = config["password"]
+                    if auth_type != "none" and auth_credentials:
+                        encrypted_credentials_string = encrypt_credentials(
+                            auth_credentials, user
+                        )
+                        storage_config["encrypted_credentials"] = (
+                            encrypted_credentials_string
+                        )
+                    elif auth_type == "none":
+                        storage_config.pop("encrypted_credentials", None)
+                    for field in [
+                        "api_key",
+                        "bearer_token",
+                        "encrypted_token",
+                        "username",
+                        "password",
+                        "api_key_header",
+                    ]:
+                        storage_config.pop(field, None)
+                    update_data["config"] = storage_config
+                else:
+                    update_data["config"] = data["config"]
+            if "status" in data:
+                update_data["status"] = data["status"]
+            user_tools_collection.update_one(
+                {"_id": ObjectId(data["id"]), "user": user},
+                {"$set": update_data},
+            )
+        except Exception as err:
+            current_app.logger.error(f"Error updating tool: {err}", exc_info=True)
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify({"success": True}), 200)
+
+
+@tools_ns.route("/update_tool_config")
+class UpdateToolConfig(Resource):
+    @api.expect(
+        api.model(
+            "UpdateToolConfigModel",
+            {
+                "id": fields.String(required=True, description="Tool ID"),
+                "config": fields.Raw(
+                    required=True, description="Configuration of the tool"
+                ),
+            },
+        )
+    )
+    @api.doc(description="Update the configuration of a tool")
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        data = request.get_json()
+        required_fields = ["id", "config"]
+        missing_fields = check_required_fields(data, required_fields)
+        if missing_fields:
+            return missing_fields
+        try:
+            user_tools_collection.update_one(
+                {"_id": ObjectId(data["id"]), "user": user},
+                {"$set": {"config": data["config"]}},
+            )
+        except Exception as err:
+            current_app.logger.error(
+                f"Error updating tool config: {err}", exc_info=True
+            )
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify({"success": True}), 200)
+
+
+@tools_ns.route("/update_tool_actions")
+class UpdateToolActions(Resource):
+    @api.expect(
+        api.model(
+            "UpdateToolActionsModel",
+            {
+                "id": fields.String(required=True, description="Tool ID"),
+                "actions": fields.List(
+                    fields.Raw,
+                    required=True,
+                    description="Actions the tool can perform",
+                ),
+            },
+        )
+    )
+    @api.doc(description="Update the actions of a tool")
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        data = request.get_json()
+        required_fields = ["id", "actions"]
+        missing_fields = check_required_fields(data, required_fields)
+        if missing_fields:
+            return missing_fields
+        try:
+            user_tools_collection.update_one(
+                {"_id": ObjectId(data["id"]), "user": user},
+                {"$set": {"actions": data["actions"]}},
+            )
+        except Exception as err:
+            current_app.logger.error(
+                f"Error updating tool actions: {err}", exc_info=True
+            )
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify({"success": True}), 200)
+
+
+@tools_ns.route("/update_tool_status")
+class UpdateToolStatus(Resource):
+    @api.expect(
+        api.model(
+            "UpdateToolStatusModel",
+            {
+                "id": fields.String(required=True, description="Tool ID"),
+                "status": fields.Boolean(
+                    required=True, description="Status of the tool"
+                ),
+            },
+        )
+    )
+    @api.doc(description="Update the status of a tool")
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        data = request.get_json()
+        required_fields = ["id", "status"]
+        missing_fields = check_required_fields(data, required_fields)
+        if missing_fields:
+            return missing_fields
+        try:
+            user_tools_collection.update_one(
+                {"_id": ObjectId(data["id"]), "user": user},
+                {"$set": {"status": data["status"]}},
+            )
+        except Exception as err:
+            current_app.logger.error(
+                f"Error updating tool status: {err}", exc_info=True
+            )
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify({"success": True}), 200)
+
+
+@tools_ns.route("/delete_tool")
+class DeleteTool(Resource):
+    @api.expect(
+        api.model(
+            "DeleteToolModel",
+            {"id": fields.String(required=True, description="Tool ID")},
+        )
+    )
+    @api.doc(description="Delete a tool by ID")
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        data = request.get_json()
+        required_fields = ["id"]
+        missing_fields = check_required_fields(data, required_fields)
+        if missing_fields:
+            return missing_fields
+        try:
+            result = user_tools_collection.delete_one(
+                {"_id": ObjectId(data["id"]), "user": user}
+            )
+            if result.deleted_count == 0:
+                return {"success": False, "message": "Tool not found"}, 404
+        except Exception as err:
+            current_app.logger.error(f"Error deleting tool: {err}", exc_info=True)
+            return {"success": False}, 400
+        return {"success": True}, 200

application/app.py

@@ -1,28 +1,37 @@
+import os
 import platform
+import uuid
 
 import dotenv
-from flask import Flask, redirect, request
+from flask import Flask, jsonify, redirect, request
+from jose import jwt
+
+from application.auth import handle_auth
 
-from application.api.answer.routes import answer
-from application.api.internal.routes import internal
-from application.api.user.routes import user
-from application.celery_init import celery
 from application.core.logging_config import setup_logging
-from application.core.settings import settings
-from application.extensions import api
+
+setup_logging()
+
+from application.api import api  # noqa: E402
+from application.api.answer import answer  # noqa: E402
+from application.api.internal.routes import internal  # noqa: E402
+from application.api.user.routes import user  # noqa: E402
+from application.api.connector.routes import connector  # noqa: E402
+from application.celery_init import celery  # noqa: E402
+from application.core.settings import settings  # noqa: E402
+
 
 if platform.system() == "Windows":
     import pathlib
 
     pathlib.PosixPath = pathlib.WindowsPath
-
 dotenv.load_dotenv()
-setup_logging()
 
 app = Flask(__name__)
 app.register_blueprint(user)
 app.register_blueprint(answer)
 app.register_blueprint(internal)
+app.register_blueprint(connector)
 app.config.update(
     UPLOAD_FOLDER="inputs",
     CELERY_BROKER_URL=settings.CELERY_BROKER_URL,
@@ -32,6 +41,24 @@ app.config.update(
 celery.config_from_object("application.celeryconfig")
 api.init_app(app)
 
+if settings.AUTH_TYPE in ("simple_jwt", "session_jwt") and not settings.JWT_SECRET_KEY:
+    key_file = ".jwt_secret_key"
+    try:
+        with open(key_file, "r") as f:
+            settings.JWT_SECRET_KEY = f.read().strip()
+    except FileNotFoundError:
+        new_key = os.urandom(32).hex()
+        with open(key_file, "w") as f:
+            f.write(new_key)
+        settings.JWT_SECRET_KEY = new_key
+    except Exception as e:
+        raise RuntimeError(f"Failed to setup JWT_SECRET_KEY: {e}")
+SIMPLE_JWT_TOKEN = None
+if settings.AUTH_TYPE == "simple_jwt":
+    payload = {"sub": "local"}
+    SIMPLE_JWT_TOKEN = jwt.encode(payload, settings.JWT_SECRET_KEY, algorithm="HS256")
+    print(f"Generated Simple JWT Token: {SIMPLE_JWT_TOKEN}")
+
 
 @app.route("/")
 def home():
@@ -41,11 +68,46 @@ def home():
         return "Welcome to DocsGPT Backend!"
 
 
+@app.route("/api/config")
+def get_config():
+    response = {
+        "auth_type": settings.AUTH_TYPE,
+        "requires_auth": settings.AUTH_TYPE in ["simple_jwt", "session_jwt"],
+    }
+    return jsonify(response)
+
+
+@app.route("/api/generate_token")
+def generate_token():
+    if settings.AUTH_TYPE == "session_jwt":
+        new_user_id = str(uuid.uuid4())
+        token = jwt.encode(
+            {"sub": new_user_id}, settings.JWT_SECRET_KEY, algorithm="HS256"
+        )
+        return jsonify({"token": token})
+    return jsonify({"error": "Token generation not allowed in current auth mode"}), 400
+
+
+@app.before_request
+def authenticate_request():
+    if request.method == "OPTIONS":
+        return "", 200
+    decoded_token = handle_auth(request)
+    if not decoded_token:
+        request.decoded_token = None
+    elif "error" in decoded_token:
+        return jsonify(decoded_token), 401
+    else:
+        request.decoded_token = decoded_token
+
+
 @app.after_request
 def after_request(response):
     response.headers.add("Access-Control-Allow-Origin", "*")
-    response.headers.add("Access-Control-Allow-Headers", "Content-Type,Authorization")
-    response.headers.add("Access-Control-Allow-Methods", "GET,PUT,POST,DELETE,OPTIONS")
+    response.headers.add("Access-Control-Allow-Headers", "Content-Type, Authorization")
+    response.headers.add(
+        "Access-Control-Allow-Methods", "GET, POST, PUT, DELETE, OPTIONS"
+    )
     return response
 
 

application/auth.py

@@ -0,0 +1,28 @@
+from jose import jwt
+
+from application.core.settings import settings
+
+
+def handle_auth(request, data={}):
+    if settings.AUTH_TYPE in ["simple_jwt", "session_jwt"]:
+        jwt_token = request.headers.get("Authorization")
+        if not jwt_token:
+            return None
+
+        jwt_token = jwt_token.replace("Bearer ", "")
+
+        try:
+            decoded_token = jwt.decode(
+                jwt_token,
+                settings.JWT_SECRET_KEY,
+                algorithms=["HS256"],
+                options={"verify_exp": False},
+            )
+            return decoded_token
+        except Exception as e:
+            return {
+                "message": f"Authentication error: {str(e)}",
+                "error": "invalid_token",
+            }
+    else:
+        return {"sub": "local"}

application/cache.py

@@ -1,93 +1,117 @@
-import redis
-import time
 import json
 import logging
+import time
 from threading import Lock
+
+import redis
+
 from application.core.settings import settings
 from application.utils import get_hash
 
 logger = logging.getLogger(__name__)
 
 _redis_instance = None
+_redis_creation_failed = False
 _instance_lock = Lock()
 
 def get_redis_instance():
-    global _redis_instance
-    if _redis_instance is None:
+    global _redis_instance, _redis_creation_failed
+    if _redis_instance is None and not _redis_creation_failed:
         with _instance_lock:
-            if _redis_instance is None:
+            if _redis_instance is None and not _redis_creation_failed:
                 try:
-                    _redis_instance = redis.Redis.from_url(settings.CACHE_REDIS_URL, socket_connect_timeout=2)
+                    _redis_instance = redis.Redis.from_url(
+                        settings.CACHE_REDIS_URL, socket_connect_timeout=2
+                    )
+                except ValueError as e:
+                    logger.error(f"Invalid Redis URL: {e}")
+                    _redis_creation_failed = True  # Stop future attempts
+                    _redis_instance = None
                 except redis.ConnectionError as e:
                     logger.error(f"Redis connection error: {e}")
-                    _redis_instance = None
+                    _redis_instance = None  # Keep trying for connection errors
     return _redis_instance
 
-def gen_cache_key(*messages, model="docgpt"):
+
+def gen_cache_key(messages, model="docgpt", tools=None):
     if not all(isinstance(msg, dict) for msg in messages):
         raise ValueError("All messages must be dictionaries.")
-    messages_str = json.dumps(list(messages), sort_keys=True)
-    combined = f"{model}_{messages_str}"
+    messages_str = json.dumps(messages)
+    tools_str = json.dumps(str(tools)) if tools else ""
+    combined = f"{model}_{messages_str}_{tools_str}"
     cache_key = get_hash(combined)
     return cache_key
 
+
 def gen_cache(func):
-    def wrapper(self, model, messages, *args, **kwargs):
+    def wrapper(self, model, messages, stream, tools=None, *args, **kwargs):
+        if tools is not None:
+            return func(self, model, messages, stream, tools, *args, **kwargs)
+        
         try:
-            cache_key = gen_cache_key(*messages)
-            redis_client = get_redis_instance()
-            if redis_client:
-                try:
-                    cached_response = redis_client.get(cache_key)
-                    if cached_response:
-                        return cached_response.decode('utf-8')
-                except redis.ConnectionError as e:
-                    logger.error(f"Redis connection error: {e}")
-
-            result = func(self, model, messages, *args, **kwargs)
-            if redis_client:
-                try:
-                    redis_client.set(cache_key, result, ex=1800)
-                except redis.ConnectionError as e:
-                    logger.error(f"Redis connection error: {e}")
-
-            return result
+            cache_key = gen_cache_key(messages, model, tools)
         except ValueError as e:
-            logger.error(e)
-            return "Error: No user message found in the conversation to generate a cache key."
+            logger.error(f"Cache key generation failed: {e}")
+            return func(self, model, messages, stream, tools, *args, **kwargs)
+
+        redis_client = get_redis_instance()
+        if redis_client:
+            try:
+                cached_response = redis_client.get(cache_key)
+                if cached_response:
+                    return cached_response.decode("utf-8")
+            except Exception as e:
+                logger.error(f"Error getting cached response: {e}", exc_info=True)
+
+        result = func(self, model, messages, stream, tools, *args, **kwargs)
+        if redis_client and isinstance(result, str):
+            try:
+                redis_client.set(cache_key, result, ex=1800)
+            except Exception as e:
+                logger.error(f"Error setting cache: {e}", exc_info=True)
+
+        return result
+
     return wrapper
 
+
 def stream_cache(func):
-    def wrapper(self, model, messages, stream, *args, **kwargs):
-        cache_key = gen_cache_key(*messages)
-        logger.info(f"Stream cache key: {cache_key}")
+    def wrapper(self, model, messages, stream, tools=None, *args, **kwargs):
+        if tools is not None:
+            yield from func(self, model, messages, stream, tools, *args, **kwargs)
+            return
         
+        try:
+            cache_key = gen_cache_key(messages, model, tools)
+        except ValueError as e:
+            logger.error(f"Cache key generation failed: {e}")
+            yield from func(self, model, messages, stream, tools, *args, **kwargs)
+            return
+
         redis_client = get_redis_instance()
         if redis_client:
             try:
                 cached_response = redis_client.get(cache_key)
                 if cached_response:
                     logger.info(f"Cache hit for stream key: {cache_key}")
-                    cached_response = json.loads(cached_response.decode('utf-8'))
+                    cached_response = json.loads(cached_response.decode("utf-8"))
                     for chunk in cached_response:
                         yield chunk
-                        time.sleep(0.03)
+                        time.sleep(0.03)  # Simulate streaming delay
                     return
-            except redis.ConnectionError as e:
-                logger.error(f"Redis connection error: {e}")
+            except Exception as e:
+                logger.error(f"Error getting cached stream: {e}", exc_info=True)
 
-        result = func(self, model, messages, stream, *args, **kwargs)
         stream_cache_data = []
-        
-        for chunk in result:
-            stream_cache_data.append(chunk)
+        for chunk in func(self, model, messages, stream, tools, *args, **kwargs):
             yield chunk
-        
+            stream_cache_data.append(str(chunk))
+
         if redis_client:
             try:
                 redis_client.set(cache_key, json.dumps(stream_cache_data), ex=1800)
                 logger.info(f"Stream cache saved for key: {cache_key}")
-            except redis.ConnectionError as e:
-                logger.error(f"Redis connection error: {e}")
-        
-    return wrapper
+            except Exception as e:
+                logger.error(f"Error setting stream cache: {e}", exc_info=True)
+
+    return wrapper

application/celery_init.py

@@ -2,14 +2,22 @@ from celery import Celery
 from application.core.settings import settings
 from celery.signals import setup_logging
 
+
 def make_celery(app_name=__name__):
-    celery = Celery(app_name, broker=settings.CELERY_BROKER_URL, backend=settings.CELERY_RESULT_BACKEND)
+    celery = Celery(
+        app_name,
+        broker=settings.CELERY_BROKER_URL,
+        backend=settings.CELERY_RESULT_BACKEND,
+    )
     celery.conf.update(settings)
     return celery
 
+
 @setup_logging.connect
 def config_loggers(*args, **kwargs):
     from application.core.logging_config import setup_logging
+
     setup_logging()
 
+
 celery = make_celery()

application/core/settings.py

@@ -1,25 +1,62 @@
+import os
 from pathlib import Path
 from typing import Optional
-import os
 
 from pydantic_settings import BaseSettings
 
-current_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+current_dir = os.path.dirname(
+    os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+)
 
 
 class Settings(BaseSettings):
-    LLM_NAME: str = "docsgpt"
-    MODEL_NAME: Optional[str] = None # if LLM_NAME is openai, MODEL_NAME can be gpt-4 or gpt-3.5-turbo
+    AUTH_TYPE: Optional[str] = None  # simple_jwt, session_jwt, or None
+    LLM_PROVIDER: str = "docsgpt"
+    LLM_NAME: Optional[str] = (
+        None  # if LLM_PROVIDER is openai, LLM_NAME can be gpt-4 or gpt-3.5-turbo
+    )
     EMBEDDINGS_NAME: str = "huggingface_sentence-transformers/all-mpnet-base-v2"
     CELERY_BROKER_URL: str = "redis://localhost:6379/0"
     CELERY_RESULT_BACKEND: str = "redis://localhost:6379/1"
     MONGO_URI: str = "mongodb://localhost:27017/docsgpt"
-    MODEL_PATH: str = os.path.join(current_dir, "models/docsgpt-7b-f16.gguf")
+    MONGO_DB_NAME: str = "docsgpt"
+    LLM_PATH: str = os.path.join(current_dir, "models/docsgpt-7b-f16.gguf")
     DEFAULT_MAX_HISTORY: int = 150
-    MODEL_TOKEN_LIMITS: dict = {"gpt-3.5-turbo": 4096, "claude-2": 1e5}
+    LLM_TOKEN_LIMITS: dict = {
+        "gpt-4o-mini": 128000,
+        "gpt-3.5-turbo": 4096,
+        "claude-2": 1e5,
+        "gemini-2.5-flash": 1e6,
+    }
+    DEFAULT_AGENT_LIMITS: dict = {
+        "token_limit": 50000,
+        "request_limit": 500,
+    }
     UPLOAD_FOLDER: str = "inputs"
-    VECTOR_STORE: str = "faiss" #  "faiss" or "elasticsearch" or "qdrant" or "milvus" or "lancedb"
-    RETRIEVERS_ENABLED: list = ["classic_rag", "duckduck_search"] # also brave_search
+    PARSE_PDF_AS_IMAGE: bool = False
+    PARSE_IMAGE_REMOTE: bool = False
+    VECTOR_STORE: str = (
+        "faiss"  #  "faiss" or "elasticsearch" or "qdrant" or "milvus" or "lancedb"
+    )
+    RETRIEVERS_ENABLED: list = ["classic_rag"]
+    AGENT_NAME: str = "classic"
+    FALLBACK_LLM_PROVIDER: Optional[str] = None  # provider for fallback llm
+    FALLBACK_LLM_NAME: Optional[str] = None  # model name for fallback llm
+    FALLBACK_LLM_API_KEY: Optional[str] = None  # api key for fallback llm
+
+    # Google Drive integration
+    GOOGLE_CLIENT_ID: Optional[str] = (
+        None  # Replace with your actual Google OAuth client ID
+    )
+    GOOGLE_CLIENT_SECRET: Optional[str] = (
+        None  # Replace with your actual Google OAuth client secret
+    )
+    CONNECTOR_REDIRECT_BASE_URI: Optional[str] = (
+        "http://127.0.0.1:7091/api/connectors/callback"  ##add redirect url as it is to your provider's console(gcp)
+    )
+
+    # GitHub source
+    GITHUB_ACCESS_TOKEN: Optional[str] = None # PAT token with read repo access
 
     # LLM Cache
     CACHE_REDIS_URL: str = "redis://localhost:6379/2"
@@ -27,12 +64,18 @@ class Settings(BaseSettings):
     API_URL: str = "http://localhost:7091"  # backend url for celery worker
 
     API_KEY: Optional[str] = None  # LLM api key
-    EMBEDDINGS_KEY: Optional[str] = None  # api key for embeddings (if using openai, just copy API_KEY)
+    EMBEDDINGS_KEY: Optional[str] = (
+        None  # api key for embeddings (if using openai, just copy API_KEY)
+    )
     OPENAI_API_BASE: Optional[str] = None  # azure openai api base url
     OPENAI_API_VERSION: Optional[str] = None  # azure openai api version
     AZURE_DEPLOYMENT_NAME: Optional[str] = None  # azure deployment name for answering
-    AZURE_EMBEDDINGS_DEPLOYMENT_NAME: Optional[str] = None  # azure deployment name for embeddings
-    OPENAI_BASE_URL: Optional[str] = None # openai base url for open ai compatable models
+    AZURE_EMBEDDINGS_DEPLOYMENT_NAME: Optional[str] = (
+        None  # azure deployment name for embeddings
+    )
+    OPENAI_BASE_URL: Optional[str] = (
+        None  # openai base url for open ai compatable models
+    )
 
     # elasticsearch
     ELASTIC_CLOUD_ID: Optional[str] = None  # cloud id for elasticsearch
@@ -65,18 +108,30 @@ class Settings(BaseSettings):
     QDRANT_PATH: Optional[str] = None
     QDRANT_DISTANCE_FUNC: str = "Cosine"
 
+    # PGVector vectorstore config
+    PGVECTOR_CONNECTION_STRING: Optional[str] = None
     # Milvus vectorstore config
     MILVUS_COLLECTION_NAME: Optional[str] = "docsgpt"
-    MILVUS_URI: Optional[str] = "./milvus_local.db"   # milvus lite version as default
+    MILVUS_URI: Optional[str] = "./milvus_local.db"  # milvus lite version as default
     MILVUS_TOKEN: Optional[str] = ""
 
     # LanceDB vectorstore config
     LANCEDB_PATH: str = "/tmp/lancedb"  # Path where LanceDB stores its local data
-    LANCEDB_TABLE_NAME: Optional[str] = "docsgpts"  # Name of the table to use for storing vectors
-    BRAVE_SEARCH_API_KEY: Optional[str] = None
+    LANCEDB_TABLE_NAME: Optional[str] = (
+        "docsgpts"  # Name of the table to use for storing vectors
+    )
 
     FLASK_DEBUG_MODE: bool = False
+    STORAGE_TYPE: str = "local"  # local or s3
+    URL_STRATEGY: str = "backend"  # backend or s3
 
+    JWT_SECRET_KEY: str = ""
+
+    # Encryption settings
+    ENCRYPTION_SECRET_KEY: str = "default-docsgpt-encryption-key"
+
+    TTS_PROVIDER: str = "google_tts" # google_tts or elevenlabs
+    ELEVENLABS_API_KEY: Optional[str] = None
 
 path = Path(__file__).parent.parent.absolute()
 settings = Settings(_env_file=path.joinpath(".env"), _env_file_encoding="utf-8")

application/extensions.py

@@ -1,7 +0,0 @@
-from flask_restx import Api
-
-api = Api(
-    version="1.0",
-    title="DocsGPT API",
-    description="API for DocsGPT",
-)

application/llm/anthropic.py

@@ -17,7 +17,7 @@ class AnthropicLLM(BaseLLM):
         self.AI_PROMPT = AI_PROMPT
 
     def _raw_gen(
-        self, baseself, model, messages, stream=False, max_tokens=300, **kwargs
+        self, baseself, model, messages, stream=False, tools=None, max_tokens=300, **kwargs
     ):
         context = messages[0]["content"]
         user_question = messages[-1]["content"]
@@ -34,7 +34,7 @@ class AnthropicLLM(BaseLLM):
         return completion.completion
 
     def _raw_gen_stream(
-        self, baseself, model, messages, stream=True, max_tokens=300, **kwargs
+        self, baseself, model, messages, stream=True, tools=None, max_tokens=300, **kwargs
     ):
         context = messages[0]["content"]
         user_question = messages[-1]["content"]
@@ -46,5 +46,9 @@ class AnthropicLLM(BaseLLM):
             stream=True,
         )
 
-        for completion in stream_response:
-            yield completion.completion
+        try:
+            for completion in stream_response:
+                yield completion.completion
+        finally:
+            if hasattr(stream_response, 'close'):
+                stream_response.close()

application/llm/base.py

@@ -1,29 +1,144 @@
+import logging
 from abc import ABC, abstractmethod
+
+from application.cache import gen_cache, stream_cache
+
+from application.core.settings import settings
 from application.usage import gen_token_usage, stream_token_usage
-from application.cache import stream_cache, gen_cache
+
+logger = logging.getLogger(__name__)
 
 
 class BaseLLM(ABC):
-    def __init__(self):
+    def __init__(
+        self,
+        decoded_token=None,
+    ):
+        self.decoded_token = decoded_token
         self.token_usage = {"prompt_tokens": 0, "generated_tokens": 0}
+        self.fallback_provider = settings.FALLBACK_LLM_PROVIDER
+        self.fallback_model_name = settings.FALLBACK_LLM_NAME
+        self.fallback_llm_api_key = settings.FALLBACK_LLM_API_KEY
+        self._fallback_llm = None
 
-    def _apply_decorator(self, method, decorators, *args, **kwargs):
-        for decorator in decorators:
-            method = decorator(method)
-        return method(self, *args, **kwargs)
+    @property
+    def fallback_llm(self):
+        """Lazy-loaded fallback LLM instance."""
+        if (
+            self._fallback_llm is None
+            and self.fallback_provider
+            and self.fallback_model_name
+        ):
+            try:
+                from application.llm.llm_creator import LLMCreator
+
+                self._fallback_llm = LLMCreator.create_llm(
+                    self.fallback_provider,
+                    self.fallback_llm_api_key,
+                    None,
+                    self.decoded_token,
+                )
+            except Exception as e:
+                logger.error(
+                    f"Failed to initialize fallback LLM: {str(e)}", exc_info=True
+                )
+        return self._fallback_llm
+
+    def _execute_with_fallback(
+        self, method_name: str, decorators: list, *args, **kwargs
+    ):
+        """
+        Unified method execution with fallback support.
+
+        Args:
+            method_name: Name of the raw method ('_raw_gen' or '_raw_gen_stream')
+            decorators: List of decorators to apply
+            *args: Positional arguments
+            **kwargs: Keyword arguments
+        """
+
+        def decorated_method():
+            method = getattr(self, method_name)
+            for decorator in decorators:
+                method = decorator(method)
+            return method(self, *args, **kwargs)
+
+        try:
+            return decorated_method()
+        except Exception as e:
+            if not self.fallback_llm:
+                logger.error(f"Primary LLM failed and no fallback available: {str(e)}")
+                raise
+            logger.warning(
+                f"Falling back to {self.fallback_provider}/{self.fallback_model_name}. Error: {str(e)}"
+            )
+
+            fallback_method = getattr(
+                self.fallback_llm, method_name.replace("_raw_", "")
+            )
+            return fallback_method(*args, **kwargs)
+
+    def gen(self, model, messages, stream=False, tools=None, *args, **kwargs):
+        decorators = [gen_token_usage, gen_cache]
+        return self._execute_with_fallback(
+            "_raw_gen",
+            decorators,
+            model=model,
+            messages=messages,
+            stream=stream,
+            tools=tools,
+            *args,
+            **kwargs,
+        )
+
+    def gen_stream(self, model, messages, stream=True, tools=None, *args, **kwargs):
+        decorators = [stream_cache, stream_token_usage]
+        return self._execute_with_fallback(
+            "_raw_gen_stream",
+            decorators,
+            model=model,
+            messages=messages,
+            stream=stream,
+            tools=tools,
+            *args,
+            **kwargs,
+        )
 
     @abstractmethod
-    def _raw_gen(self, model, messages, stream, *args, **kwargs):
+    def _raw_gen(self, model, messages, stream, tools, *args, **kwargs):
         pass
 
-    def gen(self, model, messages, stream=False, *args, **kwargs):
-        decorators = [gen_token_usage, gen_cache]
-        return self._apply_decorator(self._raw_gen, decorators=decorators, model=model, messages=messages, stream=stream, *args, **kwargs)
-
     @abstractmethod
     def _raw_gen_stream(self, model, messages, stream, *args, **kwargs):
         pass
 
-    def gen_stream(self, model, messages, stream=True, *args, **kwargs):
-        decorators = [stream_cache, stream_token_usage]
-        return self._apply_decorator(self._raw_gen_stream, decorators=decorators, model=model, messages=messages, stream=stream, *args, **kwargs)
+    def supports_tools(self):
+        return hasattr(self, "_supports_tools") and callable(
+            getattr(self, "_supports_tools")
+        )
+
+    def _supports_tools(self):
+        raise NotImplementedError("Subclass must implement _supports_tools method")
+
+    def supports_structured_output(self):
+        """Check if the LLM supports structured output/JSON schema enforcement"""
+        return hasattr(self, "_supports_structured_output") and callable(
+            getattr(self, "_supports_structured_output")
+        )
+
+    def _supports_structured_output(self):
+        return False
+
+    def prepare_structured_output_format(self, json_schema):
+        """Prepare structured output format specific to the LLM provider"""
+        _ = json_schema
+        return None
+
+    def get_supported_attachment_types(self):
+        """
+        Return a list of MIME types supported by this LLM for file uploads.
+
+        Returns:
+            list: List of supported MIME types
+        """
+        return []

application/llm/docsgpt_provider.py

@@ -1,44 +1,139 @@
-from application.llm.base import BaseLLM
 import json
-import requests
+
+from application.core.settings import settings
+from application.llm.base import BaseLLM
 
 
 class DocsGPTAPILLM(BaseLLM):
 
     def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
+        from openai import OpenAI
+
         super().__init__(*args, **kwargs)
-        self.api_key = api_key
+        self.client = OpenAI(api_key="sk-docsgpt-public", base_url="https://oai.arc53.com")
         self.user_api_key = user_api_key
-        self.endpoint = "https://llm.docsgpt.co.uk"
+        self.api_key = api_key
 
-    def _raw_gen(self, baseself, model, messages, stream=False, *args, **kwargs):
-        context = messages[0]["content"]
-        user_question = messages[-1]["content"]
-        prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
+    def _clean_messages_openai(self, messages):
+        cleaned_messages = []
+        for message in messages:
+            role = message.get("role")
+            content = message.get("content")
 
-        response = requests.post(
-            f"{self.endpoint}/answer", json={"prompt": prompt, "max_new_tokens": 30}
-        )
-        response_clean = response.json()["a"].replace("###", "")
+            if role == "model":
+                role = "assistant"
 
-        return response_clean
+            if role and content is not None:
+                if isinstance(content, str):
+                    cleaned_messages.append({"role": role, "content": content})
+                elif isinstance(content, list):
+                    for item in content:
+                        if "text" in item:
+                            cleaned_messages.append(
+                                {"role": role, "content": item["text"]}
+                            )
+                        elif "function_call" in item:
+                            tool_call = {
+                                "id": item["function_call"]["call_id"],
+                                "type": "function",
+                                "function": {
+                                    "name": item["function_call"]["name"],
+                                    "arguments": json.dumps(
+                                        item["function_call"]["args"]
+                                    ),
+                                },
+                            }
+                            cleaned_messages.append(
+                                {
+                                    "role": "assistant",
+                                    "content": None,
+                                    "tool_calls": [tool_call],
+                                }
+                            )
+                        elif "function_response" in item:
+                            cleaned_messages.append(
+                                {
+                                    "role": "tool",
+                                    "tool_call_id": item["function_response"][
+                                        "call_id"
+                                    ],
+                                    "content": json.dumps(
+                                        item["function_response"]["response"]["result"]
+                                    ),
+                                }
+                            )
+                        else:
+                            raise ValueError(
+                                f"Unexpected content dictionary format: {item}"
+                            )
+                else:
+                    raise ValueError(f"Unexpected content type: {type(content)}")
 
-    def _raw_gen_stream(self, baseself, model, messages, stream=True, *args, **kwargs):
-        context = messages[0]["content"]
-        user_question = messages[-1]["content"]
-        prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
+        return cleaned_messages
 
-        # send prompt to endpoint /stream
-        response = requests.post(
-            f"{self.endpoint}/stream",
-            json={"prompt": prompt, "max_new_tokens": 256},
-            stream=True,
-        )
+    def _raw_gen(
+        self,
+        baseself,
+        model,
+        messages,
+        stream=False,
+        tools=None,
+        engine=settings.AZURE_DEPLOYMENT_NAME,
+        **kwargs,
+    ):
+        messages = self._clean_messages_openai(messages)
+        if tools:
+            response = self.client.chat.completions.create(
+                model="docsgpt",
+                messages=messages,
+                stream=stream,
+                tools=tools,
+                **kwargs,
+            )
+            return response.choices[0]
+        else:
+            response = self.client.chat.completions.create(
+                model="docsgpt", messages=messages, stream=stream, **kwargs
+            )
+            return response.choices[0].message.content
 
-        for line in response.iter_lines():
-            if line:
-                # data = json.loads(line)
-                data_str = line.decode("utf-8")
-                if data_str.startswith("data: "):
-                    data = json.loads(data_str[6:])
-                    yield data["a"]
+    def _raw_gen_stream(
+        self,
+        baseself,
+        model,
+        messages,
+        stream=True,
+        tools=None,
+        engine=settings.AZURE_DEPLOYMENT_NAME,
+        **kwargs,
+    ):
+        messages = self._clean_messages_openai(messages)
+        if tools:
+            response = self.client.chat.completions.create(
+                model="docsgpt",
+                messages=messages,
+                stream=stream,
+                tools=tools,
+                **kwargs,
+            )
+        else:
+            response = self.client.chat.completions.create(
+                model="docsgpt", messages=messages, stream=stream, **kwargs
+            )
+
+        try:
+            for line in response:
+                if (
+                    len(line.choices) > 0
+                    and line.choices[0].delta.content is not None
+                    and len(line.choices[0].delta.content) > 0
+                ):
+                    yield line.choices[0].delta.content
+                elif len(line.choices) > 0:
+                    yield line.choices[0]
+        finally:
+            if hasattr(response, 'close'):
+                response.close()
+
+    def _supports_tools(self):
+        return True

application/llm/google_ai.py

@@ -0,0 +1,468 @@
+import json
+import logging
+
+from google import genai
+from google.genai import types
+
+from application.core.settings import settings
+
+from application.llm.base import BaseLLM
+from application.storage.storage_creator import StorageCreator
+
+
+class GoogleLLM(BaseLLM):
+    def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.api_key = api_key
+        self.user_api_key = user_api_key
+        self.client = genai.Client(api_key=self.api_key)
+        self.storage = StorageCreator.get_storage()
+
+    def get_supported_attachment_types(self):
+        """
+        Return a list of MIME types supported by Google Gemini for file uploads.
+
+        Returns:
+            list: List of supported MIME types
+        """
+        return [
+            "application/pdf",
+            "image/png",
+            "image/jpeg",
+            "image/jpg",
+            "image/webp",
+            "image/gif",
+        ]
+
+    def prepare_messages_with_attachments(self, messages, attachments=None):
+        """
+        Process attachments using Google AI's file API for more efficient handling.
+
+        Args:
+            messages (list): List of message dictionaries.
+            attachments (list): List of attachment dictionaries with content and metadata.
+
+        Returns:
+            list: Messages formatted with file references for Google AI API.
+        """
+        if not attachments:
+            return messages
+
+        prepared_messages = messages.copy()
+
+        # Find the user message to attach files to the last one
+        user_message_index = None
+        for i in range(len(prepared_messages) - 1, -1, -1):
+            if prepared_messages[i].get("role") == "user":
+                user_message_index = i
+                break
+
+        if user_message_index is None:
+            user_message = {"role": "user", "content": []}
+            prepared_messages.append(user_message)
+            user_message_index = len(prepared_messages) - 1
+
+        if isinstance(prepared_messages[user_message_index].get("content"), str):
+            text_content = prepared_messages[user_message_index]["content"]
+            prepared_messages[user_message_index]["content"] = [
+                {"type": "text", "text": text_content}
+            ]
+        elif not isinstance(prepared_messages[user_message_index].get("content"), list):
+            prepared_messages[user_message_index]["content"] = []
+
+        files = []
+        for attachment in attachments:
+            mime_type = attachment.get("mime_type")
+
+            if mime_type in self.get_supported_attachment_types():
+                try:
+                    file_uri = self._upload_file_to_google(attachment)
+                    logging.info(
+                        f"GoogleLLM: Successfully uploaded file, got URI: {file_uri}"
+                    )
+                    files.append({"file_uri": file_uri, "mime_type": mime_type})
+                except Exception as e:
+                    logging.error(
+                        f"GoogleLLM: Error uploading file: {e}", exc_info=True
+                    )
+                    if "content" in attachment:
+                        prepared_messages[user_message_index]["content"].append(
+                            {
+                                "type": "text",
+                                "text": f"[File could not be processed: {attachment.get('path', 'unknown')}]",
+                            }
+                        )
+
+        if files:
+            logging.info(f"GoogleLLM: Adding {len(files)} files to message")
+            prepared_messages[user_message_index]["content"].append({"files": files})
+
+        return prepared_messages
+
+    def _upload_file_to_google(self, attachment):
+        """
+        Upload a file to Google AI and return the file URI.
+
+        Args:
+            attachment (dict): Attachment dictionary with path and metadata.
+
+        Returns:
+            str: Google AI file URI for the uploaded file.
+        """
+        if "google_file_uri" in attachment:
+            return attachment["google_file_uri"]
+
+        file_path = attachment.get("path")
+        if not file_path:
+            raise ValueError("No file path provided in attachment")
+
+        if not self.storage.file_exists(file_path):
+            raise FileNotFoundError(f"File not found: {file_path}")
+
+        try:
+            file_uri = self.storage.process_file(
+                file_path,
+                lambda local_path, **kwargs: self.client.files.upload(
+                    file=local_path
+                ).uri,
+            )
+
+            from application.core.mongo_db import MongoDB
+
+            mongo = MongoDB.get_client()
+            db = mongo[settings.MONGO_DB_NAME]
+            attachments_collection = db["attachments"]
+            if "_id" in attachment:
+                attachments_collection.update_one(
+                    {"_id": attachment["_id"]}, {"$set": {"google_file_uri": file_uri}}
+                )
+
+            return file_uri
+        except Exception as e:
+            logging.error(f"Error uploading file to Google AI: {e}", exc_info=True)
+            raise
+
+    def _clean_messages_google(self, messages):
+        """Convert OpenAI format messages to Google AI format."""
+        cleaned_messages = []
+        for message in messages:
+            role = message.get("role")
+            content = message.get("content")
+
+            if role == "assistant":
+                role = "model"
+            elif role == "tool":
+                role = "model"
+
+            parts = []
+            if role and content is not None:
+                if isinstance(content, str):
+                    parts = [types.Part.from_text(text=content)]
+                elif isinstance(content, list):
+                    for item in content:
+                        if "text" in item:
+                            parts.append(types.Part.from_text(text=item["text"]))
+                        elif "function_call" in item:
+                            parts.append(
+                                types.Part.from_function_call(
+                                    name=item["function_call"]["name"],
+                                    args=item["function_call"]["args"],
+                                )
+                            )
+                        elif "function_response" in item:
+                            parts.append(
+                                types.Part.from_function_response(
+                                    name=item["function_response"]["name"],
+                                    response=item["function_response"]["response"],
+                                )
+                            )
+                        elif "files" in item:
+                            for file_data in item["files"]:
+                                parts.append(
+                                    types.Part.from_uri(
+                                        file_uri=file_data["file_uri"],
+                                        mime_type=file_data["mime_type"],
+                                    )
+                                )
+                        else:
+                            raise ValueError(
+                                f"Unexpected content dictionary format:{item}"
+                            )
+                else:
+                    raise ValueError(f"Unexpected content type: {type(content)}")
+
+                if parts:
+                    cleaned_messages.append(types.Content(role=role, parts=parts))
+
+        return cleaned_messages
+
+    def _clean_schema(self, schema_obj):
+        """
+        Recursively remove unsupported fields from schema objects
+        and validate required properties.
+        """
+        if not isinstance(schema_obj, dict):
+            return schema_obj
+        allowed_fields = {
+            "type",
+            "description",
+            "items",
+            "properties",
+            "required",
+            "enum",
+            "pattern",
+            "minimum",
+            "maximum",
+            "nullable",
+            "default",
+        }
+
+        cleaned = {}
+        for key, value in schema_obj.items():
+            if key not in allowed_fields:
+                continue
+            elif key == "type" and isinstance(value, str):
+                cleaned[key] = value.upper()
+            elif isinstance(value, dict):
+                cleaned[key] = self._clean_schema(value)
+            elif isinstance(value, list):
+                cleaned[key] = [self._clean_schema(item) for item in value]
+            else:
+                cleaned[key] = value
+
+        # Validate that required properties actually exist in properties
+        if "required" in cleaned and "properties" in cleaned:
+            valid_required = []
+            properties_keys = set(cleaned["properties"].keys())
+            for required_prop in cleaned["required"]:
+                if required_prop in properties_keys:
+                    valid_required.append(required_prop)
+            if valid_required:
+                cleaned["required"] = valid_required
+            else:
+                cleaned.pop("required", None)
+        elif "required" in cleaned and "properties" not in cleaned:
+            cleaned.pop("required", None)
+
+        return cleaned
+
+    def _clean_tools_format(self, tools_list):
+        """Convert OpenAI format tools to Google AI format."""
+        genai_tools = []
+        for tool_data in tools_list:
+            if tool_data["type"] == "function":
+                function = tool_data["function"]
+                parameters = function["parameters"]
+                properties = parameters.get("properties", {})
+
+                if properties:
+                    cleaned_properties = {}
+                    for k, v in properties.items():
+                        cleaned_properties[k] = self._clean_schema(v)
+
+                    genai_function = dict(
+                        name=function["name"],
+                        description=function["description"],
+                        parameters={
+                            "type": "OBJECT",
+                            "properties": cleaned_properties,
+                            "required": (
+                                parameters["required"]
+                                if "required" in parameters
+                                else []
+                            ),
+                        },
+                    )
+                else:
+                    genai_function = dict(
+                        name=function["name"],
+                        description=function["description"],
+                    )
+
+                genai_tool = types.Tool(function_declarations=[genai_function])
+                genai_tools.append(genai_tool)
+
+        return genai_tools
+
+    def _raw_gen(
+        self,
+        baseself,
+        model,
+        messages,
+        stream=False,
+        tools=None,
+        formatting="openai",
+        response_schema=None,
+        **kwargs,
+    ):
+        """Generate content using Google AI API without streaming."""
+        client = genai.Client(api_key=self.api_key)
+        if formatting == "openai":
+            messages = self._clean_messages_google(messages)
+        config = types.GenerateContentConfig()
+        if messages[0].role == "system":
+            config.system_instruction = messages[0].parts[0].text
+            messages = messages[1:]
+
+        if tools:
+            cleaned_tools = self._clean_tools_format(tools)
+            config.tools = cleaned_tools
+
+        # Add response schema for structured output if provided
+        if response_schema:
+            config.response_schema = response_schema
+            config.response_mime_type = "application/json"
+
+        response = client.models.generate_content(
+            model=model,
+            contents=messages,
+            config=config,
+        )
+
+        if tools:
+            return response
+        else:
+            return response.text
+
+    def _raw_gen_stream(
+        self,
+        baseself,
+        model,
+        messages,
+        stream=True,
+        tools=None,
+        formatting="openai",
+        response_schema=None,
+        **kwargs,
+    ):
+        """Generate content using Google AI API with streaming."""
+        client = genai.Client(api_key=self.api_key)
+        if formatting == "openai":
+            messages = self._clean_messages_google(messages)
+        config = types.GenerateContentConfig()
+        if messages[0].role == "system":
+            config.system_instruction = messages[0].parts[0].text
+            messages = messages[1:]
+
+        if tools:
+            cleaned_tools = self._clean_tools_format(tools)
+            config.tools = cleaned_tools
+
+        # Add response schema for structured output if provided
+        if response_schema:
+            config.response_schema = response_schema
+            config.response_mime_type = "application/json"
+
+        # Check if we have both tools and file attachments
+        has_attachments = False
+        for message in messages:
+            for part in message.parts:
+                if hasattr(part, "file_data") and part.file_data is not None:
+                    has_attachments = True
+                    break
+            if has_attachments:
+                break
+
+        logging.info(
+            f"GoogleLLM: Starting stream generation. Model: {model}, Messages: {json.dumps(messages, default=str)}, Has attachments: {has_attachments}"
+        )
+
+        response = client.models.generate_content_stream(
+            model=model,
+            contents=messages,
+            config=config,
+        )
+
+        try:
+            for chunk in response:
+                if hasattr(chunk, "candidates") and chunk.candidates:
+                    for candidate in chunk.candidates:
+                        if candidate.content and candidate.content.parts:
+                            for part in candidate.content.parts:
+                                if part.function_call:
+                                    yield part
+                                elif part.text:
+                                    yield part.text
+                elif hasattr(chunk, "text"):
+                    yield chunk.text
+        finally:
+            if hasattr(response, 'close'):
+                response.close()
+
+    def _supports_tools(self):
+        """Return whether this LLM supports function calling."""
+        return True
+
+    def _supports_structured_output(self):
+        """Return whether this LLM supports structured JSON output."""
+        return True
+
+    def prepare_structured_output_format(self, json_schema):
+        """Convert JSON schema to Google AI structured output format."""
+        if not json_schema:
+            return None
+
+        type_map = {
+            "object": "OBJECT",
+            "array": "ARRAY",
+            "string": "STRING",
+            "integer": "INTEGER",
+            "number": "NUMBER",
+            "boolean": "BOOLEAN",
+        }
+
+        def convert(schema):
+            if not isinstance(schema, dict):
+                return schema
+
+            result = {}
+            schema_type = schema.get("type")
+            if schema_type:
+                result["type"] = type_map.get(schema_type.lower(), schema_type.upper())
+
+            for key in [
+                "description",
+                "nullable",
+                "enum",
+                "minItems",
+                "maxItems",
+                "required",
+                "propertyOrdering",
+            ]:
+                if key in schema:
+                    result[key] = schema[key]
+
+            if "format" in schema:
+                format_value = schema["format"]
+                if schema_type == "string":
+                    if format_value == "date":
+                        result["format"] = "date-time"
+                    elif format_value in ["enum", "date-time"]:
+                        result["format"] = format_value
+                else:
+                    result["format"] = format_value
+
+            if "properties" in schema:
+                result["properties"] = {
+                    k: convert(v) for k, v in schema["properties"].items()
+                }
+                if "propertyOrdering" not in result and result.get("type") == "OBJECT":
+                    result["propertyOrdering"] = list(result["properties"].keys())
+
+            if "items" in schema:
+                result["items"] = convert(schema["items"])
+
+            for field in ["anyOf", "oneOf", "allOf"]:
+                if field in schema:
+                    result[field] = [convert(s) for s in schema[field]]
+
+            return result
+
+        try:
+            return convert(json_schema)
+        except Exception as e:
+            logging.error(
+                f"Error preparing structured output format for Google: {e}",
+                exc_info=True,
+            )
+            return None

application/llm/groq.py

@@ -1,45 +1,32 @@
 from application.llm.base import BaseLLM
-
+from openai import OpenAI
 
 
 class GroqLLM(BaseLLM):
-
     def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
-        from openai import OpenAI
-
         super().__init__(*args, **kwargs)
         self.client = OpenAI(api_key=api_key, base_url="https://api.groq.com/openai/v1")
         self.api_key = api_key
         self.user_api_key = user_api_key
 
-    def _raw_gen(
-        self,
-        baseself,
-        model,
-        messages,
-        stream=False,
-        **kwargs
-    ):  
-        response = self.client.chat.completions.create(
-            model=model, messages=messages, stream=stream, **kwargs
-        )
-
-        return response.choices[0].message.content
+    def _raw_gen(self, baseself, model, messages, stream=False, tools=None, **kwargs):
+        if tools:
+            response = self.client.chat.completions.create(
+                model=model, messages=messages, stream=stream, tools=tools, **kwargs
+            )
+            return response.choices[0]
+        else:
+            response = self.client.chat.completions.create(
+                model=model, messages=messages, stream=stream, **kwargs
+            )
+            return response.choices[0].message.content
 
     def _raw_gen_stream(
-        self,
-        baseself,
-        model,
-        messages,
-        stream=True,
-        **kwargs
-    ):  
+        self, baseself, model, messages, stream=True, tools=None, **kwargs
+    ):
         response = self.client.chat.completions.create(
             model=model, messages=messages, stream=stream, **kwargs
         )
-
         for line in response:
-            # import sys
-            # print(line.choices[0].delta.content, file=sys.stderr)
             if line.choices[0].delta.content is not None:
                 yield line.choices[0].delta.content

application/llm/handlers/base.py

@@ -0,0 +1,351 @@
+import logging
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Any, Dict, Generator, List, Optional, Union
+
+from application.logging import build_stack_data
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ToolCall:
+    """Represents a tool/function call from the LLM."""
+
+    id: str
+    name: str
+    arguments: Union[str, Dict]
+    index: Optional[int] = None
+
+    @classmethod
+    def from_dict(cls, data: Dict) -> "ToolCall":
+        """Create ToolCall from dictionary."""
+        return cls(
+            id=data.get("id", ""),
+            name=data.get("name", ""),
+            arguments=data.get("arguments", {}),
+            index=data.get("index"),
+        )
+
+
+@dataclass
+class LLMResponse:
+    """Represents a response from the LLM."""
+
+    content: str
+    tool_calls: List[ToolCall]
+    finish_reason: str
+    raw_response: Any
+
+    @property
+    def requires_tool_call(self) -> bool:
+        """Check if the response requires tool calls."""
+        return bool(self.tool_calls) and self.finish_reason == "tool_calls"
+
+
+class LLMHandler(ABC):
+    """Abstract base class for LLM handlers."""
+
+    def __init__(self):
+        self.llm_calls = []
+        self.tool_calls = []
+
+    @abstractmethod
+    def parse_response(self, response: Any) -> LLMResponse:
+        """Parse raw LLM response into standardized format."""
+        pass
+
+    @abstractmethod
+    def create_tool_message(self, tool_call: ToolCall, result: Any) -> Dict:
+        """Create a tool result message for the conversation history."""
+        pass
+
+    @abstractmethod
+    def _iterate_stream(self, response: Any) -> Generator:
+        """Iterate through streaming response chunks."""
+        pass
+
+    def process_message_flow(
+        self,
+        agent,
+        initial_response,
+        tools_dict: Dict,
+        messages: List[Dict],
+        attachments: Optional[List] = None,
+        stream: bool = False,
+    ) -> Union[str, Generator]:
+        """
+        Main orchestration method for processing LLM message flow.
+
+        Args:
+            agent: The agent instance
+            initial_response: Initial LLM response
+            tools_dict: Dictionary of available tools
+            messages: Conversation history
+            attachments: Optional attachments
+            stream: Whether to use streaming
+
+        Returns:
+            Final response or generator for streaming
+        """
+        messages = self.prepare_messages(agent, messages, attachments)
+
+        if stream:
+            return self.handle_streaming(agent, initial_response, tools_dict, messages)
+        else:
+            return self.handle_non_streaming(
+                agent, initial_response, tools_dict, messages
+            )
+
+    def prepare_messages(
+        self, agent, messages: List[Dict], attachments: Optional[List] = None
+    ) -> List[Dict]:
+        """
+        Prepare messages with attachments and provider-specific formatting.
+
+        Args:
+            agent: The agent instance
+            messages: Original messages
+            attachments: List of attachments
+
+        Returns:
+            Prepared messages list
+        """
+        if not attachments:
+            return messages
+        logger.info(f"Preparing messages with {len(attachments)} attachments")
+        supported_types = agent.llm.get_supported_attachment_types()
+
+        supported_attachments = [
+            a for a in attachments if a.get("mime_type") in supported_types
+        ]
+        unsupported_attachments = [
+            a for a in attachments if a.get("mime_type") not in supported_types
+        ]
+
+        # Process supported attachments with the LLM's custom method
+
+        if supported_attachments:
+            logger.info(
+                f"Processing {len(supported_attachments)} supported attachments"
+            )
+            messages = agent.llm.prepare_messages_with_attachments(
+                messages, supported_attachments
+            )
+        # Process unsupported attachments with default method
+
+        if unsupported_attachments:
+            logger.info(
+                f"Processing {len(unsupported_attachments)} unsupported attachments"
+            )
+            messages = self._append_unsupported_attachments(
+                messages, unsupported_attachments
+            )
+        return messages
+
+    def _append_unsupported_attachments(
+        self, messages: List[Dict], attachments: List[Dict]
+    ) -> List[Dict]:
+        """
+        Default method to append unsupported attachment content to system prompt.
+
+        Args:
+            messages: Current messages
+            attachments: List of unsupported attachments
+
+        Returns:
+            Updated messages list
+        """
+        prepared_messages = messages.copy()
+        attachment_texts = []
+
+        for attachment in attachments:
+            logger.info(f"Adding attachment {attachment.get('id')} to context")
+            if "content" in attachment:
+                attachment_texts.append(
+                    f"Attached file content:\n\n{attachment['content']}"
+                )
+        if attachment_texts:
+            combined_text = "\n\n".join(attachment_texts)
+
+            system_msg = next(
+                (msg for msg in prepared_messages if msg.get("role") == "system"),
+                {"role": "system", "content": ""},
+            )
+
+            if system_msg not in prepared_messages:
+                prepared_messages.insert(0, system_msg)
+            system_msg["content"] += f"\n\n{combined_text}"
+        return prepared_messages
+
+    def handle_tool_calls(
+        self, agent, tool_calls: List[ToolCall], tools_dict: Dict, messages: List[Dict]
+    ) -> Generator:
+        """
+        Execute tool calls and update conversation history.
+
+        Args:
+            agent: The agent instance
+            tool_calls: List of tool calls to execute
+            tools_dict: Available tools dictionary
+            messages: Current conversation history
+
+        Returns:
+            Updated messages list
+        """
+        updated_messages = messages.copy()
+
+        for call in tool_calls:
+            try:
+                self.tool_calls.append(call)
+                tool_executor_gen = agent._execute_tool_action(tools_dict, call)
+                while True:
+                    try:
+                        yield next(tool_executor_gen)
+                    except StopIteration as e:
+                        tool_response, call_id = e.value
+                        break
+                updated_messages.append(
+                    {
+                        "role": "assistant",
+                        "content": [
+                            {
+                                "function_call": {
+                                    "name": call.name,
+                                    "args": call.arguments,
+                                    "call_id": call_id,
+                                }
+                            }
+                        ],
+                    }
+                )
+
+                updated_messages.append(self.create_tool_message(call, tool_response))
+            except Exception as e:
+                logger.error(f"Error executing tool: {str(e)}", exc_info=True)
+                error_call = ToolCall(
+                    id=call.id, name=call.name, arguments=call.arguments
+                )
+                error_response = f"Error executing tool: {str(e)}"
+                error_message = self.create_tool_message(error_call, error_response)
+                updated_messages.append(error_message)
+
+                call_parts = call.name.split("_")
+                if len(call_parts) >= 2:
+                    tool_id = call_parts[-1]  # Last part is tool ID (e.g., "1")
+                    action_name = "_".join(call_parts[:-1])
+                    tool_name = tools_dict.get(tool_id, {}).get("name", "unknown_tool")
+                    full_action_name = f"{action_name}_{tool_id}"
+                else:
+                    tool_name = "unknown_tool"
+                    action_name = call.name
+                    full_action_name = call.name
+                yield {
+                    "type": "tool_call",
+                    "data": {
+                        "tool_name": tool_name,
+                        "call_id": call.id,
+                        "action_name": full_action_name,
+                        "arguments": call.arguments,
+                        "error": error_response,
+                        "status": "error",
+                    },
+                }
+        return updated_messages
+
+    def handle_non_streaming(
+        self, agent, response: Any, tools_dict: Dict, messages: List[Dict]
+    ) -> Generator:
+        """
+        Handle non-streaming response flow.
+
+        Args:
+            agent: The agent instance
+            response: Current LLM response
+            tools_dict: Available tools dictionary
+            messages: Conversation history
+
+        Returns:
+            Final response after processing all tool calls
+        """
+        parsed = self.parse_response(response)
+        self.llm_calls.append(build_stack_data(agent.llm))
+
+        while parsed.requires_tool_call:
+            tool_handler_gen = self.handle_tool_calls(
+                agent, parsed.tool_calls, tools_dict, messages
+            )
+            while True:
+                try:
+                    yield next(tool_handler_gen)
+                except StopIteration as e:
+                    messages = e.value
+                    break
+            response = agent.llm.gen(
+                model=agent.gpt_model, messages=messages, tools=agent.tools
+            )
+            parsed = self.parse_response(response)
+            self.llm_calls.append(build_stack_data(agent.llm))
+        return parsed.content
+
+    def handle_streaming(
+        self, agent, response: Any, tools_dict: Dict, messages: List[Dict]
+    ) -> Generator:
+        """
+        Handle streaming response flow.
+
+        Args:
+            agent: The agent instance
+            response: Current LLM response
+            tools_dict: Available tools dictionary
+            messages: Conversation history
+
+        Yields:
+            Streaming response chunks
+        """
+        buffer = ""
+        tool_calls = {}
+
+        for chunk in self._iterate_stream(response):
+            if isinstance(chunk, str):
+                yield chunk
+                continue
+            parsed = self.parse_response(chunk)
+
+            if parsed.tool_calls:
+                for call in parsed.tool_calls:
+                    if call.index not in tool_calls:
+                        tool_calls[call.index] = call
+                    else:
+                        existing = tool_calls[call.index]
+                        if call.id:
+                            existing.id = call.id
+                        if call.name:
+                            existing.name = call.name
+                        if call.arguments:
+                            existing.arguments += call.arguments
+            if parsed.finish_reason == "tool_calls":
+                tool_handler_gen = self.handle_tool_calls(
+                    agent, list(tool_calls.values()), tools_dict, messages
+                )
+                while True:
+                    try:
+                        yield next(tool_handler_gen)
+                    except StopIteration as e:
+                        messages = e.value
+                        break
+                tool_calls = {}
+
+                response = agent.llm.gen_stream(
+                    model=agent.gpt_model, messages=messages, tools=agent.tools
+                )
+                self.llm_calls.append(build_stack_data(agent.llm))
+
+                yield from self.handle_streaming(agent, response, tools_dict, messages)
+                return
+            if parsed.content:
+                buffer += parsed.content
+                yield buffer
+                buffer = ""
+            if parsed.finish_reason == "stop":
+                return

application/llm/handlers/google.py

@@ -0,0 +1,78 @@
+import uuid
+from typing import Any, Dict, Generator
+
+from application.llm.handlers.base import LLMHandler, LLMResponse, ToolCall
+
+
+class GoogleLLMHandler(LLMHandler):
+    """Handler for Google's GenAI API."""
+
+    def parse_response(self, response: Any) -> LLMResponse:
+        """Parse Google response into standardized format."""
+
+        if isinstance(response, str):
+            return LLMResponse(
+                content=response,
+                tool_calls=[],
+                finish_reason="stop",
+                raw_response=response,
+            )
+        if hasattr(response, "candidates"):
+            parts = response.candidates[0].content.parts if response.candidates else []
+            tool_calls = [
+                ToolCall(
+                    id=str(uuid.uuid4()),
+                    name=part.function_call.name,
+                    arguments=part.function_call.args,
+                )
+                for part in parts
+                if hasattr(part, "function_call") and part.function_call is not None
+            ]
+
+            content = " ".join(
+                part.text
+                for part in parts
+                if hasattr(part, "text") and part.text is not None
+            )
+            return LLMResponse(
+                content=content,
+                tool_calls=tool_calls,
+                finish_reason="tool_calls" if tool_calls else "stop",
+                raw_response=response,
+            )
+        else:
+            tool_calls = []
+            if hasattr(response, "function_call"):
+                tool_calls.append(
+                    ToolCall(
+                        id=str(uuid.uuid4()),
+                        name=response.function_call.name,
+                        arguments=response.function_call.args,
+                    )
+                )
+            return LLMResponse(
+                content=response.text if hasattr(response, "text") else "",
+                tool_calls=tool_calls,
+                finish_reason="tool_calls" if tool_calls else "stop",
+                raw_response=response,
+            )
+
+    def create_tool_message(self, tool_call: ToolCall, result: Any) -> Dict:
+        """Create Google-style tool message."""
+
+        return {
+            "role": "model",
+            "content": [
+                {
+                    "function_response": {
+                        "name": tool_call.name,
+                        "response": {"result": result},
+                    }
+                }
+            ],
+        }
+
+    def _iterate_stream(self, response: Any) -> Generator:
+        """Iterate through Google streaming response."""
+        for chunk in response:
+            yield chunk

application/llm/handlers/handler_creator.py

@@ -0,0 +1,18 @@
+from application.llm.handlers.base import LLMHandler
+from application.llm.handlers.google import GoogleLLMHandler
+from application.llm.handlers.openai import OpenAILLMHandler
+
+
+class LLMHandlerCreator:
+    handlers = {
+        "openai": OpenAILLMHandler,
+        "google": GoogleLLMHandler,
+        "default": OpenAILLMHandler,
+    }
+
+    @classmethod
+    def create_handler(cls, llm_type: str, *args, **kwargs) -> LLMHandler:
+        handler_class = cls.handlers.get(llm_type.lower())
+        if not handler_class:
+            handler_class = OpenAILLMHandler
+        return handler_class(*args, **kwargs)

application/llm/handlers/openai.py

@@ -0,0 +1,57 @@
+from typing import Any, Dict, Generator
+
+from application.llm.handlers.base import LLMHandler, LLMResponse, ToolCall
+
+
+class OpenAILLMHandler(LLMHandler):
+    """Handler for OpenAI API."""
+
+    def parse_response(self, response: Any) -> LLMResponse:
+        """Parse OpenAI response into standardized format."""
+        if isinstance(response, str):
+            return LLMResponse(
+                content=response,
+                tool_calls=[],
+                finish_reason="stop",
+                raw_response=response,
+            )
+
+        message = getattr(response, "message", None) or getattr(response, "delta", None)
+
+        tool_calls = []
+        if hasattr(message, "tool_calls"):
+            tool_calls = [
+                ToolCall(
+                    id=getattr(tc, "id", ""),
+                    name=getattr(tc.function, "name", ""),
+                    arguments=getattr(tc.function, "arguments", ""),
+                    index=getattr(tc, "index", None),
+                )
+                for tc in message.tool_calls or []
+            ]
+        return LLMResponse(
+            content=getattr(message, "content", ""),
+            tool_calls=tool_calls,
+            finish_reason=getattr(response, "finish_reason", ""),
+            raw_response=response,
+        )
+
+    def create_tool_message(self, tool_call: ToolCall, result: Any) -> Dict:
+        """Create OpenAI-style tool message."""
+        return {
+            "role": "tool",
+            "content": [
+                {
+                    "function_response": {
+                        "name": tool_call.name,
+                        "response": {"result": result},
+                        "call_id": tool_call.id,
+                    }
+                }
+            ],
+        }
+
+    def _iterate_stream(self, response: Any) -> Generator:
+        """Iterate through OpenAI streaming response."""
+        for chunk in response:
+            yield chunk

application/llm/llama_cpp.py

@@ -2,6 +2,7 @@ from application.llm.base import BaseLLM
 from application.core.settings import settings
 import threading
 
+
 class LlamaSingleton:
     _instances = {}
     _lock = threading.Lock()  # Add a lock for thread synchronization
@@ -29,7 +30,7 @@ class LlamaCpp(BaseLLM):
         self,
         api_key=None,
         user_api_key=None,
-        llm_name=settings.MODEL_PATH,
+        llm_name=settings.LLM_PATH,
         *args,
         **kwargs,
     ):
@@ -42,14 +43,18 @@ class LlamaCpp(BaseLLM):
         context = messages[0]["content"]
         user_question = messages[-1]["content"]
         prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
-        result = LlamaSingleton.query_model(self.llama, prompt, max_tokens=150, echo=False)
+        result = LlamaSingleton.query_model(
+            self.llama, prompt, max_tokens=150, echo=False
+        )
         return result["choices"][0]["text"].split("### Answer \n")[-1]
 
     def _raw_gen_stream(self, baseself, model, messages, stream=True, **kwargs):
         context = messages[0]["content"]
         user_question = messages[-1]["content"]
         prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
-        result = LlamaSingleton.query_model(self.llama, prompt, max_tokens=150, echo=False, stream=stream)
+        result = LlamaSingleton.query_model(
+            self.llama, prompt, max_tokens=150, echo=False, stream=stream
+        )
         for item in result:
             for choice in item["choices"]:
-                yield choice["text"]
+                yield choice["text"]

application/llm/llm_creator.py

@@ -6,6 +6,8 @@ from application.llm.llama_cpp import LlamaCpp
 from application.llm.anthropic import AnthropicLLM
 from application.llm.docsgpt_provider import DocsGPTAPILLM
 from application.llm.premai import PremAILLM
+from application.llm.google_ai import GoogleLLM
+from application.llm.novita import NovitaLLM
 
 
 class LLMCreator:
@@ -18,12 +20,16 @@ class LLMCreator:
         "anthropic": AnthropicLLM,
         "docsgpt": DocsGPTAPILLM,
         "premai": PremAILLM,
-        "groq": GroqLLM
+        "groq": GroqLLM,
+        "google": GoogleLLM,
+        "novita": NovitaLLM,
     }
 
     @classmethod
-    def create_llm(cls, type, api_key, user_api_key, *args, **kwargs):
+    def create_llm(cls, type, api_key, user_api_key, decoded_token, *args, **kwargs):
         llm_class = cls.llms.get(type.lower())
         if not llm_class:
             raise ValueError(f"No LLM class found for type {type}")
-        return llm_class(api_key, user_api_key, *args, **kwargs)
+        return llm_class(
+            api_key, user_api_key, decoded_token=decoded_token, *args, **kwargs
+        )