build(deps): bump packaging from 24.2 to 25.0 in /application

Bumps [packaging](https://github.com/pypa/packaging) from 24.2 to 25.0.
- [Release notes](https://github.com/pypa/packaging/releases)
- [Changelog](https://github.com/pypa/packaging/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pypa/packaging/compare/24.2...25.0)

---
updated-dependencies:
- dependency-name: packaging
  dependency-version: '25.0'
  dependency-type: direct:production
  update-type: version-update:semver-major
...

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

.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

.gitignore

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

CONTRIBUTING.md

@@ -35,18 +35,40 @@ Tech Stack Overview:
 
 - 🖥 Backend: Developed in Python 🐍
 
-### 🌐 If you are looking to contribute to frontend (⚛️React, Vite):
+### 🌐 Frontend Contributions (⚛️ React, Vite)
 
+*   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.
 
-- 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.
-
-### 🖥 If you are looking to contribute to Backend (🐍 Python):
+### 🖥 Backend Contributions (🐍 Python)
 
 - 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
 

README.md

@@ -15,14 +15,14 @@
   <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://docs.docsgpt.cloud/quickstart">⚡️ Quickstart</a> • <a href="https://app.docsgpt.cloud/">☁️ Cloud Version</a> • <a href="https://discord.gg/n5BX8dh8rU">💬 Discord</a>
   <br>
-
-  [☁️ Cloud Version](https://app.docsgpt.cloud/) • [💬 Discord](https://discord.gg/n5BX8dh8rU) • [📖 Guides](https://docs.docsgpt.cloud/)
+  <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>
-  [👫 Contribute](https://github.com/arc53/DocsGPT/blob/main/CONTRIBUTING.md) • [🏠 Self-host](https://docs.docsgpt.cloud/Guides/How-to-use-different-LLM) • [⚡️ Quickstart](https://github.com/arc53/DocsGPT#quickstart) 
 
 </div>
 <div align="center">
@@ -35,6 +35,7 @@
     <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>
@@ -45,12 +46,15 @@
 
 - [x] Full GoogleAI compatibility (Jan 2025)
 - [x] Add tools (Jan 2025)
-- [ ] Anthropic Tool compatibility
-- [ ] Add triggerable actions / tools (webhook)
+- [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)
+- [ ] Anthropic Tool compatibility (May 2025)
 - [ ] Add OAuth 2.0 authentication for tools and sources
-- [ ] Manually updating chunks in the app UI
-- [ ] Devcontainer for easy development
-- [ ] Chatbots menu re-design to handle tools, scheduling, and more
+- [ ] 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!
 
@@ -62,51 +66,53 @@ We're eager to provide personalized assistance when deploying your DocsGPT to a
 
 [Send Email :email:](mailto:support@docsgpt.cloud?subject=DocsGPT%20support%2Fsolutions)
 
+## Join the Lighthouse Program 🌟
+
+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.  
+
+[Learn More & Apply →](https://docs.google.com/forms/d/1KAADiJinUJ8EMQyfTXUIGyFbqINNClNR3jBNWq7DgTE)
+
 
 ## QuickStart
 
 > [!Note]
 > Make sure you have [Docker](https://docs.docker.com/engine/install/) installed
 
+A more detailed [Quickstart](https://docs.docsgpt.cloud/quickstart) is available in our documentation
 
-1. Clone the repository and run the following command:
-  ```bash
-  git clone https://github.com/arc53/DocsGPT.git
-  cd DocsGPT
-  ```
-
-On Mac OS or Linux, write:
-
-
-2. Run the following command:
-  ```bash
-  ./setup.sh
-  ```
-
-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:
-
-On windows:
-
-2. Create a `.env` file in your root directory and set the env variables.
-   It should look like this inside:
-
-   ```
-   LLM_NAME=[docsgpt or openai or others] 
-   API_KEY=[if LLM_NAME is openai]
-   ```
-
-   See optional environment variables in the [/application/.env_sample](https://github.com/arc53/DocsGPT/blob/main/application/.env_sample) file.
-
-3. Run the following command:
+1. **Clone the repository:**
 
    ```bash
-   docker compose up --build
+   git clone https://github.com/arc53/DocsGPT.git
+   cd DocsGPT
    ```
-4. Navigate to http://localhost:5173/.
 
-To stop, just run `Ctrl + C`.
+**For macOS and Linux:**
+
+2. **Run the setup script:**
+
+   ```bash
+   ./setup.sh
+   ```
+
+**For Windows:**
+
+2. **Run the PowerShell setup script:**
+
+   ```powershell
+   PowerShell -ExecutionPolicy Bypass -File .\setup.ps1
+   ```
+
+Either script will guide you through setting up DocsGPT. Four options available: using the public API, running locally, connecting to a local inference engine, or using a cloud API provider.  Scripts will automatically configure your `.env` file and handle necessary downloads and installations based on your chosen option.
+
+**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
+```
+(or use the specific `docker compose down` command shown after running the setup script).
 
 > [!Note]
 > For development environment setup instructions, please refer to the [Development Environment Guide](https://docs.docsgpt.cloud/Deploying/Development-Environment).

application/Dockerfile

@@ -6,7 +6,6 @@ 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 python3.12 python3.12-venv && \
     rm -rf /var/lib/apt/lists/* 
@@ -20,7 +19,7 @@ RUN if [ -f /usr/bin/python3.12 ]; then \
 
 # 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
@@ -49,7 +48,6 @@ 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 python3.12 && \
     ln -s /usr/bin/python3.12 /usr/bin/python && \
     rm -rf /var/lib/apt/lists/*
@@ -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 +1,277 @@
+import uuid
+from abc import ABC, abstractmethod
+from typing import Dict, Generator, List, Optional
+
+from application.agents.llm_handler import get_llm_handler
+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.llm.llm_creator import LLMCreator
+from application.logging import build_stack_data, log_activity, LogContext
+from application.retriever.base import BaseRetriever
+from application.core.settings import settings
+from bson.objectid import ObjectId
+
+
+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,
+    ):
+        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 = 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 = get_llm_handler(llm_name)
+        self.attachments = attachments or []
+
+    @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)
+        tools_by_id = {str(tool["_id"]): tool for tool in user_tools}
+        return tools_by_id
+
+    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)
+
+        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={})
+        tool = tm.load_tool(
+            tool_data["name"],
+            tool_config=(
+                {
+                    "url": tool_data["config"]["actions"][action_name]["url"],
+                    "method": tool_data["config"]["actions"][action_name]["method"],
+                    "headers": headers,
+                    "query_params": query_params,
+                }
+                if tool_data["name"] == "api_tool"
+                else tool_data["config"]
+            ),
+        )
+        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)
+        call_id = getattr(call, "id", None)
+
+        tool_call_data = {
+            "tool_name": tool_data["name"],
+            "call_id": call_id if call_id is not None else "None",
+            "action_name": f"{action_name}_{tool_id}",
+            "arguments": call_args,
+            "result": result,
+        }
+        self.tool_calls.append(tool_call_data)
+
+        return result, call_id
+
+    def _build_messages(
+        self,
+        system_prompt: str,
+        query: str,
+        retrieved_data: List[Dict],
+    ) -> List[Dict]:
+        docs_together = "\n".join([doc["text"] for doc in retrieved_data])
+        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):
+        resp = self.llm.gen_stream(
+            model=self.gpt_model, messages=messages, tools=self.tools
+        )
+        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.handle_response(
+            self, resp, tools_dict, messages, attachments
+        )
+        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

application/agents/classic_agent.py

@@ -0,0 +1,64 @@
+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):
+    def _gen_inner(
+        self, query: str, retriever: BaseRetriever, log_context: LogContext
+    ) -> Generator[Dict, None, None]:
+        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)
+
+        messages = self._build_messages(self.prompt, query, retrieved_data)
+
+        resp = self._llm_gen(messages, log_context)
+
+        attachments = self.attachments
+
+        if isinstance(resp, str):
+            yield {"answer": resp}
+            return
+        if (
+            hasattr(resp, "message")
+            and hasattr(resp.message, "content")
+            and resp.message.content is not None
+        ):
+            yield {"answer": resp.message.content}
+            return
+
+        resp = self._llm_handler(resp, tools_dict, messages, log_context, attachments)
+
+        if isinstance(resp, str):
+            yield {"answer": resp}
+        elif (
+            hasattr(resp, "message")
+            and hasattr(resp.message, "content")
+            and resp.message.content is not None
+        ):
+            yield {"answer": resp.message.content}
+        else:
+            for line in resp:
+                if isinstance(line, str):
+                    yield {"answer": line}
+
+        log_context.stacks.append(
+            {"component": "agent", "data": {"tool_calls": self.tool_calls.copy()}}
+        )
+
+        yield {"sources": retrieved_data}
+        # clean tool_call_data only send first 50 characters of tool_call['result']
+        for tool_call in self.tool_calls:
+            if len(str(tool_call["result"])) > 50:
+                tool_call["result"] = str(tool_call["result"])[:50] + "..."
+        yield {"tool_calls": self.tool_calls.copy()}

application/agents/llm_handler.py

@@ -0,0 +1,351 @@
+import json
+import logging
+from abc import ABC, abstractmethod
+
+from application.logging import build_stack_data
+
+logger = logging.getLogger(__name__)
+
+
+class LLMHandler(ABC):
+    def __init__(self):
+        self.llm_calls = []
+        self.tool_calls = []
+
+    @abstractmethod
+    def handle_response(self, agent, resp, tools_dict, messages, attachments=None, **kwargs):
+        pass
+
+    def prepare_messages_with_attachments(self, agent, messages, attachments=None):
+        """
+        Prepare messages with attachment content if available.
+
+        Args:
+            agent: The current agent instance.
+            messages (list): List of message dictionaries.
+            attachments (list): List of attachment dictionaries with content.
+
+        Returns:
+            list: Messages with attachment context added to the system prompt.
+        """
+        if not attachments:
+            return messages
+
+        logger.info(f"Preparing messages with {len(attachments)} attachments")
+
+        supported_types = agent.llm.get_supported_attachment_types()
+
+        supported_attachments = []
+        unsupported_attachments = []
+
+        for attachment in attachments:
+            mime_type = attachment.get('mime_type')
+            if mime_type in supported_types:
+                supported_attachments.append(attachment)
+            else:
+                unsupported_attachments.append(attachment)
+
+        # Process supported attachments with the LLM's custom method
+        prepared_messages = messages
+        if supported_attachments:
+            logger.info(f"Processing {len(supported_attachments)} supported attachments with {agent.llm.__class__.__name__}'s method")
+            prepared_messages = agent.llm.prepare_messages_with_attachments(messages, supported_attachments)
+
+        # Process unsupported attachments with the default method
+        if unsupported_attachments:
+            logger.info(f"Processing {len(unsupported_attachments)} unsupported attachments with default method")
+            prepared_messages = self._append_attachment_content_to_system(prepared_messages, unsupported_attachments)
+
+        return prepared_messages
+
+    def _append_attachment_content_to_system(self, messages, attachments):
+        """
+        Default method to append attachment content to the system prompt.
+
+        Args:
+            messages (list): List of message dictionaries.
+            attachments (list): List of attachment dictionaries with content.
+
+        Returns:
+            list: Messages with attachment context added to the system prompt.
+        """
+        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_attachment_text = "\n\n".join(attachment_texts)
+
+            system_found = False
+            for i in range(len(prepared_messages)):
+                if prepared_messages[i].get("role") == "system":
+                    prepared_messages[i]["content"] += f"\n\n{combined_attachment_text}"
+                    system_found = True
+                    break
+
+            if not system_found:
+                prepared_messages.insert(0, {"role": "system", "content": combined_attachment_text})
+
+        return prepared_messages
+
+class OpenAILLMHandler(LLMHandler):
+    def handle_response(self, agent, resp, tools_dict, messages, attachments=None, stream: bool = True):
+
+        messages = self.prepare_messages_with_attachments(agent, messages, attachments)
+        logger.info(f"Messages with attachments: {messages}")
+        if not stream:
+            while hasattr(resp, "finish_reason") and resp.finish_reason == "tool_calls":
+                message = json.loads(resp.model_dump_json())["message"]
+                keys_to_remove = {"audio", "function_call", "refusal"}
+                filtered_data = {
+                    k: v for k, v in message.items() if k not in keys_to_remove
+                }
+                messages.append(filtered_data)
+
+                tool_calls = resp.message.tool_calls
+                for call in tool_calls:
+                    try:
+                        self.tool_calls.append(call)
+                        tool_response, call_id = agent._execute_tool_action(
+                            tools_dict, call
+                        )
+                        function_call_dict = {
+                            "function_call": {
+                                "name": call.function.name,
+                                "args": call.function.arguments,
+                                "call_id": call_id,
+                            }
+                        }
+                        function_response_dict = {
+                            "function_response": {
+                                "name": call.function.name,
+                                "response": {"result": tool_response},
+                                "call_id": call_id,
+                            }
+                        }
+
+                        messages.append(
+                            {"role": "assistant", "content": [function_call_dict]}
+                        )
+                        messages.append(
+                            {"role": "tool", "content": [function_response_dict]}
+                        )
+
+                        messages = self.prepare_messages_with_attachments(agent, messages, attachments)
+                    except Exception as e:
+                        logging.error(f"Error executing tool: {str(e)}", exc_info=True)
+                        messages.append(
+                            {
+                                "role": "tool",
+                                "content": f"Error executing tool: {str(e)}",
+                                "tool_call_id": call_id,
+                            }
+                        )
+                resp = agent.llm.gen_stream(
+                    model=agent.gpt_model, messages=messages, tools=agent.tools
+                )
+                self.llm_calls.append(build_stack_data(agent.llm))
+            return resp
+
+        else:
+            text_buffer = ""
+            while True:
+                tool_calls = {}
+                for chunk in resp:
+                    if isinstance(chunk, str) and len(chunk) > 0:
+                        yield chunk
+                        continue
+                    elif hasattr(chunk, "delta"):
+                        chunk_delta = chunk.delta
+
+                        if (
+                            hasattr(chunk_delta, "tool_calls")
+                            and chunk_delta.tool_calls is not None
+                        ):
+                            for tool_call in chunk_delta.tool_calls:
+                                index = tool_call.index
+                                if index not in tool_calls:
+                                    tool_calls[index] = {
+                                        "id": "",
+                                        "function": {"name": "", "arguments": ""},
+                                    }
+
+                                current = tool_calls[index]
+                                if tool_call.id:
+                                    current["id"] = tool_call.id
+                                if tool_call.function.name:
+                                    current["function"][
+                                        "name"
+                                    ] = tool_call.function.name
+                                if tool_call.function.arguments:
+                                    current["function"][
+                                        "arguments"
+                                    ] += tool_call.function.arguments
+                                tool_calls[index] = current
+
+                        if (
+                            hasattr(chunk, "finish_reason")
+                            and chunk.finish_reason == "tool_calls"
+                        ):
+                            for index in sorted(tool_calls.keys()):
+                                call = tool_calls[index]
+                                try:
+                                    self.tool_calls.append(call)
+                                    tool_response, call_id = agent._execute_tool_action(
+                                        tools_dict, call
+                                    )
+                                    if isinstance(call["function"]["arguments"], str):
+                                        call["function"]["arguments"] = json.loads(call["function"]["arguments"])
+
+                                    function_call_dict = {
+                                        "function_call": {
+                                            "name": call["function"]["name"],
+                                            "args": call["function"]["arguments"],
+                                            "call_id": call["id"],
+                                        }
+                                    }
+                                    function_response_dict = {
+                                        "function_response": {
+                                            "name": call["function"]["name"],
+                                            "response": {"result": tool_response},
+                                            "call_id": call["id"],
+                                        }
+                                    }
+
+                                    messages.append(
+                                        {
+                                            "role": "assistant",
+                                            "content": [function_call_dict],
+                                        }
+                                    )
+                                    messages.append(
+                                        {
+                                            "role": "tool",
+                                            "content": [function_response_dict],
+                                        }
+                                    )
+
+                                except Exception as e:
+                                    logging.error(f"Error executing tool: {str(e)}", exc_info=True)
+                                    messages.append(
+                                        {
+                                            "role": "assistant",
+                                            "content": f"Error executing tool: {str(e)}",
+                                        }
+                                    )
+                            tool_calls = {}
+                        if hasattr(chunk_delta, "content") and chunk_delta.content:
+                            # Add to buffer or yield immediately based on your preference
+                            text_buffer += chunk_delta.content
+                            yield text_buffer
+                            text_buffer = ""
+
+                        if (
+                            hasattr(chunk, "finish_reason")
+                            and chunk.finish_reason == "stop"
+                        ):
+                            return resp
+                    elif isinstance(chunk, str) and len(chunk) == 0:
+                            continue
+
+                logger.info(f"Regenerating with messages: {messages}")
+                resp = agent.llm.gen_stream(
+                    model=agent.gpt_model, messages=messages, tools=agent.tools
+                )
+                self.llm_calls.append(build_stack_data(agent.llm))
+
+
+class GoogleLLMHandler(LLMHandler):
+    def handle_response(self, agent, resp, tools_dict, messages, attachments=None, stream: bool = True):
+        from google.genai import types
+
+        messages = self.prepare_messages_with_attachments(agent, messages, attachments)
+
+        while True:
+            if not stream:
+                response = agent.llm.gen(
+                    model=agent.gpt_model, messages=messages, tools=agent.tools
+                )
+                self.llm_calls.append(build_stack_data(agent.llm))
+                if response.candidates and response.candidates[0].content.parts:
+                    tool_call_found = False
+                    for part in response.candidates[0].content.parts:
+                        if part.function_call:
+                            tool_call_found = True
+                            self.tool_calls.append(part.function_call)
+                            tool_response, call_id = agent._execute_tool_action(
+                                tools_dict, part.function_call
+                            )
+                            function_response_part = types.Part.from_function_response(
+                                name=part.function_call.name,
+                                response={"result": tool_response},
+                            )
+
+                            messages.append(
+                                {"role": "model", "content": [part.to_json_dict()]}
+                            )
+                            messages.append(
+                                {
+                                    "role": "tool",
+                                    "content": [function_response_part.to_json_dict()],
+                                }
+                            )
+
+                    if (
+                        not tool_call_found
+                        and response.candidates[0].content.parts
+                        and response.candidates[0].content.parts[0].text
+                    ):
+                        return response.candidates[0].content.parts[0].text
+                    elif not tool_call_found:
+                        return response.candidates[0].content.parts
+
+                else:
+                    return response
+
+            else:
+                response = agent.llm.gen_stream(
+                    model=agent.gpt_model, messages=messages, tools=agent.tools
+                )
+                self.llm_calls.append(build_stack_data(agent.llm))
+
+                tool_call_found = False
+                for result in response:
+                    if hasattr(result, "function_call"):
+                        tool_call_found = True
+                        self.tool_calls.append(result.function_call)
+                        tool_response, call_id = agent._execute_tool_action(
+                            tools_dict, result.function_call
+                        )
+                        function_response_part = types.Part.from_function_response(
+                            name=result.function_call.name,
+                            response={"result": tool_response},
+                        )
+
+                        messages.append(
+                            {"role": "model", "content": [result.to_json_dict()]}
+                        )
+                        messages.append(
+                            {
+                                "role": "tool",
+                                "content": [function_response_part.to_json_dict()],
+                            }
+                        )
+                    else:
+                        tool_call_found = False
+                        yield result
+
+                if not tool_call_found:
+                    return response
+
+
+def get_llm_handler(llm_type):
+    handlers = {
+        "openai": OpenAILLMHandler(),
+        "google": GoogleLLMHandler(),
+    }
+    return handlers.get(llm_type, OpenAILLMHandler())

application/agents/react_agent.py

@@ -0,0 +1,132 @@
+import os
+from typing import Dict, Generator, List
+
+from application.agents.base import BaseAgent
+from application.logging import build_stack_data, LogContext
+from application.retriever.base import BaseRetriever
+
+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 = f.read()
+with open(
+    os.path.join(current_dir, "application/prompts", "react_final_prompt.txt"),
+    "r",
+) as f:
+    final_prompt = f.read()
+
+
+class ReActAgent(BaseAgent):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.plan = ""
+        self.observations: List[str] = []
+
+    def _gen_inner(
+        self, query: str, retriever: BaseRetriever, log_context: LogContext
+    ) -> Generator[Dict, None, None]:
+        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])
+        plan = self._create_plan(query, docs_together, log_context)
+        for line in plan:
+            if isinstance(line, str):
+                self.plan += line
+                yield {"thought": line}
+
+        prompt = self.prompt + f"\nFollow this plan: {self.plan}"
+        messages = self._build_messages(prompt, query, retrieved_data)
+
+        resp = self._llm_gen(messages, log_context)
+
+        if isinstance(resp, str):
+            self.observations.append(resp)
+        if (
+            hasattr(resp, "message")
+            and hasattr(resp.message, "content")
+            and resp.message.content is not None
+        ):
+            self.observations.append(resp.message.content)
+
+        resp = self._llm_handler(resp, tools_dict, messages, log_context)
+
+        for tool_call in self.tool_calls:
+            observation = (
+                f"Action '{tool_call['action_name']}' of tool '{tool_call['tool_name']}' "
+                f"with arguments '{tool_call['arguments']}' returned: '{tool_call['result']}'"
+            )
+            self.observations.append(observation)
+
+        if isinstance(resp, str):
+            self.observations.append(resp)
+        elif (
+            hasattr(resp, "message")
+            and hasattr(resp.message, "content")
+            and resp.message.content is not None
+        ):
+            self.observations.append(resp.message.content)
+        else:
+            completion = self.llm.gen_stream(
+                model=self.gpt_model, messages=messages, tools=self.tools
+            )
+            for line in completion:
+                if isinstance(line, str):
+                    self.observations.append(line)
+
+        log_context.stacks.append(
+            {"component": "agent", "data": {"tool_calls": self.tool_calls.copy()}}
+        )
+
+        yield {"sources": retrieved_data}
+        # clean tool_call_data only send first 50 characters of tool_call['result']
+        for tool_call in self.tool_calls:
+            if len(str(tool_call["result"])) > 50:
+                tool_call["result"] = str(tool_call["result"])[:50] + "..."
+        yield {"tool_calls": self.tool_calls.copy()}
+
+        final_answer = self._create_final_answer(query, self.observations, log_context)
+        for line in final_answer:
+            if isinstance(line, str):
+                yield {"answer": line}
+
+    def _create_plan(
+        self, query: str, docs_data: str, log_context: LogContext = None
+    ) -> Generator[str, None, None]:
+        plan_prompt = planning_prompt.replace("{query}", query)
+        if "{summaries}" in planning_prompt:
+            summaries = docs_data
+            plan_prompt = plan_prompt.replace("{summaries}", summaries)
+
+        messages = [{"role": "user", "content": plan_prompt}]
+        print(self.tools)
+        plan = self.llm.gen_stream(
+            model=self.gpt_model, messages=messages, tools=self.tools
+        )
+        if log_context:
+            data = build_stack_data(self.llm)
+            log_context.stacks.append({"component": "planning_llm", "data": data})
+        return plan
+
+    def _create_final_answer(
+        self, query: str, observations: List[str], log_context: LogContext = None
+    ) -> str:
+        observation_string = "\n".join(observations)
+        final_answer_prompt = final_prompt.format(
+            query=query, observations=observation_string
+        )
+
+        messages = [{"role": "user", "content": final_answer_prompt}]
+        final_answer = self.llm.gen_stream(model=self.gpt_model, messages=messages)
+        if log_context:
+            data = build_stack_data(self.llm)
+            log_context.stacks.append({"component": "final_answer_llm", "data": data})
+        return final_answer

application/agents/tools/api_tool.py

@@ -1,7 +1,7 @@
 import json
 
 import requests
-from application.tools.base import Tool
+from application.agents.tools.base import Tool
 
 
 class APITool(Tool):
@@ -25,16 +25,34 @@ class APITool(Tool):
     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)
+        # 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()
-            try:
-                data = response.json()
-            except ValueError:
+            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,

application/agents/tools/brave.py

@@ -0,0 +1,217 @@
+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"
+        
+        # Build query parameters
+        params = {
+            "q": query,
+            "country": country,
+            "search_lang": search_lang,
+            "count": min(count, 20),
+            "offset": min(offset, 9),
+            "safesearch": safesearch
+        }
+        
+        # Add optional parameters only if they have values
+        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
+        
+        # Set up headers
+        headers = {
+            "Accept": "application/json",
+            "Accept-Encoding": "gzip",
+            "X-Subscription-Token": self.token
+        }
+        
+        # Make the request
+        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"
+        
+        # Build query parameters
+        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
+        }
+        
+        # Set up headers
+        headers = {
+            "Accept": "application/json",
+            "Accept-Encoding": "gzip",
+            "X-Subscription-Token": self.token
+        }
+        
+        # Make the request
+        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)",
+                        },
+                        # "country": {
+                        #     "type": "string",
+                        #     "description": "The 2-character country code (default: US)",
+                        # },
+                        "search_lang": {
+                            "type": "string",
+                            "description": "The search language preference (default: en)",
+                        },
+                        # "count": {
+                        #     "type": "integer",
+                        #     "description": "Number of results to return (max 20, default: 10)",
+                        # },
+                        # "offset": {
+                        #     "type": "integer",
+                        #     "description": "Pagination offset (max 9, default: 0)",
+                        # },
+                        # "safesearch": {
+                        #     "type": "string",
+                        #     "description": "Filter level for adult content (off, moderate, strict)",
+                        # },
+                        "freshness": {
+                            "type": "string",
+                            "description": "Time filter for results (pd: last 24h, pw: last week, pm: last month, py: last year)",
+                        },
+                        # "result_filter": {
+                        #     "type": "string",
+                        #     "description": "Comma-delimited list of result types to include",
+                        # },
+                        # "extra_snippets": {
+                        #     "type": "boolean",
+                        #     "description": "Get additional excerpts from result pages",
+                        # },
+                        # "summary": {
+                        #     "type": "boolean",
+                        #     "description": "Enable summary generation in search results",
+                        # }
+                    },
+                    "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)",
+                        },
+                        # "country": {
+                        #     "type": "string",
+                        #     "description": "The 2-character country code (default: US)",
+                        # },
+                        # "search_lang": {
+                        #     "type": "string",
+                        #     "description": "The search language preference (default: en)",
+                        # },
+                        "count": {
+                            "type": "integer",
+                            "description": "Number of results to return (max 100, default: 5)",
+                        },
+                        # "safesearch": {
+                        #     "type": "string",
+                        #     "description": "Filter level for adult content (off, strict). Default: strict",
+                        # },
+                        # "spellcheck": {
+                        #     "type": "boolean",
+                        #     "description": "Whether to spellcheck provided query (default: true)",
+                        # }
+                    },
+                    "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

@@ -1,5 +1,5 @@
 import requests
-from application.tools.base import Tool
+from application.agents.tools.base import Tool
 
 
 class CryptoPriceTool(Tool):
@@ -31,7 +31,6 @@ class CryptoPriceTool(Tool):
         response = requests.get(url)
         if response.status_code == 200:
             data = response.json()
-            # data will be like {"USD": <price>} if the call is successful
             if currency.upper() in data:
                 return {
                     "status_code": response.status_code,

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

@@ -1,5 +1,5 @@
 import psycopg2
-from application.tools.base import Tool
+from application.agents.tools.base import Tool
 
 class PostgresTool(Tool):
     """

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

@@ -1,5 +1,5 @@
 import requests
-from application.tools.base import Tool
+from application.agents.tools.base import Tool
 
 
 class TelegramTool(Tool):

application/agents/tools/tool_action_parser.py

@@ -0,0 +1,42 @@
+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):
+        if isinstance(call, dict):
+            try:
+                call_args = json.loads(call["function"]["arguments"])
+                tool_id = call["function"]["name"].split("_")[-1]
+                action_name = call["function"]["name"].rsplit("_", 1)[0]
+            except (KeyError, TypeError) as e:
+                logger.error(f"Error parsing OpenAI LLM call: {e}")
+                return None, None, None
+        else:
+            try:
+                call_args = json.loads(call.function.arguments)
+                tool_id = call.function.name.split("_")[-1]
+                action_name = call.function.name.rsplit("_", 1)[0]
+            except (AttributeError, TypeError) 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):
+        call_args = call.args
+        tool_id = call.name.split("_")[-1]
+        action_name = call.name.rsplit("_", 1)[0]
+        return tool_id, action_name, call_args

application/agents/tools/tool_manager.py

@@ -3,7 +3,7 @@ import inspect
 import os
 import pkgutil
 
-from application.tools.base import Tool
+from application.agents.tools.base import Tool
 
 
 class ToolManager:
@@ -13,13 +13,11 @@ class ToolManager:
         self.load_tools()
 
     def load_tools(self):
-        tools_dir = os.path.join(os.path.dirname(__file__), "implementations")
+        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.tools.implementations.{name}"
-            )
+            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, {})
@@ -27,9 +25,7 @@ class ToolManager:
 
     def load_tool(self, tool_name, tool_config):
         self.config[tool_name] = tool_config
-        module = importlib.import_module(
-            f"application.tools.implementations.{tool_name}"
-        )
+        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:
                 return obj(tool_config)

application/api/answer/routes.py

@@ -3,14 +3,14 @@ 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 import Blueprint, make_response, request, Response
 from flask_restx import fields, Namespace, Resource
 
+from application.agents.agent_creator import AgentCreator
 
 from application.core.mongo_db import MongoDB
 from application.core.settings import settings
@@ -23,12 +23,13 @@ from application.utils import check_required_fields, limit_chat_history
 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"]
 prompts_collection = db["prompts"]
-api_key_collection = db["api_keys"]
+agents_collection = db["agents"]
 user_logs_collection = db["user_logs"]
+attachments_collection = db["attachments"]
 
 answer = Blueprint("answer", __name__)
 answer_ns = Namespace("answer", description="Answer related operations", path="/")
@@ -42,6 +43,8 @@ elif settings.LLM_NAME == "anthropic":
     gpt_model = "claude-2"
 elif settings.LLM_NAME == "groq":
     gpt_model = "llama3-8b-8192"
+elif settings.LLM_NAME == "novita":
+    gpt_model = "deepseek/deepseek-r1"
 
 if settings.MODEL_NAME:  # in case there is particular model name configured
     gpt_model = settings.MODEL_NAME
@@ -83,22 +86,51 @@ def run_async_chain(chain, question, chat_history):
     return result
 
 
+def get_agent_key(agent_id, user_id):
+    if not agent_id:
+        return None, False, None
+
+    try:
+        agent = agents_collection.find_one({"_id": ObjectId(agent_id)})
+        if agent is None:
+            raise Exception("Agent not found", 404)
+
+        is_owner = agent.get("user") == user_id
+
+        if is_owner:
+            agents_collection.update_one(
+                {"_id": ObjectId(agent_id)},
+                {"$set": {"lastUsedAt": datetime.datetime.now(datetime.timezone.utc)}},
+            )
+            return str(agent["key"]), False, None
+
+        is_shared_with_user = agent.get(
+            "shared_publicly", False
+        ) or user_id in agent.get("shared_with", [])
+
+        if is_shared_with_user:
+            return str(agent["key"]), True, agent.get("shared_token")
+
+        raise Exception("Unauthorized access to the agent", 403)
+
+    except Exception as e:
+        logger.error(f"Error in get_agent_key: {str(e)}", exc_info=True)
+        raise
+
+
 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)
+    data = agents_collection.find_one({"key": api_key})
+    if not data:
+        raise Exception("Invalid API Key, please generate a 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"])
+    source = data.get("source")
+    if isinstance(source, DBRef):
+        source_doc = db.dereference(source)
         data["source"] = str(source_doc["_id"])
-        if "retriever" in source_doc:
-            data["retriever"] = source_doc["retriever"]
+        data["retriever"] = source_doc.get("retriever", data.get("retriever"))
     else:
         data["source"] = {}
+
     return data
 
 
@@ -118,7 +150,22 @@ def is_azure_configured():
     )
 
 
-def save_conversation(conversation_id, question, response, source_log_docs, llm,index=None):
+def save_conversation(
+    conversation_id,
+    question,
+    response,
+    thought,
+    source_log_docs,
+    tool_calls,
+    llm,
+    decoded_token,
+    index=None,
+    api_key=None,
+    agent_id=None,
+    is_shared_usage=False,
+    shared_token=None,
+):
+    current_time = datetime.datetime.now(datetime.timezone.utc)
     if conversation_id is not None and index is not None:
         conversations_collection.update_one(
             {"_id": ObjectId(conversation_id), f"queries.{index}": {"$exists": True}},
@@ -126,21 +173,17 @@ def save_conversation(conversation_id, question, response, source_log_docs, llm,
                 "$set": {
                     f"queries.{index}.prompt": question,
                     f"queries.{index}.response": response,
+                    f"queries.{index}.thought": thought,
                     f"queries.{index}.sources": source_log_docs,
+                    f"queries.{index}.tool_calls": tool_calls,
+                    f"queries.{index}.timestamp": current_time,
                 }
-            }
+            },
         )
         ##remove following queries from the array
         conversations_collection.update_one(
             {"_id": ObjectId(conversation_id), f"queries.{index}": {"$exists": True}},
-            {
-                "$push":{
-                    "queries":{
-                        "$each":[],
-                        "$slice":index+1
-                    }
-                }
-            }
+            {"$push": {"queries": {"$each": [], "$slice": index + 1}}},
         )
     elif conversation_id is not None and conversation_id != "None":
         conversations_collection.update_one(
@@ -150,7 +193,10 @@ def save_conversation(conversation_id, question, response, source_log_docs, llm,
                     "queries": {
                         "prompt": question,
                         "response": response,
+                        "thought": thought,
                         "sources": source_log_docs,
+                        "tool_calls": tool_calls,
+                        "timestamp": current_time,
                     }
                 }
             },
@@ -170,28 +216,37 @@ def save_conversation(conversation_id, question, response, source_log_docs, llm,
                 "role": "user",
                 "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,
+                "system \n\nUser: " + question + "\n\n" + "AI: " + response,
             },
         ]
 
         completion = llm.gen(model=gpt_model, messages=messages_summary, max_tokens=30)
+        conversation_data = {
+            "user": decoded_token.get("sub"),
+            "date": datetime.datetime.utcnow(),
+            "name": completion,
+            "queries": [
+                {
+                    "prompt": question,
+                    "response": response,
+                    "thought": thought,
+                    "sources": source_log_docs,
+                    "tool_calls": tool_calls,
+                    "timestamp": current_time,
+                }
+            ],
+        }
+        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
+            api_key_doc = agents_collection.find_one({"key": api_key})
+            if api_key_doc:
+                conversation_data["api_key"] = api_key_doc["key"]
         conversation_id = conversations_collection.insert_one(
-            {
-                "user": "local",
-                "date": datetime.datetime.utcnow(),
-                "name": completion,
-                "queries": [
-                    {
-                        "prompt": question,
-                        "response": response,
-                        "sources": source_log_docs,
-                    }
-                ],
-            }
+            conversation_data
         ).inserted_id
     return conversation_id
 
@@ -209,67 +264,116 @@ def get_prompt(prompt_id):
 
 
 def complete_stream(
-    question, retriever, conversation_id, user_api_key, isNoneDoc=False,index=None
+    question,
+    agent,
+    retriever,
+    conversation_id,
+    user_api_key,
+    decoded_token,
+    isNoneDoc=False,
+    index=None,
+    should_save_conversation=True,
+    attachments=None,
+    agent_id=None,
+    is_shared_usage=False,
+    shared_token=None,
 ):
-
     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"
+        response_full, thought, source_log_docs, tool_calls = "", "", [], []
+        attachment_ids = []
+
+        if attachments:
+            attachment_ids = [attachment["id"] for attachment in attachments]
+            logger.info(
+                f"Processing request with {len(attachments)} attachments: {attachment_ids}"
+            )
+
+        answer = agent.gen(query=question, retriever=retriever)
+
         for line in answer:
             if "answer" in line:
                 response_full += str(line["answer"])
-                data = json.dumps(line)
+                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 len(truncated_sources) > 0:
+                    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 "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
+            settings.LLM_NAME,
+            api_key=settings.API_KEY,
+            user_api_key=user_api_key,
+            decoded_token=decoded_token,
         )
-        if user_api_key is None:
+
+        if should_save_conversation:
             conversation_id = save_conversation(
-                conversation_id, question, response_full, source_log_docs, llm,index
+                conversation_id,
+                question,
+                response_full,
+                thought,
+                source_log_docs,
+                tool_calls,
+                llm,
+                decoded_token,
+                index,
+                api_key=user_api_key,
+                agent_id=agent_id,
+                is_shared_usage=is_shared_usage,
+                shared_token=shared_token,
             )
-            # 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"
+        else:
+            conversation_id = None
+
+        # 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",
+                "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),
             }
         )
         data = json.dumps({"type": "end"})
         yield f"data: {data}\n\n"
     except Exception as e:
-        print("\033[91merr", str(e), file=sys.stderr)
-        traceback.print_exc()
+        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.",
-                "error_exception": str(e),
             }
         )
         yield f"data: {data}\n\n"
@@ -305,8 +409,16 @@ class Stream(Resource):
             "isNoneDoc": fields.Boolean(
                 required=False, description="Flag indicating if no document is used"
             ),
-            "index":fields.Integer(
-                required=False, description="The position where query is to be updated"
+            "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"
             ),
         },
     )
@@ -317,22 +429,37 @@ class Stream(Resource):
         data = request.get_json()
         required_fields = ["question"]
         if "index" in data:
-            required_fields = ["question","conversation_id"]
+            required_fields = ["question", "conversation_id"]
         missing_fields = check_required_fields(data, required_fields)
         if missing_fields:
             return missing_fields
 
+        save_conv = data.get("save_conversation", True)
+
         try:
             question = data["question"]
-            history = limit_chat_history(json.loads(data.get("history", [])), gpt_model=gpt_model)
+            history = limit_chat_history(
+                json.loads(data.get("history", [])), gpt_model=gpt_model
+            )
             conversation_id = data.get("conversation_id")
             prompt_id = data.get("prompt_id", "default")
-            
-            index=data.get("index",None)
+            attachment_ids = data.get("attachments", [])
+
+            index = data.get("index", None)
             chunks = int(data.get("chunks", 2))
             token_limit = data.get("token_limit", settings.DEFAULT_MAX_HISTORY)
             retriever_name = data.get("retriever", "classic")
-            
+            agent_id = data.get("agent_id", None)
+            agent_type = settings.AGENT_NAME
+            agent_key, is_shared_usage, shared_token = get_agent_key(
+                agent_id, request.decoded_token.get("sub")
+            )
+
+            if agent_key:
+                data.update({"api_key": agent_key})
+            else:
+                agent_id = None
+
             if "api_key" in data:
                 data_key = get_data_from_api_key(data["api_key"])
                 chunks = int(data_key.get("chunks", 2))
@@ -340,27 +467,55 @@ class Stream(Resource):
                 source = {"active_docs": data_key.get("source")}
                 retriever_name = data_key.get("retriever", retriever_name)
                 user_api_key = data["api_key"]
+                agent_type = data_key.get("agent_type", agent_type)
+                if is_shared_usage:
+                    decoded_token = request.decoded_token
+                else:
+                    decoded_token = {"sub": data_key.get("user")}
+                    is_shared_usage = False
 
             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
+                decoded_token = request.decoded_token
 
             else:
                 source = {}
                 user_api_key = None
+                decoded_token = request.decoded_token
 
-            current_app.logger.info(
-                f"/stream - request_data: {data}, source: {source}",
+            if not decoded_token:
+                return make_response({"error": "Unauthorized"}, 401)
+
+            attachments = get_attachments_content(
+                attachment_ids, decoded_token.get("sub")
+            )
+
+            logger.info(
+                f"/stream - request_data: {data}, source: {source}, attachments: {len(attachments)}",
                 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
+
+            agent = AgentCreator.create_agent(
+                agent_type,
+                endpoint="stream",
+                llm_name=settings.LLM_NAME,
+                gpt_model=gpt_model,
+                api_key=settings.API_KEY,
+                user_api_key=user_api_key,
+                prompt=prompt,
+                chat_history=history,
+                decoded_token=decoded_token,
+                attachments=attachments,
+            )
+
             retriever = RetrieverCreator.create_retriever(
                 retriever_name,
-                question=question,
                 source=source,
                 chat_history=history,
                 prompt=prompt,
@@ -368,40 +523,44 @@ class Stream(Resource):
                 token_limit=token_limit,
                 gpt_model=gpt_model,
                 user_api_key=user_api_key,
+                decoded_token=decoded_token,
             )
-            
+            is_shared_usage_val = data.get("is_shared_usage", False)
+            is_shared_token_val = data.get("shared_token", None)
             return Response(
                 complete_stream(
                     question=question,
+                    agent=agent,
                     retriever=retriever,
                     conversation_id=conversation_id,
                     user_api_key=user_api_key,
+                    decoded_token=decoded_token,
                     isNoneDoc=data.get("isNoneDoc"),
                     index=index,
+                    should_save_conversation=save_conv,
+                    agent_id=agent_id,
+                    is_shared_usage=is_shared_usage_val,
+                    shared_token=is_shared_token_val,
                 ),
                 mimetype="text/event-stream",
             )
 
         except ValueError:
             message = "Malformed request body"
-            print("\033[91merr", str(message), file=sys.stderr)
+            logger.error(f"/stream - error: {message}")
             return Response(
                 error_stream_generate(message),
                 status=400,
                 mimetype="text/event-stream",
             )
         except Exception as e:
-            current_app.logger.error(
+            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),
+                error_stream_generate("Unknown error occurred"),
                 status=status_code,
                 mimetype="text/event-stream",
             )
@@ -448,19 +607,22 @@ class Answer(Resource):
     @api.doc(description="Provide an answer based on the question and retriever")
     def post(self):
         data = request.get_json()
-        required_fields = ["question"]       
+        required_fields = ["question"]
         missing_fields = check_required_fields(data, required_fields)
         if missing_fields:
             return missing_fields
 
         try:
             question = data["question"]
-            history = limit_chat_history(json.loads(data.get("history", [])), gpt_model=gpt_model)
+            history = limit_chat_history(
+                json.loads(data.get("history", [])), gpt_model=gpt_model
+            )
             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")
+            agent_type = settings.AGENT_NAME
 
             if "api_key" in data:
                 data_key = get_data_from_api_key(data["api_key"])
@@ -469,24 +631,44 @@ class Answer(Resource):
                 source = {"active_docs": data_key.get("source")}
                 retriever_name = data_key.get("retriever", retriever_name)
                 user_api_key = data["api_key"]
+                agent_type = data_key.get("agent_type", agent_type)
+                decoded_token = {"sub": data_key.get("user")}
+
             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
+                decoded_token = request.decoded_token
+
             else:
                 source = {}
                 user_api_key = None
+                decoded_token = request.decoded_token
+
+            if not decoded_token:
+                return make_response({"error": "Unauthorized"}, 401)
 
             prompt = get_prompt(prompt_id)
 
-            current_app.logger.info(
+            logger.info(
                 f"/api/answer - request_data: {data}, source: {source}",
                 extra={"data": json.dumps({"request_data": data, "source": source})},
             )
 
+            agent = AgentCreator.create_agent(
+                agent_type,
+                endpoint="api/answer",
+                llm_name=settings.LLM_NAME,
+                gpt_model=gpt_model,
+                api_key=settings.API_KEY,
+                user_api_key=user_api_key,
+                prompt=prompt,
+                chat_history=history,
+                decoded_token=decoded_token,
+            )
+
             retriever = RetrieverCreator.create_retriever(
                 retriever_name,
-                question=question,
                 source=source,
                 chat_history=history,
                 prompt=prompt,
@@ -494,36 +676,84 @@ class Answer(Resource):
                 token_limit=token_limit,
                 gpt_model=gpt_model,
                 user_api_key=user_api_key,
+                decoded_token=decoded_token,
             )
 
-            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"]
+            source_log_docs = []
+            tool_calls = []
+            stream_ended = False
+            thought = ""
+
+            for line in complete_stream(
+                question=question,
+                agent=agent,
+                retriever=retriever,
+                conversation_id=conversation_id,
+                user_api_key=user_api_key,
+                decoded_token=decoded_token,
+                isNoneDoc=data.get("isNoneDoc"),
+                index=None,
+                should_save_conversation=False,
+            ):
+                try:
+                    event_data = line.replace("data: ", "").strip()
+                    event = json.loads(event_data)
+
+                    if event["type"] == "answer":
+                        response_full += event["answer"]
+                    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 bad_request(500, 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 bad_request(500, "Stream ended unexpectedly.")
 
             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
+                settings.LLM_NAME,
+                api_key=settings.API_KEY,
+                user_api_key=user_api_key,
+                decoded_token=decoded_token,
             )
 
             result = {"answer": response_full, "sources": source_log_docs}
             result["conversation_id"] = str(
                 save_conversation(
-                    conversation_id, question, response_full, source_log_docs, llm
+                    conversation_id,
+                    question,
+                    response_full,
+                    thought,
+                    source_log_docs,
+                    tool_calls,
+                    llm,
+                    decoded_token,
+                    api_key=user_api_key,
                 )
             )
+
             retriever_params = retriever.get_params()
             user_logs_collection.insert_one(
                 {
                     "action": "api_answer",
                     "level": "info",
-                    "user": "local",
+                    "user": decoded_token.get("sub"),
                     "api_key": user_api_key,
                     "question": question,
                     "response": response_full,
@@ -534,7 +764,7 @@ class Answer(Resource):
             )
 
         except Exception as e:
-            current_app.logger.error(
+            logger.error(
                 f"/api/answer - error: {str(e)} - traceback: {traceback.format_exc()}",
                 extra={"error": str(e), "traceback": traceback.format_exc()},
             )
@@ -592,21 +822,28 @@ class Search(Resource):
                 chunks = int(data_key.get("chunks", 2))
                 source = {"active_docs": data_key.get("source")}
                 user_api_key = data["api_key"]
+                decoded_token = {"sub": data_key.get("user")}
+
             elif "active_docs" in data:
                 source = {"active_docs": data["active_docs"]}
                 user_api_key = None
+                decoded_token = request.decoded_token
+
             else:
                 source = {}
                 user_api_key = None
+                decoded_token = request.decoded_token
 
-            current_app.logger.info(
+            if not decoded_token:
+                return make_response({"error": "Unauthorized"}, 401)
+
+            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",
@@ -614,16 +851,17 @@ class Search(Resource):
                 token_limit=token_limit,
                 gpt_model=gpt_model,
                 user_api_key=user_api_key,
+                decoded_token=decoded_token,
             )
 
-            docs = retriever.search()
+            docs = retriever.search(question)
             retriever_params = retriever.get_params()
 
             user_logs_collection.insert_one(
                 {
                     "action": "api_search",
                     "level": "info",
-                    "user": "local",
+                    "user": decoded_token.get("sub"),
                     "api_key": user_api_key,
                     "question": question,
                     "sources": docs,
@@ -637,10 +875,41 @@ class Search(Resource):
                     doc["source"] = "None"
 
         except Exception as e:
-            current_app.logger.error(
+            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)
+
+
+def get_attachments_content(attachment_ids, user):
+    """
+    Retrieve content from attachment documents based on their IDs.
+
+    Args:
+        attachment_ids (list): List of attachment document IDs
+        user (str): User identifier to verify ownership
+
+    Returns:
+        list: List of dictionaries containing attachment content and metadata
+    """
+    if not attachment_ids:
+        return []
+
+    attachments = []
+    for attachment_id in attachment_ids:
+        try:
+            attachment_doc = attachments_collection.find_one(
+                {"_id": ObjectId(attachment_id), "user": user}
+            )
+
+            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

application/api/internal/routes.py

@@ -3,12 +3,15 @@ import datetime
 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"]
 
@@ -45,26 +48,26 @@ def upload_index_files():
     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
 
-    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
+        storage.save_file(file_faiss, f"{index_base_path}/index.faiss")
+        storage.save_file(file_pkl, f"{index_base_path}/index.pkl")
 
     existing_entry = sources_collection.find_one({"_id": ObjectId(id)})
     if existing_entry:

application/api/user/tasks.py

@@ -1,7 +1,13 @@
 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,
+    remote_worker,
+    sync_worker,
+)
 
 
 @celery.task(bind=True)
@@ -22,6 +28,18 @@ def schedule_syncs(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.on_after_configure.connect
 def setup_periodic_tasks(sender, **kwargs):
     sender.add_periodic_task(

application/app.py

@@ -1,15 +1,24 @@
+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.answer.routes import answer  # noqa: E402
+from application.api.internal.routes import internal  # noqa: E402
+from application.api.user.routes import user  # noqa: E402
+from application.celery_init import celery  # noqa: E402
+from application.core.settings import settings  # noqa: E402
+from application.extensions import api  # noqa: E402
+
 
 if platform.system() == "Windows":
     import pathlib
@@ -17,7 +26,6 @@ if platform.system() == "Windows":
     pathlib.PosixPath = pathlib.WindowsPath
 
 dotenv.load_dotenv()
-setup_logging()
 
 app = Flask(__name__)
 app.register_blueprint(user)
@@ -32,6 +40,25 @@ 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 +68,47 @@ 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

@@ -11,21 +11,25 @@ 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
                     )
+                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
 
 
@@ -41,36 +45,48 @@ def gen_cache_key(messages, model="docgpt", tools=None):
 
 def gen_cache(func):
     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, model, tools)
-            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, stream, tools, *args, **kwargs)
-            if redis_client and isinstance(result, str):
-                try:
-                    redis_client.set(cache_key, result, ex=1800)
-                except redis.ConnectionError as e:
-                    logger.error(f"Redis connection error: {e}")
-
-            return result
         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, tools=None, *args, **kwargs):
-        cache_key = gen_cache_key(messages, model, tools)
-        logger.info(f"Stream cache key: {cache_key}")
+        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:
@@ -81,23 +97,21 @@ def stream_cache(func):
                     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, tools=tools, *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}")
+            except Exception as e:
+                logger.error(f"Error setting stream cache: {e}", exc_info=True)
 
     return wrapper

application/core/settings.py

@@ -1,26 +1,40 @@
+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):
+    AUTH_TYPE: Optional[str] = None
     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
+    MODEL_NAME: Optional[str] = (
+        None  # if LLM_NAME is openai, MODEL_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"
+    MONGO_DB_NAME: str = "docsgpt"
     MODEL_PATH: str = os.path.join(current_dir, "models/docsgpt-7b-f16.gguf")
     DEFAULT_MAX_HISTORY: int = 150
-    MODEL_TOKEN_LIMITS: dict = {"gpt-4o-mini": 128000, "gpt-3.5-turbo": 4096, "claude-2": 1e5}
+    MODEL_TOKEN_LIMITS: dict = {
+        "gpt-4o-mini": 128000,
+        "gpt-3.5-turbo": 4096,
+        "claude-2": 1e5,
+        "gemini-2.0-flash-exp": 1e6,
+    }
     UPLOAD_FOLDER: str = "inputs"
     PARSE_PDF_AS_IMAGE: bool = False
-    VECTOR_STORE: str = "faiss" #  "faiss" or "elasticsearch" or "qdrant" or "milvus" or "lancedb"
-    RETRIEVERS_ENABLED: list = ["classic_rag", "duckduck_search"] # also brave_search
+    VECTOR_STORE: str = (
+        "faiss"  #  "faiss" or "elasticsearch" or "qdrant" or "milvus" or "lancedb"
+    )
+    RETRIEVERS_ENABLED: list = ["classic_rag", "duckduck_search"]  # also brave_search
+    AGENT_NAME: str = "classic"
 
     # LLM Cache
     CACHE_REDIS_URL: str = "redis://localhost:6379/2"
@@ -28,12 +42,18 @@ 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
@@ -68,15 +88,21 @@ class Settings(BaseSettings):
 
     # 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
+    LANCEDB_TABLE_NAME: Optional[str] = (
+        "docsgpts"  # Name of the table to use for storing vectors
+    )
     BRAVE_SEARCH_API_KEY: Optional[str] = None
 
     FLASK_DEBUG_MODE: bool = False
+    STORAGE_TYPE: str = "local"  # local or s3 
+
+
+    JWT_SECRET_KEY: str = ""
 
 
 path = Path(__file__).parent.parent.absolute()

application/llm/base.py

@@ -5,7 +5,8 @@ from application.usage import gen_token_usage, stream_token_usage
 
 
 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}
 
     def _apply_decorator(self, method, decorators, *args, **kwargs):
@@ -54,3 +55,12 @@ class BaseLLM(ABC):
 
     def _supports_tools(self):
         raise NotImplementedError("Subclass must implement _supports_tools method")
+        
+    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 []  # Default: no attachments supported

application/llm/docsgpt_provider.py

@@ -1,34 +1,131 @@
-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.arc53.com"
+        self.api_key = api_key
 
-    def _raw_gen(self, baseself, model, messages, stream=False, *args, **kwargs):
-        response = requests.post(
-            f"{self.endpoint}/answer", json={"messages": messages, "max_new_tokens": 30}
-        )
-        response_clean = response.json()["a"].replace("###", "")
+    def _clean_messages_openai(self, messages):
+        cleaned_messages = []
+        for message in messages:
+            role = message.get("role")
+            content = message.get("content")
 
-        return response_clean
+            if role == "model":
+                role = "assistant"
 
-    def _raw_gen_stream(self, baseself, model, messages, stream=True, *args, **kwargs):
-        response = requests.post(
-            f"{self.endpoint}/stream",
-            json={"messages": messages, "max_new_tokens": 256},
-            stream=True,
-        )
+            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)}")
 
-        for line in response.iter_lines():
-            if line:
-                data_str = line.decode("utf-8")
-                if data_str.startswith("data: "):
-                    data = json.loads(data_str[6:])
-                    yield data["a"]
+        return cleaned_messages
+
+    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
+
+    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
+            )
+
+        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]
+
+    def _supports_tools(self):
+        return True

application/llm/google_ai.py

@@ -1,7 +1,11 @@
 from google import genai
 from google.genai import types
+import logging
+import json
 
 from application.llm.base import BaseLLM
+from application.storage.storage_creator import StorageCreator
+from application.core.settings import settings
 
 
 class GoogleLLM(BaseLLM):
@@ -9,6 +13,126 @@ class GoogleLLM(BaseLLM):
         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):
         cleaned_messages = []
@@ -22,11 +146,11 @@ class GoogleLLM(BaseLLM):
             parts = []
             if role and content is not None:
                 if isinstance(content, str):
-                    parts = [types.Part.from_text(content)]
+                    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(item["text"]))
+                            parts.append(types.Part.from_text(text=item["text"]))
                         elif "function_call" in item:
                             parts.append(
                                 types.Part.from_function_call(
@@ -41,6 +165,14 @@ class GoogleLLM(BaseLLM):
                                     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}"
@@ -146,13 +278,35 @@ class GoogleLLM(BaseLLM):
             cleaned_tools = self._clean_tools_format(tools)
             config.tools = cleaned_tools
 
+        # 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,
         )
+
+
         for chunk in response:
-            if chunk.text is not None:
+            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
 
     def _supports_tools(self):

application/llm/llm_creator.py

@@ -7,6 +7,7 @@ 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:
@@ -20,12 +21,15 @@ class LLMCreator:
         "docsgpt": DocsGPTAPILLM,
         "premai": PremAILLM,
         "groq": GroqLLM,
-        "google": GoogleLLM
+        "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
+        )

application/llm/novita.py

@@ -0,0 +1,32 @@
+from application.llm.base import BaseLLM
+from openai import OpenAI
+
+
+class NovitaLLM(BaseLLM):
+    def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.client = OpenAI(api_key=api_key, base_url="https://api.novita.ai/v3/openai")
+        self.api_key = api_key
+        self.user_api_key = user_api_key
+
+    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, tools=None, **kwargs
+    ):
+        response = self.client.chat.completions.create(
+            model=model, messages=messages, stream=stream, **kwargs
+        )
+        for line in response:
+            if line.choices[0].delta.content is not None:
+                yield line.choices[0].delta.content

application/llm/openai.py

@@ -1,5 +1,10 @@
+import json
+import base64
+import logging
+
 from application.core.settings import settings
 from application.llm.base import BaseLLM
+from application.storage.storage_creator import StorageCreator
 
 
 class OpenAILLM(BaseLLM):
@@ -8,12 +13,82 @@ class OpenAILLM(BaseLLM):
         from openai import OpenAI
 
         super().__init__(*args, **kwargs)
-        if settings.OPENAI_BASE_URL:
+        if isinstance(settings.OPENAI_BASE_URL, str) and settings.OPENAI_BASE_URL.strip():
             self.client = OpenAI(api_key=api_key, base_url=settings.OPENAI_BASE_URL)
         else:
-            self.client = OpenAI(api_key=api_key)
+            DEFAULT_OPENAI_API_BASE = "https://api.openai.com/v1"
+            self.client = OpenAI(api_key=api_key, base_url=DEFAULT_OPENAI_API_BASE)
         self.api_key = api_key
         self.user_api_key = user_api_key
+        self.storage = StorageCreator.get_storage()
+
+    def _clean_messages_openai(self, messages):
+        cleaned_messages = []
+        for message in messages:
+            role = message.get("role")
+            content = message.get("content")
+
+            if role == "model":
+                role = "assistant"
+
+            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"]
+                                    ),
+                                }
+                            )
+                        elif isinstance(item, dict):
+                            content_parts = []
+                            if "text" in item:
+                                content_parts.append({"type": "text", "text": item["text"]})
+                            elif "type" in item and item["type"] == "text" and "text" in item:
+                                content_parts.append(item)
+                            elif "type" in item and item["type"] == "file" and "file" in item:
+                                content_parts.append(item)
+                            elif "type" in item and item["type"] == "image_url" and "image_url" in item:
+                                content_parts.append(item)
+                            cleaned_messages.append({"role": role, "content": content_parts})
+                        else:
+                            raise ValueError(
+                                f"Unexpected content dictionary format: {item}"
+                            )
+                else:
+                    raise ValueError(f"Unexpected content type: {type(content)}")
+
+        return cleaned_messages
 
     def _raw_gen(
         self,
@@ -25,9 +100,14 @@ class OpenAILLM(BaseLLM):
         engine=settings.AZURE_DEPLOYMENT_NAME,
         **kwargs,
     ):
+        messages = self._clean_messages_openai(messages)
         if tools:
             response = self.client.chat.completions.create(
-                model=model, messages=messages, stream=stream, tools=tools, **kwargs
+                model=model,
+                messages=messages,
+                stream=stream,
+                tools=tools,
+                **kwargs,
             )
             return response.choices[0]
         else:
@@ -46,32 +126,200 @@ class OpenAILLM(BaseLLM):
         engine=settings.AZURE_DEPLOYMENT_NAME,
         **kwargs,
     ):
-        response = self.client.chat.completions.create(
-            model=model, messages=messages, stream=stream, **kwargs
-        )
+        messages = self._clean_messages_openai(messages)
+        if tools:
+            response = self.client.chat.completions.create(
+                model=model,
+                messages=messages,
+                stream=stream,
+                tools=tools,
+                **kwargs,
+            )
+        else:
+            response = self.client.chat.completions.create(
+                model=model, messages=messages, stream=stream, **kwargs
+            )
 
         for line in response:
-            if line.choices[0].delta.content is not None:
+            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]
 
     def _supports_tools(self):
         return True
 
+    def get_supported_attachment_types(self):
+        """
+        Return a list of MIME types supported by OpenAI 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 OpenAI'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 OpenAI API.
+        """
+        if not attachments:
+            return messages
+
+        prepared_messages = messages.copy()
+
+        # Find the user message to attach file_id 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"] = []
+
+        for attachment in attachments:
+            mime_type = attachment.get('mime_type')
+
+            if mime_type and mime_type.startswith('image/'):
+                try:
+                    base64_image = self._get_base64_image(attachment)
+                    prepared_messages[user_message_index]["content"].append({
+                        "type": "image_url",
+                        "image_url": {
+                            "url": f"data:{mime_type};base64,{base64_image}"
+                        }
+                    })
+                except Exception as e:
+                    logging.error(f"Error processing image attachment: {e}", exc_info=True)
+                    if 'content' in attachment:
+                        prepared_messages[user_message_index]["content"].append({
+                            "type": "text",
+                            "text": f"[Image could not be processed: {attachment.get('path', 'unknown')}]"
+                        })
+            # Handle PDFs using the file API
+            elif mime_type == 'application/pdf':
+                try:
+                    file_id = self._upload_file_to_openai(attachment)
+                    prepared_messages[user_message_index]["content"].append({
+                        "type": "file",
+                        "file": {"file_id": file_id}
+                    })
+                except Exception as e:
+                    logging.error(f"Error uploading PDF to OpenAI: {e}", exc_info=True)
+                    if 'content' in attachment:
+                        prepared_messages[user_message_index]["content"].append({
+                            "type": "text",
+                            "text": f"File content:\n\n{attachment['content']}"
+                        })
+
+        return prepared_messages
+
+    def _get_base64_image(self, attachment):
+        """
+        Convert an image file to base64 encoding.
+
+        Args:
+            attachment (dict): Attachment dictionary with path and metadata.
+
+        Returns:
+            str: Base64-encoded image data.
+        """
+        file_path = attachment.get('path')
+        if not file_path:
+            raise ValueError("No file path provided in attachment")
+
+        try:
+            with self.storage.get_file(file_path) as image_file:
+                return base64.b64encode(image_file.read()).decode('utf-8')
+        except FileNotFoundError:
+            raise FileNotFoundError(f"File not found: {file_path}")
+
+    def _upload_file_to_openai(self, attachment):
+        """
+        Upload a file to OpenAI and return the file_id.
+
+        Args:
+            attachment (dict): Attachment dictionary with path and metadata.
+                Expected keys:
+                - path: Path to the file
+                - id: Optional MongoDB ID for caching
+
+        Returns:
+            str: OpenAI file_id for the uploaded file.
+        """
+        import logging
+
+        if 'openai_file_id' in attachment:
+            return attachment['openai_file_id']
+
+        file_path = attachment.get('path')
+
+        if not self.storage.file_exists(file_path):
+            raise FileNotFoundError(f"File not found: {file_path}")
+
+        try:
+            file_id = self.storage.process_file(
+                file_path,
+                lambda local_path, **kwargs: self.client.files.create(
+                    file=open(local_path, 'rb'),
+                    purpose="assistants"
+                ).id
+            )
+
+            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": {"openai_file_id": file_id}}
+                )
+
+            return file_id
+        except Exception as e:
+            logging.error(f"Error uploading file to OpenAI: {e}", exc_info=True)
+            raise
+
 
 class AzureOpenAILLM(OpenAILLM):
 
     def __init__(
-        self, openai_api_key, openai_api_base, openai_api_version, deployment_name
+        self, api_key, user_api_key, *args, **kwargs
     ):
-        super().__init__(openai_api_key)
+
+        super().__init__(api_key)
         self.api_base = (settings.OPENAI_API_BASE,)
         self.api_version = (settings.OPENAI_API_VERSION,)
         self.deployment_name = (settings.AZURE_DEPLOYMENT_NAME,)
         from openai import AzureOpenAI
 
         self.client = AzureOpenAI(
-            api_key=openai_api_key,
+            api_key=api_key,
             api_version=settings.OPENAI_API_VERSION,
-            api_base=settings.OPENAI_API_BASE,
-            deployment_name=settings.AZURE_DEPLOYMENT_NAME,
+            azure_endpoint=settings.OPENAI_API_BASE
         )

application/logging.py

@@ -0,0 +1,154 @@
+import datetime
+import functools
+import inspect
+
+import logging
+import uuid
+from typing import Any, Callable, Dict, Generator, List
+
+from application.core.mongo_db import MongoDB
+from application.core.settings import settings
+
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
+
+
+class LogContext:
+    def __init__(self, endpoint, activity_id, user, api_key, query):
+        self.endpoint = endpoint
+        self.activity_id = activity_id
+        self.user = user
+        self.api_key = api_key
+        self.query = query
+        self.stacks = []
+
+
+def build_stack_data(
+    obj: Any,
+    include_attributes: List[str] = None,
+    exclude_attributes: List[str] = None,
+    custom_data: Dict = None,
+) -> Dict:
+    if obj is None:
+        raise ValueError("The 'obj' parameter cannot be None")
+    data = {}
+    if include_attributes is None:
+        include_attributes = []
+        for name, value in inspect.getmembers(obj):
+            if (
+                not name.startswith("_")
+                and not inspect.ismethod(value)
+                and not inspect.isfunction(value)
+            ):
+                include_attributes.append(name)
+    for attr_name in include_attributes:
+        if exclude_attributes and attr_name in exclude_attributes:
+            continue
+        try:
+            attr_value = getattr(obj, attr_name)
+            if attr_value is not None:
+                if isinstance(attr_value, (int, float, str, bool)):
+                    data[attr_name] = attr_value
+                elif isinstance(attr_value, list):
+                    if all(isinstance(item, dict) for item in attr_value):
+                        data[attr_name] = attr_value
+                    elif all(hasattr(item, "__dict__") for item in attr_value):
+                        data[attr_name] = [item.__dict__ for item in attr_value]
+                    else:
+                        data[attr_name] = [str(item) for item in attr_value]
+                elif isinstance(attr_value, dict):
+                    data[attr_name] = {k: str(v) for k, v in attr_value.items()}
+        except AttributeError as e:
+            logging.warning(f"AttributeError while accessing {attr_name}: {e}")
+        except AttributeError:
+            pass
+    if custom_data:
+        data.update(custom_data)
+    return data
+
+
+def log_activity() -> Callable:
+    def decorator(func: Callable) -> Callable:
+        @functools.wraps(func)
+        def wrapper(*args: Any, **kwargs: Any) -> Any:
+            activity_id = str(uuid.uuid4())
+            data = build_stack_data(args[0])
+            endpoint = data.get("endpoint", "")
+            user = data.get("user", "local")
+            api_key = data.get("user_api_key", "")
+            query = kwargs.get("query", getattr(args[0], "query", ""))
+
+            context = LogContext(endpoint, activity_id, user, api_key, query)
+            kwargs["log_context"] = context
+
+            logging.info(
+                f"Starting activity: {endpoint} - {activity_id} - User: {user}"
+            )
+
+            generator = func(*args, **kwargs)
+            yield from _consume_and_log(generator, context)
+
+        return wrapper
+
+    return decorator
+
+
+def _consume_and_log(generator: Generator, context: "LogContext"):
+    try:
+        for item in generator:
+            yield item
+    except Exception as e:
+        logging.exception(f"Error in {context.endpoint} - {context.activity_id}: {e}")
+        context.stacks.append({"component": "error", "data": {"message": str(e)}})
+        _log_to_mongodb(
+            endpoint=context.endpoint,
+            activity_id=context.activity_id,
+            user=context.user,
+            api_key=context.api_key,
+            query=context.query,
+            stacks=context.stacks,
+            level="error",
+        )
+        raise
+    finally:
+        _log_to_mongodb(
+            endpoint=context.endpoint,
+            activity_id=context.activity_id,
+            user=context.user,
+            api_key=context.api_key,
+            query=context.query,
+            stacks=context.stacks,
+            level="info",
+        )
+
+
+def _log_to_mongodb(
+    endpoint: str,
+    activity_id: str,
+    user: str,
+    api_key: str,
+    query: str,
+    stacks: List[Dict],
+    level: str,
+) -> None:
+    try:
+        mongo = MongoDB.get_client()
+        db = mongo[settings.MONGO_DB_NAME]
+        user_logs_collection = db["stack_logs"]
+
+        log_entry = {
+            "endpoint": endpoint,
+            "id": activity_id,
+            "level": level,
+            "user": user,
+            "api_key": api_key,
+            "query": query,
+            "stacks": stacks,
+            "timestamp": datetime.datetime.now(datetime.timezone.utc),
+        }
+        user_logs_collection.insert_one(log_entry)
+        logging.debug(f"Logged activity to MongoDB: {activity_id}")
+
+    except Exception as e:
+        logging.error(f"Failed to log to MongoDB: {e}", exc_info=True)

application/parser/embedding_pipeline.py

@@ -19,7 +19,7 @@ def add_text_to_store_with_retry(store, doc, source_id):
         doc.metadata["source_id"] = str(source_id)
         store.add_texts([doc.page_content], metadatas=[doc.metadata])
     except Exception as e:
-        logging.error(f"Failed to add document with retry: {e}")
+        logging.error(f"Failed to add document with retry: {e}", exc_info=True)
         raise
 
 
@@ -75,7 +75,7 @@ def embed_and_store_documents(docs, folder_name, source_id, task_status):
             # Add document to vector store
             add_text_to_store_with_retry(store, doc, source_id)
         except Exception as e:
-            logging.error(f"Error embedding document {idx}: {e}")
+            logging.error(f"Error embedding document {idx}: {e}", exc_info=True)
             logging.info(f"Saving progress at document {idx} out of {total_docs}")
             store.save_local(folder_name)
             break

application/parser/file/bulk.py

@@ -158,7 +158,7 @@ class SimpleDirectoryReader(BaseReader):
                     data = f.read()
             # Prepare metadata for this file
             if self.file_metadata is not None:
-                file_metadata = self.file_metadata(str(input_file))
+                file_metadata = self.file_metadata(input_file.name)
             else:
                 # Provide a default empty metadata
                 file_metadata = {'title': '', 'store': ''}

application/parser/file/docs_parser.py

@@ -24,26 +24,27 @@ class PDFParser(BaseParser):
             # alternatively you can use local vision capable LLM
             with open(file, "rb") as file_loaded:
                 files = {'file': file_loaded}
-                response = requests.post(doc2md_service, files=files)   
-                data = response.json()["markdown"] 
+                response = requests.post(doc2md_service, files=files)
+                data = response.json()["markdown"]
             return data
 
         try:
-            import PyPDF2
+            from pypdf import PdfReader
         except ImportError:
-            raise ValueError("PyPDF2 is required to read PDF files.")
+            raise ValueError("pypdf is required to read PDF files.")
         text_list = []
         with open(file, "rb") as fp:
             # Create a PDF object
-            pdf = PyPDF2.PdfReader(fp)
+            pdf = PdfReader(fp)
 
             # Get the number of pages in the PDF document
             num_pages = len(pdf.pages)
 
             # Iterate over every page
-            for page in range(num_pages):
+            for page_index in range(num_pages):
                 # Extract the text from the page
-                page_text = pdf.pages[page].extract_text()
+                page = pdf.pages[page_index]
+                page_text = page.extract_text()
                 text_list.append(page_text)
         text = "\n".join(text_list)
 
@@ -66,4 +67,4 @@ class DocxParser(BaseParser):
 
         text = docx2txt.process(file)
 
-        return text
+        return text

application/parser/file/tabular_parser.py

@@ -73,7 +73,13 @@ class PandasCSVParser(BaseParser):
             for more information.
             Set to empty dict by default, this means pandas will try to figure
             out the separators, table head, etc. on its own.
-
+            
+        header_period (int): Controls how headers are included in output:
+            - 0: Headers only at the beginning
+            - 1: Headers in every row
+            - N > 1: Headers every N rows
+            
+        header_prefix (str): Prefix for header rows. Default is "HEADERS: ".
     """
 
     def __init__(
@@ -83,6 +89,8 @@ class PandasCSVParser(BaseParser):
             col_joiner: str = ", ",
             row_joiner: str = "\n",
             pandas_config: dict = {},
+            header_period: int = 20,
+            header_prefix: str = "HEADERS: ",
             **kwargs: Any
     ) -> None:
         """Init params."""
@@ -91,6 +99,8 @@ class PandasCSVParser(BaseParser):
         self._col_joiner = col_joiner
         self._row_joiner = row_joiner
         self._pandas_config = pandas_config
+        self._header_period = header_period
+        self._header_prefix = header_prefix
 
     def _init_parser(self) -> Dict:
         """Init parser."""
@@ -104,15 +114,26 @@ class PandasCSVParser(BaseParser):
             raise ValueError("pandas module is required to read CSV files.")
 
         df = pd.read_csv(file, **self._pandas_config)
+        headers = df.columns.tolist()
+        header_row = f"{self._header_prefix}{self._col_joiner.join(headers)}"
 
-        text_list = df.apply(
-            lambda row: (self._col_joiner).join(row.astype(str).tolist()), axis=1
-        ).tolist()
+        if not self._concat_rows:
+            return df.apply(
+                lambda row: (self._col_joiner).join(row.astype(str).tolist()), axis=1
+            ).tolist()
+        
+        text_list = []
+        if self._header_period != 1:
+            text_list.append(header_row)
+        
+        for i, row in df.iterrows():
+            if (self._header_period > 1 and i > 0 and i % self._header_period == 0):
+                text_list.append(header_row)
+            text_list.append(self._col_joiner.join(row.astype(str).tolist()))
+            if self._header_period == 1 and i < len(df) - 1:
+                text_list.append(header_row)
 
-        if self._concat_rows:
-            return (self._row_joiner).join(text_list)
-        else:
-            return text_list
+        return self._row_joiner.join(text_list)
 
 
 class ExcelParser(BaseParser):
@@ -138,7 +159,13 @@ class ExcelParser(BaseParser):
             for more information.
             Set to empty dict by default, this means pandas will try to figure
             out the table structure on its own.
-
+            
+        header_period (int): Controls how headers are included in output:
+            - 0: Headers only at the beginning (default)
+            - 1: Headers in every row
+            - N > 1: Headers every N rows
+            
+        header_prefix (str): Prefix for header rows. Default is "HEADERS: ".
     """
 
     def __init__(
@@ -148,6 +175,8 @@ class ExcelParser(BaseParser):
             col_joiner: str = ", ",
             row_joiner: str = "\n",
             pandas_config: dict = {},
+            header_period: int = 20,
+            header_prefix: str = "HEADERS: ",
             **kwargs: Any
     ) -> None:
         """Init params."""
@@ -156,6 +185,8 @@ class ExcelParser(BaseParser):
         self._col_joiner = col_joiner
         self._row_joiner = row_joiner
         self._pandas_config = pandas_config
+        self._header_period = header_period
+        self._header_prefix = header_prefix
 
     def _init_parser(self) -> Dict:
         """Init parser."""
@@ -169,12 +200,22 @@ class ExcelParser(BaseParser):
             raise ValueError("pandas module is required to read Excel files.")
 
         df = pd.read_excel(file, **self._pandas_config)
+        headers = df.columns.tolist()
+        header_row = f"{self._header_prefix}{self._col_joiner.join(headers)}"
+        
+        if not self._concat_rows:
+            return df.apply(
+                lambda row: (self._col_joiner).join(row.astype(str).tolist()), axis=1
+            ).tolist()
+        
+        text_list = []
+        if self._header_period != 1:
+            text_list.append(header_row)
 
-        text_list = df.apply(
-            lambda row: (self._col_joiner).join(row.astype(str).tolist()), axis=1
-        ).tolist()
-
-        if self._concat_rows:
-            return (self._row_joiner).join(text_list)
-        else:
-            return text_list
+        for i, row in df.iterrows():
+            if (self._header_period > 1 and i > 0 and i % self._header_period == 0):
+                text_list.append(header_row)
+            text_list.append(self._col_joiner.join(row.astype(str).tolist()))
+            if self._header_period == 1 and i < len(df) - 1:
+                text_list.append(header_row)
+        return self._row_joiner.join(text_list)

application/parser/remote/crawler_loader.py

@@ -1,3 +1,4 @@
+import logging
 import requests
 from urllib.parse import urlparse, urljoin
 from bs4 import BeautifulSoup
@@ -42,7 +43,7 @@ class CrawlerLoader(BaseRemote):
                         )
                     )
             except Exception as e:
-                print(f"Error processing URL {current_url}: {e}")
+                logging.error(f"Error processing URL {current_url}: {e}", exc_info=True)
                 continue
 
             # Parse the HTML content to extract all links
@@ -61,4 +62,4 @@ class CrawlerLoader(BaseRemote):
             if self.limit is not None and len(visited_urls) >= self.limit:
                 break
 
-        return loaded_content
+        return loaded_content

application/parser/remote/sitemap_loader.py

@@ -1,3 +1,4 @@
+import logging
 import requests
 import re  # Import regular expression library
 import xml.etree.ElementTree as ET
@@ -32,7 +33,7 @@ class SitemapLoader(BaseRemote):
                 documents.extend(loader.load())
                 processed_urls += 1  # Increment the counter after processing each URL
             except Exception as e:
-                print(f"Error processing URL {url}: {e}")
+                logging.error(f"Error processing URL {url}: {e}", exc_info=True)
                 continue
 
         return documents

application/parser/remote/web_loader.py

@@ -1,3 +1,4 @@
+import logging
 from application.parser.remote.base import BaseRemote
 from application.parser.schema.base import Document
 from langchain_community.document_loaders import WebBaseLoader
@@ -39,6 +40,6 @@ class WebLoader(BaseRemote):
                         )
                     )
             except Exception as e:
-                print(f"Error processing URL {url}: {e}")
+                logging.error(f"Error processing URL {url}: {e}", exc_info=True)
                 continue
-        return documents
+        return documents

application/prompts/chat_combine_creative.txt

@@ -1,9 +1,15 @@
-You are a DocsGPT, friendly and helpful AI assistant by Arc53 that provides help with documents. You give thorough answers with code examples if possible.
-Use the following pieces of context to help answer the users question. If its not relevant to the question, provide friendly responses.
-You have access to chat history, and can use it to help answer the question.
-When using code examples, use the following format:
+You are a helpful AI assistant, DocsGPT. You are proactive and helpful. Try to use tools, if they are available to you, 
+be proactive and fill in missing information.
+Users can Upload documents for your context as attachments or sources via UI using the Conversation input box.
+If appropriate, your answers can include code examples, formatted as follows:
 ```(language)
 (code)
 ```
+Users are also able to see charts and diagrams if you use them with valid mermaid syntax in your responses.
+Try to respond with mermaid charts if visualization helps with users queries.
+You effectively utilize chat history, ensuring relevant and tailored responses. 
+Try to use additional provided context if it's available, otherwise use your knowledge and tool capabilities.
+Allow yourself to be very creative and use your imagination.
 ----------------
+Possible additional context from uploaded sources:
 {summaries}

application/prompts/chat_combine_default.txt

@@ -1,9 +1,14 @@
-You are a helpful AI assistant, DocsGPT, specializing in document assistance, designed to offer detailed and informative responses. 
+You are a helpful AI assistant, DocsGPT. You are proactive and helpful. Try to use tools, if they are available to you, 
+be proactive and fill in missing information.
+Users can Upload documents for your context as attachments or sources via UI using the Conversation input box.
 If appropriate, your answers can include code examples, formatted as follows:
 ```(language)
 (code)
 ```
+Users are also able to see charts and diagrams if you use them with valid mermaid syntax in your responses.
+Try to respond with mermaid charts if visualization helps with users queries.
 You effectively utilize chat history, ensuring relevant and tailored responses. 
-If a question doesn't align with your context, you provide friendly and helpful replies.
+Try to use additional provided context if it's available, otherwise use your knowledge and tool capabilities.
 ----------------
+Possible additional context from uploaded sources:
 {summaries}

application/prompts/chat_combine_strict.txt

@@ -1,13 +1,17 @@
-You are an AI Assistant, DocsGPT, adept at offering document assistance. 
-Your expertise lies in providing answer on top of provided context. 
-You can leverage the chat history if needed.
-Answer the question based on the context below. 
-Keep the answer concise. Respond "Irrelevant context" if not sure about the answer.
-If question is not related to the context, respond "Irrelevant context".
-When using code examples, use the following format:
+You are a helpful AI assistant, DocsGPT. You are proactive and helpful. Try to use tools, if they are available to you, 
+be proactive and fill in missing information.
+Users can Upload documents for your context as attachments or sources via UI using the Conversation input box.
+If appropriate, your answers can include code examples, formatted as follows:
 ```(language)
 (code)
 ```
- ----------------
- Context:
- {summaries}
+Users are also able to see charts and diagrams if you use them with valid mermaid syntax in your responses.
+Try to respond with mermaid charts if visualization helps with users queries.
+You effectively utilize chat history, ensuring relevant and tailored responses. 
+Use context provided below or use available tools tool capabilities to answer user queries.
+If you dont have enough information from the context or tools, answer "I don't know" or "I don't have enough information".
+Never make up information or provide false information!
+Allow yourself to be very creative and use your imagination.
+----------------
+Context from uploaded sources:
+{summaries}

application/prompts/react_final_prompt.txt

@@ -0,0 +1,3 @@
+Query: {query}
+Observations: {observations}
+Now, using the insights from the observations, formulate a well-structured and precise final answer.

application/prompts/react_planning_prompt.txt

@@ -0,0 +1,10 @@
+You are an AI assistant and talk like you're thinking out loud. Given the following query, outline a concise thought process that includes key steps and considerations necessary for effective analysis and response. Avoid pointwise formatting. The goal is to break down the query into manageable components without excessive detail, focusing on clarity and logical progression.
+
+Include the following elements in your thought process:
+1. Identify the main objective of the query.
+2. Determine any relevant context or background information needed to understand the query.
+3. List potential approaches or methods to address the query.
+4. Highlight any critical factors or constraints that may influence the outcome.
+
+Query: {query}  
+Summaries: {summaries}

application/requirements.txt

@@ -1,87 +1,77 @@
-anthropic==0.40.0
-boto3==1.35.97
-beautifulsoup4==4.12.3
+anthropic==0.49.0
+boto3==1.38.18
+beautifulsoup4==4.13.4
 celery==5.4.0
 dataclasses-json==0.6.7
 docx2txt==0.8
-duckduckgo-search==6.3.0
+duckduckgo-search==7.5.2
 ebooklib==0.18
-elastic-transport==8.17.0
-elasticsearch==8.17.0
 escodegen==1.0.11
 esprima==4.0.1
 esutils==1.0.1
-Flask==3.1.0
+Flask==3.1.1
 faiss-cpu==1.9.0.post1
 flask-restx==1.3.0
-google-genai==0.5.0
-google-generativeai==0.8.3
+google-genai==1.3.0
 gTTS==2.5.4
 gunicorn==23.0.0
-html2text==2024.2.26
 javalang==0.13.0
-jinja2==3.1.5
+jinja2==3.1.6
 jiter==0.8.2
 jmespath==1.0.1
 joblib==1.4.2
 jsonpatch==1.33
 jsonpointer==3.0.0
-jsonschema==4.23.0
-jsonschema-spec==0.2.4
-jsonschema-specifications==2023.7.1
 kombu==5.4.2
-langchain==0.3.14
-langchain-community==0.3.14
-langchain-core==0.3.29
-langchain-openai==0.3.0
-langchain-text-splitters==0.3.5
-langsmith==0.2.10
+langchain==0.3.20
+langchain-community==0.3.19
+langchain-core==0.3.59
+langchain-openai==0.3.16
+langchain-text-splitters==0.3.8
+langsmith==0.3.42
 lazy-object-proxy==1.10.0
-lxml==5.3.0
+lxml==5.3.1
 markupsafe==3.0.2
-marshmallow==3.24.1
+marshmallow==3.26.1
 mpmath==1.3.0
-multidict==6.1.0
+multidict==6.4.3
 mypy-extensions==1.0.0
 networkx==3.4.2
 numpy==2.2.1
-openai==1.59.5
-openapi-schema-validator==0.6.2
-openapi-spec-validator==0.6.0
-openapi3-parser==1.1.19
+openai==1.78.1
+openapi3-parser==1.1.21
 orjson==3.10.14
-packaging==24.1
+packaging==25.0
 pandas==2.2.3
 openpyxl==3.1.5
 pathable==0.4.4
 pillow==11.1.0
-portalocker==2.10.1
+portalocker==3.1.1
 prance==23.6.21.0
-primp==0.10.0
-prompt-toolkit==3.0.48
+prompt-toolkit==3.0.51
 protobuf==5.29.3
 psycopg2-binary==2.9.10
 py==1.11.0
-pydantic==2.10.4
+pydantic==2.10.6
 pydantic-core==2.27.2
 pydantic-settings==2.7.1
-pymongo==4.10.1
-pypdf2==3.0.1
+pymongo==4.11.3
+pypdf==5.5.0
 python-dateutil==2.9.0.post0
 python-dotenv==1.0.1
+python-jose==3.4.0
 python-pptx==1.0.2
-qdrant-client==1.12.2
 redis==5.2.1
-referencing==0.30.2
+referencing==0.36.2
 regex==2024.11.6
 requests==2.32.3
 retry==0.9.2
 sentence-transformers==3.3.1
 tiktoken==0.8.0
 tokenizers==0.21.0
-torch==2.5.1
+torch==2.7.0
 tqdm==4.67.1
-transformers==4.48.0
+transformers==4.51.3
 typing-extensions==4.12.2
 typing-inspect==0.9.0
 tzdata==2024.2
@@ -89,7 +79,7 @@ urllib3==2.3.0
 vine==5.1.0
 wcwidth==0.2.13
 werkzeug==3.1.3
-yarl==1.18.3
-markdownify==0.14.1
+yarl==1.20.0
+markdownify==1.1.0
 tldextract==5.1.3
 websockets==14.1

application/retriever/brave_search.py

@@ -1,15 +1,16 @@
 import json
-from application.retriever.base import BaseRetriever
+
+from langchain_community.tools import BraveSearch
+
 from application.core.settings import settings
 from application.llm.llm_creator import LLMCreator
-from langchain_community.tools import BraveSearch
+from application.retriever.base import BaseRetriever
 
 
 class BraveRetSearch(BaseRetriever):
 
     def __init__(
         self,
-        question,
         source,
         chat_history,
         prompt,
@@ -17,8 +18,9 @@ class BraveRetSearch(BaseRetriever):
         token_limit=150,
         gpt_model="docsgpt",
         user_api_key=None,
+        decoded_token=None,
     ):
-        self.question = question
+        self.question = ""
         self.source = source
         self.chat_history = chat_history
         self.prompt = prompt
@@ -35,6 +37,7 @@ class BraveRetSearch(BaseRetriever):
             )
         )
         self.user_api_key = user_api_key
+        self.decoded_token = decoded_token
 
     def _get_data(self):
         if self.chunks == 0:
@@ -81,14 +84,19 @@ class BraveRetSearch(BaseRetriever):
         messages_combine.append({"role": "user", "content": self.question})
 
         llm = LLMCreator.create_llm(
-            settings.LLM_NAME, api_key=settings.API_KEY, user_api_key=self.user_api_key
+            settings.LLM_NAME,
+            api_key=settings.API_KEY,
+            user_api_key=self.user_api_key,
+            decoded_token=self.decoded_token,
         )
 
         completion = llm.gen_stream(model=self.gpt_model, messages=messages_combine)
         for line in completion:
             yield {"answer": str(line)}
 
-    def search(self):
+    def search(self, query: str = ""):
+        if query:
+            self.question = query
         return self._get_data()
 
     def get_params(self):
@@ -100,5 +108,5 @@ class BraveRetSearch(BaseRetriever):
             "chunks": self.chunks,
             "token_limit": self.token_limit,
             "gpt_model": self.gpt_model,
-            "user_api_key": self.user_api_key
+            "user_api_key": self.user_api_key,
         }

application/retriever/classic_rag.py

@@ -1,26 +1,27 @@
+import logging
 from application.core.settings import settings
+from application.llm.llm_creator import LLMCreator
 from application.retriever.base import BaseRetriever
-from application.tools.agent import Agent
 
 from application.vectorstore.vector_creator import VectorCreator
 
 
 class ClassicRAG(BaseRetriever):
-
     def __init__(
         self,
-        question,
         source,
-        chat_history,
-        prompt,
+        chat_history=None,
+        prompt="",
         chunks=2,
         token_limit=150,
         gpt_model="docsgpt",
         user_api_key=None,
+        llm_name=settings.LLM_NAME,
+        api_key=settings.API_KEY,
+        decoded_token=None,
     ):
-        self.question = question
-        self.vectorstore = source["active_docs"] if "active_docs" in source else None
-        self.chat_history = chat_history
+        self.original_question = ""
+        self.chat_history = chat_history if chat_history is not None else []
         self.prompt = prompt
         self.chunks = chunks
         self.gpt_model = gpt_model
@@ -35,6 +36,45 @@ class ClassicRAG(BaseRetriever):
             )
         )
         self.user_api_key = user_api_key
+        self.llm_name = llm_name
+        self.api_key = api_key
+        self.llm = LLMCreator.create_llm(
+            self.llm_name,
+            api_key=self.api_key,
+            user_api_key=self.user_api_key,
+            decoded_token=decoded_token,
+        )
+        self.question = self._rephrase_query()
+        self.vectorstore = source["active_docs"] if "active_docs" in source else None
+        self.decoded_token = decoded_token
+
+    def _rephrase_query(self):
+        if (
+            not self.original_question
+            or not self.chat_history
+            or self.chat_history == []
+        ):
+            return self.original_question
+
+        prompt = f"""Given the following conversation history:
+        {self.chat_history}
+
+        Rephrase the following user question to be a standalone search query 
+        that captures all relevant context from the conversation:
+        """
+
+        messages = [
+            {"role": "system", "content": prompt},
+            {"role": "user", "content": self.original_question},
+        ]
+
+        try:
+            rephrased_query = self.llm.gen(model=self.gpt_model, messages=messages)
+            print(f"Rephrased query: {rephrased_query}")
+            return rephrased_query if rephrased_query else self.original_question
+        except Exception as e:
+            logging.error(f"Error rephrasing query: {e}", exc_info=True)
+            return self.original_question
 
     def _get_data(self):
         if self.chunks == 0:
@@ -61,47 +101,20 @@ class ClassicRAG(BaseRetriever):
 
         return docs
 
-    def gen(self):
-        docs = self._get_data()
+    def gen():
+        pass
 
-        # join all page_content together with a newline
-        docs_together = "\n".join([doc["text"] for doc in docs])
-        p_chat_combine = self.prompt.replace("{summaries}", docs_together)
-        messages_combine = [{"role": "system", "content": p_chat_combine}]
-        for doc in docs:
-            yield {"source": doc}
-
-        if len(self.chat_history) > 0:
-            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"]}
-                    )
-        messages_combine.append({"role": "user", "content": self.question})
-        # llm = LLMCreator.create_llm(
-        #     settings.LLM_NAME, api_key=settings.API_KEY, user_api_key=self.user_api_key
-        # )
-        # completion = llm.gen_stream(model=self.gpt_model, messages=messages_combine)
-        agent = Agent(
-            llm_name=settings.LLM_NAME,
-            gpt_model=self.gpt_model,
-            api_key=settings.API_KEY,
-            user_api_key=self.user_api_key,
-        )
-        completion = agent.gen(messages_combine)
-        for line in completion:
-            yield {"answer": str(line)}
-
-    def search(self):
+    def search(self, query: str = ""):
+        if query:
+            self.original_question = query
+            self.question = self._rephrase_query()
         return self._get_data()
 
     def get_params(self):
         return {
-            "question": self.question,
+            "question": self.original_question,
+            "rephrased_question": self.question,
             "source": self.vectorstore,
-            "chat_history": self.chat_history,
-            "prompt": self.prompt,
             "chunks": self.chunks,
             "token_limit": self.token_limit,
             "gpt_model": self.gpt_model,

application/retriever/duckduck_search.py

@@ -1,15 +1,15 @@
-from application.retriever.base import BaseRetriever
-from application.core.settings import settings
-from application.llm.llm_creator import LLMCreator
 from langchain_community.tools import DuckDuckGoSearchResults
 from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
 
+from application.core.settings import settings
+from application.llm.llm_creator import LLMCreator
+from application.retriever.base import BaseRetriever
+
 
 class DuckDuckSearch(BaseRetriever):
 
     def __init__(
         self,
-        question,
         source,
         chat_history,
         prompt,
@@ -17,8 +17,9 @@ class DuckDuckSearch(BaseRetriever):
         token_limit=150,
         gpt_model="docsgpt",
         user_api_key=None,
+        decoded_token=None,
     ):
-        self.question = question
+        self.question = ""
         self.source = source
         self.chat_history = chat_history
         self.prompt = prompt
@@ -35,42 +36,26 @@ class DuckDuckSearch(BaseRetriever):
             )
         )
         self.user_api_key = user_api_key
-
-    def _parse_lang_string(self, input_string):
-        result = []
-        current_item = ""
-        inside_brackets = False
-        for char in input_string:
-            if char == "[":
-                inside_brackets = True
-            elif char == "]":
-                inside_brackets = False
-                result.append(current_item)
-                current_item = ""
-            elif inside_brackets:
-                current_item += char
-
-        if inside_brackets:
-            result.append(current_item)
-
-        return result
+        self.decoded_token = decoded_token
 
     def _get_data(self):
         if self.chunks == 0:
             docs = []
         else:
             wrapper = DuckDuckGoSearchAPIWrapper(max_results=self.chunks)
-            search = DuckDuckGoSearchResults(api_wrapper=wrapper)
+            search = DuckDuckGoSearchResults(api_wrapper=wrapper, output_format="list")
             results = search.run(self.question)
-            results = self._parse_lang_string(results)
 
             docs = []
             for i in results:
                 try:
-                    text = i.split("title:")[0]
-                    title = i.split("title:")[1].split("link:")[0]
-                    link = i.split("link:")[1]
-                    docs.append({"text": text, "title": title, "link": link})
+                    docs.append(
+                        {
+                            "text": i.get("snippet", "").strip(),
+                            "title": i.get("title", "").strip(),
+                            "link": i.get("link", "").strip(),
+                        }
+                    )
                 except IndexError:
                     pass
         if settings.LLM_NAME == "llama.cpp":
@@ -88,26 +73,31 @@ class DuckDuckSearch(BaseRetriever):
         for doc in docs:
             yield {"source": doc}
 
-        if len(self.chat_history) > 0:      
+        if len(self.chat_history) > 0:
             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 "prompt" in i and "response" in i:
+                    messages_combine.append({"role": "user", "content": i["prompt"]})
+                    messages_combine.append(
+                        {"role": "assistant", "content": i["response"]}
+                    )
         messages_combine.append({"role": "user", "content": self.question})
 
         llm = LLMCreator.create_llm(
-            settings.LLM_NAME, api_key=settings.API_KEY, user_api_key=self.user_api_key
+            settings.LLM_NAME,
+            api_key=settings.API_KEY,
+            user_api_key=self.user_api_key,
+            decoded_token=self.decoded_token,
         )
 
         completion = llm.gen_stream(model=self.gpt_model, messages=messages_combine)
         for line in completion:
             yield {"answer": str(line)}
 
-    def search(self):
+    def search(self, query: str = ""):
+        if query:
+            self.question = query
         return self._get_data()
-    
+
     def get_params(self):
         return {
             "question": self.question,
@@ -117,5 +107,5 @@ class DuckDuckSearch(BaseRetriever):
             "chunks": self.chunks,
             "token_limit": self.token_limit,
             "gpt_model": self.gpt_model,
-            "user_api_key": self.user_api_key
+            "user_api_key": self.user_api_key,
         }

application/storage/base.py

@@ -0,0 +1,94 @@
+"""Base storage class for file system abstraction."""
+from abc import ABC, abstractmethod
+from typing import BinaryIO, List, Callable
+
+
+class BaseStorage(ABC):
+    """Abstract base class for storage implementations."""
+
+    @abstractmethod
+    def save_file(self, file_data: BinaryIO, path: str) -> dict:
+        """
+        Save a file to storage.
+
+        Args:
+            file_data: File-like object containing the data
+            path: Path where the file should be stored
+
+        Returns:
+            dict: A dictionary containing metadata about the saved file, including:
+                - 'path': The path where the file was saved
+                - 'storage_type': The type of storage (e.g., 'local', 's3')
+                - Other storage-specific metadata (e.g., 'uri', 'bucket_name', etc.)
+        """
+        pass
+
+    @abstractmethod
+    def get_file(self, path: str) -> BinaryIO:
+        """
+        Retrieve a file from storage.
+
+        Args:
+            path: Path to the file
+
+        Returns:
+            BinaryIO: File-like object containing the file data
+        """
+        pass
+
+    @abstractmethod
+    def process_file(self, path: str, processor_func: Callable, **kwargs):
+        """
+        Process a file using the provided processor function.
+
+        This method handles the details of retrieving the file and providing
+        it to the processor function in an appropriate way based on the storage type.
+
+        Args:
+            path: Path to the file
+            processor_func: Function that processes the file
+            **kwargs: Additional arguments to pass to the processor function
+
+        Returns:
+            The result of the processor function
+        """
+        pass
+
+    @abstractmethod
+    def delete_file(self, path: str) -> bool:
+        """
+        Delete a file from storage.
+
+        Args:
+            path: Path to the file
+
+        Returns:
+            bool: True if deletion was successful
+        """
+        pass
+
+    @abstractmethod
+    def file_exists(self, path: str) -> bool:
+        """
+        Check if a file exists.
+
+        Args:
+            path: Path to the file
+
+        Returns:
+            bool: True if the file exists
+        """
+        pass
+
+    @abstractmethod
+    def list_files(self, directory: str) -> List[str]:
+        """
+        List all files in a directory.
+
+        Args:
+            directory: Directory path to list
+
+        Returns:
+            List[str]: List of file paths
+        """
+        pass

application/storage/local.py

@@ -0,0 +1,103 @@
+"""Local file system implementation."""
+import os
+import shutil
+from typing import BinaryIO, List, Callable
+
+from application.storage.base import BaseStorage
+
+
+class LocalStorage(BaseStorage):
+    """Local file system storage implementation."""
+
+    def __init__(self, base_dir: str = None):
+        """
+        Initialize local storage.
+
+        Args:
+            base_dir: Base directory for all operations. If None, uses current directory.
+        """
+        self.base_dir = base_dir or os.path.dirname(
+            os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+        )
+
+    def _get_full_path(self, path: str) -> str:
+        """Get absolute path by combining base_dir and path."""
+        if os.path.isabs(path):
+            return path
+        return os.path.join(self.base_dir, path)
+
+    def save_file(self, file_data: BinaryIO, path: str) -> dict:
+        """Save a file to local storage."""
+        full_path = self._get_full_path(path)
+
+        os.makedirs(os.path.dirname(full_path), exist_ok=True)
+
+        if hasattr(file_data, 'save'):
+            file_data.save(full_path)
+        else:
+            with open(full_path, 'wb') as f:
+                shutil.copyfileobj(file_data, f)
+
+        return {
+            'storage_type': 'local'
+        }
+
+    def get_file(self, path: str) -> BinaryIO:
+        """Get a file from local storage."""
+        full_path = self._get_full_path(path)
+
+        if not os.path.exists(full_path):
+            raise FileNotFoundError(f"File not found: {full_path}")
+
+        return open(full_path, 'rb')
+
+    def delete_file(self, path: str) -> bool:
+        """Delete a file from local storage."""
+        full_path = self._get_full_path(path)
+
+        if not os.path.exists(full_path):
+            return False
+
+        os.remove(full_path)
+        return True
+
+    def file_exists(self, path: str) -> bool:
+        """Check if a file exists in local storage."""
+        full_path = self._get_full_path(path)
+        return os.path.exists(full_path)
+
+    def list_files(self, directory: str) -> List[str]:
+        """List all files in a directory in local storage."""
+        full_path = self._get_full_path(directory)
+
+        if not os.path.exists(full_path):
+            return []
+
+        result = []
+        for root, _, files in os.walk(full_path):
+            for file in files:
+                rel_path = os.path.relpath(os.path.join(root, file), self.base_dir)
+                result.append(rel_path)
+
+        return result
+
+    def process_file(self, path: str, processor_func: Callable, **kwargs):
+        """
+        Process a file using the provided processor function.
+
+        For local storage, we can directly pass the full path to the processor.
+
+        Args:
+            path: Path to the file
+            processor_func: Function that processes the file
+            **kwargs: Additional arguments to pass to the processor function
+
+        Returns:
+            The result of the processor function
+        """
+        full_path = self._get_full_path(path)
+
+        if not os.path.exists(full_path):
+            raise FileNotFoundError(f"File not found: {full_path}")
+
+        return processor_func(local_path=full_path, **kwargs)

application/storage/s3.py

@@ -0,0 +1,120 @@
+"""S3 storage implementation."""
+import io
+from typing import BinaryIO, List, Callable
+import os
+
+import boto3
+from botocore.exceptions import ClientError
+
+from application.storage.base import BaseStorage
+from application.core.settings import settings
+
+
+class S3Storage(BaseStorage):
+    """AWS S3 storage implementation."""
+
+    def __init__(self, bucket_name=None):
+        """
+        Initialize S3 storage.
+
+        Args:
+            bucket_name: S3 bucket name (optional, defaults to settings)
+        """
+        self.bucket_name = bucket_name or getattr(settings, "S3_BUCKET_NAME", "docsgpt-test-bucket")
+
+        # Get credentials from settings
+        aws_access_key_id = getattr(settings, "SAGEMAKER_ACCESS_KEY", None)
+        aws_secret_access_key = getattr(settings, "SAGEMAKER_SECRET_KEY", None)
+        region_name = getattr(settings, "SAGEMAKER_REGION", None)
+
+        self.s3 = boto3.client(
+            's3',
+            aws_access_key_id=aws_access_key_id,
+            aws_secret_access_key=aws_secret_access_key,
+            region_name=region_name
+        )
+
+    def save_file(self, file_data: BinaryIO, path: str) -> dict:
+        """Save a file to S3 storage."""
+        self.s3.upload_fileobj(file_data, self.bucket_name, path)
+
+        region = getattr(settings, "SAGEMAKER_REGION", None)
+
+        return {
+            'storage_type': 's3',
+            'bucket_name': self.bucket_name,
+            'uri': f's3://{self.bucket_name}/{path}',
+            'region': region
+        }
+
+    def get_file(self, path: str) -> BinaryIO:
+        """Get a file from S3 storage."""
+        if not self.file_exists(path):
+            raise FileNotFoundError(f"File not found: {path}")
+
+        file_obj = io.BytesIO()
+        self.s3.download_fileobj(self.bucket_name, path, file_obj)
+        file_obj.seek(0)
+        return file_obj
+
+    def delete_file(self, path: str) -> bool:
+        """Delete a file from S3 storage."""
+        try:
+            self.s3.delete_object(Bucket=self.bucket_name, Key=path)
+            return True
+        except ClientError:
+            return False
+
+    def file_exists(self, path: str) -> bool:
+        """Check if a file exists in S3 storage."""
+        try:
+            self.s3.head_object(Bucket=self.bucket_name, Key=path)
+            return True
+        except ClientError:
+            return False
+
+    def list_files(self, directory: str) -> List[str]:
+        """List all files in a directory in S3 storage."""
+        # Ensure directory ends with a slash if it's not empty
+        if directory and not directory.endswith('/'):
+            directory += '/'
+
+        result = []
+        paginator = self.s3.get_paginator('list_objects_v2')
+        pages = paginator.paginate(Bucket=self.bucket_name, Prefix=directory)
+
+        for page in pages:
+            if 'Contents' in page:
+                for obj in page['Contents']:
+                    result.append(obj['Key'])
+
+        return result
+
+    def process_file(self, path: str, processor_func: Callable, **kwargs):
+        """
+        Process a file using the provided processor function.
+
+        Args:
+            path: Path to the file
+            processor_func: Function that processes the file
+            **kwargs: Additional arguments to pass to the processor function
+        
+        Returns:
+            The result of the processor function
+        """
+        import tempfile
+        import logging
+        
+        if not self.file_exists(path):
+            raise FileNotFoundError(f"File not found in S3: {path}")
+        
+        with tempfile.NamedTemporaryFile(suffix=os.path.splitext(path)[1], delete=True) as temp_file:
+            try:
+                # Download the file from S3 to the temporary file
+                self.s3.download_fileobj(self.bucket_name, path, temp_file)
+                temp_file.flush()
+                
+                return processor_func(local_path=temp_file.name, **kwargs)
+            except Exception as e:
+                logging.error(f"Error processing S3 file {path}: {e}", exc_info=True)
+                raise

application/storage/storage_creator.py

@@ -0,0 +1,32 @@
+"""Storage factory for creating different storage implementations."""
+from typing import Dict, Type
+
+from application.storage.base import BaseStorage
+from application.storage.local import LocalStorage
+from application.storage.s3 import S3Storage
+from application.core.settings import settings
+
+
+class StorageCreator:
+    storages: Dict[str, Type[BaseStorage]] = {
+        "local": LocalStorage,
+        "s3": S3Storage,
+    }
+    
+    _instance = None
+    
+    @classmethod
+    def get_storage(cls) -> BaseStorage:
+        if cls._instance is None:
+            storage_type = getattr(settings, "STORAGE_TYPE", "local")
+            cls._instance = cls.create_storage(storage_type)
+        
+        return cls._instance
+    
+    @classmethod
+    def create_storage(cls, type_name: str, *args, **kwargs) -> BaseStorage:
+        storage_class = cls.storages.get(type_name.lower())
+        if not storage_class:
+            raise ValueError(f"No storage implementation found for type {type_name}")
+        
+        return storage_class(*args, **kwargs)

application/tools/agent.py

@@ -1,160 +0,0 @@
-from application.core.mongo_db import MongoDB
-from application.llm.llm_creator import LLMCreator
-from application.tools.llm_handler import get_llm_handler
-from application.tools.tool_action_parser import ToolActionParser
-from application.tools.tool_manager import ToolManager
-
-
-class Agent:
-    def __init__(self, llm_name, gpt_model, api_key, user_api_key=None):
-        # Initialize the LLM with the provided parameters
-        self.llm = LLMCreator.create_llm(
-            llm_name, api_key=api_key, user_api_key=user_api_key
-        )
-        self.llm_handler = get_llm_handler(llm_name)
-        self.gpt_model = gpt_model
-        # Static tool configuration (to be replaced later)
-        self.tools = []
-        self.tool_config = {}
-
-    def _get_user_tools(self, user="local"):
-        mongo = MongoDB.get_client()
-        db = mongo["docsgpt"]
-        user_tools_collection = db["user_tools"]
-        user_tools = user_tools_collection.find({"user": user, "status": True})
-        user_tools = list(user_tools)
-        tools_by_id = {str(tool["_id"]): tool for tool in user_tools}
-        return tools_by_id
-
-    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()
-            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)
-
-        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={})
-        tool = tm.load_tool(
-            tool_data["name"],
-            tool_config=(
-                {
-                    "url": tool_data["config"]["actions"][action_name]["url"],
-                    "method": tool_data["config"]["actions"][action_name]["method"],
-                    "headers": headers,
-                    "query_params": query_params,
-                }
-                if tool_data["name"] == "api_tool"
-                else tool_data["config"]
-            ),
-        )
-        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)
-        call_id = getattr(call, "id", None)
-        return result, call_id
-
-    def _simple_tool_agent(self, messages):
-        tools_dict = self._get_user_tools()
-        self._prepare_tools(tools_dict)
-
-        resp = self.llm.gen(model=self.gpt_model, messages=messages, tools=self.tools)
-
-        if isinstance(resp, str):
-            yield resp
-            return
-        if hasattr(resp, "message") and hasattr(resp.message, "content"):
-            yield resp.message.content
-            return
-
-        resp = self.llm_handler.handle_response(self, resp, tools_dict, messages)
-
-        if isinstance(resp, str):
-            yield resp
-        elif hasattr(resp, "message") and hasattr(resp.message, "content"):
-            yield resp.message.content
-        else:
-            completion = self.llm.gen_stream(
-                model=self.gpt_model, messages=messages, tools=self.tools
-            )
-            for line in completion:
-                yield line
-
-        return
-
-    def gen(self, messages):
-        if self.llm.supports_tools():
-            resp = self._simple_tool_agent(messages)
-            for line in resp:
-                yield line
-        else:
-            resp = self.llm.gen_stream(model=self.gpt_model, messages=messages)
-            for line in resp:
-                yield line

application/tools/llm_handler.py

@@ -1,97 +0,0 @@
-import json
-from abc import ABC, abstractmethod
-
-
-class LLMHandler(ABC):
-    @abstractmethod
-    def handle_response(self, agent, resp, tools_dict, messages, **kwargs):
-        pass
-
-
-class OpenAILLMHandler(LLMHandler):
-    def handle_response(self, agent, resp, tools_dict, messages):
-        while resp.finish_reason == "tool_calls":
-            message = json.loads(resp.model_dump_json())["message"]
-            keys_to_remove = {"audio", "function_call", "refusal"}
-            filtered_data = {
-                k: v for k, v in message.items() if k not in keys_to_remove
-            }
-            messages.append(filtered_data)
-
-            tool_calls = resp.message.tool_calls
-            for call in tool_calls:
-                try:
-                    tool_response, call_id = agent._execute_tool_action(
-                        tools_dict, call
-                    )
-                    messages.append(
-                        {
-                            "role": "tool",
-                            "content": str(tool_response),
-                            "tool_call_id": call_id,
-                        }
-                    )
-                except Exception as e:
-                    messages.append(
-                        {
-                            "role": "tool",
-                            "content": f"Error executing tool: {str(e)}",
-                            "tool_call_id": call_id,
-                        }
-                    )
-            resp = agent.llm.gen(
-                model=agent.gpt_model, messages=messages, tools=agent.tools
-            )
-        return resp
-
-
-class GoogleLLMHandler(LLMHandler):
-    def handle_response(self, agent, resp, tools_dict, messages):
-        from google.genai import types
-
-        while True:
-            response = agent.llm.gen(
-                model=agent.gpt_model, messages=messages, tools=agent.tools
-            )
-            if response.candidates and response.candidates[0].content.parts:
-                tool_call_found = False
-                for part in response.candidates[0].content.parts:
-                    if part.function_call:
-                        tool_call_found = True
-                        tool_response, call_id = agent._execute_tool_action(
-                            tools_dict, part.function_call
-                        )
-                        function_response_part = types.Part.from_function_response(
-                            name=part.function_call.name,
-                            response={"result": tool_response},
-                        )
-
-                        messages.append(
-                            {"role": "model", "content": [part.to_json_dict()]}
-                        )
-                        messages.append(
-                            {
-                                "role": "tool",
-                                "content": [function_response_part.to_json_dict()],
-                            }
-                        )
-
-                if (
-                    not tool_call_found
-                    and response.candidates[0].content.parts
-                    and response.candidates[0].content.parts[0].text
-                ):
-                    return response.candidates[0].content.parts[0].text
-                elif not tool_call_found:
-                    return response.candidates[0].content.parts
-
-            else:
-                return response
-
-
-def get_llm_handler(llm_type):
-    handlers = {
-        "openai": OpenAILLMHandler(),
-        "google": GoogleLLMHandler(),
-    }
-    return handlers.get(llm_type, OpenAILLMHandler())

application/tools/tool_action_parser.py

@@ -1,26 +0,0 @@
-import json
-
-
-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):
-        call_args = json.loads(call.function.arguments)
-        tool_id = call.function.name.split("_")[-1]
-        action_name = call.function.name.rsplit("_", 1)[0]
-        return tool_id, action_name, call_args
-
-    def _parse_google_llm(self, call):
-        call_args = call.args
-        tool_id = call.name.split("_")[-1]
-        action_name = call.name.rsplit("_", 1)[0]
-        return tool_id, action_name, call_args

application/usage.py

@@ -1,17 +1,24 @@
 import sys
 from datetime import datetime
+
 from application.core.mongo_db import MongoDB
-from application.utils import num_tokens_from_string, num_tokens_from_object_or_list
+from application.core.settings import settings
+from application.utils import num_tokens_from_object_or_list, num_tokens_from_string
 
 mongo = MongoDB.get_client()
-db = mongo["docsgpt"]
+db = mongo[settings.MONGO_DB_NAME]
 usage_collection = db["token_usage"]
 
 
-def update_token_usage(user_api_key, token_usage):
+def update_token_usage(decoded_token, user_api_key, token_usage):
     if "pytest" in sys.modules:
         return
+    if decoded_token:
+        user_id = decoded_token["sub"]
+    else:
+        user_id = None
     usage_data = {
+        "user_id": user_id,
         "api_key": user_api_key,
         "prompt_tokens": token_usage["prompt_tokens"],
         "generated_tokens": token_usage["generated_tokens"],
@@ -24,14 +31,17 @@ def gen_token_usage(func):
     def wrapper(self, model, messages, stream, tools, **kwargs):
         for message in messages:
             if message["content"]:
-                self.token_usage["prompt_tokens"] += num_tokens_from_string(message["content"])
+                self.token_usage["prompt_tokens"] += num_tokens_from_string(
+                    message["content"]
+                )
         result = func(self, model, messages, stream, tools, **kwargs)
-        # check if result is a string
         if isinstance(result, str):
             self.token_usage["generated_tokens"] += num_tokens_from_string(result)
         else:
-            self.token_usage["generated_tokens"] += num_tokens_from_object_or_list(result)
-        update_token_usage(self.user_api_key, self.token_usage)
+            self.token_usage["generated_tokens"] += num_tokens_from_object_or_list(
+                result
+            )
+        update_token_usage(self.decoded_token, self.user_api_key, self.token_usage)
         return result
 
     return wrapper
@@ -40,7 +50,9 @@ def gen_token_usage(func):
 def stream_token_usage(func):
     def wrapper(self, model, messages, stream, tools, **kwargs):
         for message in messages:
-            self.token_usage["prompt_tokens"] += num_tokens_from_string(message["content"])
+            self.token_usage["prompt_tokens"] += num_tokens_from_string(
+                message["content"]
+            )
         batch = []
         result = func(self, model, messages, stream, tools, **kwargs)
         for r in result:
@@ -48,6 +60,6 @@ def stream_token_usage(func):
             yield r
         for line in batch:
             self.token_usage["generated_tokens"] += num_tokens_from_string(line)
-        update_token_usage(self.user_api_key, self.token_usage)
+        update_token_usage(self.decoded_token, self.user_api_key, self.token_usage)
 
     return wrapper

application/utils.py

@@ -1,5 +1,7 @@
-import tiktoken
 import hashlib
+import re
+
+import tiktoken
 from flask import jsonify, make_response
 
 
@@ -21,6 +23,7 @@ def num_tokens_from_string(string: str) -> int:
     else:
         return 0
 
+
 def num_tokens_from_object_or_list(thing):
     if isinstance(thing, list):
         return sum([num_tokens_from_object_or_list(x) for x in thing])
@@ -31,6 +34,7 @@ def num_tokens_from_object_or_list(thing):
     else:
         return 0
 
+
 def count_tokens_docs(docs):
     docs_content = ""
     for doc in docs:
@@ -56,7 +60,8 @@ def check_required_fields(data, required_fields):
 
 
 def get_hash(data):
-    return hashlib.md5(data.encode()).hexdigest()
+    return hashlib.md5(data.encode(), usedforsecurity=False).hexdigest()
+
 
 def limit_chat_history(history, max_token_limit=None, gpt_model="docsgpt"):
     """
@@ -66,32 +71,41 @@ def limit_chat_history(history, max_token_limit=None, gpt_model="docsgpt"):
     from application.core.settings import settings
 
     max_token_limit = (
-            max_token_limit
-            if max_token_limit and 
-            max_token_limit < settings.MODEL_TOKEN_LIMITS.get(
-                gpt_model, settings.DEFAULT_MAX_HISTORY
-            )
-            else settings.MODEL_TOKEN_LIMITS.get(
-                gpt_model, settings.DEFAULT_MAX_HISTORY
-            )
-        )
-    
+        max_token_limit
+        if max_token_limit
+        and max_token_limit
+        < settings.MODEL_TOKEN_LIMITS.get(gpt_model, settings.DEFAULT_MAX_HISTORY)
+        else settings.MODEL_TOKEN_LIMITS.get(gpt_model, settings.DEFAULT_MAX_HISTORY)
+    )
 
     if not history:
         return []
-        
-    tokens_current_history = 0
+
     trimmed_history = []
-    
+    tokens_current_history = 0
+
     for message in reversed(history):
+        tokens_batch = 0
         if "prompt" in message and "response" in message:
-            tokens_batch = num_tokens_from_string(message["prompt"]) + num_tokens_from_string(
-                message["response"]
-            )
-            if tokens_current_history + tokens_batch < max_token_limit:
-                tokens_current_history += tokens_batch
-                trimmed_history.insert(0, message)
-            else:
-                break
+            tokens_batch += num_tokens_from_string(message["prompt"])
+            tokens_batch += num_tokens_from_string(message["response"])
+
+        if "tool_calls" in message:
+            for tool_call in message["tool_calls"]:
+                tool_call_string = f"Tool: {tool_call.get('tool_name')} | Action: {tool_call.get('action_name')} | Args: {tool_call.get('arguments')} | Response: {tool_call.get('result')}"
+                tokens_batch += num_tokens_from_string(tool_call_string)
+
+        if tokens_current_history + tokens_batch < max_token_limit:
+            tokens_current_history += tokens_batch
+            trimmed_history.insert(0, message)
+        else:
+            break
 
     return trimmed_history
+
+
+def validate_function_name(function_name):
+    """Validates if a function name matches the allowed pattern."""
+    if not re.match(r"^[a-zA-Z0-9_-]+$", function_name):
+        return False
+    return True

application/vectorstore/base.py

@@ -75,9 +75,9 @@ class BaseVectorStore(ABC):
                     openai_api_key=embeddings_key
                 )
         elif embeddings_name == "huggingface_sentence-transformers/all-mpnet-base-v2":
-            if os.path.exists("./model/all-mpnet-base-v2"):
+            if os.path.exists("./models/all-mpnet-base-v2"):
                 embedding_instance = EmbeddingsSingleton.get_instance(
-                    embeddings_name="./model/all-mpnet-base-v2",
+                    embeddings_name = "./models/all-mpnet-base-v2",
                 )
             else:
                 embedding_instance = EmbeddingsSingleton.get_instance(
@@ -86,4 +86,5 @@ class BaseVectorStore(ABC):
         else:
             embedding_instance = EmbeddingsSingleton.get_instance(embeddings_name)
 
-        return embedding_instance
+        return embedding_instance
+    

application/vectorstore/elasticsearch.py

@@ -1,9 +1,6 @@
 from application.vectorstore.base import BaseVectorStore
 from application.core.settings import settings
 from application.vectorstore.document_class import Document
-import elasticsearch
-
-
 
 
 class ElasticsearchStore(BaseVectorStore):
@@ -26,8 +23,7 @@ class ElasticsearchStore(BaseVectorStore):
             else:
                 raise ValueError("Please provide either elasticsearch_url or cloud_id.")
 
-            
-
+            import elasticsearch
             ElasticsearchStore._es_connection = elasticsearch.Elasticsearch(**connection_params)
             
         self.docsearch = ElasticsearchStore._es_connection
@@ -155,8 +151,6 @@ class ElasticsearchStore(BaseVectorStore):
         **kwargs,
         ):
         
-        from elasticsearch.helpers import BulkIndexError, bulk
-
         bulk_kwargs = bulk_kwargs or {}
         import uuid
         embeddings = []
@@ -189,6 +183,7 @@ class ElasticsearchStore(BaseVectorStore):
 
 
         if len(requests) > 0:
+            from elasticsearch.helpers import BulkIndexError, bulk
             try:
                 success, failed = bulk(
                     self._es_connection,

application/vectorstore/faiss.py

@@ -1,30 +1,60 @@
-from langchain_community.vectorstores import FAISS
-from application.vectorstore.base import BaseVectorStore
-from application.core.settings import settings
 import os
+import tempfile
+
+from langchain_community.vectorstores import FAISS
+
+from application.core.settings import settings
+from application.parser.schema.base import Document
+from application.vectorstore.base import BaseVectorStore
+from application.storage.storage_creator import StorageCreator
+
 
 def get_vectorstore(path: str) -> str:
     if path:
-        vectorstore = os.path.join("application", "indexes", path)
+        vectorstore = f"indexes/{path}"
     else:
-        vectorstore = os.path.join("application")
+        vectorstore = "indexes"
     return vectorstore
 
+
 class FaissStore(BaseVectorStore):
     def __init__(self, source_id: str, embeddings_key: str, docs_init=None):
         super().__init__()
+        self.source_id = source_id
         self.path = get_vectorstore(source_id)
-        embeddings = self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key)
+        self.embeddings = self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key)
+        self.storage = StorageCreator.get_storage()
 
         try:
             if docs_init:
-                self.docsearch = FAISS.from_documents(docs_init, embeddings)
+                self.docsearch = FAISS.from_documents(docs_init, self.embeddings)
             else:
-                self.docsearch = FAISS.load_local(self.path, embeddings, allow_dangerous_deserialization=True)
-        except Exception:
-            raise
+                with tempfile.TemporaryDirectory() as temp_dir:
+                    faiss_path = f"{self.path}/index.faiss"
+                    pkl_path = f"{self.path}/index.pkl"
+                    
+                    if not self.storage.file_exists(faiss_path) or not self.storage.file_exists(pkl_path):
+                        raise FileNotFoundError(f"Index files not found in storage at {self.path}")
+                    
+                    faiss_file = self.storage.get_file(faiss_path)
+                    pkl_file = self.storage.get_file(pkl_path)
+                    
+                    local_faiss_path = os.path.join(temp_dir, "index.faiss")
+                    local_pkl_path = os.path.join(temp_dir, "index.pkl")
+                    
+                    with open(local_faiss_path, 'wb') as f:
+                        f.write(faiss_file.read())
+                    
+                    with open(local_pkl_path, 'wb') as f:
+                        f.write(pkl_file.read())
+                    
+                    self.docsearch = FAISS.load_local(
+                        temp_dir, self.embeddings, allow_dangerous_deserialization=True
+                    )
+        except Exception as e:
+            raise Exception(f"Error loading FAISS index: {str(e)}")
 
-        self.assert_embedding_dimensions(embeddings)
+        self.assert_embedding_dimensions(self.embeddings)
 
     def search(self, *args, **kwargs):
         return self.docsearch.similarity_search(*args, **kwargs)
@@ -40,11 +70,42 @@ class FaissStore(BaseVectorStore):
 
     def assert_embedding_dimensions(self, embeddings):
         """Check that the word embedding dimension of the docsearch index matches the dimension of the word embeddings used."""
-        if settings.EMBEDDINGS_NAME == "huggingface_sentence-transformers/all-mpnet-base-v2":
-            word_embedding_dimension = getattr(embeddings, 'dimension', None)
+        if (
+            settings.EMBEDDINGS_NAME
+            == "huggingface_sentence-transformers/all-mpnet-base-v2"
+        ):
+            word_embedding_dimension = getattr(embeddings, "dimension", None)
             if word_embedding_dimension is None:
-                raise AttributeError("'dimension' attribute not found in embeddings instance.")
-            
+                raise AttributeError(
+                    "'dimension' attribute not found in embeddings instance."
+                )
+
             docsearch_index_dimension = self.docsearch.index.d
             if word_embedding_dimension != docsearch_index_dimension:
-                raise ValueError(f"Embedding dimension mismatch: embeddings.dimension ({word_embedding_dimension}) != docsearch index dimension ({docsearch_index_dimension})")
+                raise ValueError(
+                    f"Embedding dimension mismatch: embeddings.dimension ({word_embedding_dimension}) != docsearch index dimension ({docsearch_index_dimension})"
+                )
+
+    def get_chunks(self):
+        chunks = []
+        if self.docsearch:
+            for doc_id, doc in self.docsearch.docstore._dict.items():
+                chunk_data = {
+                    "doc_id": doc_id,
+                    "text": doc.page_content,
+                    "metadata": doc.metadata,
+                }
+                chunks.append(chunk_data)
+        return chunks
+
+    def add_chunk(self, text, metadata=None):
+        metadata = metadata or {}
+        doc = Document(text=text, extra_info=metadata).to_langchain_format()
+        doc_id = self.docsearch.add_documents([doc])
+        self.save_local(self.path)
+        return doc_id
+
+    def delete_chunk(self, chunk_id):
+        self.delete_index([chunk_id])
+        self.save_local(self.path)
+        return True

application/vectorstore/mongodb.py

@@ -1,3 +1,4 @@
+import logging
 from application.core.settings import settings
 from application.vectorstore.base import BaseVectorStore
 from application.vectorstore.document_class import Document
@@ -124,3 +125,53 @@ class MongoDBVectorStore(BaseVectorStore):
 
     def delete_index(self, *args, **kwargs):
         self._collection.delete_many({"source_id": self._source_id})
+
+    def get_chunks(self):
+        try:
+            chunks = []
+            cursor = self._collection.find({"source_id": self._source_id})
+            for doc in cursor:
+                doc_id = str(doc.get("_id"))
+                text = doc.get(self._text_key)
+                metadata = {
+                    k: v
+                    for k, v in doc.items()
+                    if k
+                    not in ["_id", self._text_key, self._embedding_key, "source_id"]
+                }
+
+                if text:
+                    chunks.append(
+                        {"doc_id": doc_id, "text": text, "metadata": metadata}
+                    )
+
+            return chunks
+        except Exception as e:
+            logging.error(f"Error getting chunks: {e}", exc_info=True)
+            return []
+
+    def add_chunk(self, text, metadata=None):
+        metadata = metadata or {}
+        embeddings = self._embedding.embed_documents([text])
+        if not embeddings:
+            raise ValueError("Could not generate embedding for chunk")
+
+        chunk_data = {
+            self._text_key: text,
+            self._embedding_key: embeddings[0],
+            "source_id": self._source_id,
+            **metadata,
+        }
+        result = self._collection.insert_one(chunk_data)
+        return str(result.inserted_id)
+
+    def delete_chunk(self, chunk_id):
+        try:
+            from bson.objectid import ObjectId
+
+            object_id = ObjectId(chunk_id)
+            result = self._collection.delete_one({"_id": object_id})
+            return result.deleted_count > 0
+        except Exception as e:
+            logging.error(f"Error deleting chunk: {e}", exc_info=True)
+            return False

application/vectorstore/qdrant.py

@@ -1,11 +1,12 @@
-from langchain_community.vectorstores.qdrant import Qdrant
 from application.vectorstore.base import BaseVectorStore
 from application.core.settings import settings
-from qdrant_client import models
 
 
 class QdrantStore(BaseVectorStore):
     def __init__(self, source_id: str = "", embeddings_key: str = "embeddings"):
+        from qdrant_client import models
+        from langchain_community.vectorstores.qdrant import Qdrant
+
         self._filter = models.Filter(
             must=[
                 models.FieldCondition(

application/worker.py

@@ -1,25 +1,37 @@
+import datetime
+import json
 import logging
+import mimetypes
 import os
 import shutil
 import string
+import tempfile
 import zipfile
+
 from collections import Counter
 from urllib.parse import urljoin
 
 import requests
+from bson.dbref import DBRef
 from bson.objectid import ObjectId
 
+from application.agents.agent_creator import AgentCreator
+from application.api.answer.routes import get_prompt
+
 from application.core.mongo_db import MongoDB
 from application.core.settings import settings
-from application.parser.file.bulk import SimpleDirectoryReader
+from application.parser.chunking import Chunker
 from application.parser.embedding_pipeline import embed_and_store_documents
+from application.parser.file.bulk import SimpleDirectoryReader
 from application.parser.remote.remote_creator import RemoteCreator
 from application.parser.schema.base import Document
-from application.parser.chunking import Chunker
-from application.utils import count_tokens_docs
+from application.retriever.retriever_creator import RetrieverCreator
+
+from application.storage.storage_creator import StorageCreator
+from application.utils import count_tokens_docs, num_tokens_from_string
 
 mongo = MongoDB.get_client()
-db = mongo["docsgpt"]
+db = mongo[settings.MONGO_DB_NAME]
 sources_collection = db["sources"]
 
 # Constants
@@ -27,18 +39,22 @@ MIN_TOKENS = 150
 MAX_TOKENS = 1250
 RECURSION_DEPTH = 2
 
+
 # Define a function to extract metadata from a given filename.
 def metadata_from_filename(title):
     return {"title": title}
 
+
 # Define a function to generate a random string of a given length.
 def generate_random_string(length):
     return "".join([string.ascii_letters[i % 52] for i in range(length)])
 
+
 current_dir = os.path.dirname(
     os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 )
 
+
 def extract_zip_recursive(zip_path, extract_to, current_depth=0, max_depth=5):
     """
     Recursively extract zip files with a limit on recursion depth.
@@ -58,7 +74,7 @@ def extract_zip_recursive(zip_path, extract_to, current_depth=0, max_depth=5):
             zip_ref.extractall(extract_to)
         os.remove(zip_path)  # Remove the zip file after extracting
     except Exception as e:
-        logging.error(f"Error extracting zip file {zip_path}: {e}")
+        logging.error(f"Error extracting zip file {zip_path}: {e}", exc_info=True)
         return
 
     # Check for nested zip files and extract them
@@ -69,6 +85,7 @@ def extract_zip_recursive(zip_path, extract_to, current_depth=0, max_depth=5):
                 file_path = os.path.join(root, file)
                 extract_zip_recursive(file_path, root, current_depth + 1, max_depth)
 
+
 def download_file(url, params, dest_path):
     try:
         response = requests.get(url, params=params)
@@ -79,6 +96,7 @@ def download_file(url, params, dest_path):
         logging.error(f"Error downloading file: {e}")
         raise
 
+
 def upload_index(full_path, file_data):
     try:
         if settings.VECTOR_STORE == "faiss":
@@ -87,7 +105,9 @@ def upload_index(full_path, file_data):
                 "file_pkl": open(full_path + "/index.pkl", "rb"),
             }
             response = requests.post(
-                urljoin(settings.API_URL, "/api/upload_index"), files=files, data=file_data
+                urljoin(settings.API_URL, "/api/upload_index"),
+                files=files,
+                data=file_data,
             )
         else:
             response = requests.post(
@@ -102,6 +122,76 @@ def upload_index(full_path, file_data):
             for file in files.values():
                 file.close()
 
+
+def run_agent_logic(agent_config, input_data):
+    try:
+        source = agent_config.get("source")
+        retriever = agent_config.get("retriever", "classic")
+        if isinstance(source, DBRef):
+            source_doc = db.dereference(source)
+            source = str(source_doc["_id"])
+            retriever = source_doc.get("retriever", agent_config.get("retriever"))
+        else:
+            source = {}
+        source = {"active_docs": source}
+        chunks = int(agent_config.get("chunks", 2))
+        prompt_id = agent_config.get("prompt_id", "default")
+        user_api_key = agent_config["key"]
+        agent_type = agent_config.get("agent_type", "classic")
+        decoded_token = {"sub": agent_config.get("user")}
+        prompt = get_prompt(prompt_id)
+        agent = AgentCreator.create_agent(
+            agent_type,
+            endpoint="webhook",
+            llm_name=settings.LLM_NAME,
+            gpt_model=settings.MODEL_NAME,
+            api_key=settings.API_KEY,
+            user_api_key=user_api_key,
+            prompt=prompt,
+            chat_history=[],
+            decoded_token=decoded_token,
+            attachments=[],
+        )
+        retriever = RetrieverCreator.create_retriever(
+            retriever,
+            source=source,
+            chat_history=[],
+            prompt=prompt,
+            chunks=chunks,
+            token_limit=settings.DEFAULT_MAX_HISTORY,
+            gpt_model=settings.MODEL_NAME,
+            user_api_key=user_api_key,
+            decoded_token=decoded_token,
+        )
+        answer = agent.gen(query=input_data, retriever=retriever)
+        response_full = ""
+        thought = ""
+        source_log_docs = []
+        tool_calls = []
+
+        for line in answer:
+            if "answer" in line:
+                response_full += str(line["answer"])
+            elif "sources" in line:
+                source_log_docs.extend(line["sources"])
+            elif "tool_calls" in line:
+                tool_calls.extend(line["tool_calls"])
+            elif "thought" in line:
+                thought += line["thought"]
+
+        result = {
+            "answer": response_full,
+            "sources": source_log_docs,
+            "tool_calls": tool_calls,
+            "thought": thought,
+        }
+        logging.info(f"Agent response: {result}")
+        return result
+    except Exception as e:
+        logging.error(f"Error in run_agent_logic: {e}", exc_info=True)
+        raise
+
+
 # Define the main function for ingesting and processing documents.
 def ingest_worker(
     self, directory, formats, name_job, filename, user, retriever="classic"
@@ -126,62 +216,87 @@ def ingest_worker(
     limit = None
     exclude = True
     sample = False
+
+    storage = StorageCreator.get_storage()
+
     full_path = os.path.join(directory, user, name_job)
+    source_file_path = os.path.join(full_path, filename)
 
     logging.info(f"Ingest file: {full_path}", extra={"user": user, "job": name_job})
-    file_data = {"name": name_job, "file": filename, "user": user}
 
-    if not os.path.exists(full_path):
-        os.makedirs(full_path)
-    download_file(urljoin(settings.API_URL, "/api/download"), file_data, os.path.join(full_path, filename))
+    # Create temporary working directory
+    with tempfile.TemporaryDirectory() as temp_dir:
+        try:
+            os.makedirs(temp_dir, exist_ok=True)
 
-    # check if file is .zip and extract it
-    if filename.endswith(".zip"):
-        extract_zip_recursive(
-            os.path.join(full_path, filename), full_path, 0, RECURSION_DEPTH
-        )
+            # Download file from storage to temp directory
+            temp_file_path = os.path.join(temp_dir, filename)
+            file_data = storage.get_file(source_file_path)
 
-    self.update_state(state="PROGRESS", meta={"current": 1})
+            with open(temp_file_path, "wb") as f:
+                f.write(file_data.read())
 
-    raw_docs = SimpleDirectoryReader(
-        input_dir=full_path,
-        input_files=input_files,
-        recursive=recursive,
-        required_exts=formats,
-        num_files_limit=limit,
-        exclude_hidden=exclude,
-        file_metadata=metadata_from_filename,
-    ).load_data()
+            self.update_state(state="PROGRESS", meta={"current": 1})
 
-    chunker = Chunker(
-        chunking_strategy="classic_chunk",
-        max_tokens=MAX_TOKENS,
-        min_tokens=MIN_TOKENS,
-        duplicate_headers=False
-    )
-    raw_docs = chunker.chunk(documents=raw_docs)
+            # Handle zip files
+            if filename.endswith(".zip"):
+                logging.info(f"Extracting zip file: {filename}")
+                extract_zip_recursive(
+                    temp_file_path, temp_dir, current_depth=0, max_depth=RECURSION_DEPTH
+                )
 
-    docs = [Document.to_langchain_format(raw_doc) for raw_doc in raw_docs]
-    id = ObjectId()
+            if sample:
+                logging.info(f"Sample mode enabled. Using {limit} documents.")
 
-    embed_and_store_documents(docs, full_path, id, self)
-    tokens = count_tokens_docs(docs)
-    self.update_state(state="PROGRESS", meta={"current": 100})
+            reader = SimpleDirectoryReader(
+                input_dir=temp_dir,
+                input_files=input_files,
+                recursive=recursive,
+                required_exts=formats,
+                exclude_hidden=exclude,
+                file_metadata=metadata_from_filename,
+            )
+            raw_docs = reader.load_data()
 
-    if sample:
-        for i in range(min(5, len(raw_docs))):
-            logging.info(f"Sample document {i}: {raw_docs[i]}")
+            chunker = Chunker(
+                chunking_strategy="classic_chunk",
+                max_tokens=MAX_TOKENS,
+                min_tokens=MIN_TOKENS,
+                duplicate_headers=False,
+            )
+            raw_docs = chunker.chunk(documents=raw_docs)
 
-    file_data.update({
-        "tokens": tokens,
-        "retriever": retriever,
-        "id": str(id),
-        "type": "local",
-    })
-    upload_index(full_path, file_data)
+            docs = [Document.to_langchain_format(raw_doc) for raw_doc in raw_docs]
 
-    # delete local
-    shutil.rmtree(full_path)
+            id = ObjectId()
+
+            vector_store_path = os.path.join(temp_dir, "vector_store")
+            os.makedirs(vector_store_path, exist_ok=True)
+
+            embed_and_store_documents(docs, vector_store_path, id, self)
+
+            tokens = count_tokens_docs(docs)
+
+            self.update_state(state="PROGRESS", meta={"current": 100})
+
+            if sample:
+                for i in range(min(5, len(raw_docs))):
+                    logging.info(f"Sample document {i}: {raw_docs[i]}")
+            file_data = {
+                "name": name_job,
+                "file": filename,
+                "user": user,
+                "tokens": tokens,
+                "retriever": retriever,
+                "id": str(id),
+                "type": "local",
+            }
+
+            upload_index(vector_store_path, file_data)
+
+        except Exception as e:
+            logging.error(f"Error in ingest_worker: {e}", exc_info=True)
+            raise
 
     return {
         "directory": directory,
@@ -192,6 +307,7 @@ def ingest_worker(
         "limited": False,
     }
 
+
 def remote_worker(
     self,
     source_data,
@@ -203,7 +319,7 @@ def remote_worker(
     sync_frequency="never",
     operation_mode="upload",
     doc_id=None,
-):  
+):
     full_path = os.path.join(directory, user, name_job)
     if not os.path.exists(full_path):
         os.makedirs(full_path)
@@ -218,7 +334,7 @@ def remote_worker(
             chunking_strategy="classic_chunk",
             max_tokens=MAX_TOKENS,
             min_tokens=MIN_TOKENS,
-            duplicate_headers=False
+            duplicate_headers=False,
         )
         docs = chunker.chunk(documents=raw_docs)
         docs = [Document.to_langchain_format(raw_doc) for raw_doc in raw_docs]
@@ -260,6 +376,7 @@ def remote_worker(
     logging.info("remote_worker task completed successfully")
     return {"urls": source_data, "name_job": name_job, "user": user, "limited": False}
 
+
 def sync(
     self,
     source_data,
@@ -285,10 +402,11 @@ def sync(
             doc_id,
         )
     except Exception as e:
-        logging.error(f"Error during sync: {e}")
+        logging.error(f"Error during sync: {e}", exc_info=True)
         return {"status": "error", "error": str(e)}
     return {"status": "success"}
 
+
 def sync_worker(self, frequency):
     sync_counts = Counter()
     sources = sources_collection.find()
@@ -312,3 +430,121 @@ def sync_worker(self, frequency):
         key: sync_counts[key]
         for key in ["total_sync_count", "sync_success", "sync_failure"]
     }
+
+
+def attachment_worker(self, file_info, user):
+    """
+    Process and store a single attachment without vectorization.
+    """
+
+    mongo = MongoDB.get_client()
+    db = mongo[settings.MONGO_DB_NAME]
+    attachments_collection = db["attachments"]
+
+    filename = file_info["filename"]
+    attachment_id = file_info["attachment_id"]
+    relative_path = file_info["path"]
+    metadata = file_info.get("metadata", {})
+
+    try:
+        self.update_state(state="PROGRESS", meta={"current": 10})
+        storage = StorageCreator.get_storage()
+        
+        self.update_state(
+            state="PROGRESS", meta={"current": 30, "status": "Processing content"}
+        )
+
+        content = storage.process_file(
+            relative_path,
+            lambda local_path, **kwargs: SimpleDirectoryReader(
+                input_files=[local_path], exclude_hidden=True, errors="ignore"
+            ).load_data()[0].text
+        )
+            
+        token_count = num_tokens_from_string(content)
+
+        self.update_state(
+            state="PROGRESS", meta={"current": 80, "status": "Storing in database"}
+        )
+
+        mime_type = mimetypes.guess_type(filename)[0] or "application/octet-stream"
+
+        doc_id = ObjectId(attachment_id)
+        attachments_collection.insert_one(
+            {
+                "_id": doc_id,
+                "user": user,
+                "path": relative_path,
+                "content": content,
+                "token_count": token_count,
+                "mime_type": mime_type,
+                "date": datetime.datetime.now(),
+                "metadata": metadata,
+            }
+        )
+
+        logging.info(
+            f"Stored attachment with ID: {attachment_id}", extra={"user": user}
+        )
+
+        self.update_state(
+            state="PROGRESS", meta={"current": 100, "status": "Complete"}
+        )
+
+        return {
+            "filename": filename,
+            "path": relative_path,
+            "token_count": token_count,
+            "attachment_id": attachment_id,
+            "mime_type": mime_type,
+            "metadata": metadata,
+        }
+
+    except Exception as e:
+        logging.error(
+            f"Error processing file {filename}: {e}",
+            extra={"user": user},
+            exc_info=True,
+        )
+        raise
+
+
+def agent_webhook_worker(self, agent_id, payload):
+    """
+    Process the webhook payload for an agent.
+
+    Args:
+        self: Reference to the instance of the task.
+        agent_id (str): Unique identifier for the agent.
+        payload (dict): The payload data from the webhook.
+
+    Returns:
+        dict: Information about the processed webhook.
+    """
+    mongo = MongoDB.get_client()
+    db = mongo["docsgpt"]
+    agents_collection = db["agents"]
+
+    self.update_state(state="PROGRESS", meta={"current": 1})
+    try:
+        agent_oid = ObjectId(agent_id)
+        agent_config = agents_collection.find_one({"_id": agent_oid})
+        if not agent_config:
+            raise ValueError(f"Agent with ID {agent_id} not found.")
+        input_data = json.dumps(payload)
+    except Exception as e:
+        logging.error(f"Error processing agent webhook: {e}", exc_info=True)
+        return {"status": "error", "error": str(e)}
+
+    self.update_state(state="PROGRESS", meta={"current": 50})
+    try:
+        result = run_agent_logic(agent_config, input_data)
+    except Exception as e:
+        logging.error(f"Error running agent logic: {e}", exc_info=True)
+        return {"status": "error", "error": str(e)}
+    finally:
+        self.update_state(state="PROGRESS", meta={"current": 100})
+        logging.info(
+            f"Webhook processed for agent {agent_id}", extra={"agent_id": agent_id}
+        )
+        return {"status": "success", "result": result}

deployment/docker-compose-azure.yaml

@@ -1,6 +1,6 @@
 services:
   frontend:
-    build: ./frontend
+    build: ../frontend
     environment:
       - VITE_API_HOST=http://localhost:7091
       - VITE_API_STREAMING=$VITE_API_STREAMING
@@ -10,7 +10,7 @@ services:
       - backend
 
   backend:
-    build: ./application
+    build: ../application
     environment:
       - API_KEY=$OPENAI_API_KEY
       - EMBEDDINGS_KEY=$OPENAI_API_KEY
@@ -25,15 +25,15 @@ services:
     ports:
       - "7091:7091"
     volumes:
-      - ./application/indexes:/app/application/indexes
-      - ./application/inputs:/app/application/inputs
-      - ./application/vectors:/app/application/vectors
+      - ../application/indexes:/app/application/indexes
+      - ../application/inputs:/app/application/inputs
+      - ../application/vectors:/app/application/vectors
     depends_on:
         - redis
         - mongo
 
   worker:
-    build: ./application
+    build: ../application
     command: celery -A application.app.celery worker -l INFO
     environment:
       - API_KEY=$OPENAI_API_KEY

deployment/docker-compose-dev.yaml

@@ -0,0 +1,18 @@
+services:
+
+  redis:
+    image: redis:6-alpine
+    ports:
+      - 6379:6379
+
+  mongo:
+    image: mongo:6
+    ports:
+      - 27017:27017
+    volumes:
+      - mongodb_data_container:/data/db
+
+
+
+volumes:
+  mongodb_data_container:

deployment/docker-compose-local.yaml

@@ -1,8 +1,8 @@
 services:
   frontend:
-    build: ./frontend
+    build: ../frontend
     volumes:
-    - ./frontend/src:/app/src
+    - ../frontend/src:/app/src
     environment:
       - VITE_API_HOST=http://localhost:7091
       - VITE_API_STREAMING=$VITE_API_STREAMING

deployment/docker-compose.yaml

@@ -1,8 +1,8 @@
 services:
   frontend:
-    build: ./frontend
+    build: ../frontend
     volumes:
-      - ./frontend/src:/app/src
+      - ../frontend/src:/app/src
     environment:
       - VITE_API_HOST=http://localhost:7091
       - VITE_API_STREAMING=$VITE_API_STREAMING
@@ -12,7 +12,8 @@ services:
       - backend
 
   backend:
-    build: ./application
+    user: root
+    build: ../application
     environment:
       - API_KEY=$API_KEY
       - EMBEDDINGS_KEY=$API_KEY
@@ -21,18 +22,21 @@ services:
       - CELERY_RESULT_BACKEND=redis://redis:6379/1
       - MONGO_URI=mongodb://mongo:27017/docsgpt
       - CACHE_REDIS_URL=redis://redis:6379/2
+      - OPENAI_BASE_URL=$OPENAI_BASE_URL
+      - MODEL_NAME=$MODEL_NAME
     ports:
       - "7091:7091"
     volumes:
-      - ./application/indexes:/app/application/indexes
-      - ./application/inputs:/app/application/inputs
-      - ./application/vectors:/app/application/vectors
+      - ../application/indexes:/app/application/indexes
+      - ../application/inputs:/app/inputs
+      - ../application/vectors:/app/application/vectors
     depends_on:
       - redis
       - mongo
 
   worker:
-    build: ./application
+    user: root
+    build: ../application
     command: celery -A application.app.celery worker -l INFO -B
     environment:
       - API_KEY=$API_KEY
@@ -43,6 +47,10 @@ services:
       - MONGO_URI=mongodb://mongo:27017/docsgpt
       - API_URL=http://backend:7091
       - CACHE_REDIS_URL=redis://redis:6379/2
+    volumes:
+      - ../application/indexes:/app/application/indexes
+      - ../application/inputs:/app/inputs
+      - ../application/vectors:/app/application/vectors
     depends_on:
       - redis
       - mongo

deployment/optional/docker-compose.optional.ollama-cpu.yaml

@@ -0,0 +1,11 @@
+version: "3.8"
+services:
+  ollama:
+    image: ollama/ollama
+    ports:
+      - "11434:11434"
+    volumes:
+      - ollama_data:/root/.ollama
+
+volumes:
+  ollama_data:

deployment/optional/docker-compose.optional.ollama-gpu.yaml

@@ -0,0 +1,16 @@
+version: "3.8"
+services:
+  ollama:
+    image: ollama/ollama
+    ports:
+      - "11434:11434"
+    volumes:
+      - ollama_data:/root/.ollama
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - capabilities: [gpu]
+
+volumes:
+  ollama_data:

docker-compose-mock.yaml

@@ -1,20 +0,0 @@
-services:
-  frontend:
-    build: ./frontend
-    environment:
-      - VITE_API_HOST=http://localhost:7091
-      - VITE_API_STREAMING=$VITE_API_STREAMING
-    ports:
-      - "5173:5173"
-    depends_on:
-      - mock-backend
-
-  mock-backend:
-    build: ./mock-backend
-    ports:
-      - "7091:7091"
-
-  redis:
-    image: redis:6-alpine
-    ports:
-      - 6379:6379

docs/components/DeploymentCards.jsx

@@ -0,0 +1,120 @@
+import Image from 'next/image';
+
+const iconMap = {
+    'Amazon Lightsail': '/lightsail.png',
+    'Railway': '/railway.png',
+    'Civo Compute Cloud': '/civo.png',
+    'DigitalOcean Droplet': '/digitalocean.png',
+    'Kamatera Cloud': '/kamatera.png',
+};
+
+
+export function DeploymentCards({ items }) {
+    return (
+        <>
+            <div className="deployment-cards">
+                {items.map(({ title, link, description }) => {
+                    const isExternal = link.startsWith('https://');
+                    const iconSrc = iconMap[title] || '/default-icon.png'; // Default icon if not found
+
+                    return (
+                        <div
+                            key={title}
+                            className={`card${isExternal ? ' external' : ''}`}
+                        >
+                            <a href={link} target={isExternal ? '_blank' : undefined} rel="noopener noreferrer" className="card-link-wrapper">
+                                <div className="card-icon-container">
+                                    {iconSrc && <div className="card-icon"><Image src={iconSrc} alt={title} width={32} height={32} /></div>} {/* Reduced icon size */}
+                                </div>
+                                <h3 className="card-title">{title}</h3>
+                                {description && <p className="card-description">{description}</p>}
+                                <p className="card-url">{new URL(link).hostname.replace('www.', '')}</p>
+                            </a>
+                        </div>
+                    );
+                })}
+            </div>
+
+            <style jsx>{`
+               .deployment-cards {
+                    margin-top: 24px;
+                    display: grid;
+                    grid-template-columns: 1fr;
+                    gap: 16px;
+                }
+                @media (min-width: 768px) {
+                    .deployment-cards {
+                        grid-template-columns: 1fr 1fr;
+                    }
+                }
+                .card {
+                    background-color: #222222;
+                    border-radius: 8px;
+                    padding: 16px;
+                    transition: background-color 0.3s;
+                    position: relative;
+                    color: #ffffff;
+                    /* Make the card a flex container */
+                    display: flex;
+                    flex-direction: column;
+                    align-items: center; /* Center horizontally */
+                    justify-content: center; /* Center vertically */
+                    height: 100%; /* Fill the height of the grid cell */
+                  
+                }
+                .card:hover {
+                    background-color: #333333;
+                }
+                .card.external::after {
+                    content: "↗";
+                    position: absolute;
+                    top: 12px; /* Adjusted position */
+                    right: 12px; /* Adjusted position */
+                    color: #ffffff;
+                    font-size: 0.7em; /* Reduced size */
+                    opacity: 0.8; /* Slightly faded */
+                }
+                .card-link-wrapper {
+                    display: flex;
+                    flex-direction: column;
+                    align-items:center;
+                    color: inherit;
+                    text-decoration: none;
+                    width:100%; /* Important: make link wrapper take full width */
+                }
+               .card-icon-container{
+                display:flex;
+                justify-content:center;
+                 width: 100%;
+                 margin-bottom: 8px; /* Space between icon and title */
+               }
+                .card-icon {
+                   display: block;
+                   margin: 0 auto;
+
+                }
+                .card-title {
+                    font-weight: 600;
+                    margin-bottom: 4px;
+                    font-size: 16px;
+                    text-align: center;
+                    color: #f0f0f0; /* Lighter title color if needed */
+                }
+                .card-description {
+                    margin-bottom: 0;
+                    font-size: 13px;
+                    color: #aaaaaa;
+                    text-align: center;
+                    line-height: 1.4;
+                }
+                .card-url {
+                    margin-top: 8px; /*Keep space consistent */
+                    font-size: 11px;
+                    color: #777777;
+                    text-align: center;
+                    font-family: monospace;
+                }
+            `}</style>
+        </>
+    );
+}

docs/package-lock.json

@@ -7,8 +7,8 @@
       "license": "MIT",
       "dependencies": {
         "@vercel/analytics": "^1.1.1",
-        "docsgpt-react": "^0.4.9",
-        "next": "^14.2.22",
+        "docsgpt-react": "^0.5.0",
+        "next": "^14.2.26",
         "nextra": "^2.13.2",
         "nextra-theme-docs": "^2.13.2",
         "react": "^18.2.0",
@@ -422,6 +422,13 @@
         "node": ">=6.9.0"
       }
     },
+    "node_modules/@bpmn-io/snarkdown": {
+      "version": "2.2.0",
+      "resolved": "https://registry.npmjs.org/@bpmn-io/snarkdown/-/snarkdown-2.2.0.tgz",
+      "integrity": "sha512-bVD7FIoaBDZeCJkMRgnBPDeptPlto87wt2qaCjf5t8iLaevDmTPaREd6FpBEGsHlUdHFFZWRk4qAoEC5So2M0Q==",
+      "deprecated": "Package no longer supported. Contact Support at https://www.npmjs.com/support for more info.",
+      "license": "MIT"
+    },
     "node_modules/@braintree/sanitize-url": {
       "version": "6.0.4",
       "resolved": "https://registry.npmjs.org/@braintree/sanitize-url/-/sanitize-url-6.0.4.tgz",
@@ -931,17 +938,19 @@
       }
     },
     "node_modules/@next/env": {
-      "version": "14.2.22",
-      "resolved": "https://registry.npmjs.org/@next/env/-/env-14.2.22.tgz",
-      "integrity": "sha512-EQ6y1QeNQglNmNIXvwP/Bb+lf7n9WtgcWvtoFsHquVLCJUuxRs+6SfZ5EK0/EqkkLex4RrDySvKgKNN7PXip7Q=="
+      "version": "14.2.26",
+      "resolved": "https://registry.npmjs.org/@next/env/-/env-14.2.26.tgz",
+      "integrity": "sha512-vO//GJ/YBco+H7xdQhzJxF7ub3SUwft76jwaeOyVVQFHCi5DCnkP16WHB+JBylo4vOKPoZBlR94Z8xBxNBdNJA==",
+      "license": "MIT"
     },
     "node_modules/@next/swc-darwin-arm64": {
-      "version": "14.2.22",
-      "resolved": "https://registry.npmjs.org/@next/swc-darwin-arm64/-/swc-darwin-arm64-14.2.22.tgz",
-      "integrity": "sha512-HUaLiehovgnqY4TMBZJ3pDaOsTE1spIXeR10pWgdQVPYqDGQmHJBj3h3V6yC0uuo/RoY2GC0YBFRkOX3dI9WVQ==",
+      "version": "14.2.26",
+      "resolved": "https://registry.npmjs.org/@next/swc-darwin-arm64/-/swc-darwin-arm64-14.2.26.tgz",
+      "integrity": "sha512-zDJY8gsKEseGAxG+C2hTMT0w9Nk9N1Sk1qV7vXYz9MEiyRoF5ogQX2+vplyUMIfygnjn9/A04I6yrUTRTuRiyQ==",
       "cpu": [
         "arm64"
       ],
+      "license": "MIT",
       "optional": true,
       "os": [
         "darwin"
@@ -951,12 +960,13 @@
       }
     },
     "node_modules/@next/swc-darwin-x64": {
-      "version": "14.2.22",
-      "resolved": "https://registry.npmjs.org/@next/swc-darwin-x64/-/swc-darwin-x64-14.2.22.tgz",
-      "integrity": "sha512-ApVDANousaAGrosWvxoGdLT0uvLBUC+srqOcpXuyfglA40cP2LBFaGmBjhgpxYk5z4xmunzqQvcIgXawTzo2uQ==",
+      "version": "14.2.26",
+      "resolved": "https://registry.npmjs.org/@next/swc-darwin-x64/-/swc-darwin-x64-14.2.26.tgz",
+      "integrity": "sha512-U0adH5ryLfmTDkahLwG9sUQG2L0a9rYux8crQeC92rPhi3jGQEY47nByQHrVrt3prZigadwj/2HZ1LUUimuSbg==",
       "cpu": [
         "x64"
       ],
+      "license": "MIT",
       "optional": true,
       "os": [
         "darwin"
@@ -966,12 +976,13 @@
       }
     },
     "node_modules/@next/swc-linux-arm64-gnu": {
-      "version": "14.2.22",
-      "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-gnu/-/swc-linux-arm64-gnu-14.2.22.tgz",
-      "integrity": "sha512-3O2J99Bk9aM+d4CGn9eEayJXHuH9QLx0BctvWyuUGtJ3/mH6lkfAPRI4FidmHMBQBB4UcvLMfNf8vF0NZT7iKw==",
+      "version": "14.2.26",
+      "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-gnu/-/swc-linux-arm64-gnu-14.2.26.tgz",
+      "integrity": "sha512-SINMl1I7UhfHGM7SoRiw0AbwnLEMUnJ/3XXVmhyptzriHbWvPPbbm0OEVG24uUKhuS1t0nvN/DBvm5kz6ZIqpg==",
       "cpu": [
         "arm64"
       ],
+      "license": "MIT",
       "optional": true,
       "os": [
         "linux"
@@ -981,12 +992,13 @@
       }
     },
     "node_modules/@next/swc-linux-arm64-musl": {
-      "version": "14.2.22",
-      "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-musl/-/swc-linux-arm64-musl-14.2.22.tgz",
-      "integrity": "sha512-H/hqfRz75yy60y5Eg7DxYfbmHMjv60Dsa6IWHzpJSz4MRkZNy5eDnEW9wyts9bkxwbOVZNPHeb3NkqanP+nGPg==",
+      "version": "14.2.26",
+      "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-musl/-/swc-linux-arm64-musl-14.2.26.tgz",
+      "integrity": "sha512-s6JaezoyJK2DxrwHWxLWtJKlqKqTdi/zaYigDXUJ/gmx/72CrzdVZfMvUc6VqnZ7YEvRijvYo+0o4Z9DencduA==",
       "cpu": [
         "arm64"
       ],
+      "license": "MIT",
       "optional": true,
       "os": [
         "linux"
@@ -996,12 +1008,13 @@
       }
     },
     "node_modules/@next/swc-linux-x64-gnu": {
-      "version": "14.2.22",
-      "resolved": "https://registry.npmjs.org/@next/swc-linux-x64-gnu/-/swc-linux-x64-gnu-14.2.22.tgz",
-      "integrity": "sha512-LckLwlCLcGR1hlI5eiJymR8zSHPsuruuwaZ3H2uudr25+Dpzo6cRFjp/3OR5UYJt8LSwlXv9mmY4oI2QynwpqQ==",
+      "version": "14.2.26",
+      "resolved": "https://registry.npmjs.org/@next/swc-linux-x64-gnu/-/swc-linux-x64-gnu-14.2.26.tgz",
+      "integrity": "sha512-FEXeUQi8/pLr/XI0hKbe0tgbLmHFRhgXOUiPScz2hk0hSmbGiU8aUqVslj/6C6KA38RzXnWoJXo4FMo6aBxjzg==",
       "cpu": [
         "x64"
       ],
+      "license": "MIT",
       "optional": true,
       "os": [
         "linux"
@@ -1011,12 +1024,13 @@
       }
     },
     "node_modules/@next/swc-linux-x64-musl": {
-      "version": "14.2.22",
-      "resolved": "https://registry.npmjs.org/@next/swc-linux-x64-musl/-/swc-linux-x64-musl-14.2.22.tgz",
-      "integrity": "sha512-qGUutzmh0PoFU0fCSu0XYpOfT7ydBZgDfcETIeft46abPqP+dmePhwRGLhFKwZWxNWQCPprH26TjaTxM0Nv8mw==",
+      "version": "14.2.26",
+      "resolved": "https://registry.npmjs.org/@next/swc-linux-x64-musl/-/swc-linux-x64-musl-14.2.26.tgz",
+      "integrity": "sha512-BUsomaO4d2DuXhXhgQCVt2jjX4B4/Thts8nDoIruEJkhE5ifeQFtvW5c9JkdOtYvE5p2G0hcwQ0UbRaQmQwaVg==",
       "cpu": [
         "x64"
       ],
+      "license": "MIT",
       "optional": true,
       "os": [
         "linux"
@@ -1026,12 +1040,13 @@
       }
     },
     "node_modules/@next/swc-win32-arm64-msvc": {
-      "version": "14.2.22",
-      "resolved": "https://registry.npmjs.org/@next/swc-win32-arm64-msvc/-/swc-win32-arm64-msvc-14.2.22.tgz",
-      "integrity": "sha512-K6MwucMWmIvMb9GlvT0haYsfIPxfQD8yXqxwFy4uLFMeXIb2TcVYQimxkaFZv86I7sn1NOZnpOaVk5eaxThGIw==",
+      "version": "14.2.26",
+      "resolved": "https://registry.npmjs.org/@next/swc-win32-arm64-msvc/-/swc-win32-arm64-msvc-14.2.26.tgz",
+      "integrity": "sha512-5auwsMVzT7wbB2CZXQxDctpWbdEnEW/e66DyXO1DcgHxIyhP06awu+rHKshZE+lPLIGiwtjo7bsyeuubewwxMw==",
       "cpu": [
         "arm64"
       ],
+      "license": "MIT",
       "optional": true,
       "os": [
         "win32"
@@ -1041,12 +1056,13 @@
       }
     },
     "node_modules/@next/swc-win32-ia32-msvc": {
-      "version": "14.2.22",
-      "resolved": "https://registry.npmjs.org/@next/swc-win32-ia32-msvc/-/swc-win32-ia32-msvc-14.2.22.tgz",
-      "integrity": "sha512-5IhDDTPEbzPR31ZzqHe90LnNe7BlJUZvC4sA1thPJV6oN5WmtWjZ0bOYfNsyZx00FJt7gggNs6SrsX0UEIcIpA==",
+      "version": "14.2.26",
+      "resolved": "https://registry.npmjs.org/@next/swc-win32-ia32-msvc/-/swc-win32-ia32-msvc-14.2.26.tgz",
+      "integrity": "sha512-GQWg/Vbz9zUGi9X80lOeGsz1rMH/MtFO/XqigDznhhhTfDlDoynCM6982mPCbSlxJ/aveZcKtTlwfAjwhyxDpg==",
       "cpu": [
         "ia32"
       ],
+      "license": "MIT",
       "optional": true,
       "os": [
         "win32"
@@ -1056,12 +1072,13 @@
       }
     },
     "node_modules/@next/swc-win32-x64-msvc": {
-      "version": "14.2.22",
-      "resolved": "https://registry.npmjs.org/@next/swc-win32-x64-msvc/-/swc-win32-x64-msvc-14.2.22.tgz",
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+      "version": "14.2.26",
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+      "version": "3.0.11",
+      "resolved": "https://registry.npmjs.org/base-x/-/base-x-3.0.11.tgz",
+      "integrity": "sha512-xz7wQ8xDhdyP7tQxwdteLYeFfS68tSMNCZ/Y37WJ4bhGfKPpqEIlmIyueQHqOyoPhE6xNUqjzRr8ra0eF9VRvA==",
+      "license": "MIT",
       "peer": true,
       "dependencies": {
         "safe-buffer": "^5.0.1"
@@ -3413,6 +3478,7 @@
       "version": "2.1.2",
       "resolved": "https://registry.npmjs.org/clone/-/clone-2.1.2.tgz",
       "integrity": "sha512-3Pe/CF1Nn94hyhIYpjtiLhdCoEoz0DqQ+988E9gmeEdQZlojxnOb74wctFyuwWQHzqyf9X7C7MG8juUpqBJT8w==",
+      "license": "MIT",
       "peer": true,
       "engines": {
         "node": ">=0.8"
@@ -4057,11 +4123,13 @@
       }
     },
     "node_modules/docsgpt-react": {
-      "version": "0.4.9",
-      "resolved": "https://registry.npmjs.org/docsgpt-react/-/docsgpt-react-0.4.9.tgz",
-      "integrity": "sha512-mGGbd4IGVHrQVVdgoej991Vpl/hYkTuKz5Ax95hvqSbWDZELZnEx2/AZajAII5AayUZKWYaEFRluewUiGJVSbA==",
+      "version": "0.5.0",
+      "resolved": "https://registry.npmjs.org/docsgpt-react/-/docsgpt-react-0.5.0.tgz",
+      "integrity": "sha512-5tDfFxBHG9432URaE8rQaYmBE8tbEUg74L85ykg/WbcoL84U3ixrt0tG7T0SfoTfxQT46H3afliYdv1rDmFGLw==",
+      "license": "Apache-2.0",
       "dependencies": {
         "@babel/plugin-transform-flow-strip-types": "^7.23.3",
+        "@bpmn-io/snarkdown": "^2.2.0",
         "@parcel/resolver-glob": "^2.12.0",
         "@parcel/transformer-svg-react": "^2.12.0",
         "@parcel/transformer-typescript-tsc": "^2.12.0",
@@ -4149,6 +4217,7 @@
       "version": "16.4.7",
       "resolved": "https://registry.npmjs.org/dotenv/-/dotenv-16.4.7.tgz",
       "integrity": "sha512-47qPchRCykZC03FhkYAhrvwU4xDBFIj1QPqaarj6mdM/hgUzfPHcpkHJOn3mJAufFeeAxAzeGsr5X0M4k6fLZQ==",
+      "license": "BSD-2-Clause",
       "peer": true,
       "engines": {
         "node": ">=12"
@@ -4161,6 +4230,7 @@
       "version": "11.0.7",
       "resolved": "https://registry.npmjs.org/dotenv-expand/-/dotenv-expand-11.0.7.tgz",
       "integrity": "sha512-zIHwmZPRshsCdpMDyVsqGmgyP0yT8GAgXUnkdAoJisxvf33k7yO6OuoKmcTGuXPWSsm8Oh88nZicRLA9Y0rUeA==",
+      "license": "BSD-2-Clause",
       "peer": true,
       "dependencies": {
         "dotenv": "^16.4.5"
@@ -6758,11 +6828,12 @@
       }
     },
     "node_modules/next": {
-      "version": "14.2.22",
-      "resolved": "https://registry.npmjs.org/next/-/next-14.2.22.tgz",
-      "integrity": "sha512-Ps2caobQ9hlEhscLPiPm3J3SYhfwfpMqzsoCMZGWxt9jBRK9hoBZj2A37i8joKhsyth2EuVKDVJCTF5/H4iEDw==",
+      "version": "14.2.26",
+      "resolved": "https://registry.npmjs.org/next/-/next-14.2.26.tgz",
+      "integrity": "sha512-b81XSLihMwCfwiUVRRja3LphLo4uBBMZEzBBWMaISbKTwOmq3wPknIETy/8000tr7Gq4WmbuFYPS7jOYIf+ZJw==",
+      "license": "MIT",
       "dependencies": {
-        "@next/env": "14.2.22",
+        "@next/env": "14.2.26",
         "@swc/helpers": "0.5.5",
         "busboy": "1.6.0",
         "caniuse-lite": "^1.0.30001579",
@@ -6777,15 +6848,15 @@
         "node": ">=18.17.0"
       },
       "optionalDependencies": {
-        "@next/swc-darwin-arm64": "14.2.22",
-        "@next/swc-darwin-x64": "14.2.22",
-        "@next/swc-linux-arm64-gnu": "14.2.22",
-        "@next/swc-linux-arm64-musl": "14.2.22",
-        "@next/swc-linux-x64-gnu": "14.2.22",
-        "@next/swc-linux-x64-musl": "14.2.22",
-        "@next/swc-win32-arm64-msvc": "14.2.22",
-        "@next/swc-win32-ia32-msvc": "14.2.22",
-        "@next/swc-win32-x64-msvc": "14.2.22"
+        "@next/swc-darwin-arm64": "14.2.26",
+        "@next/swc-darwin-x64": "14.2.26",
+        "@next/swc-linux-arm64-gnu": "14.2.26",
+        "@next/swc-linux-arm64-musl": "14.2.26",
+        "@next/swc-linux-x64-gnu": "14.2.26",
+        "@next/swc-linux-x64-musl": "14.2.26",
+        "@next/swc-win32-arm64-msvc": "14.2.26",
+        "@next/swc-win32-ia32-msvc": "14.2.26",
+        "@next/swc-win32-x64-msvc": "14.2.26"
       },
       "peerDependencies": {
         "@opentelemetry/api": "^1.1.0",
@@ -10123,6 +10194,7 @@
           "url": "https://feross.org/support"
         }
       ],
+      "license": "MIT",
       "peer": true
     },
     "node_modules/safer-buffer": {
@@ -10470,9 +10542,10 @@
       }
     },
     "node_modules/typescript": {
-      "version": "5.7.2",
-      "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.7.2.tgz",
-      "integrity": "sha512-i5t66RHxDvVN40HfDd1PsEThGNnlMCMT3jMUuoh9/0TaqWevNontacunWyN02LA9/fIbEWlcHZcgTKb9QoaLfg==",
+      "version": "5.8.2",
+      "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.8.2.tgz",
+      "integrity": "sha512-aJn6wq13/afZp/jT9QZmwEjDqqvSGp1VT5GVg+f/t6/oVyrgXM6BY1h9BRh/O5p3PlUPAe+WuiEZOmb/49RqoQ==",
+      "license": "Apache-2.0",
       "peer": true,
       "bin": {
         "tsc": "bin/tsc",

docs/package.json

@@ -7,8 +7,8 @@
   "license": "MIT",
   "dependencies": {
     "@vercel/analytics": "^1.1.1",
-    "docsgpt-react": "^0.4.9",
-    "next": "^14.2.22",
+    "docsgpt-react": "^0.5.0",
+    "next": "^14.2.26",
     "nextra": "^2.13.2",
     "nextra-theme-docs": "^2.13.2",
     "react": "^18.2.0",

docs/pages/API/API-docs.md

@@ -1,350 +0,0 @@
-# API Endpoints Documentation
-
-*Currently, the application provides the following main API endpoints:*
-
-
-### 1. /api/answer 
-**Description:**
-
-This endpoint is used to request answers to user-provided questions.
-
-**Request:**
-
-**Method**: `POST`
-
-**Headers**: Content-Type should be set to `application/json; charset=utf-8`
-
-**Request Body**: JSON object with the following fields:
-* `question` — The user's question.
-* `history`  —  (Optional) Previous conversation history.
-* `api_key`— Your API key.
-* `embeddings_key`  —  Your embeddings key.
-* `active_docs` — The location of active documentation.
-
-Here is a JavaScript Fetch Request example:
-```js
-// answer (POST http://127.0.0.1:5000/api/answer)
-fetch("http://127.0.0.1:5000/api/answer", {
-      "method": "POST",
-      "headers": {
-            "Content-Type": "application/json; charset=utf-8"
-      },
-      "body": JSON.stringify({"question":"Hi","history":null,"api_key":"OPENAI_API_KEY","embeddings_key":"OPENAI_API_KEY",
-      "active_docs": "javascript/.project/ES2015/openai_text-embedding-ada-002/"})
-})
-.then((res) => res.text())
-.then(console.log.bind(console))
-```
-
-**Response**
-
-In response, you will get a JSON document containing the `answer`, `query` and `result`:
-```json
-{
-  "answer": "Hi there! How can I help you?\n",
-  "query": "Hi",
-  "result": "Hi there! How can I help you?\nSOURCES:"
-}
-```
-
-### 2. /api/docs_check
-
-**Description:**
-
-This endpoint will make sure documentation is loaded on the server (just run it every time user is switching between libraries (documentations)).
-
-**Request:**
-
-**Method**: `POST`
-
-**Headers**: Content-Type should be set to `application/json; charset=utf-8`
-
-**Request Body**: JSON object with the field:
-* `docs` — The location of the documentation:
-```js
-// docs_check (POST http://127.0.0.1:5000/api/docs_check)
-fetch("http://127.0.0.1:5000/api/docs_check", {
-      "method": "POST",
-      "headers": {
-            "Content-Type": "application/json; charset=utf-8"
-      },
-      "body": JSON.stringify({"docs":"javascript/.project/ES2015/openai_text-embedding-ada-002/"})
-})
-.then((res) => res.text())
-.then(console.log.bind(console))
-```
-
-**Response:**
-
-In response, you will get a JSON document like this one indicating whether the documentation exists or not:
-```json
-{
-  "status": "exists"
-}
-```
-
-
-### 3. /api/combine
-**Description:**
-
-This endpoint provides information about available vectors and their locations with a simple GET request.
-
-**Request:**
-
-**Method**: `GET`
-
-**Response:**
-
-Response will include:
-* `date`
-* `description`
-* `docLink`
-* `fullName`
-* `language`
-* `location` (local or docshub)
-* `model`
-* `name`
-* `version`
-
-Example of JSON in Docshub and local:
-
-<img width="295" alt="image" src="https://user-images.githubusercontent.com/15183589/224714085-f09f51a4-7a9a-4efb-bd39-798029bb4273.png">
-
-### 4. /api/upload
-**Description:**
-
-This endpoint is used to upload a file that needs to be trained, response is JSON with task ID, which can be used to check on task's progress.
-
-**Request:**
-
-**Method**: `POST`
-
-**Request Body**: A multipart/form-data form with file upload and additional fields, including `user` and `name`.
-
-HTML example:
-
-```html
-<form action="/api/upload" method="post" enctype="multipart/form-data" class="mt-2">
-    <input type="file" name="file" class="py-4" id="file-upload">
-    <input type="text" name="user" value="local" hidden>
-    <input type="text" name="name" placeholder="Name:">
-    
-    <button type="submit" class="py-2 px-4 text-white bg-purple-30 rounded-md hover:bg-purple-30 focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-purple-30">
-        Upload
-    </button>
-</form>
-```
-
-**Response:**
-
-JSON response with a status and a task ID that can be used to check the task's progress.
-
-
-### 5. /api/task_status
-**Description:**
-
-This endpoint is used to get the status of a task (`task_id`) from `/api/upload`
-
-**Request:**
-
-**Method**: `GET`
-
-**Query Parameter**: `task_id` (task ID to check)
-
-**Sample JavaScript Fetch Request:**
-```js
-// Task status (Get http://127.0.0.1:5000/api/task_status)
-fetch("http://localhost:5001/api/task_status?task_id=YOUR_TASK_ID", {
-      "method": "GET",
-      "headers": {
-            "Content-Type": "application/json; charset=utf-8"
-      },
-})
-.then((res) => res.text())
-.then(console.log.bind(console))
-```
-
-**Response:**
-
-There are two types of responses:
-
-1. While the task is still running, the 'current' value will show progress from 0 to 100.
-   ```json
-   {
-     "result": {
-       "current": 1
-     },
-     "status": "PROGRESS"
-   }
-   ```
-
-2. When task is completed:
-   ```json
-   {
-     "result": {
-       "directory": "temp",
-       "filename": "install.rst",
-       "formats": [
-         ".rst",
-         ".md",
-         ".pdf"
-       ],
-       "name_job": "somename",
-       "user": "local"
-     },
-     "status": "SUCCESS"
-   }
-   ```
-
-### 6. /api/delete_old
-**Description:**
-
-This endpoint is used to delete old Vector Stores.
-
-**Request:**
-
-**Method**: `GET`
-
-**Query Parameter**: `task_id`
-
-**Sample JavaScript Fetch Request:**
-```js
-// delete_old (GET http://127.0.0.1:5000/api/delete_old)
-fetch("http://localhost:5001/api/delete_old?task_id=YOUR_TASK_ID", {
-      "method": "GET",
-      "headers": {
-            "Content-Type": "application/json; charset=utf-8"
-      },
-})
-.then((res) => res.text())
-.then(console.log.bind(console))
-
-```
-**Response:**
-
-JSON response indicating the status of the operation:
-
-```json
-{ "status": "ok" }
-```
-
-### 7. /api/get_api_keys
-**Description:**
-
-The endpoint retrieves a list of API keys for the user.
-
-**Request:**
-
-**Method**: `GET`
-
-**Sample JavaScript Fetch Request:**
-```js
-// get_api_keys (GET http://127.0.0.1:5000/api/get_api_keys)
-fetch("http://localhost:5001/api/get_api_keys", {
-      "method": "GET",
-      "headers": {
-            "Content-Type": "application/json; charset=utf-8"
-      },
-})
-.then((res) => res.text())
-.then(console.log.bind(console))
-
-```
-**Response:**
-
-JSON response with a list of created API keys:
-
-```json
-[
-      {
-        "id": "string",
-        "name": "string",
-        "key": "string",
-        "source": "string"
-      },
-      ...
-    ]
-```
-
-### 8. /api/create_api_key
-
-**Description:**
-
-Create a new API key for the user.
-
-**Request:**
-
-**Method**: `POST`
-
-**Headers**: Content-Type should be set to `application/json; charset=utf-8`
-
-**Request Body**: JSON object with the following fields:
-* `name` — A name for the API key.
-* `source` — The source documents that will be used.
-* `prompt_id` — The prompt ID.
-* `chunks` — The number of chunks used to process an answer.
-
-Here is a JavaScript Fetch Request example:
-```js
-// create_api_key (POST http://127.0.0.1:5000/api/create_api_key)
-fetch("http://127.0.0.1:5000/api/create_api_key", {
-      "method": "POST",
-      "headers": {
-            "Content-Type": "application/json; charset=utf-8"
-      },
-      "body": JSON.stringify({"name":"Example Key Name",
-          "source":"Example Source",
-          "prompt_id":"creative",
-          "chunks":"2"})
-})
-.then((res) => res.json())
-.then(console.log.bind(console))
-```
-
-**Response**
-
-In response, you will get a JSON document containing the `id` and `key`:
-```json
-{
-  "id": "string",
-  "key": "string"
-}
-```
-
-### 9. /api/delete_api_key
-
-**Description:**
-
-Delete an API key for the user.
-
-**Request:**
-
-**Method**: `POST`
-
-**Headers**: Content-Type should be set to `application/json; charset=utf-8`
-
-**Request Body**: JSON object with the field:
-* `id` — The unique identifier of the API key to be deleted.
-
-Here is a JavaScript Fetch Request example:
-```js
-// delete_api_key (POST http://127.0.0.1:5000/api/delete_api_key)
-fetch("http://127.0.0.1:5000/api/delete_api_key", {
-      "method": "POST",
-      "headers": {
-            "Content-Type": "application/json; charset=utf-8"
-      },
-      "body": JSON.stringify({"id":"API_KEY_ID"})
-})
-.then((res) => res.json())
-.then(console.log.bind(console))
-```
-
-**Response:**
-
-In response, you will get a JSON document indicating the status of the operation:
-```json
-{
-  "status": "ok"
-}
-```

docs/pages/API/_meta.json

@@ -1,10 +0,0 @@
-{
-  "API-docs": {
-    "title": "🗂️️ API-docs",
-    "href": "/API/API-docs"
-  },
-  "api-key-guide": {
-    "title": "🔐 API Keys guide",
-    "href": "/API/api-key-guide"
-  }
-}

docs/pages/Deploying/Amazon-Lightsail.mdx

@@ -1,3 +1,9 @@
+---
+title: Hosting DocsGPT on Amazon Lightsail
+description:
+display: hidden
+---
+
 # Self-hosting DocsGPT on Amazon Lightsail
 
 Here's a step-by-step guide on how to set up an Amazon Lightsail instance to host DocsGPT.
@@ -73,7 +79,7 @@ To save the file, press CTRL+X, then Y, and then ENTER.
 
 Next, set the correct IP for the Backend by opening the docker-compose.yml file:
 
-`nano docker-compose.yml`
+`nano deployment/docker-compose.yaml`
 
 And Change line 7 to: `VITE_API_HOST=http://localhost:7091`
 to this `VITE_API_HOST=http://<your instance public IP>:7091`
@@ -84,7 +90,7 @@ This will allow the frontend to connect to the backend.
 
 You're almost there! Now that all the necessary bits and pieces have been installed, it is time to run the application. To do so, use the following command:
 
-`sudo docker-compose up -d`
+`sudo docker compose -f deployment/docker-compose.yaml up -d`
 
 Launching it for the first time will take a few minutes to download all the necessary dependencies and build.
 
@@ -101,10 +107,4 @@ Repeat the process for port `7091`.
 
 #### Access your instance
 
-Your instance is now available at your Public IP Address on port 5173. Enjoy using DocsGPT!
-
-## Other Deployment Options
-
-- [Deploy DocsGPT on Civo Compute Cloud](https://dev.to/rutamhere/deploying-docsgpt-on-civo-compute-c)
-- [Deploy DocsGPT on DigitalOcean Droplet](https://dev.to/rutamhere/deploying-docsgpt-on-digitalocean-droplet-50ea)
-- [Deploy DocsGPT on Kamatera Performance Cloud](https://dev.to/rutamhere/deploying-docsgpt-on-kamatera-performance-cloud-1bj)
+Your instance is now available at your Public IP Address on port 5173. Enjoy using DocsGPT!

docs/pages/Deploying/Development-Environment.md

@@ -1,78 +0,0 @@
-## Development Environments
-
-### Spin up Mongo and Redis
-
-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](https://github.com/arc53/DocsGPT/blob/main/docker-compose-dev.yaml).
-
-Run
-
-```
-docker compose -f docker-compose-dev.yaml build
-docker compose -f docker-compose-dev.yaml up -d
-```
-
-### Run the Backend
-
-> [!Note]
-> Make sure you have Python 3.12 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`.
-
-> [!Note]
-> You can also launch the in a debugger mode in vscode by accessing SHIFT + CMD + D or SHIFT + Windows + D on windows and selecting Flask or Celery.
-
-
-### 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`.