auto-rag

Need vectorstores testing for all except faiss

.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/FUNDING.yml

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

.github/dependabot.yml

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

.github/workflows/bandit.yaml

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

.github/workflows/ci.yml

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

.github/workflows/cife.yml

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

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

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

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

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

.github/workflows/pytest.yml

@@ -6,7 +6,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.11"]
+        python-version: ["3.12"]
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
@@ -23,8 +23,8 @@ jobs:
         run: |
           python -m pytest --cov=application --cov-report=xml
       - name: Upload coverage reports to Codecov
-        if: github.event_name == 'pull_request' && matrix.python-version == '3.11'
-        uses: codecov/codecov-action@v4
+        if: github.event_name == 'pull_request' && matrix.python-version == '3.12'
+        uses: codecov/codecov-action@v5
         env:
           CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
 

.gitignore

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

.vscode/launch.json

@@ -11,6 +11,44 @@
       "skipFiles": [
         "<node_internals>/**"
       ]
+    },
+    {
+        "name": "Flask Debugger",
+        "type": "debugpy",
+        "request": "launch",
+        "module": "flask",
+        "env": {
+            "FLASK_APP": "application/app.py",
+            "PYTHONPATH": "${workspaceFolder}",
+            "FLASK_ENV": "development",
+            "FLASK_DEBUG": "1",
+            "FLASK_RUN_PORT": "7091",
+            "FLASK_RUN_HOST": "0.0.0.0"
+
+        },
+        "args": [
+            "run",
+            "--no-debugger"
+        ],
+        "cwd": "${workspaceFolder}",
+    },
+    {
+      "name": "Celery Debugger",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "celery",
+      "env": {
+        "PYTHONPATH": "${workspaceFolder}",
+      },
+      "args": [
+        "-A",
+        "application.app.celery",
+        "worker",
+        "-l",
+        "INFO",
+        "--pool=solo"
+      ],
+      "cwd": "${workspaceFolder}"
     }
   ]
 }

CONTRIBUTING.md

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

HACKTOBERFEST.md

@@ -1,41 +0,0 @@
-# **🎉 Join the Hacktoberfest with DocsGPT and win a Free T-shirt and other prizes! 🎉**
-
-Welcome, contributors! We're excited to announce that DocsGPT is participating in Hacktoberfest. Get involved by submitting meaningful pull requests.
-
-All contributors with accepted PRs will receive a cool Holopin! 🤩 (Watch out for a reply in your PR to collect it).
-
-### 🏆 Top 50 contributors will receive a special T-shirt
-
-### 🏆 [LLM Document analysis by LexEU competition](https://github.com/arc53/DocsGPT/blob/main/lexeu-competition.md): 
-A separate competition is available for those who submit new retrieval / workflow method that will analyze a Document using EU laws.
-With 200$, 100$, 50$ prize for 1st, 2nd and 3rd place respectively.
-You can find more information [here](https://github.com/arc53/DocsGPT/blob/main/lexeu-competition.md)
-
-## 📜 Here's How to Contribute:
-```text
-🛠️ Code: This is the golden ticket! Make meaningful contributions through PRs.
-
-🧩 API extension: Build an app utilising DocsGPT API. We prefer submissions that showcase original ideas and turn the API into an AI agent.
-They can be a completely separate repos. 
-For example: 
-https://github.com/arc53/tg-bot-docsgpt-extenstion or 
-https://github.com/arc53/DocsGPT-cli
-
-Non-Code Contributions:
-
-📚 Wiki: Improve our documentation, create a guide or change existing documentation.
-
-🖥️ Design: Improve the UI/UX or design a new feature.
-
-📝 Blogging or Content Creation: Write articles or create videos to showcase DocsGPT or highlight your contributions!
-```
-
-### 📝 Guidelines for Pull Requests:
-- Familiarize yourself with the current contributions and our [Roadmap](https://github.com/orgs/arc53/projects/2).
-- Before contributing we highly advise that you check existing [issues](https://github.com/arc53/DocsGPT/issues) or [create](https://github.com/arc53/DocsGPT/issues/new/choose) an issue and wait to get assigned.
-- Once you are finished with your contribution, please fill in this [form](https://airtable.com/appikMaJwdHhC1SDP/pagoblCJ9W29wf6Hf/form).
-- Refer to the [Documentation](https://docs.docsgpt.cloud/).
-- Feel free to join our [Discord](https://discord.gg/n5BX8dh8rU) server. We're here to help newcomers, so don't hesitate to jump in! Join us [here](https://discord.gg/n5BX8dh8rU).
-  
-Thank you very much for considering contributing to DocsGPT during Hacktoberfest! 🙏 Your contributions (not just simple typos) could earn you a stylish new t-shirt and other prizes as a token of our appreciation. 🎁 Join us, and let's code together! 🚀
-

README.md

@@ -3,13 +3,11 @@
 </h1>
 
 <p align="center">
-  <strong>Open-Source Documentation Assistant</strong>
+  <strong>Open-Source RAG Assistant</strong>
 </p>
 
 <p align="left">
-  <strong><a href="https://www.docsgpt.cloud/">DocsGPT</a></strong> is a cutting-edge open-source solution that streamlines the process of finding information in the project documentation. With its integration of the powerful <strong>GPT</strong> models, developers can easily ask questions about a project and receive accurate answers.
-  
-Say goodbye to time-consuming manual searches, and let <strong><a href="https://www.docsgpt.cloud/">DocsGPT</a></strong> help you quickly find the information you need. Try it out and see how it revolutionizes your project documentation experience. Contribute to its development and be a part of the future of AI-powered assistance.
+  <strong><a href="https://www.docsgpt.cloud/">DocsGPT</a></strong> is an open-source genAI tool that helps users get reliable answers from any knowledge source, while avoiding hallucinations. It enables quick and reliable information retrieval, with tooling and agentic system capability built in.
 </p>
 
 <div align="center">
@@ -17,180 +15,131 @@ Say goodbye to time-consuming manual searches, and let <strong><a href="https://
   <a href="https://github.com/arc53/DocsGPT">![link to main GitHub showing Stars number](https://img.shields.io/github/stars/arc53/docsgpt?style=social)</a>
   <a href="https://github.com/arc53/DocsGPT">![link to main GitHub showing Forks number](https://img.shields.io/github/forks/arc53/docsgpt?style=social)</a>
   <a href="https://github.com/arc53/DocsGPT/blob/main/LICENSE">![link to license file](https://img.shields.io/github/license/arc53/docsgpt)</a>
+  <a href="https://www.bestpractices.dev/projects/9907"><img src="https://www.bestpractices.dev/projects/9907/badge"></a>
   <a href="https://discord.gg/n5BX8dh8rU">![link to discord](https://img.shields.io/discord/1070046503302877216)</a>
   <a href="https://twitter.com/docsgptai">![X (formerly Twitter) URL](https://img.shields.io/twitter/follow/docsgptai)</a>
 
- 
+  <a href="https://docs.docsgpt.cloud/quickstart">⚡️ Quickstart</a> • <a href="https://app.docsgpt.cloud/">☁️ Cloud Version</a> • <a href="https://discord.gg/n5BX8dh8rU">💬 Discord</a>
+  <br>
+  <a href="https://docs.docsgpt.cloud/">📖 Documentation</a> • <a href="https://github.com/arc53/DocsGPT/blob/main/CONTRIBUTING.md">👫 Contribute</a> • <a href="https://blog.docsgpt.cloud/">🗞 Blog</a>
+  <br>
+
 </div>
+<div align="center">
+<img src="https://d3dg1063dc54p9.cloudfront.net/videos/demov7.gif" alt="video-example-of-docs-gpt" width="800" height="450">
+</div>
+<h3 align="left">
+  <strong>Key Features:</strong>
+</h3>
+<ul align="left">
+    <li><strong>🗂️ Wide Format Support:</strong> Reads PDF, DOCX, CSV, XLSX, EPUB, MD, RST, HTML, MDX, JSON, PPTX, and images.</li>
+    <li><strong>🌐 Web & Data Integration:</strong> Ingests from URLs, sitemaps, Reddit, GitHub and web crawlers.</li>
+    <li><strong>✅ Reliable Answers:</strong> Get accurate, hallucination-free responses with source citations viewable in a clean UI.</li>
+    <li><strong>🔑 Streamlined API Keys:</strong>  Generate keys linked to your settings, documents, and models, simplifying chatbot and integration setup.</li>
+    <li><strong>🔗 Actionable Tooling:</strong> Connect to APIs, tools, and other services to enable LLM actions.</li>
+    <li><strong>🧩 Pre-built Integrations:</strong> Use readily available HTML/React chat widgets, search tools, Discord/Telegram bots, and more.</li>
+    <li><strong>🔌 Flexible Deployment:</strong> Works with major LLMs (OpenAI, Google, Anthropic) and local models (Ollama, llama_cpp).</li>
+    <li><strong>🏢 Secure & Scalable:</strong> Run privately and securely with Kubernetes support, designed for enterprise-grade reliability.</li>
+</ul>
+
+## Roadmap
+
+- [x] Full GoogleAI compatibility (Jan 2025)
+- [x] Add tools (Jan 2025)
+- [x] Manually updating chunks in the app UI (Feb 2025)
+- [x] Devcontainer for easy development (Feb 2025)
+- [x] ReACT agent (March 2025)
+- [x] Chatbots menu re-design to handle tools, agent types, and more (April 2025)
+- [x] New input box in the conversation menu (April 2025)
+- [x] Add triggerable actions / tools (webhook) (April 2025)
+- [ ] Anthropic Tool compatibility (May 2025)
+- [ ] Add OAuth 2.0 authentication for tools and sources
+- [ ] Agent scheduling
+
+You can find our full roadmap [here](https://github.com/orgs/arc53/projects/2). Please don't hesitate to contribute or create issues, it helps us improve DocsGPT!
 
 ### Production Support / Help for Companies:
 
 We're eager to provide personalized assistance when deploying your DocsGPT to a live environment.
 
-<a href ="https://cal.com/arc53/docsgpt-demo-b2b">
-    <img alt="Let's chat" src="https://cal.com/book-with-cal-dark.svg" />
-</a>
+[Get a Demo :wave:](https://www.docsgpt.cloud/contact)⁠
 
-[Send Email :email:](mailto:contact@arc53.com?subject=DocsGPT%20support%2Fsolutions)
+[Send Email :email:](mailto:support@docsgpt.cloud?subject=DocsGPT%20support%2Fsolutions)
 
+## Join the Lighthouse Program 🌟
 
-<img src="https://github.com/user-attachments/assets/9a1f21de-7a15-4e42-9424-70d22ba5a913" alt="video-example-of-docs-gpt" width="1000" height="500">
+Calling all developers and GenAI innovators! The **DocsGPT Lighthouse Program** connects technical leaders actively deploying or extending DocsGPT in real-world scenarios. Collaborate directly with our team to shape the roadmap, access priority support, and build enterprise-ready solutions with exclusive community insights.  
 
-## Roadmap
+[Learn More & Apply →](https://docs.google.com/forms/d/1KAADiJinUJ8EMQyfTXUIGyFbqINNClNR3jBNWq7DgTE)
 
-You can find our roadmap [here](https://github.com/orgs/arc53/projects/2). Please don't hesitate to contribute or create issues, it helps us improve DocsGPT!
-
-## Our Open-Source Models Optimized for DocsGPT:
-
-| Name                                                                  | Base Model  | Requirements (or similar) |
-| --------------------------------------------------------------------- | ----------- | ------------------------- |
-| [Docsgpt-7b-mistral](https://huggingface.co/Arc53/docsgpt-7b-mistral)   | Mistral-7b   | 1xA10G gpu                |
-| [Docsgpt-14b](https://huggingface.co/Arc53/docsgpt-14b)               | llama-2-14b | 2xA10 gpu's               |
-| [Docsgpt-40b-falcon](https://huggingface.co/Arc53/docsgpt-40b-falcon) | falcon-40b  | 8xA10G gpu's              |
-
-If you don't have enough resources to run it, you can use bitsnbytes to quantize.
-
-## End to End AI Framework for Information Retrieval
-
-![Architecture chart](https://github.com/user-attachments/assets/fc6a7841-ddfc-45e6-b5a0-d05fe648cbe2)
-
-## Useful Links
-
-- :mag: :fire: [Cloud Version](https://app.docsgpt.cloud/)
-
-- :speech_balloon: :tada: [Join our Discord](https://discord.gg/n5BX8dh8rU)
-
-- :books: :sunglasses: [Guides](https://docs.docsgpt.cloud/)
-
-- :couple: [Interested in contributing?](https://github.com/arc53/DocsGPT/blob/main/CONTRIBUTING.md)
-
-- :file_folder: :rocket: [How to use any other documentation](https://docs.docsgpt.cloud/Guides/How-to-train-on-other-documentation)
-
-- :house: :closed_lock_with_key: [How to host it locally (so all data will stay on-premises)](https://docs.docsgpt.cloud/Guides/How-to-use-different-LLM)
-
-## Project Structure
-
-- Application - Flask app (main application).
-
-- Extensions - Chrome extension.
-
-- Scripts - Script that creates similarity search index for other libraries.
-
-- Frontend - Frontend uses <a href="https://vitejs.dev/">Vite</a> and <a href="https://react.dev/">React</a>.
 
 ## QuickStart
 
 > [!Note]
 > Make sure you have [Docker](https://docs.docker.com/engine/install/) installed
 
-On Mac OS or Linux, write:
+A more detailed [Quickstart](https://docs.docsgpt.cloud/quickstart) is available in our documentation
 
-`./setup.sh`
+1. **Clone the repository:**
 
-It will install all the dependencies and allow you to download the local model, use OpenAI or use our LLM API.
-
-Otherwise, refer to this Guide for Windows:
-
-1. Download and open this repository with `git clone https://github.com/arc53/DocsGPT.git`
-2. Create a `.env` file in your root directory and set the env variables and `VITE_API_STREAMING` to true or false, depending on whether you want streaming answers or not.
-   It should look like this inside:
-
-   ```
-   LLM_NAME=[docsgpt or openai or others] 
-   VITE_API_STREAMING=true
-   API_KEY=[if LLM_NAME is openai]
+   ```bash
+   git clone https://github.com/arc53/DocsGPT.git
+   cd DocsGPT
    ```
 
-   See optional environment variables in the [/.env-template](https://github.com/arc53/DocsGPT/blob/main/.env-template) and [/application/.env_sample](https://github.com/arc53/DocsGPT/blob/main/application/.env_sample) files.
+**For macOS and Linux:**
 
-3. Run [./run-with-docker-compose.sh](https://github.com/arc53/DocsGPT/blob/main/run-with-docker-compose.sh).
-4. Navigate to http://localhost:5173/.
+2. **Run the setup script:**
 
-To stop, just run `Ctrl + C`.
+   ```bash
+   ./setup.sh
+   ```
 
-## Development Environments
+**For Windows:**
 
-### Spin up Mongo and Redis
+2. **Run the PowerShell setup script:**
 
-For development, only two containers are used from [docker-compose.yaml](https://github.com/arc53/DocsGPT/blob/main/docker-compose.yaml) (by deleting all services except for Redis and Mongo).
-See file [docker-compose-dev.yaml](./docker-compose-dev.yaml).
+   ```powershell
+   PowerShell -ExecutionPolicy Bypass -File .\setup.ps1
+   ```
 
-Run
+Either script will guide you through setting up DocsGPT. 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
 ```
-docker compose -f docker-compose-dev.yaml build
-docker compose -f docker-compose-dev.yaml up -d
-```
-
-### Run the Backend
+(or use the specific `docker compose down` command shown after running the setup script).
 
 > [!Note]
-> Make sure you have Python 3.10 or 3.11 installed.
-
-1. Export required environment variables or prepare a `.env` file in the project folder:
-   - Copy [.env-template](https://github.com/arc53/DocsGPT/blob/main/application/.env-template) and create `.env`.
-
-(check out [`application/core/settings.py`](application/core/settings.py) if you want to see more config options.)
-
-2. (optional) Create a Python virtual environment:
-   You can follow the [Python official documentation](https://docs.python.org/3/tutorial/venv.html) for virtual environments.
-
-a) On Mac OS and Linux
-
-```commandline
-python -m venv venv
-. venv/bin/activate
-```
-
-b) On Windows
-
-```commandline
-python -m venv venv
- venv/Scripts/activate
-```
-
-3. Download embedding model and save it in the `model/` folder:
-You can use the script below, or download it manually from [here](https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip), unzip it and save it in the `model/` folder.
-
-```commandline
-wget https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip
-unzip mpnet-base-v2.zip -d model
-rm mpnet-base-v2.zip
-```
-
-4. Install dependencies for the backend:
-
-```commandline
-pip install -r application/requirements.txt
-```
-
-5. Run the app using `flask --app application/app.py run --host=0.0.0.0 --port=7091`.
-6. Start worker with `celery -A application.app.celery worker -l INFO`.
-
-### Start Frontend
-
-> [!Note]
-> Make sure you have Node version 16 or higher.
-
-1. Navigate to the [/frontend](https://github.com/arc53/DocsGPT/tree/main/frontend) folder.
-2. Install the required packages `husky` and `vite` (ignore if already installed).
-
-```commandline
-npm install husky -g
-npm install vite -g
-```
-
-3. Install dependencies by running `npm install --include=dev`.
-4. Run the app using `npm run dev`.
+> For development environment setup instructions, please refer to the [Development Environment Guide](https://docs.docsgpt.cloud/Deploying/Development-Environment).
 
 ## Contributing
 
 Please refer to the [CONTRIBUTING.md](CONTRIBUTING.md) file for information about how to get involved. We welcome issues, questions, and pull requests.
 
+## Architecture
+
+![Architecture chart](https://github.com/user-attachments/assets/fc6a7841-ddfc-45e6-b5a0-d05fe648cbe2)
+
+## Project Structure
+
+- Application - Flask app (main application).
+
+- Extensions - Extensions, like react widget or discord bot.
+
+- Frontend - Frontend uses <a href="https://vitejs.dev/">Vite</a> and <a href="https://react.dev/">React</a>.
+
+- Scripts - Miscellaneous scripts.
+
 ## Code Of Conduct
 
 We as members, contributors, and leaders, pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. Please refer to the [CODE_OF_CONDUCT.md](CODE_OF_CONDUCT.md) file for more information about contributing.
 
+
 ## Many Thanks To Our Contributors⚡
 
 <a href="https://github.com/arc53/DocsGPT/graphs/contributors" alt="View Contributors">

application/Dockerfile

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

application/agents/agent_creator.py

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

application/agents/base.py

@@ -0,0 +1,277 @@
+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 +1,229 @@
+import os
+from typing import Dict, Generator, List, Any
+import logging
+
+from application.agents.base import BaseAgent
+from application.logging import build_stack_data, LogContext
+from application.retriever.base import BaseRetriever
+
+logger = logging.getLogger(__name__)
+
+current_dir = os.path.dirname(
+    os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+)
+with open(
+    os.path.join(current_dir, "application/prompts", "react_planning_prompt.txt"), "r"
+) as f:
+    planning_prompt_template = f.read()
+with open(
+    os.path.join(current_dir, "application/prompts", "react_final_prompt.txt"),
+    "r",
+) as f:
+    final_prompt_template = f.read()
+    
+MAX_ITERATIONS_REASONING = 10
+
+class ReActAgent(BaseAgent):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.plan: str = ""
+        self.observations: List[str] = []
+
+    def _extract_content_from_llm_response(self, resp: Any) -> str:
+        """
+        Helper to extract string content from various LLM response types.
+        Handles strings, message objects (OpenAI-like), and streams.
+        Adapt stream handling for your specific LLM client if not OpenAI.
+        """
+        collected_content = []
+        if isinstance(resp, str):
+            collected_content.append(resp)
+        elif ( # OpenAI non-streaming or Anthropic non-streaming (older SDK style)
+            hasattr(resp, "message")
+            and hasattr(resp.message, "content")
+            and resp.message.content is not None
+        ):
+            collected_content.append(resp.message.content)
+        elif ( # OpenAI non-streaming (Pydantic model), Anthropic new SDK non-streaming
+            hasattr(resp, "choices") and resp.choices and
+            hasattr(resp.choices[0], "message") and
+            hasattr(resp.choices[0].message, "content") and
+            resp.choices[0].message.content is not None
+        ):
+            collected_content.append(resp.choices[0].message.content) # OpenAI
+        elif ( # Anthropic new SDK non-streaming content block
+             hasattr(resp, "content") and isinstance(resp.content, list) and resp.content and
+             hasattr(resp.content[0], "text")
+        ):
+            collected_content.append(resp.content[0].text) # Anthropic
+        else:
+            # Assume resp is a stream if not a recognized object
+            try:
+                for chunk in resp: # This will fail if resp is not iterable (e.g. a non-streaming response object)
+                    content_piece = ""
+                    # OpenAI-like stream
+                    if hasattr(chunk, 'choices') and len(chunk.choices) > 0 and \
+                       hasattr(chunk.choices[0], 'delta') and \
+                       hasattr(chunk.choices[0].delta, 'content') and \
+                       chunk.choices[0].delta.content is not None:
+                        content_piece = chunk.choices[0].delta.content
+                    # Anthropic-like stream (ContentBlockDelta)
+                    elif hasattr(chunk, 'type') and chunk.type == 'content_block_delta' and \
+                         hasattr(chunk, 'delta') and hasattr(chunk.delta, 'text'):
+                        content_piece = chunk.delta.text
+                    elif isinstance(chunk, str): # Simplest case: stream of strings
+                        content_piece = chunk
+
+                    if content_piece:
+                        collected_content.append(content_piece)
+            except TypeError: # If resp is not iterable (e.g. a final response object that wasn't caught above)
+                logger.debug(f"Response type {type(resp)} could not be iterated as a stream. It might be a non-streaming object not handled by specific checks.")
+            except Exception as e:
+                logger.error(f"Error processing potential stream chunk: {e}, chunk was: {getattr(chunk, '__dict__', chunk)}")
+
+
+        return "".join(collected_content)
+
+    def _gen_inner(
+        self, query: str, retriever: BaseRetriever, log_context: LogContext
+    ) -> Generator[Dict, None, None]:
+        # Reset state for this generation call
+        self.plan = ""
+        self.observations = []
+        retrieved_data = self._retriever_search(retriever, query, log_context)
+
+        if self.user_api_key:
+            tools_dict = self._get_tools(self.user_api_key)
+        else:
+            tools_dict = self._get_user_tools(self.user)
+        self._prepare_tools(tools_dict)
+
+        docs_together = "\n".join([doc["text"] for doc in retrieved_data])
+        iterating_reasoning = 0
+        while iterating_reasoning < MAX_ITERATIONS_REASONING:
+            iterating_reasoning += 1
+            # 1. Create Plan
+            logger.info("ReActAgent: Creating plan...")
+            plan_stream = self._create_plan(query, docs_together, log_context)
+            current_plan_parts = []
+            yield {"thought": f"Reasoning... (iteration {iterating_reasoning})\n\n"}
+            for line_chunk in plan_stream:
+                current_plan_parts.append(line_chunk)
+                yield {"thought": line_chunk}
+            self.plan = "".join(current_plan_parts)
+            if self.plan:
+                self.observations.append(f"Plan: {self.plan} Iteration: {iterating_reasoning}")
+
+
+            max_obs_len = 20000
+            obs_str = "\n".join(self.observations)
+            if len(obs_str) > max_obs_len:
+                obs_str = obs_str[:max_obs_len] + "\n...[observations truncated]"
+            execution_prompt_str = (
+                (self.prompt or "")
+                + f"\n\nFollow this plan:\n{self.plan}"
+                + f"\n\nObservations:\n{obs_str}"
+                + f"\n\nIf there is enough data to complete user query '{query}', Respond with 'SATISFIED' only. Otherwise, continue. Dont Menstion 'SATISFIED' in your response if you are not ready. "
+            )
+            
+            messages = self._build_messages(execution_prompt_str, query, retrieved_data)
+
+            resp_from_llm_gen = self._llm_gen(messages, log_context)
+
+            initial_llm_thought_content = self._extract_content_from_llm_response(resp_from_llm_gen)
+            if initial_llm_thought_content:
+                self.observations.append(f"Initial thought/response: {initial_llm_thought_content}")
+            else:
+                logger.info("ReActAgent: Initial LLM response (before handler) had no textual content (might be only tool calls).")
+            resp_after_handler = self._llm_handler(resp_from_llm_gen, tools_dict, messages, log_context)
+            
+            for tool_call_info in self.tool_calls: # Iterate over self.tool_calls populated by _llm_handler
+                observation_string = (
+                    f"Executed Action: Tool '{tool_call_info.get('tool_name', 'N/A')}' "
+                    f"with arguments '{tool_call_info.get('arguments', '{}')}'. Result: '{str(tool_call_info.get('result', ''))[:200]}...'"
+                )
+                self.observations.append(observation_string)
+
+            content_after_handler = self._extract_content_from_llm_response(resp_after_handler)
+            if content_after_handler:
+                self.observations.append(f"Response after tool execution: {content_after_handler}")
+            else:
+                logger.info("ReActAgent: LLM response after handler had no textual content.")
+
+            if log_context:
+                log_context.stacks.append(
+                    {"component": "agent_tool_calls", "data": {"tool_calls": self.tool_calls.copy()}}
+                )
+
+            yield {"sources": retrieved_data}
+
+            display_tool_calls = []
+            for tc in self.tool_calls:
+                cleaned_tc = tc.copy()
+                if len(str(cleaned_tc.get("result", ""))) > 50:
+                    cleaned_tc["result"] = str(cleaned_tc["result"])[:50] + "..."
+                display_tool_calls.append(cleaned_tc)
+            if display_tool_calls:
+                yield {"tool_calls": display_tool_calls}
+            
+            if "SATISFIED" in content_after_handler:
+                logger.info("ReActAgent: LLM satisfied with the plan and data. Stopping reasoning.")
+                break
+
+        # 3. Create Final Answer based on all observations
+        final_answer_stream = self._create_final_answer(query, self.observations, log_context)
+        for answer_chunk in final_answer_stream:
+            yield {"answer": answer_chunk}
+        logger.info("ReActAgent: Finished generating final answer.")
+
+    def _create_plan(
+        self, query: str, docs_data: str, log_context: LogContext = None
+    ) -> Generator[str, None, None]:
+        plan_prompt_filled = planning_prompt_template.replace("{query}", query)
+        if "{summaries}" in plan_prompt_filled:
+            summaries = docs_data if docs_data else "No documents retrieved."
+            plan_prompt_filled = plan_prompt_filled.replace("{summaries}", summaries)
+        plan_prompt_filled = plan_prompt_filled.replace("{prompt}", self.prompt or "")
+        plan_prompt_filled = plan_prompt_filled.replace("{observations}", "\n".join(self.observations))
+
+        messages = [{"role": "user", "content": plan_prompt_filled}]
+
+        plan_stream_from_llm = self.llm.gen_stream(
+            model=self.gpt_model, messages=messages, tools=getattr(self, 'tools', None) # Use self.tools
+        )
+        if log_context:
+            data = build_stack_data(self.llm)
+            log_context.stacks.append({"component": "planning_llm", "data": data})
+
+        for chunk in plan_stream_from_llm:
+            content_piece = self._extract_content_from_llm_response(chunk)
+            if content_piece:
+                yield content_piece
+
+    def _create_final_answer(
+        self, query: str, observations: List[str], log_context: LogContext = None
+    ) -> Generator[str, None, None]:
+        observation_string = "\n".join(observations)
+        max_obs_len = 10000
+        if len(observation_string) > max_obs_len:
+            observation_string = observation_string[:max_obs_len] + "\n...[observations truncated]"
+            logger.warning("ReActAgent: Truncated observations for final answer prompt due to length.")
+
+        final_answer_prompt_filled = final_prompt_template.format(
+            query=query, observations=observation_string
+        )
+
+        messages = [{"role": "user", "content": final_answer_prompt_filled}]
+
+        # Final answer should synthesize, not call tools.
+        final_answer_stream_from_llm = self.llm.gen_stream(
+            model=self.gpt_model, messages=messages, tools=None
+        )
+        if log_context:
+            data = build_stack_data(self.llm)
+            log_context.stacks.append({"component": "final_answer_llm", "data": data})
+
+        for chunk in final_answer_stream_from_llm:
+            content_piece = self._extract_content_from_llm_response(chunk)
+            if content_piece:
+                yield content_piece

application/agents/tools/api_tool.py

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

application/agents/tools/base.py

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

application/agents/tools/brave.py

@@ -0,0 +1,217 @@
+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

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

application/agents/tools/ntfy.py

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

application/agents/tools/postgres.py

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

application/agents/tools/read_webpage.py

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

application/agents/tools/telegram.py

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

application/agents/tools/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

@@ -0,0 +1,42 @@
+import importlib
+import inspect
+import os
+import pkgutil
+
+from application.agents.tools.base import Tool
+
+
+class ToolManager:
+    def __init__(self, config):
+        self.config = config
+        self.tools = {}
+        self.load_tools()
+
+    def load_tools(self):
+        tools_dir = os.path.join(os.path.dirname(__file__))
+        for finder, name, ispkg in pkgutil.iter_modules([tools_dir]):
+            if name == "base" or name.startswith("__"):
+                continue
+            module = importlib.import_module(f"application.agents.tools.{name}")
+            for member_name, obj in inspect.getmembers(module, inspect.isclass):
+                if issubclass(obj, Tool) and obj is not Tool:
+                    tool_config = self.config.get(name, {})
+                    self.tools[name] = obj(tool_config)
+
+    def load_tool(self, tool_name, tool_config):
+        self.config[tool_name] = tool_config
+        module = importlib.import_module(f"application.agents.tools.{tool_name}")
+        for member_name, obj in inspect.getmembers(module, inspect.isclass):
+            if issubclass(obj, Tool) and obj is not Tool:
+                return obj(tool_config)
+
+    def execute_action(self, tool_name, action_name, **kwargs):
+        if tool_name not in self.tools:
+            raise ValueError(f"Tool '{tool_name}' not loaded")
+        return self.tools[tool_name].execute_action(action_name, **kwargs)
+
+    def get_all_actions_metadata(self):
+        metadata = []
+        for tool in self.tools.values():
+            metadata.extend(tool.get_actions_metadata())
+        return metadata

application/api/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
@@ -18,17 +18,18 @@ from application.error import bad_request
 from application.extensions import api
 from application.llm.llm_creator import LLMCreator
 from application.retriever.retriever_creator import RetrieverCreator
-from application.utils import check_required_fields
+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="/")
@@ -37,11 +38,13 @@ api.add_namespace(answer_ns)
 gpt_model = ""
 # to have some kind of default behaviour
 if settings.LLM_NAME == "openai":
-    gpt_model = "gpt-3.5-turbo"
+    gpt_model = "gpt-4o-mini"
 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
 
 
@@ -118,8 +150,42 @@ def is_azure_configured():
     )
 
 
-def save_conversation(conversation_id, question, response, source_log_docs, llm):
-    if conversation_id is not None and conversation_id != "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}": {"$exists": True}},
+            {
+                "$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}}},
+        )
+    elif conversation_id is not None and conversation_id != "None":
         conversations_collection.update_one(
             {"_id": ObjectId(conversation_id)},
             {
@@ -127,7 +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,
                     }
                 }
             },
@@ -141,34 +210,43 @@ def save_conversation(conversation_id, question, response, source_log_docs, llm)
                 "role": "assistant",
                 "content": "Summarise following conversation in no more than 3 "
                 "words, respond ONLY with the summary, use the same "
-                "language as the system \n\nUser: "
-                + question
-                + "\n\n"
-                + "AI: "
-                + response,
+                "language as the system",
             },
             {
                 "role": "user",
                 "content": "Summarise following conversation in no more than 3 words, "
                 "respond ONLY with the summary, use the same language as the "
-                "system",
+                "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
 
@@ -186,66 +264,116 @@ def get_prompt(prompt_id):
 
 
 def complete_stream(
-    question, retriever, conversation_id, user_api_key, isNoneDoc=False
+    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
+                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)
+        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"
@@ -281,6 +409,17 @@ class Stream(Resource):
             "isNoneDoc": fields.Boolean(
                 required=False, description="Flag indicating if no document is used"
             ),
+            "index": fields.Integer(
+                required=False, description="Index of the query to update"
+            ),
+            "save_conversation": fields.Boolean(
+                required=False,
+                default=True,
+                description="Whether to save the conversation",
+            ),
+            "attachments": fields.List(
+                fields.String, required=False, description="List of attachment IDs"
+            ),
         },
     )
 
@@ -289,22 +428,40 @@ class Stream(Resource):
     def post(self):
         data = request.get_json()
         required_fields = ["question"]
-
+        if "index" in data:
+            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 = data.get("history", [])
-            history = json.loads(history)
+            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")
-            
+            attachment_ids = data.get("attachments", [])
 
-            chunks = int(data.get("chunks", 2))
+            index = data.get("index", None)
+            chunks_from_request = data.get("chunks", 2)
+            chunks = chunks_from_request if str(chunks_from_request) == 'Auto' else int(chunks_from_request)
             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
+            decoded_token = getattr(request, "decoded_token", None)
+            user_sub = decoded_token.get("sub") if decoded_token else None
+            agent_key, is_shared_usage, shared_token = get_agent_key(
+                agent_id, user_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"])
@@ -313,27 +470,55 @@ 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,
@@ -341,39 +526,43 @@ class Stream(Resource):
                 token_limit=token_limit,
                 gpt_model=gpt_model,
                 user_api_key=user_api_key,
+                decoded_token=decoded_token,
             )
 
             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,
+                    shared_token=shared_token,
                 ),
                 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",
             )
@@ -420,19 +609,23 @@ 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 = data.get("history", [])
+            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))
+            chunks_from_request = data.get("chunks", 2)
+            chunks = chunks_from_request if str(chunks_from_request) == 'Auto' else int(chunks_from_request)
             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"])
@@ -441,24 +634,44 @@ 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,
@@ -466,36 +679,84 @@ 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,
@@ -506,7 +767,7 @@ 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()},
             )
@@ -555,7 +816,8 @@ class Search(Resource):
 
         try:
             question = data["question"]
-            chunks = int(data.get("chunks", 2))
+            chunks_from_request = data.get("chunks", 2)
+            chunks = chunks_from_request if str(chunks_from_request) == 'Auto' else int(chunks_from_request)
             token_limit = data.get("token_limit", settings.DEFAULT_MAX_HISTORY)
             retriever_name = data.get("retriever", "classic")
 
@@ -564,21 +826,28 @@ 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",
@@ -586,16 +855,17 @@ 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,
@@ -609,10 +879,41 @@ 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

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

application/celery_init.py

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

application/core/settings.py

@@ -1,25 +1,40 @@
+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-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"
-    VECTOR_STORE: str = "faiss" #  "faiss" or "elasticsearch" or "qdrant" or "milvus" or "lancedb"
-    RETRIEVERS_ENABLED: list = ["classic_rag", "duckduck_search"] # also brave_search
+    PARSE_PDF_AS_IMAGE: bool = False
+    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"
@@ -27,12 +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
@@ -67,15 +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/anthropic.py

@@ -17,7 +17,7 @@ class AnthropicLLM(BaseLLM):
         self.AI_PROMPT = AI_PROMPT
 
     def _raw_gen(
-        self, baseself, model, messages, stream=False, max_tokens=300, **kwargs
+        self, baseself, model, messages, stream=False, tools=None, max_tokens=300, **kwargs
     ):
         context = messages[0]["content"]
         user_question = messages[-1]["content"]
@@ -34,7 +34,7 @@ class AnthropicLLM(BaseLLM):
         return completion.completion
 
     def _raw_gen_stream(
-        self, baseself, model, messages, stream=True, max_tokens=300, **kwargs
+        self, baseself, model, messages, stream=True, tools=None, max_tokens=300, **kwargs
     ):
         context = messages[0]["content"]
         user_question = messages[-1]["content"]

application/llm/base.py

@@ -1,10 +1,12 @@
 from abc import ABC, abstractmethod
+
+from application.cache import gen_cache, stream_cache
 from application.usage import gen_token_usage, stream_token_usage
-from application.cache import stream_cache, gen_cache
 
 
 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):
@@ -13,17 +15,52 @@ class BaseLLM(ABC):
         return method(self, *args, **kwargs)
 
     @abstractmethod
-    def _raw_gen(self, model, messages, stream, *args, **kwargs):
+    def _raw_gen(self, model, messages, stream, tools, *args, **kwargs):
         pass
 
-    def gen(self, model, messages, stream=False, *args, **kwargs):
+    def gen(self, model, messages, stream=False, tools=None, *args, **kwargs):
         decorators = [gen_token_usage, gen_cache]
-        return self._apply_decorator(self._raw_gen, decorators=decorators, model=model, messages=messages, stream=stream, *args, **kwargs)
+        return self._apply_decorator(
+            self._raw_gen,
+            decorators=decorators,
+            model=model,
+            messages=messages,
+            stream=stream,
+            tools=tools,
+            *args,
+            **kwargs
+        )
 
     @abstractmethod
     def _raw_gen_stream(self, model, messages, stream, *args, **kwargs):
         pass
 
-    def gen_stream(self, model, messages, stream=True, *args, **kwargs):
+    def gen_stream(self, model, messages, stream=True, tools=None, *args, **kwargs):
         decorators = [stream_cache, stream_token_usage]
-        return self._apply_decorator(self._raw_gen_stream, decorators=decorators, model=model, messages=messages, stream=stream, *args, **kwargs)
+        return self._apply_decorator(
+            self._raw_gen_stream,
+            decorators=decorators,
+            model=model,
+            messages=messages,
+            stream=stream,
+            tools=tools,
+            *args,
+            **kwargs
+        )
+
+    def supports_tools(self):
+        return hasattr(self, "_supports_tools") and callable(
+            getattr(self, "_supports_tools")
+        )
+
+    def _supports_tools(self):
+        raise NotImplementedError("Subclass must implement _supports_tools method")
+        
+    def 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

@@ -1,44 +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.docsgpt.co.uk"
+        self.api_key = api_key
 
-    def _raw_gen(self, baseself, model, messages, stream=False, *args, **kwargs):
-        context = messages[0]["content"]
-        user_question = messages[-1]["content"]
-        prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
+    def _clean_messages_openai(self, messages):
+        cleaned_messages = []
+        for message in messages:
+            role = message.get("role")
+            content = message.get("content")
 
-        response = requests.post(
-            f"{self.endpoint}/answer", json={"prompt": prompt, "max_new_tokens": 30}
-        )
-        response_clean = response.json()["a"].replace("###", "")
+            if role == "model":
+                role = "assistant"
 
-        return response_clean
+            if role and content is not None:
+                if isinstance(content, str):
+                    cleaned_messages.append({"role": role, "content": content})
+                elif isinstance(content, list):
+                    for item in content:
+                        if "text" in item:
+                            cleaned_messages.append(
+                                {"role": role, "content": item["text"]}
+                            )
+                        elif "function_call" in item:
+                            tool_call = {
+                                "id": item["function_call"]["call_id"],
+                                "type": "function",
+                                "function": {
+                                    "name": item["function_call"]["name"],
+                                    "arguments": json.dumps(
+                                        item["function_call"]["args"]
+                                    ),
+                                },
+                            }
+                            cleaned_messages.append(
+                                {
+                                    "role": "assistant",
+                                    "content": None,
+                                    "tool_calls": [tool_call],
+                                }
+                            )
+                        elif "function_response" in item:
+                            cleaned_messages.append(
+                                {
+                                    "role": "tool",
+                                    "tool_call_id": item["function_response"][
+                                        "call_id"
+                                    ],
+                                    "content": json.dumps(
+                                        item["function_response"]["response"]["result"]
+                                    ),
+                                }
+                            )
+                        else:
+                            raise ValueError(
+                                f"Unexpected content dictionary format: {item}"
+                            )
+                else:
+                    raise ValueError(f"Unexpected content type: {type(content)}")
 
-    def _raw_gen_stream(self, baseself, model, messages, stream=True, *args, **kwargs):
-        context = messages[0]["content"]
-        user_question = messages[-1]["content"]
-        prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
+        return cleaned_messages
 
-        # send prompt to endpoint /stream
-        response = requests.post(
-            f"{self.endpoint}/stream",
-            json={"prompt": prompt, "max_new_tokens": 256},
-            stream=True,
-        )
+    def _raw_gen(
+        self,
+        baseself,
+        model,
+        messages,
+        stream=False,
+        tools=None,
+        engine=settings.AZURE_DEPLOYMENT_NAME,
+        **kwargs,
+    ):
+        messages = self._clean_messages_openai(messages)
+        if tools:
+            response = self.client.chat.completions.create(
+                model="docsgpt",
+                messages=messages,
+                stream=stream,
+                tools=tools,
+                **kwargs,
+            )
+            return response.choices[0]
+        else:
+            response = self.client.chat.completions.create(
+                model="docsgpt", messages=messages, stream=stream, **kwargs
+            )
+            return response.choices[0].message.content
 
-        for line in response.iter_lines():
-            if line:
-                # data = json.loads(line)
-                data_str = line.decode("utf-8")
-                if data_str.startswith("data: "):
-                    data = json.loads(data_str[6:])
-                    yield data["a"]
+    def _raw_gen_stream(
+        self,
+        baseself,
+        model,
+        messages,
+        stream=True,
+        tools=None,
+        engine=settings.AZURE_DEPLOYMENT_NAME,
+        **kwargs,
+    ):
+        messages = self._clean_messages_openai(messages)
+        if tools:
+            response = self.client.chat.completions.create(
+                model="docsgpt",
+                messages=messages,
+                stream=stream,
+                tools=tools,
+                **kwargs,
+            )
+        else:
+            response = self.client.chat.completions.create(
+                model="docsgpt", messages=messages, stream=stream, **kwargs
+            )
+
+        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

@@ -0,0 +1,313 @@
+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):
+    def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.api_key = api_key
+        self.user_api_key = user_api_key
+        self.client = genai.Client(api_key=self.api_key)
+        self.storage = StorageCreator.get_storage()
+
+    def get_supported_attachment_types(self):
+        """
+        Return a list of MIME types supported by Google Gemini for file uploads.
+
+        Returns:
+            list: List of supported MIME types
+        """
+        return [
+            'application/pdf',
+            'image/png',
+            'image/jpeg',
+            'image/jpg',
+            'image/webp',
+            'image/gif'
+        ]
+
+    def prepare_messages_with_attachments(self, messages, attachments=None):
+        """
+        Process attachments using Google AI's file API for more efficient handling.
+
+        Args:
+            messages (list): List of message dictionaries.
+            attachments (list): List of attachment dictionaries with content and metadata.
+
+        Returns:
+            list: Messages formatted with file references for Google AI API.
+        """
+        if not attachments:
+            return messages
+
+        prepared_messages = messages.copy()
+
+        # Find the user message to attach files to the last one
+        user_message_index = None
+        for i in range(len(prepared_messages) - 1, -1, -1):
+            if prepared_messages[i].get("role") == "user":
+                user_message_index = i
+                break
+
+        if user_message_index is None:
+            user_message = {"role": "user", "content": []}
+            prepared_messages.append(user_message)
+            user_message_index = len(prepared_messages) - 1
+
+        if isinstance(prepared_messages[user_message_index].get("content"), str):
+            text_content = prepared_messages[user_message_index]["content"]
+            prepared_messages[user_message_index]["content"] = [
+                {"type": "text", "text": text_content}
+            ]
+        elif not isinstance(prepared_messages[user_message_index].get("content"), list):
+            prepared_messages[user_message_index]["content"] = []
+
+        files = []
+        for attachment in attachments:
+            mime_type = attachment.get('mime_type')
+
+            if mime_type in self.get_supported_attachment_types():
+                try:
+                    file_uri = self._upload_file_to_google(attachment)
+                    logging.info(f"GoogleLLM: Successfully uploaded file, got URI: {file_uri}")
+                    files.append({"file_uri": file_uri, "mime_type": mime_type})
+                except Exception as e:
+                    logging.error(f"GoogleLLM: Error uploading file: {e}", exc_info=True)
+                    if 'content' in attachment:
+                        prepared_messages[user_message_index]["content"].append({
+                            "type": "text",
+                            "text": f"[File could not be processed: {attachment.get('path', 'unknown')}]"
+                        })
+
+        if files:
+            logging.info(f"GoogleLLM: Adding {len(files)} files to message")
+            prepared_messages[user_message_index]["content"].append({
+                "files": files
+            })
+
+        return prepared_messages
+
+    def _upload_file_to_google(self, attachment):
+        """
+        Upload a file to Google AI and return the file URI.
+
+        Args:
+            attachment (dict): Attachment dictionary with path and metadata.
+
+        Returns:
+            str: Google AI file URI for the uploaded file.
+        """
+        if 'google_file_uri' in attachment:
+            return attachment['google_file_uri']
+
+        file_path = attachment.get('path')
+        if not file_path:
+            raise ValueError("No file path provided in attachment")
+
+        if not self.storage.file_exists(file_path):
+            raise FileNotFoundError(f"File not found: {file_path}")
+
+        try:
+            file_uri = self.storage.process_file(
+                file_path,
+                lambda local_path, **kwargs: self.client.files.upload(file=local_path).uri
+            )
+
+            from application.core.mongo_db import MongoDB
+            mongo = MongoDB.get_client()
+            db = mongo[settings.MONGO_DB_NAME]
+            attachments_collection = db["attachments"]
+            if '_id' in attachment:
+                attachments_collection.update_one(
+                    {"_id": attachment['_id']},
+                    {"$set": {"google_file_uri": file_uri}}
+                )
+
+            return file_uri
+        except Exception as e:
+            logging.error(f"Error uploading file to Google AI: {e}", exc_info=True)
+            raise
+
+    def _clean_messages_google(self, messages):
+        cleaned_messages = []
+        for message in messages:
+            role = message.get("role")
+            content = message.get("content")
+
+            if role == "assistant":
+                role = "model"
+
+            parts = []
+            if role and content is not None:
+                if isinstance(content, str):
+                    parts = [types.Part.from_text(text=content)]
+                elif isinstance(content, list):
+                    for item in content:
+                        if "text" in item:
+                            parts.append(types.Part.from_text(text=item["text"]))
+                        elif "function_call" in item:
+                            parts.append(
+                                types.Part.from_function_call(
+                                    name=item["function_call"]["name"],
+                                    args=item["function_call"]["args"],
+                                )
+                            )
+                        elif "function_response" in item:
+                            parts.append(
+                                types.Part.from_function_response(
+                                    name=item["function_response"]["name"],
+                                    response=item["function_response"]["response"],
+                                )
+                            )
+                        elif "files" in item:
+                                for file_data in item["files"]:
+                                    parts.append(
+                                        types.Part.from_uri(
+                                            file_uri=file_data["file_uri"],
+                                            mime_type=file_data["mime_type"]
+                                        )
+                                    )
+                        else:
+                            raise ValueError(
+                                f"Unexpected content dictionary format:{item}"
+                            )
+                else:
+                    raise ValueError(f"Unexpected content type: {type(content)}")
+
+                cleaned_messages.append(types.Content(role=role, parts=parts))
+
+        return cleaned_messages
+
+    def _clean_tools_format(self, tools_list):
+        genai_tools = []
+        for tool_data in tools_list:
+            if tool_data["type"] == "function":
+                function = tool_data["function"]
+                parameters = function["parameters"]
+                properties = parameters.get("properties", {})
+
+                if properties:
+                    genai_function = dict(
+                        name=function["name"],
+                        description=function["description"],
+                        parameters={
+                            "type": "OBJECT",
+                            "properties": {
+                                k: {
+                                    **v,
+                                    "type": v["type"].upper() if v["type"] else None,
+                                }
+                                for k, v in properties.items()
+                            },
+                            "required": (
+                                parameters["required"]
+                                if "required" in parameters
+                                else []
+                            ),
+                        },
+                    )
+                else:
+                    genai_function = dict(
+                        name=function["name"],
+                        description=function["description"],
+                    )
+
+                genai_tool = types.Tool(function_declarations=[genai_function])
+                genai_tools.append(genai_tool)
+
+        return genai_tools
+
+    def _raw_gen(
+        self,
+        baseself,
+        model,
+        messages,
+        stream=False,
+        tools=None,
+        formatting="openai",
+        **kwargs,
+    ):
+        client = genai.Client(api_key=self.api_key)
+        if formatting == "openai":
+            messages = self._clean_messages_google(messages)
+        config = types.GenerateContentConfig()
+        if messages[0].role == "system":
+            config.system_instruction = messages[0].parts[0].text
+            messages = messages[1:]
+
+        if tools:
+            cleaned_tools = self._clean_tools_format(tools)
+            config.tools = cleaned_tools
+            response = client.models.generate_content(
+                model=model,
+                contents=messages,
+                config=config,
+            )
+            return response
+        else:
+            response = client.models.generate_content(
+                model=model, contents=messages, config=config
+            )
+            return response.text
+
+    def _raw_gen_stream(
+        self,
+        baseself,
+        model,
+        messages,
+        stream=True,
+        tools=None,
+        formatting="openai",
+        **kwargs,
+    ):
+        client = genai.Client(api_key=self.api_key)
+        if formatting == "openai":
+            messages = self._clean_messages_google(messages)
+        config = types.GenerateContentConfig()
+        if messages[0].role == "system":
+            config.system_instruction = messages[0].parts[0].text
+            messages = messages[1:]
+
+        if tools:
+            cleaned_tools = self._clean_tools_format(tools)
+            config.tools = cleaned_tools
+
+        # 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 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):
+        return True

application/llm/groq.py

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

application/llm/llm_creator.py

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

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

@@ -1,6 +1,10 @@
-from application.llm.base import BaseLLM
-from application.core.settings import settings
+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):
@@ -9,15 +13,82 @@ class OpenAILLM(BaseLLM):
         from openai import OpenAI
 
         super().__init__(*args, **kwargs)
-        if settings.OPENAI_BASE_URL:
-            self.client = OpenAI(
-                api_key=api_key,
-                base_url=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,
@@ -25,14 +96,25 @@ class OpenAILLM(BaseLLM):
         model,
         messages,
         stream=False,
+        tools=None,
         engine=settings.AZURE_DEPLOYMENT_NAME,
-        **kwargs
-    ):  
-        response = self.client.chat.completions.create(
-            model=model, messages=messages, stream=stream, **kwargs
-        )
-
-        return response.choices[0].message.content
+        **kwargs,
+    ):
+        messages = self._clean_messages_openai(messages)
+        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,
@@ -40,34 +122,204 @@ class OpenAILLM(BaseLLM):
         model,
         messages,
         stream=True,
+        tools=None,
         engine=settings.AZURE_DEPLOYMENT_NAME,
-        **kwargs
-    ):  
-        response = self.client.chat.completions.create(
-            model=model, messages=messages, stream=stream, **kwargs
-        )
+        **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:
-            # import sys
-            # print(line.choices[0].delta.content, file=sys.stderr)
-            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/llm/sagemaker.py

@@ -76,7 +76,7 @@ class SagemakerAPILLM(BaseLLM):
         self.endpoint = settings.SAGEMAKER_ENDPOINT
         self.runtime = runtime
 
-    def _raw_gen(self, baseself, model, messages, stream=False, **kwargs):
+    def _raw_gen(self, baseself, model, messages, stream=False, tools=None, **kwargs):
         context = messages[0]["content"]
         user_question = messages[-1]["content"]
         prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
@@ -105,7 +105,7 @@ class SagemakerAPILLM(BaseLLM):
         print(result[0]["generated_text"], file=sys.stderr)
         return result[0]["generated_text"][len(prompt) :]
 
-    def _raw_gen_stream(self, baseself, model, messages, stream=True, **kwargs):
+    def _raw_gen_stream(self, baseself, model, messages, stream=True, tools=None, **kwargs):
         context = messages[0]["content"]
         user_question = messages[-1]["content"]
         prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"

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/chunking.py

@@ -0,0 +1,118 @@
+import re
+from typing import List, Tuple
+import logging
+from application.parser.schema.base import Document
+from application.utils import get_encoding
+
+logger = logging.getLogger(__name__)
+
+class Chunker:
+    def __init__(
+        self,
+        chunking_strategy: str = "classic_chunk",
+        max_tokens: int = 2000,
+        min_tokens: int = 150,
+        duplicate_headers: bool = False,
+    ):
+        if chunking_strategy not in ["classic_chunk"]:
+            raise ValueError(f"Unsupported chunking strategy: {chunking_strategy}")
+        self.chunking_strategy = chunking_strategy
+        self.max_tokens = max_tokens
+        self.min_tokens = min_tokens
+        self.duplicate_headers = duplicate_headers
+        self.encoding = get_encoding()
+
+    def separate_header_and_body(self, text: str) -> Tuple[str, str]:
+        header_pattern = r"^(.*?\n){3}"
+        match = re.match(header_pattern, text)
+        if match:
+            header = match.group(0)
+            body = text[len(header):]
+        else:
+            header, body = "", text  # No header, treat entire text as body
+        return header, body
+
+    def combine_documents(self, doc: Document, next_doc: Document) -> Document:
+        combined_text = doc.text + " " + next_doc.text
+        combined_token_count = len(self.encoding.encode(combined_text))
+        new_doc = Document(
+            text=combined_text,
+            doc_id=doc.doc_id,
+            embedding=doc.embedding,
+            extra_info={**(doc.extra_info or {}), "token_count": combined_token_count}
+        )
+        return new_doc
+    
+    def split_document(self, doc: Document) -> List[Document]:
+        split_docs = []
+        header, body = self.separate_header_and_body(doc.text)
+        header_tokens = self.encoding.encode(header) if header else []
+        body_tokens = self.encoding.encode(body)
+
+        current_position = 0
+        part_index = 0
+        while current_position < len(body_tokens):
+            end_position = current_position + self.max_tokens - len(header_tokens)
+            chunk_tokens = (header_tokens + body_tokens[current_position:end_position]
+                            if self.duplicate_headers or part_index == 0 else body_tokens[current_position:end_position])
+            chunk_text = self.encoding.decode(chunk_tokens)
+            new_doc = Document(
+                text=chunk_text,
+                doc_id=f"{doc.doc_id}-{part_index}",
+                embedding=doc.embedding,
+                extra_info={**(doc.extra_info or {}), "token_count": len(chunk_tokens)}
+            )
+            split_docs.append(new_doc)
+            current_position = end_position
+            part_index += 1
+            header_tokens = []
+        return split_docs
+
+    def classic_chunk(self, documents: List[Document]) -> List[Document]:
+        processed_docs = []
+        i = 0
+        while i < len(documents):
+            doc = documents[i]
+            tokens = self.encoding.encode(doc.text)
+            token_count = len(tokens)
+
+            if self.min_tokens <= token_count <= self.max_tokens:
+                doc.extra_info = doc.extra_info or {}
+                doc.extra_info["token_count"] = token_count
+                processed_docs.append(doc)
+                i += 1
+            elif token_count < self.min_tokens:
+                if i + 1 < len(documents):
+                    next_doc = documents[i + 1]
+                    next_tokens = self.encoding.encode(next_doc.text)
+                    if token_count + len(next_tokens) <= self.max_tokens:
+                        # Combine small documents
+                        combined_doc = self.combine_documents(doc, next_doc)
+                        processed_docs.append(combined_doc)
+                        i += 2
+                    else:
+                        # Keep the small document as is if adding next_doc would exceed max_tokens
+                        doc.extra_info = doc.extra_info or {}
+                        doc.extra_info["token_count"] = token_count
+                        processed_docs.append(doc)
+                        i += 1
+                else:
+                    # No next document to combine with; add the small document as is
+                    doc.extra_info = doc.extra_info or {}
+                    doc.extra_info["token_count"] = token_count
+                    processed_docs.append(doc)
+                    i += 1
+            else:
+                # Split large documents
+                processed_docs.extend(self.split_document(doc))
+                i += 1
+        return processed_docs
+
+    def chunk(
+        self,
+        documents: List[Document]
+    ) -> List[Document]:
+        if self.chunking_strategy == "classic_chunk":
+            return self.classic_chunk(documents)
+        else:
+            raise ValueError("Unsupported chunking strategy")

application/parser/embedding_pipeline.py

@@ -0,0 +1,86 @@
+import os
+import logging
+from retry import retry
+from tqdm import tqdm
+from application.core.settings import settings
+from application.vectorstore.vector_creator import VectorCreator
+
+
+@retry(tries=10, delay=60)
+def add_text_to_store_with_retry(store, doc, source_id):
+    """
+    Add a document's text and metadata to the vector store with retry logic.
+    Args:
+        store: The vector store object.
+        doc: The document to be added.
+        source_id: Unique identifier for the source.
+    """
+    try:
+        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}", exc_info=True)
+        raise
+
+
+def embed_and_store_documents(docs, folder_name, source_id, task_status):
+    """
+    Embeds documents and stores them in a vector store.
+
+    Args:
+        docs (list): List of documents to be embedded and stored.
+        folder_name (str): Directory to save the vector store.
+        source_id (str): Unique identifier for the source.
+        task_status: Task state manager for progress updates.
+
+    Returns:
+        None
+    """
+    # Ensure the folder exists
+    if not os.path.exists(folder_name):
+        os.makedirs(folder_name)
+
+    # Initialize vector store
+    if settings.VECTOR_STORE == "faiss":
+        docs_init = [docs.pop(0)]
+        store = VectorCreator.create_vectorstore(
+            settings.VECTOR_STORE,
+            docs_init=docs_init,
+            source_id=folder_name,
+            embeddings_key=os.getenv("EMBEDDINGS_KEY"),
+        )
+    else:
+        store = VectorCreator.create_vectorstore(
+            settings.VECTOR_STORE,
+            source_id=source_id,
+            embeddings_key=os.getenv("EMBEDDINGS_KEY"),
+        )
+        store.delete_index()
+
+    total_docs = len(docs)
+
+    # Process and embed documents
+    for idx, doc in tqdm(
+        enumerate(docs),
+        desc="Embedding 🦖",
+        unit="docs",
+        total=total_docs,
+        bar_format="{l_bar}{bar}| Time Left: {remaining}",
+    ):
+        try:
+            # Update task status for progress tracking
+            progress = int(((idx + 1) / total_docs) * 100)
+            task_status.update_state(state="PROGRESS", meta={"current": progress})
+
+            # 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}", exc_info=True)
+            logging.info(f"Saving progress at document {idx} out of {total_docs}")
+            store.save_local(folder_name)
+            break
+
+    # Save the vector store
+    if settings.VECTOR_STORE == "faiss":
+        store.save_local(folder_name)
+    logging.info("Vector store saved successfully.")

application/parser/file/bulk.py

@@ -13,6 +13,7 @@ from application.parser.file.rst_parser import RstParser
 from application.parser.file.tabular_parser import PandasCSVParser,ExcelParser
 from application.parser.file.json_parser import JSONParser
 from application.parser.file.pptx_parser import PPTXParser
+from application.parser.file.image_parser import ImageParser
 from application.parser.schema.base import Document
 
 DEFAULT_FILE_EXTRACTOR: Dict[str, BaseParser] = {
@@ -27,6 +28,9 @@ DEFAULT_FILE_EXTRACTOR: Dict[str, BaseParser] = {
     ".mdx": MarkdownParser(),
     ".json":JSONParser(),
     ".pptx":PPTXParser(),
+    ".png": ImageParser(),
+    ".jpg": ImageParser(),
+    ".jpeg": ImageParser(),
 }
 
 
@@ -154,7 +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

@@ -7,7 +7,8 @@ from pathlib import Path
 from typing import Dict
 
 from application.parser.file.base_parser import BaseParser
-
+from application.core.settings import settings
+import requests
 
 class PDFParser(BaseParser):
     """PDF parser."""
@@ -18,22 +19,32 @@ class PDFParser(BaseParser):
 
     def parse_file(self, file: Path, errors: str = "ignore") -> str:
         """Parse file."""
+        if settings.PARSE_PDF_AS_IMAGE:
+            doc2md_service = "https://llm.arc53.com/doc2md"
+            # 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"]
+            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)
 
@@ -56,4 +67,4 @@ class DocxParser(BaseParser):
 
         text = docx2txt.process(file)
 
-        return text
+        return text

application/parser/file/image_parser.py

@@ -0,0 +1,27 @@
+"""Image parser.
+
+Contains parser for .png, .jpg, .jpeg files.
+
+"""
+from pathlib import Path
+import requests
+from typing import Dict, Union
+
+from application.parser.file.base_parser import BaseParser
+
+
+class ImageParser(BaseParser):
+    """Image parser."""
+
+    def _init_parser(self) -> Dict:
+        """Init parser."""
+        return {}
+
+    def parse_file(self, file: Path, errors: str = "ignore") -> Union[str, list[str]]:
+        doc2md_service = "https://llm.arc53.com/doc2md"
+        # 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"] 
+        return data

application/parser/file/rst_parser.py

@@ -91,6 +91,25 @@ class RstParser(BaseParser):
             ]
         return rst_tups
 
+    def chunk_by_token_count(self, text: str, max_tokens: int = 100) -> List[str]:
+        """Chunk text by token count."""
+    
+        avg_token_length = 5
+    
+        chunk_size = max_tokens * avg_token_length
+
+        chunks = []
+        for i in range(0, len(text), chunk_size):
+            chunk = text[i:i+chunk_size]
+            if i + chunk_size < len(text):
+                last_space = chunk.rfind(' ')
+                if last_space != -1:
+                    chunk = chunk[:last_space]
+            
+            chunks.append(chunk.strip())
+        
+        return chunks
+    
     def remove_images(self, content: str) -> str:
         pattern = r"\.\. image:: (.*)"
         content = re.sub(pattern, "", content)
@@ -136,7 +155,7 @@ class RstParser(BaseParser):
         return {}
 
     def parse_tups(
-            self, filepath: Path, errors: str = "ignore"
+            self, filepath: Path, errors: str = "ignore",max_tokens: Optional[int] = 1000
     ) -> List[Tuple[Optional[str], str]]:
         """Parse file into tuples."""
         with open(filepath, "r") as f:
@@ -156,6 +175,15 @@ class RstParser(BaseParser):
             rst_tups = self.remove_whitespaces_excess(rst_tups)
         if self._remove_characters_excess:
             rst_tups = self.remove_characters_excess(rst_tups)
+
+        # Apply chunking if max_tokens is provided
+        if max_tokens is not None:
+            chunked_tups = []
+            for header, text in rst_tups:
+                chunks = self.chunk_by_token_count(text, max_tokens)
+                for idx, chunk in enumerate(chunks):
+                    chunked_tups.append((f"{header} - Chunk {idx + 1}", chunk))
+            return chunked_tups    
         return rst_tups
 
     def parse_file(

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/java2doc.py

@@ -1,66 +0,0 @@
-import os
-
-import javalang
-
-
-def find_files(directory):
-    files_list = []
-    for root, dirs, files in os.walk(directory):
-        for file in files:
-            if file.endswith('.java'):
-                files_list.append(os.path.join(root, file))
-    return files_list
-
-
-def extract_functions(file_path):
-    with open(file_path, "r") as file:
-        java_code = file.read()
-        methods = {}
-        tree = javalang.parse.parse(java_code)
-        for _, node in tree.filter(javalang.tree.MethodDeclaration):
-            method_name = node.name
-            start_line = node.position.line - 1
-            end_line = start_line
-            brace_count = 0
-            for line in java_code.splitlines()[start_line:]:
-                end_line += 1
-                brace_count += line.count("{") - line.count("}")
-                if brace_count == 0:
-                    break
-            method_source_code = "\n".join(java_code.splitlines()[start_line:end_line])
-            methods[method_name] = method_source_code
-    return methods
-
-
-def extract_classes(file_path):
-    with open(file_path, 'r') as file:
-        source_code = file.read()
-        classes = {}
-        tree = javalang.parse.parse(source_code)
-        for class_decl in tree.types:
-            class_name = class_decl.name
-            declarations = []
-            methods = []
-            for field_decl in class_decl.fields:
-                field_name = field_decl.declarators[0].name
-                field_type = field_decl.type.name
-                declarations.append(f"{field_type} {field_name}")
-            for method_decl in class_decl.methods:
-                methods.append(method_decl.name)
-            class_string = "Declarations: " + ", ".join(declarations) + "\n  Method name: " + ", ".join(methods)
-            classes[class_name] = class_string
-    return classes
-
-
-def extract_functions_and_classes(directory):
-    files = find_files(directory)
-    functions_dict = {}
-    classes_dict = {}
-    for file in files:
-        functions = extract_functions(file)
-        if functions:
-            functions_dict[file] = functions
-        classes = extract_classes(file)
-        if classes:
-            classes_dict[file] = classes
-    return functions_dict, classes_dict

application/parser/js2doc.py

@@ -1,70 +0,0 @@
-import os
-
-import escodegen
-import esprima
-
-
-def find_files(directory):
-    files_list = []
-    for root, dirs, files in os.walk(directory):
-        for file in files:
-            if file.endswith('.js'):
-                files_list.append(os.path.join(root, file))
-    return files_list
-
-
-def extract_functions(file_path):
-    with open(file_path, 'r') as file:
-        source_code = file.read()
-        functions = {}
-        tree = esprima.parseScript(source_code)
-        for node in tree.body:
-            if node.type == 'FunctionDeclaration':
-                func_name = node.id.name if node.id else '<anonymous>'
-                functions[func_name] = escodegen.generate(node)
-            elif node.type == 'VariableDeclaration':
-                for declaration in node.declarations:
-                    if declaration.init and declaration.init.type == 'FunctionExpression':
-                        func_name = declaration.id.name if declaration.id else '<anonymous>'
-                        functions[func_name] = escodegen.generate(declaration.init)
-            elif node.type == 'ClassDeclaration':
-                for subnode in node.body.body:
-                    if subnode.type == 'MethodDefinition':
-                        func_name = subnode.key.name
-                        functions[func_name] = escodegen.generate(subnode.value)
-                    elif subnode.type == 'VariableDeclaration':
-                        for declaration in subnode.declarations:
-                            if declaration.init and declaration.init.type == 'FunctionExpression':
-                                func_name = declaration.id.name if declaration.id else '<anonymous>'
-                                functions[func_name] = escodegen.generate(declaration.init)
-        return functions
-
-
-def extract_classes(file_path):
-    with open(file_path, 'r') as file:
-        source_code = file.read()
-        classes = {}
-        tree = esprima.parseScript(source_code)
-        for node in tree.body:
-            if node.type == 'ClassDeclaration':
-                class_name = node.id.name
-                function_names = []
-                for subnode in node.body.body:
-                    if subnode.type == 'MethodDefinition':
-                        function_names.append(subnode.key.name)
-                classes[class_name] = ", ".join(function_names)
-    return classes
-
-
-def extract_functions_and_classes(directory):
-    files = find_files(directory)
-    functions_dict = {}
-    classes_dict = {}
-    for file in files:
-        functions = extract_functions(file)
-        if functions:
-            functions_dict[file] = functions
-        classes = extract_classes(file)
-        if classes:
-            classes_dict[file] = classes
-    return functions_dict, classes_dict

application/parser/open_ai_func.py

@@ -1,75 +0,0 @@
-import os
-
-from retry import retry
-
-from application.core.settings import settings
-
-from application.vectorstore.vector_creator import VectorCreator
-
-
-# from langchain_community.embeddings import HuggingFaceEmbeddings
-# from langchain_community.embeddings import HuggingFaceInstructEmbeddings
-# from langchain_community.embeddings import CohereEmbeddings
-
-
-@retry(tries=10, delay=60)
-def store_add_texts_with_retry(store, i, id):
-    # add source_id to the metadata
-    i.metadata["source_id"] = str(id)
-    store.add_texts([i.page_content], metadatas=[i.metadata])
-    # store_pine.add_texts([i.page_content], metadatas=[i.metadata])
-
-
-def call_openai_api(docs, folder_name, id, task_status):
-    # Function to create a vector store from the documents and save it to disk
-
-    if not os.path.exists(f"{folder_name}"):
-        os.makedirs(f"{folder_name}")
-
-    from tqdm import tqdm
-
-    c1 = 0
-    if settings.VECTOR_STORE == "faiss":
-        docs_init = [docs[0]]
-        docs.pop(0)
-
-        store = VectorCreator.create_vectorstore(
-            settings.VECTOR_STORE,
-            docs_init=docs_init,
-            source_id=f"{folder_name}",
-            embeddings_key=os.getenv("EMBEDDINGS_KEY"),
-        )
-    else:
-        store = VectorCreator.create_vectorstore(
-            settings.VECTOR_STORE,
-            source_id=str(id),
-            embeddings_key=os.getenv("EMBEDDINGS_KEY"),
-        )
-        store.delete_index()
-    # Uncomment for MPNet embeddings
-    # model_name = "sentence-transformers/all-mpnet-base-v2"
-    # hf = HuggingFaceEmbeddings(model_name=model_name)
-    # store = FAISS.from_documents(docs_test, hf)
-    s1 = len(docs)
-    for i in tqdm(
-        docs,
-        desc="Embedding 🦖",
-        unit="docs",
-        total=len(docs),
-        bar_format="{l_bar}{bar}| Time Left: {remaining}",
-    ):
-        try:
-            task_status.update_state(
-                state="PROGRESS", meta={"current": int((c1 / s1) * 100)}
-            )
-            store_add_texts_with_retry(store, i, id)
-        except Exception as e:
-            print(e)
-            print("Error on ", i)
-            print("Saving progress")
-            print(f"stopped at {c1} out of {len(docs)}")
-            store.save_local(f"{folder_name}")
-            break
-        c1 += 1
-    if settings.VECTOR_STORE == "faiss":
-        store.save_local(f"{folder_name}")

application/parser/py2doc.py

@@ -1,121 +0,0 @@
-import ast
-import os
-from pathlib import Path
-
-import tiktoken
-from langchain.llms import OpenAI
-from langchain.prompts import PromptTemplate
-
-
-def find_files(directory):
-    files_list = []
-    for root, dirs, files in os.walk(directory):
-        for file in files:
-            if file.endswith('.py'):
-                files_list.append(os.path.join(root, file))
-    return files_list
-
-
-def extract_functions(file_path):
-    with open(file_path, 'r') as file:
-        source_code = file.read()
-        functions = {}
-        tree = ast.parse(source_code)
-        for node in ast.walk(tree):
-            if isinstance(node, ast.FunctionDef):
-                func_name = node.name
-                func_def = ast.get_source_segment(source_code, node)
-                functions[func_name] = func_def
-    return functions
-
-
-def extract_classes(file_path):
-    with open(file_path, 'r') as file:
-        source_code = file.read()
-        classes = {}
-        tree = ast.parse(source_code)
-        for node in ast.walk(tree):
-            if isinstance(node, ast.ClassDef):
-                class_name = node.name
-                function_names = []
-                for subnode in ast.walk(node):
-                    if isinstance(subnode, ast.FunctionDef):
-                        function_names.append(subnode.name)
-                classes[class_name] = ", ".join(function_names)
-    return classes
-
-
-def extract_functions_and_classes(directory):
-    files = find_files(directory)
-    functions_dict = {}
-    classes_dict = {}
-    for file in files:
-        functions = extract_functions(file)
-        if functions:
-            functions_dict[file] = functions
-        classes = extract_classes(file)
-        if classes:
-            classes_dict[file] = classes
-    return functions_dict, classes_dict
-
-
-def parse_functions(functions_dict, formats, dir):
-    c1 = len(functions_dict)
-    for i, (source, functions) in enumerate(functions_dict.items(), start=1):
-        print(f"Processing file {i}/{c1}")
-        source_w = source.replace(dir + "/", "").replace("." + formats, ".md")
-        subfolders = "/".join(source_w.split("/")[:-1])
-        Path(f"outputs/{subfolders}").mkdir(parents=True, exist_ok=True)
-        for j, (name, function) in enumerate(functions.items(), start=1):
-            print(f"Processing function {j}/{len(functions)}")
-            prompt = PromptTemplate(
-                input_variables=["code"],
-                template="Code: \n{code}, \nDocumentation: ",
-            )
-            llm = OpenAI(temperature=0)
-            response = llm(prompt.format(code=function))
-            mode = "a" if Path(f"outputs/{source_w}").exists() else "w"
-            with open(f"outputs/{source_w}", mode) as f:
-                f.write(
-                    f"\n\n# Function name: {name} \n\nFunction: \n```\n{function}\n```, \nDocumentation: \n{response}")
-
-
-def parse_classes(classes_dict, formats, dir):
-    c1 = len(classes_dict)
-    for i, (source, classes) in enumerate(classes_dict.items()):
-        print(f"Processing file {i + 1}/{c1}")
-        source_w = source.replace(dir + "/", "").replace("." + formats, ".md")
-        subfolders = "/".join(source_w.split("/")[:-1])
-        Path(f"outputs/{subfolders}").mkdir(parents=True, exist_ok=True)
-        for name, function_names in classes.items():
-            print(f"Processing Class {i + 1}/{c1}")
-            prompt = PromptTemplate(
-                input_variables=["class_name", "functions_names"],
-                template="Class name: {class_name} \nFunctions: {functions_names}, \nDocumentation: ",
-            )
-            llm = OpenAI(temperature=0)
-            response = llm(prompt.format(class_name=name, functions_names=function_names))
-
-            with open(f"outputs/{source_w}", "a" if Path(f"outputs/{source_w}").exists() else "w") as f:
-                f.write(f"\n\n# Class name: {name} \n\nFunctions: \n{function_names}, \nDocumentation: \n{response}")
-
-
-def transform_to_docs(functions_dict, classes_dict, formats, dir):
-    docs_content = ''.join([str(key) + str(value) for key, value in functions_dict.items()])
-    docs_content += ''.join([str(key) + str(value) for key, value in classes_dict.items()])
-
-    num_tokens = len(tiktoken.get_encoding("cl100k_base").encode(docs_content))
-    total_price = ((num_tokens / 1000) * 0.02)
-
-    print(f"Number of Tokens = {num_tokens:,d}")
-    print(f"Approx Cost = ${total_price:,.2f}")
-
-    user_input = input("Price Okay? (Y/N)\n").lower()
-    if user_input == "y" or user_input == "":
-        if not Path("outputs").exists():
-            Path("outputs").mkdir()
-        parse_functions(functions_dict, formats, dir)
-        parse_classes(classes_dict, formats, dir)
-        print("All done!")
-    else:
-        print("The API was not called. No money was spent.")

application/parser/remote/crawler_loader.py

@@ -1,17 +1,18 @@
+import logging
 import requests
 from urllib.parse import urlparse, urljoin
 from bs4 import BeautifulSoup
 from application.parser.remote.base import BaseRemote
+from application.parser.schema.base import Document
+from langchain_community.document_loaders import WebBaseLoader
 
 class CrawlerLoader(BaseRemote):
     def __init__(self, limit=10):
-        from langchain_community.document_loaders import WebBaseLoader
         self.loader = WebBaseLoader  # Initialize the document loader
         self.limit = limit  # Set the limit for the number of pages to scrape
 
     def load_data(self, inputs):
         url = inputs
-        # Check if the input is a list and if it is, use the first element
         if isinstance(url, list) and url:
             url = url[0]
 
@@ -19,25 +20,30 @@ class CrawlerLoader(BaseRemote):
         if not urlparse(url).scheme:
             url = "http://" + url
 
-        visited_urls = set()  # Keep track of URLs that have been visited
-        base_url = urlparse(url).scheme + "://" + urlparse(url).hostname  # Extract the base URL
-        urls_to_visit = [url]  # List of URLs to be visited, starting with the initial URL
-        loaded_content = []  # Store the loaded content from each URL
+        visited_urls = set()
+        base_url = urlparse(url).scheme + "://" + urlparse(url).hostname
+        urls_to_visit = [url]
+        loaded_content = []
 
-        # Continue crawling until there are no more URLs to visit
         while urls_to_visit:
-            current_url = urls_to_visit.pop(0)  # Get the next URL to visit
-            visited_urls.add(current_url)  # Mark the URL as visited
+            current_url = urls_to_visit.pop(0)
+            visited_urls.add(current_url)
 
-            # Try to load and process the content from the current URL
             try:
-                response = requests.get(current_url)  # Fetch the content of the current URL
-                response.raise_for_status()  # Raise an exception for HTTP errors
-                loader = self.loader([current_url])  # Initialize the document loader for the current URL
-                loaded_content.extend(loader.load())  # Load the content and add it to the loaded_content list
+                response = requests.get(current_url)
+                response.raise_for_status()
+                loader = self.loader([current_url])
+                docs = loader.load()
+                # Convert the loaded documents to your Document schema
+                for doc in docs:
+                    loaded_content.append(
+                        Document(
+                            doc.page_content,
+                            extra_info=doc.metadata
+                        )
+                    )
             except Exception as e:
-                # Print an error message if loading or processing fails and continue with the next URL
-                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
@@ -45,15 +51,15 @@ class CrawlerLoader(BaseRemote):
             all_links = [
                 urljoin(current_url, a['href'])
                 for a in soup.find_all('a', href=True)
-                if base_url in urljoin(current_url, a['href'])  # Ensure links are from the same domain
+                if base_url in urljoin(current_url, a['href'])
             ]
 
             # Add new links to the list of URLs to visit if they haven't been visited yet
             urls_to_visit.extend([link for link in all_links if link not in visited_urls])
-            urls_to_visit = list(set(urls_to_visit))  # Remove duplicate URLs
+            urls_to_visit = list(set(urls_to_visit))
 
             # Stop crawling if the limit of pages to scrape is reached
             if self.limit is not None and len(visited_urls) >= self.limit:
                 break
 
-        return loaded_content  # Return the loaded content from all visited URLs
+        return loaded_content

application/parser/remote/crawler_markdown.py

@@ -0,0 +1,139 @@
+import requests
+from urllib.parse import urlparse, urljoin
+from bs4 import BeautifulSoup
+from application.parser.remote.base import BaseRemote
+import re
+from markdownify import markdownify
+from application.parser.schema.base import Document
+import tldextract
+
+class CrawlerLoader(BaseRemote):
+    def __init__(self, limit=10, allow_subdomains=False):
+        """
+        Given a URL crawl web pages up to `self.limit`,
+        convert HTML content to Markdown, and returning a list of Document objects.
+
+        :param limit: The maximum number of pages to crawl.
+        :param allow_subdomains: If True, crawl pages on subdomains of the base domain.
+        """
+        self.limit = limit
+        self.allow_subdomains = allow_subdomains
+        self.session = requests.Session()
+
+    def load_data(self, inputs):
+        url = inputs
+        if isinstance(url, list) and url:
+            url = url[0]
+
+        # Ensure the URL has a scheme (if not, default to http)
+        if not urlparse(url).scheme:
+            url = "http://" + url
+
+        # Keep track of visited URLs to avoid revisiting the same page
+        visited_urls = set()
+
+        # Determine the base domain for link filtering using tldextract
+        base_domain = self._get_base_domain(url)
+        urls_to_visit = {url}
+        documents = []
+
+        while urls_to_visit:
+            current_url = urls_to_visit.pop()
+
+            # Skip if already visited
+            if current_url in visited_urls:
+                continue
+            visited_urls.add(current_url)
+
+            # Fetch the page content
+            html_content = self._fetch_page(current_url)
+            if html_content is None:
+                continue
+
+            # Convert the HTML to Markdown for cleaner text formatting
+            title, language, processed_markdown = self._process_html_to_markdown(html_content, current_url)
+            if processed_markdown:
+                # Create a Document for each visited page
+                documents.append(
+                    Document(
+                        processed_markdown,  # content
+                        None,  # doc_id
+                        None,  # embedding
+                        {"source": current_url, "title": title, "language": language} # extra_info
+                    )
+                )
+
+            # Extract links and filter them according to domain rules
+            new_links = self._extract_links(html_content, current_url)
+            filtered_links = self._filter_links(new_links, base_domain)
+
+            # Add any new, not-yet-visited links to the queue
+            urls_to_visit.update(link for link in filtered_links if link not in visited_urls)
+
+            # If we've reached the limit, stop crawling
+            if self.limit is not None and len(visited_urls) >= self.limit:
+                break
+
+        return documents
+
+    def _fetch_page(self, url):
+        try:
+            response = self.session.get(url, timeout=10)
+            response.raise_for_status()
+            return response.text
+        except requests.exceptions.RequestException as e:
+            print(f"Error fetching URL {url}: {e}")
+            return None
+
+    def _process_html_to_markdown(self, html_content, current_url):
+        soup = BeautifulSoup(html_content, 'html.parser')
+        title_tag = soup.find('title')
+        title = title_tag.text.strip() if title_tag else "No Title"
+
+        # Extract language
+        language_tag = soup.find('html')
+        language = language_tag.get('lang', 'en') if language_tag else "en"
+
+        markdownified = markdownify(html_content, heading_style="ATX", newline_style="BACKSLASH")
+        # Reduce sequences of more than two newlines to exactly three
+        markdownified = re.sub(r'\n{3,}', '\n\n\n', markdownified)
+        return title, language, markdownified
+
+    def _extract_links(self, html_content, current_url):
+        soup = BeautifulSoup(html_content, 'html.parser')
+        links = []
+        for a in soup.find_all('a', href=True):
+            full_url = urljoin(current_url, a['href'])
+            links.append((full_url, a.text.strip()))
+        return links
+
+    def _get_base_domain(self, url):
+        extracted = tldextract.extract(url)
+        # Reconstruct the domain as domain.suffix
+        base_domain = f"{extracted.domain}.{extracted.suffix}"
+        return base_domain
+
+    def _filter_links(self, links, base_domain):
+        """
+        Filter the extracted links to only include those that match the crawling criteria:
+        - If allow_subdomains is True, allow any link whose domain ends with the base_domain.
+        - If allow_subdomains is False, only allow exact matches of the base_domain.
+        """
+        filtered = []
+        for link, _ in links:
+            parsed_link = urlparse(link)
+            if not parsed_link.netloc:
+                continue
+
+            extracted = tldextract.extract(parsed_link.netloc)
+            link_base = f"{extracted.domain}.{extracted.suffix}"
+
+            if self.allow_subdomains:
+                # For subdomains: sub.example.com ends with example.com
+                if link_base == base_domain or link_base.endswith("." + base_domain):
+                    filtered.append(link)
+            else:
+                # Exact domain match
+                if link_base == base_domain:
+                    filtered.append(link)
+        return filtered

application/parser/remote/reddit_loader.py

@@ -1,10 +1,19 @@
 from application.parser.remote.base import BaseRemote
 from langchain_community.document_loaders import RedditPostsLoader
+import json
 
 
 class RedditPostsLoaderRemote(BaseRemote):
     def load_data(self, inputs):
-        data = eval(inputs)
+        try:
+            data = json.loads(inputs)
+        except json.JSONDecodeError as e:
+            raise ValueError(f"Invalid JSON input: {e}")
+
+        required_fields = ["client_id", "client_secret", "user_agent", "search_queries"]
+        missing_fields = [field for field in required_fields if field not in data]
+        if missing_fields:
+            raise ValueError(f"Missing required fields: {', '.join(missing_fields)}")
         client_id = data.get("client_id")
         client_secret = data.get("client_secret")
         user_agent = data.get("user_agent")

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

@@ -1,5 +1,8 @@
+import logging
 from application.parser.remote.base import BaseRemote
+from application.parser.schema.base import Document
 from langchain_community.document_loaders import WebBaseLoader
+from urllib.parse import urlparse
 
 headers = {
     "User-Agent": "Mozilla/5.0",
@@ -23,10 +26,20 @@ class WebLoader(BaseRemote):
             urls = [urls]
         documents = []
         for url in urls:
+            # Check if the URL scheme is provided, if not, assume http
+            if not urlparse(url).scheme:
+                url = "http://" + url
             try:
                 loader = self.loader([url], header_template=headers)
-                documents.extend(loader.load())
+                loaded_docs = loader.load()
+                for doc in loaded_docs:
+                    documents.append(
+                        Document(
+                            doc.page_content,
+                            extra_info=doc.metadata,
+                        )
+                    )
             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/token_func.py

@@ -1,79 +0,0 @@
-import re
-from math import ceil
-from typing import List
-
-import tiktoken
-from application.parser.schema.base import Document
-
-
-def separate_header_and_body(text):
-    header_pattern = r"^(.*?\n){3}"
-    match = re.match(header_pattern, text)
-    header = match.group(0)
-    body = text[len(header):]
-    return header, body
-
-
-def group_documents(documents: List[Document], min_tokens: int, max_tokens: int) -> List[Document]:
-    docs = []
-    current_group = None
-
-    for doc in documents:
-        doc_len = len(tiktoken.get_encoding("cl100k_base").encode(doc.text))
-
-        # Check if current group is empty or if the document can be added based on token count and matching metadata
-        if (current_group is None or 
-            (len(tiktoken.get_encoding("cl100k_base").encode(current_group.text)) + doc_len < max_tokens and 
-             doc_len < min_tokens and 
-             current_group.extra_info == doc.extra_info)):
-            if current_group is None:
-                current_group = doc  # Use the document directly to retain its metadata
-            else:
-                current_group.text += " " + doc.text  # Append text to the current group
-        else:
-            docs.append(current_group)
-            current_group = doc  # Start a new group with the current document
-
-    if current_group is not None:
-        docs.append(current_group)
-
-    return docs
-
-
-def split_documents(documents: List[Document], max_tokens: int) -> List[Document]:
-    docs = []
-    for doc in documents:
-        token_length = len(tiktoken.get_encoding("cl100k_base").encode(doc.text))
-        if token_length <= max_tokens:
-            docs.append(doc)
-        else:
-            header, body = separate_header_and_body(doc.text)
-            if len(tiktoken.get_encoding("cl100k_base").encode(header)) > max_tokens:
-                body = doc.text
-                header = ""
-            num_body_parts = ceil(token_length / max_tokens)
-            part_length = ceil(len(body) / num_body_parts)
-            body_parts = [body[i:i + part_length] for i in range(0, len(body), part_length)]
-            for i, body_part in enumerate(body_parts):
-                new_doc = Document(text=header + body_part.strip(),
-                                   doc_id=f"{doc.doc_id}-{i}",
-                                   embedding=doc.embedding,
-                                   extra_info=doc.extra_info)
-                docs.append(new_doc)
-    return docs
-
-
-def group_split(documents: List[Document], max_tokens: int = 2000, min_tokens: int = 150, token_check: bool = True):
-    if not token_check:
-        return documents
-    print("Grouping small documents")
-    try:
-        documents = group_documents(documents=documents, min_tokens=min_tokens, max_tokens=max_tokens)
-    except Exception:
-        print("Grouping failed, try running without token_check")
-    print("Separating large documents")
-    try:
-        documents = split_documents(documents=documents, max_tokens=max_tokens)
-    except Exception:
-        print("Grouping failed, try running without token_check")
-    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 +1,13 @@
+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 and execution 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.
+5. Plan with available tools to help you with the analysis but dont execute them. Tools will be executed by another AI.
+
+Query: {query}  
+Summaries: {summaries}
+Prompt: {prompt}
+Observations(potentially previous tool calls): {observations}

application/requirements.txt

@@ -1,89 +1,85 @@
-anthropic==0.34.2
-boto3==1.34.153
-beautifulsoup4==4.12.3
-celery==5.3.6
+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==8.15.0
-elasticsearch==8.15.1
 escodegen==1.0.11
 esprima==4.0.1
 esutils==1.0.1
-Flask==3.0.3
-faiss-cpu==1.8.0.post1
+Flask==3.1.1
+faiss-cpu==1.9.0.post1
 flask-restx==1.3.0
-gTTS==2.3.2
+google-genai==1.3.0
+gTTS==2.5.4
 gunicorn==23.0.0
-html2text==2024.2.26
 javalang==0.13.0
-jinja2==3.1.4
-jiter==0.5.0
+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==0.3.0
-langchain-community==0.3.0
-langchain-core==0.3.2
-langchain-openai==0.2.0
-langchain-text-splitters==0.3.0
-langsmith==0.1.125
+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
-markupsafe==2.1.5
-marshmallow==3.22.0
+lxml==5.3.1
+markupsafe==3.0.2
+marshmallow==3.26.1
 mpmath==1.3.0
-multidict==6.1.0
+multidict==6.4.3
 mypy-extensions==1.0.0
-networkx==3.3
-numpy==1.26.4
-openai==1.46.1
-openapi-schema-validator==0.6.2
-openapi-spec-validator==0.6.0
-openapi3-parser==1.1.18
-orjson==3.10.7
-packaging==24.1
+networkx==3.4.2
+numpy==2.2.1
+openai==1.78.1
+openapi3-parser==1.1.21
+orjson==3.10.14
+packaging==24.2
 pandas==2.2.3
 openpyxl==3.1.5
-pathable==0.4.3
-pillow==10.4.0
-portalocker==2.10.1
+pathable==0.4.4
+pillow==11.1.0
+portalocker>=2.7.0,<3.0.0
 prance==23.6.21.0
-primp==0.6.3
-prompt-toolkit==3.0.47
-protobuf==5.28.2
+prompt-toolkit==3.0.51
+protobuf==5.29.3
+psycopg2-binary==2.9.10
 py==1.11.0
-pydantic==2.9.2
-pydantic-core==2.23.4
-pydantic-settings==2.4.0
-pymongo==4.8.0
-pypdf2==3.0.1
+pydantic==2.10.6
+pydantic-core==2.27.2
+pydantic-settings==2.7.1
+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==1.11.0
-redis==5.0.1
-referencing==0.30.2
-regex==2024.9.11
+redis==5.2.1
+referencing>=0.28.0,<0.31.0
+regex==2024.11.6
 requests==2.32.3
 retry==0.9.2
-sentence-transformers==3.0.1
-tiktoken==0.7.0
-tokenizers==0.19.1
-torch==2.4.1
-tqdm==4.66.5
-transformers==4.44.2
+sentence-transformers==3.3.1
+tiktoken==0.8.0
+tokenizers==0.21.0
+torch==2.7.0
+tqdm==4.67.1
+transformers==4.51.3
 typing-extensions==4.12.2
 typing-inspect==0.9.0
 tzdata==2024.2
-urllib3==2.2.3
+urllib3==2.3.0
 vine==5.1.0
 wcwidth==0.2.13
-werkzeug==3.0.4
-yarl==1.11.1
+werkzeug==3.1.3
+yarl==1.20.0
+markdownify==1.1.0
+tldextract==5.1.3
+websockets==14.1

application/retriever/brave_search.py

@@ -1,16 +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 application.utils import num_tokens_from_string
-from langchain_community.tools import BraveSearch
+from application.retriever.base import BaseRetriever
 
 
 class BraveRetSearch(BaseRetriever):
 
     def __init__(
         self,
-        question,
         source,
         chat_history,
         prompt,
@@ -18,8 +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
@@ -36,6 +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:
@@ -72,33 +74,29 @@ class BraveRetSearch(BaseRetriever):
         for doc in docs:
             yield {"source": doc}
 
-        if len(self.chat_history) > 1:
-            tokens_current_history = 0
-            # count tokens in history
+        if len(self.chat_history) > 0:
             for i in self.chat_history:
                 if "prompt" in i and "response" in i:
-                    tokens_batch = num_tokens_from_string(i["prompt"]) + num_tokens_from_string(
-                        i["response"]
+                    messages_combine.append({"role": "user", "content": i["prompt"]})
+                    messages_combine.append(
+                        {"role": "assistant", "content": i["response"]}
                     )
-                    if tokens_current_history + tokens_batch < self.token_limit:
-                        tokens_current_history += tokens_batch
-                        messages_combine.append(
-                            {"role": "user", "content": i["prompt"]}
-                        )
-                        messages_combine.append(
-                            {"role": "system", "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):
@@ -110,5 +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

@@ -1,27 +1,32 @@
-from application.retriever.base import BaseRetriever
+import logging
 from application.core.settings import settings
-from application.vectorstore.vector_creator import VectorCreator
 from application.llm.llm_creator import LLMCreator
+from application.retriever.base import BaseRetriever
+from application.vectorstore.vector_creator import VectorCreator
 
-from application.utils import num_tokens_from_string
-
+logger = logging.getLogger(__name__)
 
 class ClassicRAG(BaseRetriever):
-
+    # Settings for Auto-Chunking
+    AUTO_CHUNK_MIN: int = 0
+    AUTO_CHUNK_MAX: int = 10
+    SIMILARITY_SCORE_THRESHOLD: float = 0.5
+    
     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
@@ -36,16 +41,113 @@ 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
+        self.actual_chunks_retrieved = 0
+
+    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 == 'Auto':
+            return self._get_data_auto()
+        else:
+            return self._get_data_classic()
+
+    def _get_data_auto(self):
+        if not self.vectorstore:
+            self.actual_chunks_retrieved = 0
+            return []
+
+        docsearch = VectorCreator.create_vectorstore(
+            settings.VECTOR_STORE, self.vectorstore, settings.EMBEDDINGS_KEY
+        )
+        
+        try:
+            docs_with_scores = docsearch.search_with_scores(self.question, k=self.AUTO_CHUNK_MAX)
+        except Exception as e:
+            logger.error(f"Error during search_with_scores: {e}", exc_info=True)
+            self.actual_chunks_retrieved = 0
+            return []
+        
+        if not docs_with_scores:
+            self.actual_chunks_retrieved = 0
+            return []
+
+        candidate_docs = []
+        for doc, score in docs_with_scores:
+            if score >= self.SIMILARITY_SCORE_THRESHOLD:
+                candidate_docs.append(doc)
+                
+        if len(candidate_docs) < self.AUTO_CHUNK_MIN and self.AUTO_CHUNK_MIN > 0:
+            final_docs_to_format = [doc for doc, score in docs_with_scores[:self.AUTO_CHUNK_MIN]]
+        else:
+            final_docs_to_format = candidate_docs
+            
+        self.actual_chunks_retrieved = len(final_docs_to_format)
+        
+        if not final_docs_to_format:
+            return []
+
+        formatted_docs = [
+            {
+                "title": i.metadata.get(
+                    "title", i.metadata.get("post_title", i.page_content)
+                ).split("/")[-1],
+                "text": i.page_content,
+                "source": (
+                    i.metadata.get("source")
+                    if i.metadata.get("source")
+                    else "local"
+                ),
+            }
+            for i in final_docs_to_format
+        ]
+        logger.info(f"AutoRAG: Retrieved {self.actual_chunks_retrieved} chunks for query '{self.original_question}'.")
+        return formatted_docs
+
+    def _get_data_classic(self):
         if self.chunks == 0:
-            docs = []
+            return []
         else:
             docsearch = VectorCreator.create_vectorstore(
                 settings.VECTOR_STORE, self.vectorstore, settings.EMBEDDINGS_KEY
             )
             docs_temp = docsearch.search(self.question, k=self.chunks)
-            print(docs_temp)
             docs = [
                 {
                     "title": i.metadata.get(
@@ -60,57 +162,36 @@ class ClassicRAG(BaseRetriever):
                 }
                 for i in docs_temp
             ]
-        if settings.LLM_NAME == "llama.cpp":
-            docs = [docs[0]]
+            return docs
 
-        return docs
+    def gen():
+        pass
 
-    def gen(self):
-        docs = self._get_data()
-
-        # 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) > 1:
-            tokens_current_history = 0
-            # count tokens in history
-            for i in self.chat_history:
-                if "prompt" in i and "response" in i:
-                    tokens_batch = num_tokens_from_string(i["prompt"]) + num_tokens_from_string(
-                        i["response"]
-                    )
-                    if tokens_current_history + tokens_batch < self.token_limit:
-                        tokens_current_history += tokens_batch
-                        messages_combine.append(
-                            {"role": "user", "content": i["prompt"]}
-                        )
-                        messages_combine.append(
-                            {"role": "system", "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)
-        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,
+        params = {
+            "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,
-            "user_api_key": self.user_api_key
+            "user_api_key": self.user_api_key,
         }
+        if self.chunks == 'Auto':
+            params.update({
+                "chunks_mode": "Auto",
+                "chunks_retrieved_auto": self.actual_chunks_retrieved,
+                "auto_chunk_min_setting": self.AUTO_CHUNK_MIN,
+                "auto_chunk_max_setting": self.AUTO_CHUNK_MAX,
+                "similarity_threshold_setting": self.SIMILARITY_SCORE_THRESHOLD,
+            })
+        else:
+            params["chunks_mode"] = "Classic"
+            params["chunks"] = self.chunks
+
+        return params

application/retriever/duckduck_search.py

@@ -1,16 +1,15 @@
-from application.retriever.base import BaseRetriever
-from application.core.settings import settings
-from application.llm.llm_creator import LLMCreator
-from application.utils import num_tokens_from_string
 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,
@@ -18,8 +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
@@ -36,42 +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":
@@ -89,35 +73,31 @@ class DuckDuckSearch(BaseRetriever):
         for doc in docs:
             yield {"source": doc}
 
-        if len(self.chat_history) > 1:
-            tokens_current_history = 0
-            # count tokens in history
+        if len(self.chat_history) > 0:
             for i in self.chat_history:
                 if "prompt" in i and "response" in i:
-                    tokens_batch = num_tokens_from_string(i["prompt"]) + num_tokens_from_string(
-                        i["response"]
+                    messages_combine.append({"role": "user", "content": i["prompt"]})
+                    messages_combine.append(
+                        {"role": "assistant", "content": i["response"]}
                     )
-                    if tokens_current_history + tokens_batch < self.token_limit:
-                        tokens_current_history += tokens_batch
-                        messages_combine.append(
-                            {"role": "user", "content": i["prompt"]}
-                        )
-                        messages_combine.append(
-                            {"role": "system", "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,
@@ -127,5 +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/retriever/retriever_creator.py

@@ -2,19 +2,18 @@ from application.retriever.classic_rag import ClassicRAG
 from application.retriever.duckduck_search import DuckDuckSearch
 from application.retriever.brave_search import BraveRetSearch
 
-
-
 class RetrieverCreator:
     retrievers = {
-        'classic': ClassicRAG,
-        'duckduck_search': DuckDuckSearch,
-        'brave_search': BraveRetSearch,
-        'default': ClassicRAG
+        "classic": ClassicRAG,
+        "duckduck_search": DuckDuckSearch,
+        "brave_search": BraveRetSearch,
+        "default": ClassicRAG,
     }
 
     @classmethod
     def create_retriever(cls, type, *args, **kwargs):
-        retiever_class = cls.retrievers.get(type.lower())
+        retriever_type = (type or "default").lower()
+        retiever_class = cls.retrievers.get(retriever_type)
         if not retiever_class:
             raise ValueError(f"No retievers class found for type {type}")
-        return retiever_class(*args, **kwargs)
+        return retiever_class(*args, **kwargs)

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/tts/elevenlabs.py

@@ -1,29 +1,84 @@
-from io import BytesIO
+import asyncio
+import websockets
+import json
 import base64
+from io import BytesIO
 from application.tts.base import BaseTTS
 
 
 class ElevenlabsTTS(BaseTTS):
-    def __init__(self):
-        from elevenlabs.client import ElevenLabs
-
-        self.client = ElevenLabs(
-            api_key="ELEVENLABS_API_KEY",
-            )
-    
+    def __init__(self):        
+        self.api_key = 'ELEVENLABS_API_KEY'# here you should put your api key
+        self.model = "eleven_flash_v2_5"
+        self.voice = "VOICE_ID" # this is the hash code for the voice not the name!
+        self.write_audio = 1
 
     def text_to_speech(self, text):
-        lang = "en"
-        audio = self.client.generate(
-            text=text,
-            model="eleven_multilingual_v2",
-            voice="Brian",
-        )
-        audio_data = BytesIO()
-        for chunk in audio:
-            audio_data.write(chunk)
-        audio_bytes = audio_data.getvalue()
+        asyncio.run(self._text_to_speech_websocket(text))
 
-        # Encode to base64
-        audio_base64 = base64.b64encode(audio_bytes).decode("utf-8")
-        return audio_base64, lang
+    async def _text_to_speech_websocket(self, text):
+        uri = f"wss://api.elevenlabs.io/v1/text-to-speech/{self.voice}/stream-input?model_id={self.model}"
+        websocket = await websockets.connect(uri)
+        payload = {
+            "text": " ",
+            "voice_settings": {
+                "stability": 0.5,
+                "similarity_boost": 0.8,
+            },
+            "xi_api_key": self.api_key,
+        }
+
+        await websocket.send(json.dumps(payload))
+        
+        async def listen():
+            while 1:
+                try:
+                    msg = await websocket.recv()
+                    data = json.loads(msg)
+
+                    if data.get("audio"):
+                        print("audio received")
+                        yield base64.b64decode(data["audio"])
+                    elif data.get("isFinal"):
+                        break
+                except websockets.exceptions.ConnectionClosed:
+                    print("websocket closed")
+                    break
+        listen_task =  asyncio.create_task(self.stream(listen()))
+        
+        await websocket.send(json.dumps({"text": text}))
+        # this is to signal the end of the text, either use this or flush
+        await websocket.send(json.dumps({"text": ""})) 
+
+        await listen_task
+    
+    async def stream(self, audio_stream):
+        if self.write_audio:
+            audio_bytes = BytesIO()
+            async for chunk in audio_stream:
+                if chunk:
+                    audio_bytes.write(chunk)
+            with open("output_audio.mp3", "wb") as f:
+                f.write(audio_bytes.getvalue())
+        
+        else:
+            async for chunk in audio_stream:
+                pass # depends on the streamer!
+
+
+def test_elevenlabs_websocket():
+    """
+    Tests the ElevenlabsTTS text_to_speech method with a sample prompt.
+    Prints out the base64-encoded result and writes it to 'output_audio.mp3'.
+    """
+    # Instantiate your TTS class
+    tts = ElevenlabsTTS()
+
+    # Call the method with some sample text
+    tts.text_to_speech("Hello from ElevenLabs WebSocket!")
+
+    print("Saved audio to output_audio.mp3.")
+
+
+if __name__ == "__main__":
+    test_elevenlabs_websocket()

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
+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"],
@@ -21,28 +28,38 @@ def update_token_usage(user_api_key, token_usage):
 
 
 def gen_token_usage(func):
-    def wrapper(self, model, messages, stream, **kwargs):
+    def wrapper(self, model, messages, stream, tools, **kwargs):
         for message in messages:
-            self.token_usage["prompt_tokens"] += num_tokens_from_string(message["content"])
-        result = func(self, model, messages, stream, **kwargs)
-        self.token_usage["generated_tokens"] += num_tokens_from_string(result)
-        update_token_usage(self.user_api_key, self.token_usage)
+            if message["content"]:
+                self.token_usage["prompt_tokens"] += num_tokens_from_string(
+                    message["content"]
+                )
+        result = func(self, model, messages, stream, tools, **kwargs)
+        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.decoded_token, self.user_api_key, self.token_usage)
         return result
 
     return wrapper
 
 
 def stream_token_usage(func):
-    def wrapper(self, model, messages, stream, **kwargs):
+    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, **kwargs)
+        result = func(self, model, messages, stream, tools, **kwargs)
         for r in result:
             batch.append(r)
             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
 
 
@@ -15,8 +17,22 @@ def get_encoding():
 
 def num_tokens_from_string(string: str) -> int:
     encoding = get_encoding()
-    num_tokens = len(encoding.encode(string))
-    return num_tokens
+    if isinstance(string, str):
+        num_tokens = len(encoding.encode(string))
+        return num_tokens
+    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])
+    elif isinstance(thing, dict):
+        return sum([num_tokens_from_object_or_list(x) for x in thing.values()])
+    elif isinstance(thing, str):
+        return num_tokens_from_string(thing)
+    else:
+        return 0
 
 
 def count_tokens_docs(docs):
@@ -44,5 +60,52 @@ 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"):
+    """
+    Limits chat history based on token count.
+    Returns a list of messages that fit within the token limit.
+    """
+    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)
+    )
+
+    if not history:
+        return []
+
+    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"])
+            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

@@ -58,6 +58,10 @@ class BaseVectorStore(ABC):
     def search(self, *args, **kwargs):
         pass
 
+    @abstractmethod
+    def search_with_scores(self, query: str, k: int, *args, **kwargs):
+        pass
+
     def is_azure_configured(self):
         return settings.OPENAI_API_BASE and settings.OPENAI_API_VERSION and settings.AZURE_DEPLOYMENT_NAME
 
@@ -75,9 +79,9 @@ 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 +90,5 @@ 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
@@ -112,6 +108,46 @@ class ElasticsearchStore(BaseVectorStore):
             
             doc_list.append(Document(page_content = hit['_source']['text'], metadata = hit['_source']['metadata']))
         return doc_list
+    
+    def search_with_scores(self, query: str, k: int, *args, **kwargs):
+        embeddings = self._get_embeddings(settings.EMBEDDINGS_NAME, self.embeddings_key)
+        vector = embeddings.embed_query(query)
+        knn = {
+            "filter": [{"match": {"metadata.source_id.keyword": self.source_id}}],
+            "field": "vector",
+            "k": k,
+            "num_candidates": 100,
+            "query_vector": vector,
+        }
+        full_query = {
+            "knn": knn,
+            "query": {
+                "bool": {
+                    "must": [
+                        {
+                            "match": {
+                                "text": {
+                                    "query": question,
+                                }
+                            }
+                        }
+                    ],
+                    "filter": [{"match": {"metadata.source_id.keyword": self.source_id}}],
+                }
+            },
+            "rank": {"rrf": {}},
+        }
+        resp = self.docsearch.search(index=self.index_name, query=full_query['query'], size=k, knn=full_query['knn'])
+
+        docs_with_scores = []
+        for hit in resp['hits']['hits']:
+            score = hit['_score']
+            # Normalize the score. Elasticsearch returns a score of 1.0 + cosine similarity.
+            similarity = max(0, score - 1.0)
+            doc = Document(page_content=hit['_source']['text'], metadata=hit['_source']['metadata'])
+            docs_with_scores.append((doc, similarity))
+
+        return docs_with_scores
 
     def _create_index_if_not_exists(
             self, index_name, dims_length
@@ -155,8 +191,6 @@ class ElasticsearchStore(BaseVectorStore):
         **kwargs,
         ):
         
-        from elasticsearch.helpers import BulkIndexError, bulk
-
         bulk_kwargs = bulk_kwargs or {}
         import uuid
         embeddings = []
@@ -189,6 +223,7 @@ class ElasticsearchStore(BaseVectorStore):
 
 
         if len(requests) > 0:
+            from elasticsearch.helpers import BulkIndexError, bulk
             try:
                 success, failed = bulk(
                     self._es_connection,

application/vectorstore/faiss.py

@@ -1,33 +1,79 @@
-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"
 
-        self.assert_embedding_dimensions(embeddings)
+                    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(self.embeddings)
 
     def search(self, *args, **kwargs):
         return self.docsearch.similarity_search(*args, **kwargs)
+    
+    def search_with_scores(self, query: str, k: int, *args, **kwargs):
+        docs_and_distances = self.docsearch.similarity_search_with_score(query, k, *args, **kwargs)
+        
+        # Convert L2 distance to a normalized similarity score (0-1, higher is better)
+        docs_and_similarities = []
+        for doc, distance in docs_and_distances:
+            if distance < 0: distance = 0
+            similarity = 1 / (1 + distance)
+            docs_and_similarities.append((doc, similarity))
+            
+        return docs_and_similarities
 
     def add_texts(self, *args, **kwargs):
         return self.docsearch.add_texts(*args, **kwargs)
@@ -40,11 +86,42 @@ 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/lancedb.py

@@ -2,6 +2,8 @@ from typing import List, Optional
 import importlib
 from application.vectorstore.base import BaseVectorStore
 from application.core.settings import settings
+from application.vectorstore.document_class import Document
+
 
 class LanceDBVectorStore(BaseVectorStore):
     """Class for LanceDB Vector Store integration."""
@@ -87,6 +89,23 @@ class LanceDBVectorStore(BaseVectorStore):
         results = self.docsearch.search(query_embedding).limit(k).to_list()
         return [(result["_distance"], result["text"], result["metadata"]) for result in results]
 
+    def search_with_scores(self, query: str, k: int, *args, **kwargs):
+        """Perform a similarity search with scores."""
+        self.ensure_table_exists()
+        query_embedding = self._get_embeddings(settings.EMBEDDINGS_NAME, self.embeddings_key).embed_query(query)
+        results = self.docsearch.search(query_embedding).limit(k).to_list()
+
+        docs_with_scores = []
+        for result in results:
+            distance = result.get('_distance', float('inf'))
+            if distance < 0: distance = 0
+            # Convert L2 distance to a normalized similarity score
+            similarity = 1 / (1 + distance)
+            doc = Document(page_content=result['text'], metadata=result["metadata"])
+            docs_with_scores.append((doc, similarity))
+            
+        return docs_with_scores
+    
     def delete_index(self):
         """Delete the entire LanceDB index (table)."""
         if self.table:

application/vectorstore/milvus.py

@@ -25,6 +25,16 @@ class MilvusStore(BaseVectorStore):
     def search(self, question, k=2, *args, **kwargs):
         expr = f"source_id == '{self._source_id}'"
         return self._docsearch.similarity_search(query=question, k=k, expr=expr, *args, **kwargs)
+    
+    def search_with_scores(self, query: str, k: int, *args, **kwargs):
+        expr = f"source_id == '{self._source_id}'"
+        docs_and_distances = self._docsearch.similarity_search_with_score(query, k, expr=expr, *args, **kwargs)
+        docs_with_scores = []
+        for doc, distance in docs_and_distances:
+            similarity = 1.0 - distance
+            docs_with_scores.append((doc, max(0, similarity)))
+        
+        return docs_with_scores
 
     def add_texts(self, texts: List[str], metadatas: Optional[List[dict]], *args, **kwargs):
         ids = [str(uuid4()) for _ in range(len(texts))]

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
@@ -61,6 +62,40 @@ class MongoDBVectorStore(BaseVectorStore):
             metadata = doc
             results.append(Document(text, metadata))
         return results
+    
+    def search_with_scores(self, query: str, k: int, *args, **kwargs):
+        query_vector = self._embedding.embed_query(query)
+
+        pipeline = [
+            {
+                "$vectorSearch": {
+                    "queryVector": query_vector,
+                    "path": self._embedding_key,
+                    "limit": k,
+                    "numCandidates": k * 10,
+                    "index": self._index_name,
+                    "filter": {"source_id": {"$eq": self._source_id}},
+                }
+            },
+            {
+                "$addFields": {
+                    "score": {"$meta": "vectorSearchScore"}
+                }
+            }
+        ]
+
+        cursor = self._collection.aggregate(pipeline)
+
+        results = []
+        for doc in cursor:
+            score = doc.pop("score", 0.0)
+            text = doc.pop(self._text_key)
+            doc.pop("_id")
+            doc.pop(self._embedding_key, None)
+            metadata = doc
+            doc = Document(page_content=text, metadata=metadata)
+            results.append((doc, score))
+        return results
 
     def _insert_texts(self, texts, metadatas):
         if not texts:
@@ -124,3 +159,53 @@ 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(
@@ -34,6 +35,9 @@ class QdrantStore(BaseVectorStore):
 
     def search(self, *args, **kwargs):
         return self._docsearch.similarity_search(filter=self._filter, *args, **kwargs)
+    
+    def search_with_scores(self, query: str, k: int, *args, **kwargs):
+        return self._docsearch.similarity_search_with_score(query=query, k=k, filter=self._filter, *args, **kwargs)
 
     def add_texts(self, *args, **kwargs):
         return self._docsearch.add_texts(*args, **kwargs)

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.chunking import Chunker
+from application.parser.embedding_pipeline import embed_and_store_documents
 from application.parser.file.bulk import SimpleDirectoryReader
-from application.parser.open_ai_func import call_openai_api
 from application.parser.remote.remote_creator import RemoteCreator
 from application.parser.schema.base import Document
-from application.parser.token_func import group_split
-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"
@@ -126,61 +216,87 @@ def ingest_worker(
     limit = None
     exclude = True
     sample = False
-    token_check = True
+
+    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()
-    raw_docs = group_split(
-        documents=raw_docs,
-        min_tokens=MIN_TOKENS,
-        max_tokens=MAX_TOKENS,
-        token_check=token_check,
-    )
+            self.update_state(state="PROGRESS", meta={"current": 1})
 
-    docs = [Document.to_langchain_format(raw_doc) for raw_doc in raw_docs]
-    id = ObjectId()
+            # 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
+                )
 
-    call_openai_api(docs, full_path, id, self)
-    tokens = count_tokens_docs(docs)
-    self.update_state(state="PROGRESS", meta={"current": 100})
+            if sample:
+                logging.info(f"Sample mode enabled. Using {limit} documents.")
 
-    if sample:
-        for i in range(min(5, len(raw_docs))):
-            logging.info(f"Sample document {i}: {raw_docs[i]}")
+            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()
 
-    file_data.update({
-        "tokens": tokens,
-        "retriever": retriever,
-        "id": str(id),
-        "type": "local",
-    })
-    upload_index(full_path, file_data)
+            chunker = Chunker(
+                chunking_strategy="classic_chunk",
+                max_tokens=MAX_TOKENS,
+                min_tokens=MIN_TOKENS,
+                duplicate_headers=False,
+            )
+            raw_docs = chunker.chunk(documents=raw_docs)
 
-    # delete local
-    shutil.rmtree(full_path)
+            docs = [Document.to_langchain_format(raw_doc) for raw_doc in raw_docs]
+
+            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,
@@ -191,6 +307,7 @@ def ingest_worker(
         "limited": False,
     }
 
+
 def remote_worker(
     self,
     source_data,
@@ -203,53 +320,63 @@ def remote_worker(
     operation_mode="upload",
     doc_id=None,
 ):
-    token_check = True
     full_path = os.path.join(directory, user, name_job)
-
     if not os.path.exists(full_path):
         os.makedirs(full_path)
+
     self.update_state(state="PROGRESS", meta={"current": 1})
-    logging.info(
-        f"Remote job: {full_path}",
-        extra={"user": user, "job": name_job, "source_data": source_data},
-    )
+    try:
+        logging.info("Initializing remote loader with type: %s", loader)
+        remote_loader = RemoteCreator.create_loader(loader)
+        raw_docs = remote_loader.load_data(source_data)
 
-    remote_loader = RemoteCreator.create_loader(loader)
-    raw_docs = remote_loader.load_data(source_data)
+        chunker = Chunker(
+            chunking_strategy="classic_chunk",
+            max_tokens=MAX_TOKENS,
+            min_tokens=MIN_TOKENS,
+            duplicate_headers=False,
+        )
+        docs = chunker.chunk(documents=raw_docs)
+        docs = [Document.to_langchain_format(raw_doc) for raw_doc in raw_docs]
+        tokens = count_tokens_docs(docs)
+        logging.info("Total tokens calculated: %d", tokens)
 
-    docs = group_split(
-        documents=raw_docs,
-        min_tokens=MIN_TOKENS,
-        max_tokens=MAX_TOKENS,
-        token_check=token_check,
-    )
-    tokens = count_tokens_docs(docs)
-    if operation_mode == "upload":
-        id = ObjectId()
-        call_openai_api(docs, full_path, id, self)
-    elif operation_mode == "sync":
-        if not doc_id or not ObjectId.is_valid(doc_id):
-            raise ValueError("doc_id must be provided for sync operation.")
-        id = ObjectId(doc_id)
-        call_openai_api(docs, full_path, id, self)
-    self.update_state(state="PROGRESS", meta={"current": 100})
+        if operation_mode == "upload":
+            id = ObjectId()
+            embed_and_store_documents(docs, full_path, id, self)
+        elif operation_mode == "sync":
+            if not doc_id or not ObjectId.is_valid(doc_id):
+                logging.error("Invalid doc_id provided for sync operation: %s", doc_id)
+                raise ValueError("doc_id must be provided for sync operation.")
+            id = ObjectId(doc_id)
+            embed_and_store_documents(docs, full_path, id, self)
 
-    file_data = {
-        "name": name_job,
-        "user": user,
-        "tokens": tokens,
-        "retriever": retriever,
-        "id": str(id),
-        "type": loader,
-        "remote_data": source_data,
-        "sync_frequency": sync_frequency,
-    }
-    upload_index(full_path, file_data)
+        self.update_state(state="PROGRESS", meta={"current": 100})
 
-    shutil.rmtree(full_path)
+        file_data = {
+            "name": name_job,
+            "user": user,
+            "tokens": tokens,
+            "retriever": retriever,
+            "id": str(id),
+            "type": loader,
+            "remote_data": source_data,
+            "sync_frequency": sync_frequency,
+        }
+        upload_index(full_path, file_data)
 
+    except Exception as e:
+        logging.error("Error in remote_worker task: %s", str(e), exc_info=True)
+        raise
+
+    finally:
+        if os.path.exists(full_path):
+            shutil.rmtree(full_path)
+
+    logging.info("remote_worker task completed successfully")
     return {"urls": source_data, "name_job": name_job, "user": user, "limited": False}
 
+
 def sync(
     self,
     source_data,
@@ -275,10 +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()
@@ -302,3 +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}