Delete docsgpt_scanner.py

-Only for api_tool for now, if this solution works well then implementation for other tools is required
- Need to check api keys creation with the current proxies
- Show connection string example at creation
- locale needs updates for other languages

.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

@@ -8,8 +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: "daily"

.github/holopin.yml

@@ -1,5 +1,11 @@
-organization: arc53
-defaultSticker: clqmdf0ed34290glbvqh0kzxd
+organization: docsgpt
+defaultSticker: cm1ulwkkl180570cl82rtzympu
 stickers:
-  - id: clqmdf0ed34290glbvqh0kzxd
-    alias: festive
+  - id: cm1ulwkkl180570cl82rtzympu
+    alias: contributor2024
+  - id: cm1ureg8o130450cl8c1po6mil
+    alias: api
+  - id: cm1urhmag148240cl8yvqxkthx
+    alias: lpc
+  - id: cm1urlcpq622090cl2tvu4w71y
+    alias: lexeu

.github/labeler.yml

@@ -1,23 +1,31 @@
 repo:
-  - '*'
+- changed-files:
+  - any-glob-to-any-file: '*'
 
 github:
-  - .github/**/*
+- changed-files:
+  - any-glob-to-any-file: '.github/**/*'
 
 application:
-  - application/**/*
+- changed-files:
+  - any-glob-to-any-file: 'application/**/*'
 
 docs:
-  - docs/**/*
+- changed-files:
+  - any-glob-to-any-file: 'docs/**/*'
 
 extensions:
-  - extensions/**/*
+- changed-files:
+  - any-glob-to-any-file: 'extensions/**/*'
 
 frontend:
-  - frontend/**/*
+- changed-files:
+  - any-glob-to-any-file: 'frontend/**/*'
 
 scripts:
-  - scripts/**/*
+- changed-files:
+  - any-glob-to-any-file: 'scripts/**/*'
 
 tests:
-  - tests/**/*
+- changed-files:
+  - any-glob-to-any-file: 'tests/**/*'

.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

@@ -1,47 +1,112 @@
 name: Build and push DocsGPT Docker image
 
 on:
-  workflow_dispatch:
-  push:
-    branches:
-      - main
+  release:
+    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@v3
+      - uses: actions/checkout@v4
 
-      - name: Set up QEMU
-        uses: docker/setup-qemu-action@v1
+      - 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@v1
+        uses: docker/setup-buildx-action@v3
+        with:
+          driver: docker-container
+          install: true
 
       - name: Login to DockerHub
-        uses: docker/login-action@v2
+        uses: docker/login-action@v3
         with:
           username: ${{ secrets.DOCKER_USERNAME }}
           password: ${{ secrets.DOCKER_PASSWORD }}
 
       - name: Login to ghcr.io
-        uses: docker/login-action@v2
+        uses: docker/login-action@v3
         with:
           registry: ghcr.io
           username: ${{ github.repository_owner }}
           password: ${{ secrets.GITHUB_TOKEN }}
 
-      - name: Build and push Docker images to docker.io and ghcr.io
-        uses: docker/build-push-action@v4
+      - 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: |
-            ${{ secrets.DOCKER_USERNAME }}/docsgpt:latest
-            ghcr.io/${{ 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

@@ -1,48 +1,112 @@
 name: Build and push DocsGPT-FE Docker image
 
 on:
-  workflow_dispatch:
-  push:
-    branches:
-      - main
+  release:
+    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@v3
+      - uses: actions/checkout@v4
 
-      - name: Set up QEMU
-        uses: docker/setup-qemu-action@v1
+      - 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@v1
+        uses: docker/setup-buildx-action@v3
+        with:
+          driver: docker-container
+          install: true
 
       - name: Login to DockerHub
-        uses: docker/login-action@v2
+        uses: docker/login-action@v3
         with:
           username: ${{ secrets.DOCKER_USERNAME }}
           password: ${{ secrets.DOCKER_PASSWORD }}
 
       - name: Login to ghcr.io
-        uses: docker/login-action@v2
+        uses: docker/login-action@v3
         with:
           registry: ghcr.io
           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
-        uses: docker/build-push-action@v4
+      - 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: |
-            ${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:latest
-            ghcr.io/${{ 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

@@ -0,0 +1,100 @@
+name: Build and push multi-arch DocsGPT Docker image
+
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+
+jobs:
+  build:
+    if: github.repository == 'arc53/DocsGPT'
+    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 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: Build and push platform-specific images
+        uses: docker/build-push-action@v6
+        with:
+          file: './application/Dockerfile'
+          platforms: ${{ matrix.platform }}
+          context: ./application
+          push: true
+          tags: |
+            ${{ secrets.DOCKER_USERNAME }}/docsgpt:develop-${{ 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

@@ -0,0 +1,104 @@
+name: Build and push DocsGPT FE Docker image for development
+
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+
+jobs:
+  build:
+    if: github.repository == 'arc53/DocsGPT'
+    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 # 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
+        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: Build and push platform-specific images
+        uses: docker/build-push-action@v6
+        with:
+          file: './frontend/Dockerfile'
+          platforms: ${{ matrix.platform }}
+          context: ./frontend
+          push: true
+          tags: |
+            ${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:develop-${{ 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/labeler.yml

@@ -10,7 +10,7 @@ jobs:
       pull-requests: write
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/labeler@v4
+      - uses: actions/labeler@v5
         with:
           repo-token: "${{ secrets.GITHUB_TOKEN }}"
           sync-labels: true

.github/workflows/lint.yml

@@ -11,7 +11,7 @@ jobs:
   ruff:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - name: Lint with Ruff
         uses: chartboost/ruff-action@v1

.github/workflows/pytest.yml

@@ -6,11 +6,11 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.11"]
+        python-version: ["3.12"]
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
       - name: Install dependencies
@@ -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@v3
+        if: github.event_name == 'pull_request' && matrix.python-version == '3.12'
+        uses: codecov/codecov-action@v5
         env:
           CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
 

.github/workflows/sync_fork.yaml

@@ -17,7 +17,7 @@ jobs:
     steps:
       # Step 1: run a standard checkout action
       - name: Checkout target repo
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       # Step 2: run the sync action
       - name: Sync upstream changes

.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

@@ -6,7 +6,7 @@ Thank you for choosing to contribute to DocsGPT! We are all very grateful!
 
 📣 **Discussions** - Engage in conversations, start new topics, or help answer questions.
 
-🐞 **Issues** - This is where we keep track of tasks. It could be bugs,fixes or suggestions for new features.
+🐞 **Issues** - This is where we keep track of tasks. It could be bugs, fixes or suggestions for new features.
 
 🛠️ **Pull requests** - Suggest changes to our repository, either by working on existing issues or adding new features.
 
@@ -21,11 +21,13 @@ Thank you for choosing to contribute to DocsGPT! We are all very grateful!
 - If you're interested in contributing code, here are some important things to know:
 
 - We have a frontend built on React (Vite) and a backend in Python.
-=======
-Before creating issues, please check out how the latest version of our app looks and works by launching it via [Quickstart](https://github.com/arc53/DocsGPT#quickstart) the version on our live demo is slightly modified with login. Your issues should relate to the version that you can launch via [Quickstart](https://github.com/arc53/DocsGPT#quickstart).
+
+  
+Before creating issues, please check out how the latest version of our app looks and works by launching it via [Quickstart](https://github.com/arc53/DocsGPT#quickstart) the version on our live demo is slightly modified with login. Your issues should relate to the version you can launch via [Quickstart](https://github.com/arc53/DocsGPT#quickstart).
 
 ### 👨‍💻 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:
 
@@ -33,19 +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; they 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
 
@@ -125,4 +148,4 @@ Thank you for considering contributing to DocsGPT! 🙏
 
 ## Questions/collaboration
 Feel free to join our [Discord](https://discord.gg/n5BX8dh8rU). We're very friendly and welcoming to new contributors, so don't hesitate to reach out.
-# Thank you so much for considering to contribute DocsGPT!🙏
+# Thank you so much for considering to contributing DocsGPT!🙏

HACKTOBERFEST.md

@@ -1,37 +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 recieve 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 sumbit best 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 extention: Build an app utilising DocsGPT API. We prefer submissions that showcase original ideas and turn the API into an AI agent.
-
-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 typo) 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 +15,126 @@ 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>
 
-### 🎃 [Hacktoberfest Prizes, Rules & Q&A](https://github.com/arc53/DocsGPT/blob/main/HACKTOBERFEST.md) 🎃
+## Roadmap
 
-### Our [Livestream to Dive into Hacktoberfest! Prizes, Rules & Q&A 🎉](https://www.youtube.com/watch?v=5QQaFFu9BC8) on 3rd of October 
+- [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)
+- [ ] Anthropic Tool compatibility
+- [ ] Add triggerable actions / tools (webhook)
+- [ ] Add OAuth 2.0 authentication for tools and sources
+- [ ] Chatbots menu re-design to handle tools, scheduling, and more
+
+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.
 
-- [Book Enterprise / teams Demo :wave:](https://cal.com/arc53/docsgpt-demo-b2b?date=2024-09-27&month=2024-09)
-- [Send Email :email:](mailto:contact@arc53.com?subject=DocsGPT%20support%2Fsolutions)
+[Get a Demo :wave:](https://www.docsgpt.cloud/contact)⁠
 
-![video-example-of-docs-gpt](https://d3dg1063dc54p9.cloudfront.net/videos/demov3.gif)
+[Send Email :email:](mailto:support@docsgpt.cloud?subject=DocsGPT%20support%2Fsolutions)
 
-## Roadmap
+## Join the Lighthouse Program 🌟
 
-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!
+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.  
 
-## Our Open-Source Models Optimized for DocsGPT:
+[Learn More & Apply →](https://docs.google.com/forms/d/1KAADiJinUJ8EMQyfTXUIGyFbqINNClNR3jBNWq7DgTE)
 
-| 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
+This interactive script will guide you through setting up DocsGPT. It offers four options: using the public API, running locally, connecting to a local inference engine, or using a cloud API provider.  The script will automatically configure your `.env` file and handle necessary downloads and installations based on your chosen option.
 
-### Spin up Mongo and Redis
+**For Windows:**
 
-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).
+2. **Follow the Docker Deployment Guide:**
 
-Run
+   Please refer to the [Docker Deployment documentation](https://docs.docsgpt.cloud/Deploying/Docker-Deploying) for detailed step-by-step instructions on setting up DocsGPT using Docker. 
 
+**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 `setup.sh`).
 
 > [!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_sample](https://github.com/arc53/DocsGPT/blob/main/application/.env_sample) 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">
@@ -201,4 +145,9 @@ We as members, contributors, and leaders, pledge to make participation in our co
 
 The source code license is [MIT](https://opensource.org/license/mit/), as described in the [LICENSE](LICENSE) file.
 
-Built with [:bird: :link: LangChain](https://github.com/hwchase17/langchain)
+<p>This project is supported by:</p>
+<p>
+  <a href="https://www.digitalocean.com/?utm_medium=opensource&utm_source=DocsGPT">
+    <img src="https://opensource.nyc3.cdn.digitaloceanspaces.com/attribution/assets/SVG/DO_Logo_horizontal_blue.svg" width="201px">
+  </a>
+</p>

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

application/agents/agent_creator.py

@@ -0,0 +1,17 @@
+from application.agents.classic_agent import ClassicAgent
+
+
+class AgentCreator:
+    agents = {
+        "classic": ClassicAgent,
+    }
+
+    @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}")
+        config = kwargs.pop('config', None)
+        if isinstance(config, dict) and 'proxy_id' in config and 'proxy_id' not in kwargs:
+            kwargs['proxy_id'] = config['proxy_id']
+        return agent_class(*args, **kwargs)

application/agents/base.py

@@ -0,0 +1,161 @@
+from typing import Dict, Generator
+
+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
+
+
+class BaseAgent:
+    def __init__(
+        self,
+        endpoint,
+        llm_name,
+        gpt_model,
+        api_key,
+        user_api_key=None,
+        decoded_token=None,
+        proxy_id=None,
+    ):
+        self.endpoint = endpoint
+        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.gpt_model = gpt_model
+        self.tools = []
+        self.tool_config = {}
+        self.tool_calls = []
+        self.proxy_id = proxy_id
+
+    def gen(self, *args, **kwargs) -> Generator[Dict, None, None]:
+        raise NotImplementedError('Method "gen" must be implemented in the child class')
+
+    def _get_user_tools(self, user="local"):
+        mongo = MongoDB.get_client()
+        db = mongo["docsgpt"]
+        user_tools_collection = db["user_tools"]
+        user_tools = user_tools_collection.find({"user": user, "status": True})
+        user_tools = list(user_tools)
+        tools_by_id = {str(tool["_id"]): tool for tool in user_tools}
+        if hasattr(self, 'proxy_id') and self.proxy_id:
+            for tool_id, tool in tools_by_id.items():
+                if 'config' not in tool:
+                    tool['config'] = {}
+                tool['config']['proxy_id'] = self.proxy_id
+        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,
+                    "proxy_id": self.proxy_id,
+                }
+                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

application/agents/classic_agent.py

@@ -0,0 +1,141 @@
+import uuid
+from typing import Dict, Generator
+
+from application.agents.base import BaseAgent
+from application.logging import build_stack_data, log_activity, LogContext
+
+from application.retriever.base import BaseRetriever
+
+
+class ClassicAgent(BaseAgent):
+    def __init__(
+        self,
+        endpoint,
+        llm_name,
+        gpt_model,
+        api_key,
+        user_api_key=None,
+        prompt="",
+        chat_history=None,
+        decoded_token=None,
+        proxy_id=None,
+    ):
+        super().__init__(
+            endpoint, llm_name, gpt_model, api_key, user_api_key, decoded_token, proxy_id
+        )
+        self.user = decoded_token.get("sub")
+        self.prompt = prompt
+        self.chat_history = chat_history if chat_history is not None else []
+
+    @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)
+
+    def _gen_inner(
+        self, query: str, retriever: BaseRetriever, log_context: LogContext
+    ) -> Generator[Dict, None, None]:
+        retrieved_data = self._retriever_search(retriever, query, log_context)
+
+        docs_together = "\n".join([doc["text"] for doc in retrieved_data])
+        p_chat_combine = self.prompt.replace("{summaries}", docs_together)
+        messages_combine = [{"role": "system", "content": p_chat_combine}]
+
+        if len(self.chat_history) > 0:
+            for i in self.chat_history:
+                if "prompt" in i and "response" in i:
+                    messages_combine.append({"role": "user", "content": i["prompt"]})
+                    messages_combine.append(
+                        {"role": "assistant", "content": i["response"]}
+                    )
+                if "tool_calls" in i:
+                    for tool_call in i["tool_calls"]:
+                        call_id = tool_call.get("call_id")
+                        if call_id is None or call_id == "None":
+                            call_id = 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})
+
+        tools_dict = self._get_user_tools(self.user)
+        self._prepare_tools(tools_dict)
+
+        resp = self._llm_gen(messages_combine, log_context)
+
+        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_combine, log_context)
+
+        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:
+            completion = self.llm.gen_stream(
+                model=self.gpt_model, messages=messages_combine, tools=self.tools
+            )
+            for line in completion:
+                if isinstance(line, str):
+                    yield {"answer": line}
+
+        yield {"sources": retrieved_data}
+        yield {"tool_calls": self.tool_calls.copy()}
+
+    def _retriever_search(self, retriever, query, log_context):
+        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_combine, log_context):
+        resp = self.llm.gen_stream(
+            model=self.gpt_model, messages=messages_combine, tools=self.tools
+        )
+        if log_context:
+            data = build_stack_data(self.llm)
+            log_context.stacks.append({"component": "llm", "data": data})
+        return resp
+
+    def _llm_handler(self, resp, tools_dict, messages_combine, log_context):
+        resp = self.llm_handler.handle_response(
+            self, resp, tools_dict, messages_combine
+        )
+        if log_context:
+            data = build_stack_data(self.llm_handler)
+            log_context.stacks.append({"component": "llm_handler", "data": data})
+        return resp

application/agents/llm_handler.py

@@ -0,0 +1,254 @@
+import json
+from abc import ABC, abstractmethod
+
+from application.logging import build_stack_data
+
+
+class LLMHandler(ABC):
+    def __init__(self):
+        self.llm_calls = []
+        self.tool_calls = []
+
+    @abstractmethod
+    def handle_response(self, agent, resp, tools_dict, messages, **kwargs):
+        pass
+
+
+class OpenAILLMHandler(LLMHandler):
+    def handle_response(self, agent, resp, tools_dict, messages, stream: bool = True):
+        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]}
+                        )
+
+                    except Exception as e:
+                        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:
+            while True:
+                tool_calls = {}
+                for chunk in resp:
+                    if isinstance(chunk, str) and len(chunk) > 0:
+                        return
+                    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:
+                                    messages.append(
+                                        {
+                                            "role": "assistant",
+                                            "content": f"Error executing tool: {str(e)}",
+                                        }
+                                    )
+                            tool_calls = {}
+
+                        if (
+                            hasattr(chunk, "finish_reason")
+                            and chunk.finish_reason == "stop"
+                        ):
+                            return
+                    elif isinstance(chunk, str) and len(chunk) == 0:
+                            continue
+
+                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, stream: bool = True):
+        from google.genai import types
+
+        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()],
+                            }
+                        )
+
+                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/tools/api_tool.py

@@ -0,0 +1,100 @@
+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):
+        sanitized_headers = {}
+        for key, value in headers.items():
+            if isinstance(value, str):
+                sanitized_value = value.encode('latin-1', errors='ignore').decode('latin-1')
+                sanitized_headers[key] = sanitized_value
+            else:
+                sanitized_headers[key] = value
+
+        if query_params:
+            url = f"{url}?{requests.compat.urlencode(query_params)}"
+        if isinstance(body, dict):
+            body = json.dumps(body)
+        response = None
+        try:
+            print(f"Making API call: {method} {url} with body: {body}")
+            if body == "{}":
+                body = None
+
+            proxy_id = self.config.get("proxy_id", None)
+            request_kwargs = {
+                'method': method,
+                'url': url,
+                'headers': sanitized_headers,
+                'data': body
+            }
+            try:
+                if proxy_id:
+                    from application.agents.tools.proxy_handler import apply_proxy_to_request
+                    response = apply_proxy_to_request(
+                        requests.request,
+                        proxy_id=proxy_id,
+                        **request_kwargs
+                    )
+                else:
+                    response = requests.request(**request_kwargs)
+            except ImportError:
+                response = requests.request(**request_kwargs)
+            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/proxy_handler.py

@@ -0,0 +1,63 @@
+import logging
+import requests
+from typing import Dict, Optional
+from bson.objectid import ObjectId
+
+from application.core.mongo_db import MongoDB
+
+logger = logging.getLogger(__name__)
+
+# Get MongoDB connection
+mongo = MongoDB.get_client()
+db = mongo["docsgpt"]
+proxies_collection = db["proxies"]
+
+def get_proxy_config(proxy_id: str) -> Optional[Dict[str, str]]:
+    """
+    Retrieve proxy configuration from the database.
+    
+    Args:
+        proxy_id: The ID of the proxy configuration
+        
+    Returns:
+        A dictionary with proxy configuration or None if not found
+    """
+    if not proxy_id or proxy_id == "none":
+        return None
+        
+    try:
+        if ObjectId.is_valid(proxy_id):
+            proxy_config = proxies_collection.find_one({"_id": ObjectId(proxy_id)})
+            if proxy_config and "connection" in proxy_config:
+                connection_str = proxy_config["connection"].strip()
+                if connection_str:
+                    # Format proxy for requests library
+                    return {
+                        "http": connection_str,
+                        "https": connection_str
+                    }
+        return None
+    except Exception as e:
+        logger.error(f"Error retrieving proxy configuration: {e}")
+        return None
+
+def apply_proxy_to_request(request_func, proxy_id=None, **kwargs):
+    """
+    Apply proxy configuration to a requests function if available.
+    This is a minimal wrapper that doesn't change the function signature.
+    
+    Args:
+        request_func: The requests function to call (e.g., requests.get, requests.post)
+        proxy_id: Optional proxy ID to use
+        **kwargs: Arguments to pass to the request function
+    
+    Returns:
+        The response from the request
+    """
+    if proxy_id:
+        proxy_config = get_proxy_config(proxy_id)
+        if proxy_config:
+            kwargs['proxies'] = proxy_config
+            logger.info(f"Using proxy for request")
+    
+    return request_func(**kwargs)

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

@@ -3,26 +3,26 @@ 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 pymongo import MongoClient
+from application.agents.agent_creator import AgentCreator
 
+from application.core.mongo_db import MongoDB
 from application.core.settings import settings
 from application.error import bad_request
 from application.extensions import api
 from application.llm.llm_creator import LLMCreator
 from application.retriever.retriever_creator import RetrieverCreator
-from application.utils import check_required_fields
+from application.utils import check_required_fields, limit_chat_history
 
 logger = logging.getLogger(__name__)
 
-mongo = MongoClient(settings.MONGO_URI)
+mongo = MongoDB.get_client()
 db = mongo["docsgpt"]
 conversations_collection = db["conversations"]
 sources_collection = db["sources"]
@@ -37,9 +37,13 @@ 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
@@ -87,9 +91,6 @@ def get_data_from_api_key(api_key):
     if data is None:
         raise Exception("Invalid API Key, please generate new key", 401)
 
-    if "retriever" not in data:
-        data["retriever"] = None
-
     if "source" in data and isinstance(data["source"], DBRef):
         source_doc = db.dereference(data["source"])
         data["source"] = str(source_doc["_id"])
@@ -116,8 +117,37 @@ 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,
+    source_log_docs,
+    tool_calls,
+    llm,
+    decoded_token,
+    index=None,
+    api_key=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}.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)},
             {
@@ -126,6 +156,8 @@ def save_conversation(conversation_id, question, response, source_log_docs, llm)
                         "prompt": question,
                         "response": response,
                         "sources": source_log_docs,
+                        "tool_calls": tool_calls,
+                        "timestamp": current_time,
                     }
                 }
             },
@@ -139,34 +171,37 @@ 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,
+                    "sources": source_log_docs,
+                    "tool_calls": tool_calls,
+                    "timestamp": current_time,
+                }
+            ],
+        }
+        if api_key:
+            api_key_doc = api_key_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
 
@@ -184,49 +219,82 @@ 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,
 ):
-
     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"
+        tool_calls = []
+
+        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 "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,
+                source_log_docs,
+                tool_calls,
+                llm,
+                decoded_token,
+                index,
+                api_key=user_api_key,
             )
-            # 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,
@@ -238,12 +306,12 @@ def complete_stream(
         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)}")
+        logger.error(traceback.format_exc())
         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"
@@ -267,8 +335,8 @@ class Stream(Resource):
             "prompt_id": fields.String(
                 required=False, default="default", description="Prompt ID"
             ),
-            "selectedDocs": fields.String(
-                required=False, description="Selected documents"
+            "proxy_id": fields.String(
+                required=False, description="Proxy ID to use for API calls"
             ),
             "chunks": fields.Integer(
                 required=False, default=2, description="Number of chunks"
@@ -282,6 +350,12 @@ class Stream(Resource):
             "isNoneDoc": fields.Boolean(
                 required=False, description="Flag indicating if no document is used"
             ),
+            "index": fields.Integer(
+                required=False, description="The position where query is to be updated"
+            ),
+            "save_conversation": fields.Boolean(
+                required=False, default=True, description="Flag to save conversation"
+            ),
         },
     )
 
@@ -290,20 +364,25 @@ 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")
-            if "selectedDocs" in data and data["selectedDocs"] is None:
-                chunks = 0
-            else:
-                chunks = int(data.get("chunks", 2))
+            proxy_id = data.get("proxy_id", None)
+
+            index = data.get("index", None)
+            chunks = int(data.get("chunks", 2))
             token_limit = data.get("token_limit", settings.DEFAULT_MAX_HISTORY)
             retriever_name = data.get("retriever", "classic")
 
@@ -311,29 +390,50 @@ class Stream(Resource):
                 data_key = get_data_from_api_key(data["api_key"])
                 chunks = int(data_key.get("chunks", 2))
                 prompt_id = data_key.get("prompt_id", "default")
+                proxy_id = data_key.get("proxy_id", None)
                 source = {"active_docs": data_key.get("source")}
                 retriever_name = data_key.get("retriever", retriever_name)
                 user_api_key = data["api_key"]
+                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
 
-            current_app.logger.info(
+            if not decoded_token:
+                return make_response({"error": "Unauthorized"}, 401)
+
+            logger.info(
                 f"/stream - request_data: {data}, source: {source}",
                 extra={"data": json.dumps({"request_data": data, "source": source})},
             )
 
             prompt = get_prompt(prompt_id)
+            if "isNoneDoc" in data and data["isNoneDoc"] is True:
+                chunks = 0
+
+            agent = AgentCreator.create_agent(
+                settings.AGENT_NAME,
+                endpoint="stream",
+                llm_name=settings.LLM_NAME,
+                gpt_model=gpt_model,
+                api_key=settings.API_KEY,
+                user_api_key=user_api_key,
+                prompt=prompt,
+                proxy_id=proxy_id,
+                chat_history=history,
+                decoded_token=decoded_token,
+            )
 
             retriever = RetrieverCreator.create_retriever(
                 retriever_name,
-                question=question,
                 source=source,
                 chat_history=history,
                 prompt=prompt,
@@ -341,39 +441,40 @@ 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,
                 ),
                 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",
             )
@@ -401,6 +502,9 @@ class Answer(Resource):
             "prompt_id": fields.String(
                 required=False, default="default", description="Prompt ID"
             ),
+            "proxy_id": fields.String(
+                required=False, description="Proxy ID to use for API calls"
+            ),
             "chunks": fields.Integer(
                 required=False, default=2, description="Number of chunks"
             ),
@@ -427,9 +531,12 @@ class Answer(Resource):
 
         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")
+            proxy_id = data.get("proxy_id", None)
             chunks = int(data.get("chunks", 2))
             token_limit = data.get("token_limit", settings.DEFAULT_MAX_HISTORY)
             retriever_name = data.get("retriever", "classic")
@@ -438,27 +545,48 @@ class Answer(Resource):
                 data_key = get_data_from_api_key(data["api_key"])
                 chunks = int(data_key.get("chunks", 2))
                 prompt_id = data_key.get("prompt_id", "default")
+                proxy_id = data_key.get("proxy_id", None)
                 source = {"active_docs": data_key.get("source")}
                 retriever_name = data_key.get("retriever", retriever_name)
                 user_api_key = data["api_key"]
+                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(
+                settings.AGENT_NAME,
+                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,
+                proxy_id=proxy_id,
+                chat_history=history,
+                decoded_token=decoded_token,
+            )
+
             retriever = RetrieverCreator.create_retriever(
                 retriever_name,
-                question=question,
                 source=source,
                 chat_history=history,
                 prompt=prompt,
@@ -466,36 +594,80 @@ 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
+
+            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"] == "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,
+                    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 +678,7 @@ 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()},
             )
@@ -564,21 +736,28 @@ 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 +765,17 @@ 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,
@@ -609,7 +789,7 @@ 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()},
             )

application/api/internal/routes.py

@@ -1,13 +1,13 @@
 import os
 import datetime
 from flask import Blueprint, request, send_from_directory
-from pymongo import MongoClient
 from werkzeug.utils import secure_filename
 from bson.objectid import ObjectId
 
+from application.core.mongo_db import MongoDB
 from application.core.settings import settings
 
-mongo = MongoClient(settings.MONGO_URI)
+mongo = MongoDB.get_client()
 db = mongo["docsgpt"]
 conversations_collection = db["conversations"]
 sources_collection = db["sources"]
@@ -75,7 +75,7 @@ def upload_index_files():
                     "user": user,
                     "name": job_name,
                     "language": job_name,
-                    "date": datetime.datetime.now().strftime("%d/%m/%Y %H:%M:%S"),
+                    "date": datetime.datetime.now(),
                     "model": settings.EMBEDDINGS_NAME,
                     "type": type,
                     "tokens": tokens,
@@ -92,7 +92,7 @@ def upload_index_files():
                 "user": user,
                 "name": job_name,
                 "language": job_name,
-                "date": datetime.datetime.now().strftime("%d/%m/%Y %H:%M:%S"),
+                "date": datetime.datetime.now(),
                 "model": settings.EMBEDDINGS_NAME,
                 "type": type,
                 "tokens": tokens,

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

@@ -0,0 +1,117 @@
+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, _redis_creation_failed
+    if _redis_instance is None and not _redis_creation_failed:
+        with _instance_lock:
+            if _redis_instance is None and not _redis_creation_failed:
+                try:
+                    _redis_instance = redis.Redis.from_url(
+                        settings.CACHE_REDIS_URL, socket_connect_timeout=2
+                    )
+                except ValueError as e:
+                    logger.error(f"Invalid Redis URL: {e}")
+                    _redis_creation_failed = True  # Stop future attempts
+                    _redis_instance = None
+                except redis.ConnectionError as e:
+                    logger.error(f"Redis connection error: {e}")
+                    _redis_instance = None  # Keep trying for connection errors
+    return _redis_instance
+
+
+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(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, stream, tools=None, *args, **kwargs):
+        if tools is not None:
+            return func(self, model, messages, stream, tools, *args, **kwargs)
+        
+        try:
+            cache_key = gen_cache_key(messages, model, tools)
+        except ValueError as e:
+            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}")
+
+        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}")
+
+        return result
+
+    return wrapper
+
+
+def stream_cache(func):
+    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"))
+                    for chunk in cached_response:
+                        yield chunk
+                        time.sleep(0.03)  # Simulate streaming delay
+                    return
+            except Exception as e:
+                logger.error(f"Error getting cached stream: {e}")
+
+        stream_cache_data = []
+        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 Exception as e:
+                logger.error(f"Error setting stream cache: {e}")
+
+    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/mongo_db.py

@@ -0,0 +1,24 @@
+from application.core.settings import settings
+from pymongo import MongoClient
+
+
+class MongoDB:
+    _client = None
+
+    @classmethod
+    def get_client(cls):
+        """
+        Get the MongoDB client instance, creating it if necessary.
+        """
+        if cls._client is None:
+            cls._client = MongoClient(settings.MONGO_URI)
+        return cls._client
+
+    @classmethod
+    def close_client(cls):
+        """
+        Close the MongoDB client connection.
+        """
+        if cls._client is not None:
+            cls._client.close()
+            cls._client = None

application/core/settings.py

@@ -1,35 +1,58 @@
+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"
     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"
-    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"
 
     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
@@ -64,13 +87,20 @@ 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
+    )
     BRAVE_SEARCH_API_KEY: Optional[str] = None
 
     FLASK_DEBUG_MODE: bool = False
 
+    JWT_SECRET_KEY: str = ""
+
 
 path = Path(__file__).parent.parent.absolute()
 settings = Settings(_env_file=path.joinpath(".env"), _env_file_encoding="utf-8")

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

@@ -1,28 +1,57 @@
 from abc import ABC, abstractmethod
+
+from application.cache import gen_cache, stream_cache
 from application.usage import gen_token_usage, stream_token_usage
 
 
 class BaseLLM(ABC):
-    def __init__(self):
+    def __init__(self, decoded_token=None):
+        self.decoded_token = decoded_token
         self.token_usage = {"prompt_tokens": 0, "generated_tokens": 0}
 
-    def _apply_decorator(self, method, decorator, *args, **kwargs):
-        return decorator(method, *args, **kwargs)
+    def _apply_decorator(self, method, decorators, *args, **kwargs):
+        for decorator in decorators:
+            method = decorator(method)
+        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):
-        return self._apply_decorator(self._raw_gen, gen_token_usage)(
-            self, model=model, messages=messages, stream=stream, *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,
+            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):
-        return self._apply_decorator(self._raw_gen_stream, stream_token_usage)(
-            self, model=model, messages=messages, stream=stream, *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,
+            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")

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 +1,167 @@
+from google import genai
+from google.genai import types
+
+from application.llm.base import BaseLLM
+
+
+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
+
+    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(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"],
+                                )
+                            )
+                        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
+
+        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

@@ -0,0 +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):
+        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, 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/llm_creator.py

@@ -1,3 +1,4 @@
+from application.llm.groq import GroqLLM
 from application.llm.openai import OpenAILLM, AzureOpenAILLM
 from application.llm.sagemaker import SagemakerAPILLM
 from application.llm.huggingface import HuggingFaceLLM
@@ -5,6 +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:
@@ -17,11 +20,16 @@ class LLMCreator:
         "anthropic": AnthropicLLM,
         "docsgpt": DocsGPTAPILLM,
         "premai": PremAILLM,
+        "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 +1,7 @@
-from application.llm.base import BaseLLM
-from application.core.settings import settings
+import json
 
+from application.core.settings import settings
+from application.llm.base import BaseLLM
 
 
 class OpenAILLM(BaseLLM):
@@ -10,29 +11,94 @@ class OpenAILLM(BaseLLM):
 
         super().__init__(*args, **kwargs)
         if settings.OPENAI_BASE_URL:
-            self.client = OpenAI(
-                api_key=api_key,
-                base_url=settings.OPENAI_BASE_URL
-            )
+            self.client = OpenAI(api_key=api_key, base_url=settings.OPENAI_BASE_URL)
         else:
             self.client = OpenAI(api_key=api_key)
         self.api_key = api_key
         self.user_api_key = user_api_key
 
+    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"]
+                                    ),
+                                }
+                            )
+                        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,
         baseself,
         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 +106,48 @@ 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
 
 
 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 +1,151 @@
+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
+
+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:
+    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()}
+                else:
+                    data[attr_name] = str(attr_value)
+        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["docsgpt"]
+        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}")

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}")
+        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}")
+            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

@@ -10,18 +10,27 @@ from application.parser.file.epub_parser import EpubParser
 from application.parser.file.html_parser import HTMLParser
 from application.parser.file.markdown_parser import MarkdownParser
 from application.parser.file.rst_parser import RstParser
-from application.parser.file.tabular_parser import PandasCSVParser
+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] = {
     ".pdf": PDFParser(),
     ".docx": DocxParser(),
     ".csv": PandasCSVParser(),
+    ".xlsx":ExcelParser(),
     ".epub": EpubParser(),
     ".md": MarkdownParser(),
     ".rst": RstParser(),
     ".html": HTMLParser(),
     ".mdx": MarkdownParser(),
+    ".json":JSONParser(),
+    ".pptx":PPTXParser(),
+    ".png": ImageParser(),
+    ".jpg": ImageParser(),
+    ".jpeg": ImageParser(),
 }
 
 

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/json_parser.py

@@ -0,0 +1,57 @@
+import json
+from typing import Any, Dict, List, Union
+from pathlib import Path
+
+from application.parser.file.base_parser import BaseParser
+
+class JSONParser(BaseParser):
+    r"""JSON (.json) parser.
+
+    Parses JSON files into a list of strings or a concatenated document.
+    It handles both JSON objects (dictionaries) and arrays (lists).
+
+    Args:
+        concat_rows (bool): Whether to concatenate all rows into one document.
+            If set to False, a Document will be created for each item in the JSON.
+            True by default.
+
+        row_joiner (str): Separator to use for joining each row.
+            Only used when `concat_rows=True`.
+            Set to "\n" by default.
+
+        json_config (dict): Options for parsing JSON. Can be used to specify options like
+        custom decoding or formatting. Set to empty dict by default.
+
+    """
+
+    def __init__(
+            self,
+            *args: Any,
+            concat_rows: bool = True,
+            row_joiner: str = "\n",
+            json_config: dict = {},
+            **kwargs: Any
+    ) -> None:
+        """Init params."""
+        super().__init__(*args, **kwargs)
+        self._concat_rows = concat_rows
+        self._row_joiner = row_joiner
+        self._json_config = json_config
+
+    def _init_parser(self) -> Dict:
+        """Init parser."""
+        return {}
+
+    def parse_file(self, file: Path, errors: str = "ignore") -> Union[str, List[str]]:
+        """Parse JSON file."""
+        
+        with open(file, 'r', encoding='utf-8') as f:
+                data = json.load(f, **self._json_config)
+
+        if isinstance(data, dict):
+            data = [data]
+
+        if self._concat_rows:
+            return self._row_joiner.join([str(item) for item in data])
+        else:
+            return data

application/parser/file/pptx_parser.py

@@ -0,0 +1,75 @@
+"""PPT parser.
+Contains parsers for presentation (.pptx) files to extract slide text.
+"""
+from pathlib import Path
+from typing import Any, Dict, List, Union
+
+from application.parser.file.base_parser import BaseParser
+
+class PPTXParser(BaseParser):
+    r"""PPTX (.pptx) parser for extracting text from PowerPoint slides.
+    Args:
+        concat_slides (bool): Specifies whether to concatenate all slide text into one document.
+            - If True, slide texts will be joined together as a single string.
+            - If False, each slide's text will be stored as a separate entry in a list.
+            Set to True by default.
+        slide_separator (str): Separator used to join slides' text content.
+            Only used when `concat_slides=True`. Default is "\n".
+        Refer to https://python-pptx.readthedocs.io/en/latest/ for more information.
+    """
+
+    def __init__(
+        self,
+        *args: Any,
+        concat_slides: bool = True,
+        slide_separator: str = "\n",
+        **kwargs: Any
+    ) -> None:
+        """Init params."""
+        super().__init__(*args, **kwargs)
+        self._concat_slides = concat_slides
+        self._slide_separator = slide_separator
+
+    def _init_parser(self) -> Dict:
+        """Init parser."""
+        return {}
+
+    def parse_file(self, file: Path, errors: str = "ignore") -> Union[str, List[str]]:
+        r"""
+        Parse a .pptx file and extract text from each slide.
+        Args:
+            file (Path): Path to the .pptx file.
+            errors (str): Error handling policy ('ignore' by default).
+        Returns:
+            Union[str, List[str]]: Concatenated text if concat_slides is True,
+            otherwise a list of slide texts.
+        """
+
+        try:
+            from pptx import Presentation
+        except ImportError:
+            raise ImportError("pptx module is required to read .PPTX files.")
+
+        try:
+            presentation = Presentation(file)
+            slide_texts=[]
+
+            # Iterate over each slide in the presentation
+            for slide in presentation.slides:
+                slide_text=""
+
+                # Iterate over each shape in the slide
+                for shape in slide.shapes:
+                    # Check if the shape has a 'text' attribute and append that to the slide_text
+                    if hasattr(shape,"text"):
+                        slide_text+=shape.text
+
+                slide_texts.append(slide_text.strip())
+
+            if self._concat_slides:
+                return self._slide_separator.join(slide_texts)
+            else:
+                return slide_texts
+
+        except Exception as e:
+            raise e

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

@@ -113,3 +113,68 @@ class PandasCSVParser(BaseParser):
             return (self._row_joiner).join(text_list)
         else:
             return text_list
+
+
+class ExcelParser(BaseParser):
+    r"""Excel (.xlsx) parser.
+
+    Parses Excel files using Pandas `read_excel` function.
+    If special parameters are required, use the `pandas_config` dict.
+
+    Args:
+        concat_rows (bool): whether to concatenate all rows into one document.
+            If set to False, a Document will be created for each row.
+            True by default.
+
+        col_joiner (str): Separator to use for joining cols per row.
+            Set to ", " by default.
+
+        row_joiner (str): Separator to use for joining each row.
+            Only used when `concat_rows=True`.
+            Set to "\n" by default.
+
+        pandas_config (dict): Options for the `pandas.read_excel` function call.
+            Refer to https://pandas.pydata.org/docs/reference/api/pandas.read_excel.html
+            for more information.
+            Set to empty dict by default, this means pandas will try to figure
+            out the table structure on its own.
+
+    """
+
+    def __init__(
+            self,
+            *args: Any,
+            concat_rows: bool = True,
+            col_joiner: str = ", ",
+            row_joiner: str = "\n",
+            pandas_config: dict = {},
+            **kwargs: Any
+    ) -> None:
+        """Init params."""
+        super().__init__(*args, **kwargs)
+        self._concat_rows = concat_rows
+        self._col_joiner = col_joiner
+        self._row_joiner = row_joiner
+        self._pandas_config = pandas_config
+
+    def _init_parser(self) -> Dict:
+        """Init parser."""
+        return {}
+
+    def parse_file(self, file: Path, errors: str = "ignore") -> Union[str, List[str]]:
+        """Parse file."""
+        try:
+            import pandas as pd
+        except ImportError:
+            raise ValueError("pandas module is required to read Excel files.")
+
+        df = pd.read_excel(file, **self._pandas_config)
+
+        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

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

@@ -2,16 +2,16 @@ 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]
 
@@ -19,24 +19,29 @@ 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}")
                 continue
 
@@ -45,15 +50,15 @@ 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/github_loader.py

@@ -0,0 +1,58 @@
+import base64
+import requests
+from typing import List
+from application.parser.remote.base import BaseRemote
+from langchain_core.documents import Document
+import mimetypes
+
+class GitHubLoader(BaseRemote):
+    def __init__(self):
+        self.access_token = None
+        self.headers = {
+            "Authorization": f"token {self.access_token}"
+        } if self.access_token else {}
+        return
+
+    def fetch_file_content(self, repo_url: str, file_path: str) -> str:
+        url = f"https://api.github.com/repos/{repo_url}/contents/{file_path}"
+        response = requests.get(url, headers=self.headers)
+
+        if response.status_code == 200:
+            content = response.json()
+            mime_type, _ = mimetypes.guess_type(file_path)  # Guess the MIME type based on the file extension
+
+            if content.get("encoding") == "base64":
+                if mime_type and mime_type.startswith("text"):  # Handle only text files
+                    try:
+                        decoded_content = base64.b64decode(content["content"]).decode("utf-8")
+                        return f"Filename: {file_path}\n\n{decoded_content}"
+                    except Exception as e:
+                        raise e
+                else:
+                    return f"Filename: {file_path} is a binary file and was skipped."
+            else:
+                return f"Filename: {file_path}\n\n{content['content']}"
+        else:
+            response.raise_for_status()
+
+    def fetch_repo_files(self, repo_url: str, path: str = "") -> List[str]:
+        url = f"https://api.github.com/repos/{repo_url}/contents/{path}"
+        response = requests.get(url, headers={**self.headers, "Accept": "application/vnd.github.v3.raw"})
+        contents = response.json()
+        files = []
+        for item in contents:
+            if item["type"] == "file":
+                files.append(item["path"])
+            elif item["type"] == "dir":
+                files.extend(self.fetch_repo_files(repo_url, item["path"]))
+        return files
+
+    def load_data(self, repo_url: str) -> List[Document]:
+        repo_name = repo_url.split("github.com/")[-1]
+        files = self.fetch_repo_files(repo_name)
+        documents = []
+        for file_path in files:
+            content = self.fetch_file_content(repo_name, file_path)
+            documents.append(Document(page_content=content, metadata={"title": file_path, 
+            "source": f"https://github.com/{repo_name}/blob/main/{file_path}"}))
+        return documents

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/remote_creator.py

@@ -2,6 +2,7 @@ from application.parser.remote.sitemap_loader import SitemapLoader
 from application.parser.remote.crawler_loader import CrawlerLoader
 from application.parser.remote.web_loader import WebLoader
 from application.parser.remote.reddit_loader import RedditPostsLoaderRemote
+from application.parser.remote.github_loader import GitHubLoader
 
 
 class RemoteCreator:
@@ -10,6 +11,7 @@ class RemoteCreator:
         "sitemap": SitemapLoader,
         "crawler": CrawlerLoader,
         "reddit": RedditPostsLoaderRemote,
+        "github": GitHubLoader,
     }
 
     @classmethod

application/parser/remote/web_loader.py

@@ -1,5 +1,7 @@
 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 +25,20 @@ 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}")
                 continue
-        return documents
+        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/requirements.txt

@@ -1,24 +1,28 @@
-anthropic==0.34.2
-boto3==1.34.153
+anthropic==0.49.0
+boto3==1.35.97
 beautifulsoup4==4.12.3
-celery==5.3.6
+celery==5.4.0
 dataclasses-json==0.6.7
 docx2txt==0.8
-duckduckgo-search==6.2.6
+duckduckgo-search==7.5.2
 ebooklib==0.18
-elastic-transport==8.15.0
-elasticsearch==8.15.1
+elastic-transport==8.17.0
+elasticsearch==8.17.1
 escodegen==1.0.11
 esprima==4.0.1
 esutils==1.0.1
-Flask==3.0.3
-faiss-cpu==1.8.0.post1
+Flask==3.1.0
+faiss-cpu==1.9.0.post1
 flask-restx==1.3.0
+gevent==24.11.1
+google-genai==1.3.0
+google-generativeai==0.8.3
+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
@@ -27,60 +31,67 @@ 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==0.3.45
+langchain-openai==0.3.8
+langchain-text-splitters==0.3.6
+langsmith==0.3.15
 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
 mypy-extensions==1.0.0
-networkx==3.3
-numpy==1.26.4
-openai==1.46.1
-openapi-schema-validator==0.6.2
+networkx==3.4.2
+numpy==2.2.1
+openai==1.66.3
+openapi-schema-validator==0.6.3
 openapi-spec-validator==0.6.0
-openapi3-parser==1.1.18
-orjson==3.10.7
+openapi3-parser==1.1.19
+orjson==3.10.14
 packaging==24.1
 pandas==2.2.3
-pathable==0.4.3
-pillow==10.4.0
+openpyxl==3.1.5
+pathable==0.4.4
+pillow==11.1.0
 portalocker==2.10.1
 prance==23.6.21.0
-primp==0.6.2
-prompt-toolkit==3.0.47
-protobuf==5.28.2
+primp==0.14.0
+prompt-toolkit==3.0.50
+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==4.10.1
+pypdf==5.2.0
 python-dateutil==2.9.0.post0
 python-dotenv==1.0.1
-qdrant-client==1.11.0
-redis==5.0.1
+python-jose==3.4.0
+python-pptx==1.0.2
+qdrant-client==1.13.2
+redis==5.2.1
 referencing==0.30.2
-regex==2024.9.11
+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==2.5.1
+tqdm==4.67.1
+transformers==4.49.0
 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==1.18.3
+markdownify==0.14.1
+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:
@@ -72,34 +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
-            self.chat_history.reverse()
+        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):
@@ -111,5 +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 +1,26 @@
-from application.retriever.base import BaseRetriever
 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.utils import num_tokens_from_string
+from application.vectorstore.vector_creator import VectorCreator
 
 
 class ClassicRAG(BaseRetriever):
-
     def __init__(
         self,
-        question,
         source,
-        chat_history,
-        prompt,
+        chat_history=None,
+        prompt="",
         chunks=2,
         token_limit=150,
         gpt_model="docsgpt",
         user_api_key=None,
+        llm_name=settings.LLM_NAME,
+        api_key=settings.API_KEY,
+        decoded_token=None,
     ):
-        self.question = question
-        self.vectorstore = source['active_docs'] if 'active_docs' in source else None
-        self.chat_history = chat_history
+        self.original_question = ""
+        self.chat_history = chat_history if chat_history is not None else []
         self.prompt = prompt
         self.chunks = chunks
         self.gpt_model = gpt_model
@@ -36,6 +35,45 @@ class ClassicRAG(BaseRetriever):
             )
         )
         self.user_api_key = user_api_key
+        self.llm_name = llm_name
+        self.api_key = api_key
+        self.llm = LLMCreator.create_llm(
+            self.llm_name,
+            api_key=self.api_key,
+            user_api_key=self.user_api_key,
+            decoded_token=decoded_token,
+        )
+        self.question = self._rephrase_query()
+        self.vectorstore = source["active_docs"] if "active_docs" in source else None
+        self.decoded_token = decoded_token
+
+    def _rephrase_query(self):
+        if (
+            not self.original_question
+            or not self.chat_history
+            or self.chat_history == []
+        ):
+            return self.original_question
+
+        prompt = f"""Given the following conversation history:
+        {self.chat_history}
+
+        Rephrase the following user question to be a standalone search query 
+        that captures all relevant context from the conversation:
+        """
+
+        messages = [
+            {"role": "system", "content": prompt},
+            {"role": "user", "content": self.original_question},
+        ]
+
+        try:
+            rephrased_query = self.llm.gen(model=self.gpt_model, messages=messages)
+            print(f"Rephrased query: {rephrased_query}")
+            return rephrased_query if rephrased_query else self.original_question
+        except Exception as e:
+            print(f"Error rephrasing query: {e}")
+            return self.original_question
 
     def _get_data(self):
         if self.chunks == 0:
@@ -45,7 +83,6 @@ class ClassicRAG(BaseRetriever):
                 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(
@@ -60,59 +97,25 @@ class ClassicRAG(BaseRetriever):
                 }
                 for i in docs_temp
             ]
-        if settings.LLM_NAME == "llama.cpp":
-            docs = [docs[0]]
 
         return docs
 
-    def gen(self):
-        docs = self._get_data()
+    def gen():
+        pass
 
-        # join all page_content together with a newline
-        docs_together = "\n".join([doc["text"] for doc in docs])
-        p_chat_combine = self.prompt.replace("{summaries}", docs_together)
-        messages_combine = [{"role": "system", "content": p_chat_combine}]
-        for doc in docs:
-            yield {"source": doc}
-
-        if len(self.chat_history) > 1:
-            tokens_current_history = 0
-            # count tokens in history
-            self.chat_history.reverse()
-            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,
+            "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,
         }

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":
@@ -89,36 +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
-            self.chat_history.reverse()
+        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,
@@ -128,5 +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/tts/base.py

@@ -0,0 +1,10 @@
+from abc import ABC, abstractmethod
+
+
+class BaseTTS(ABC):
+    def __init__(self):
+        pass
+
+    @abstractmethod
+    def text_to_speech(self, *args, **kwargs):
+        pass

application/tts/elevenlabs.py

@@ -0,0 +1,84 @@
+import asyncio
+import websockets
+import json
+import base64
+from io import BytesIO
+from application.tts.base import BaseTTS
+
+
+class ElevenlabsTTS(BaseTTS):
+    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):
+        asyncio.run(self._text_to_speech_websocket(text))
+
+    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/tts/google_tts.py

@@ -0,0 +1,19 @@
+import io
+import base64
+from gtts import gTTS
+from application.tts.base import BaseTTS
+
+
+class GoogleTTS(BaseTTS):
+    def __init__(self):
+        pass
+
+
+    def text_to_speech(self, text):
+        lang = "en"
+        audio_fp = io.BytesIO()
+        tts = gTTS(text=text, lang=lang, slow=False)
+        tts.write_to_fp(audio_fp)
+        audio_fp.seek(0)
+        audio_base64 = base64.b64encode(audio_fp.read()).decode("utf-8")
+        return audio_base64, lang

application/usage.py

@@ -1,18 +1,23 @@
 import sys
-from pymongo import MongoClient
 from datetime import datetime
-from application.core.settings import settings
-from application.utils import num_tokens_from_string
 
-mongo = MongoClient(settings.MONGO_URI)
+from application.core.mongo_db import MongoDB
+from application.utils import num_tokens_from_object_or_list, num_tokens_from_string
+
+mongo = MongoDB.get_client()
 db = mongo["docsgpt"]
 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"],
@@ -22,28 +27,38 @@ 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

@@ -1,6 +1,10 @@
+import hashlib
+import re
+
 import tiktoken
 from flask import jsonify, make_response
 
+
 _encoding = None
 
 
@@ -13,8 +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):
@@ -39,3 +57,55 @@ def check_required_fields(data, required_fields):
             400,
         )
     return None
+
+
+def get_hash(data):
+    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

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

application/vectorstore/faiss.py

@@ -1,33 +1,38 @@
-from langchain_community.vectorstores import FAISS
-from application.vectorstore.base import BaseVectorStore
-from application.core.settings import settings
 import os
 
-def get_vectorstore(path):
+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
+
+
+def get_vectorstore(path: str) -> str:
     if path:
-        vectorstore = "indexes/"+path
-        vectorstore = os.path.join("application", vectorstore)
+        vectorstore = os.path.join("application", "indexes", path)
     else:
         vectorstore = os.path.join("application")
-
     return vectorstore
 
-class FaissStore(BaseVectorStore):
 
-    def __init__(self, source_id, embeddings_key, docs_init=None):
+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)
-        if docs_init:
-            self.docsearch = FAISS.from_documents(
-                docs_init, embeddings
-            )
-        else:
-            self.docsearch = FAISS.load_local(
-                self.path, embeddings, 
-                allow_dangerous_deserialization=True
-            )
-        self.assert_embedding_dimensions(embeddings)
+        self.embeddings = self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key)
+
+        try:
+            if docs_init:
+                self.docsearch = FAISS.from_documents(docs_init, self.embeddings)
+            else:
+                self.docsearch = FAISS.load_local(
+                    self.path, self.embeddings, allow_dangerous_deserialization=True
+                )
+        except Exception:
+            raise
+
+        self.assert_embedding_dimensions(self.embeddings)
 
     def search(self, *args, **kwargs):
         return self.docsearch.similarity_search(*args, **kwargs)
@@ -42,16 +47,43 @@ class FaissStore(BaseVectorStore):
         return self.docsearch.delete(*args, **kwargs)
 
     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":
-            try:
-                word_embedding_dimension = embeddings.dimension
-            except AttributeError as e:
-                raise AttributeError("'dimension' attribute not found in embeddings instance. Make sure the embeddings object is properly initialized.") from e
+        """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 word_embedding_dimension is None:
+                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}) " +
-                                 f"!= 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

@@ -0,0 +1,119 @@
+from typing import List, Optional
+import importlib
+from application.vectorstore.base import BaseVectorStore
+from application.core.settings import settings
+
+class LanceDBVectorStore(BaseVectorStore):
+    """Class for LanceDB Vector Store integration."""
+
+    def __init__(self, path: str = settings.LANCEDB_PATH,
+                 table_name_prefix: str = settings.LANCEDB_TABLE_NAME,
+                 source_id: str = None,
+                 embeddings_key: str = "embeddings"):
+        """Initialize the LanceDB vector store."""
+        super().__init__()
+        self.path = path
+        self.table_name = f"{table_name_prefix}_{source_id}" if source_id else table_name_prefix
+        self.embeddings_key = embeddings_key
+        self._lance_db = None
+        self.docsearch = None
+        self._pa = None  # PyArrow (pa) will be lazy loaded
+
+    @property
+    def pa(self):
+        """Lazy load pyarrow module."""
+        if self._pa is None:
+            self._pa = importlib.import_module("pyarrow")
+        return self._pa
+
+    @property
+    def lancedb(self):
+        """Lazy load lancedb module."""
+        if not hasattr(self, "_lancedb_module"):
+            self._lancedb_module = importlib.import_module("lancedb")
+        return self._lancedb_module
+
+    @property
+    def lance_db(self):
+        """Lazy load the LanceDB connection."""
+        if self._lance_db is None:
+            self._lance_db = self.lancedb.connect(self.path)
+        return self._lance_db
+
+    @property
+    def table(self):
+        """Lazy load the LanceDB table."""
+        if self.docsearch is None:
+            if self.table_name in self.lance_db.table_names():
+                self.docsearch = self.lance_db.open_table(self.table_name)
+            else:
+                self.docsearch = None
+        return self.docsearch
+
+    def ensure_table_exists(self):
+        """Ensure the table exists before performing operations."""
+        if self.table is None:
+            embeddings = self._get_embeddings(settings.EMBEDDINGS_NAME, self.embeddings_key)
+            schema = self.pa.schema([
+                self.pa.field("vector", self.pa.list_(self.pa.float32(), list_size=embeddings.dimension)),
+                self.pa.field("text", self.pa.string()),
+                self.pa.field("metadata", self.pa.struct([
+                    self.pa.field("key", self.pa.string()),
+                    self.pa.field("value", self.pa.string())
+                ]))
+            ])
+            self.docsearch = self.lance_db.create_table(self.table_name, schema=schema)
+
+    def add_texts(self, texts: List[str], metadatas: Optional[List[dict]] = None, source_id: str = None):
+        """Add texts with metadata and their embeddings to the LanceDB table."""
+        embeddings = self._get_embeddings(settings.EMBEDDINGS_NAME, self.embeddings_key).embed_documents(texts)
+        vectors = []
+        for embedding, text, metadata in zip(embeddings, texts, metadatas or [{}] * len(texts)):
+            if source_id:
+                metadata["source_id"] = source_id
+            metadata_struct = [{"key": k, "value": str(v)} for k, v in metadata.items()]
+            vectors.append({
+                "vector": embedding,
+                "text": text,
+                "metadata": metadata_struct
+            })
+        self.ensure_table_exists()
+        self.docsearch.add(vectors)
+
+    def search(self, query: str, k: int = 2, *args, **kwargs):
+        """Search LanceDB for the top k most similar vectors."""
+        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()
+        return [(result["_distance"], result["text"], result["metadata"]) for result in results]
+
+    def delete_index(self):
+        """Delete the entire LanceDB index (table)."""
+        if self.table:
+            self.lance_db.drop_table(self.table_name)
+
+    def assert_embedding_dimensions(self, embeddings):
+        """Ensure that embedding dimensions match the table index dimensions."""
+        word_embedding_dimension = embeddings.dimension
+        if self.table:
+            table_index_dimension = len(self.docsearch.schema["vector"].type.value_type)
+            if word_embedding_dimension != table_index_dimension:
+                raise ValueError(
+                    f"Embedding dimension mismatch: embeddings.dimension ({word_embedding_dimension}) "
+                    f"!= table index dimension ({table_index_dimension})"
+                )
+
+    def filter_documents(self, filter_condition: dict) -> List[dict]:
+        """Filter documents based on certain conditions."""
+        self.ensure_table_exists()
+
+        # Ensure source_id exists in the filter condition
+        if 'source_id' not in filter_condition:
+            raise ValueError("filter_condition must contain 'source_id'")
+
+        source_id = filter_condition["source_id"]
+
+        # Use LanceDB's native filtering if supported, otherwise filter manually
+        filtered_data = self.docsearch.filter(lambda x: x.metadata and x.metadata.get("source_id") == source_id).to_list()
+
+        return filtered_data

application/vectorstore/milvus.py

@@ -7,7 +7,7 @@ from application.vectorstore.base import BaseVectorStore
 
 
 class MilvusStore(BaseVectorStore):
-    def __init__(self, path: str = "", embeddings_key: str = "embeddings"):
+    def __init__(self, source_id: str = "", embeddings_key: str = "embeddings"):
         super().__init__()
         from langchain_milvus import Milvus
 
@@ -20,10 +20,11 @@ class MilvusStore(BaseVectorStore):
             collection_name=settings.MILVUS_COLLECTION_NAME,
             connection_args=connection_args,
         )
-        self._path = path
+        self._source_id = source_id
 
     def search(self, question, k=2, *args, **kwargs):
-        return self._docsearch.similarity_search(query=question, k=k, filter={"path": self._path} *args, **kwargs)
+        expr = f"source_id == '{self._source_id}'"
+        return self._docsearch.similarity_search(query=question, k=k, expr=expr, *args, **kwargs)
 
     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

@@ -124,3 +124,53 @@ 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:
+            print(f"Error getting chunks: {e}")
+            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:
+            print(f"Error deleting chunk: {e}")
+            return False

application/worker.py

@@ -8,36 +8,37 @@ from urllib.parse import urljoin
 
 import requests
 from bson.objectid import ObjectId
-from pymongo import MongoClient
 
+from application.core.mongo_db import MongoDB
 from application.core.settings import settings
 from application.parser.file.bulk import SimpleDirectoryReader
-from application.parser.open_ai_func import call_openai_api
+from application.parser.embedding_pipeline import embed_and_store_documents
 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.parser.chunking import Chunker
 from application.utils import count_tokens_docs
 
-mongo = MongoClient(settings.MONGO_URI)
+mongo = MongoDB.get_client()
 db = mongo["docsgpt"]
 sources_collection = db["sources"]
 
+# Constants
+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.
@@ -52,9 +53,13 @@ def extract_zip_recursive(zip_path, extract_to, current_depth=0, max_depth=5):
         logging.warning(f"Reached maximum recursion depth of {max_depth}")
         return
 
-    with zipfile.ZipFile(zip_path, "r") as zip_ref:
-        zip_ref.extractall(extract_to)
-    os.remove(zip_path)  # Remove the zip file after extracting
+    try:
+        with zipfile.ZipFile(zip_path, "r") as zip_ref:
+            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}")
+        return
 
     # Check for nested zip files and extract them
     for root, dirs, files in os.walk(extract_to):
@@ -64,6 +69,38 @@ 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)
+        response.raise_for_status()
+        with open(dest_path, "wb") as f:
+            f.write(response.content)
+    except requests.RequestException as e:
+        logging.error(f"Error downloading file: {e}")
+        raise
+
+def upload_index(full_path, file_data):
+    try:
+        if settings.VECTOR_STORE == "faiss":
+            files = {
+                "file_faiss": open(full_path + "/index.faiss", "rb"),
+                "file_pkl": open(full_path + "/index.pkl", "rb"),
+            }
+            response = requests.post(
+                urljoin(settings.API_URL, "/api/upload_index"), files=files, data=file_data
+            )
+        else:
+            response = requests.post(
+                urljoin(settings.API_URL, "/api/upload_index"), data=file_data
+            )
+        response.raise_for_status()
+    except requests.RequestException as e:
+        logging.error(f"Error uploading index: {e}")
+        raise
+    finally:
+        if settings.VECTOR_STORE == "faiss":
+            for file in files.values():
+                file.close()
 
 # Define the main function for ingesting and processing documents.
 def ingest_worker(
@@ -84,39 +121,24 @@ def ingest_worker(
     Returns:
         dict: Information about the completed ingestion task, including input parameters and a "limited" flag.
     """
-    # directory = 'inputs' or 'temp'
-    # formats = [".rst", ".md"]
     input_files = None
     recursive = True
     limit = None
     exclude = True
-    # name_job = 'job1'
-    # filename = 'install.rst'
-    # user = 'local'
     sample = False
-    token_check = True
-    min_tokens = 150
-    max_tokens = 1250
-    recursion_depth = 2
     full_path = os.path.join(directory, user, name_job)
 
     logging.info(f"Ingest file: {full_path}", extra={"user": user, "job": name_job})
-    # check if API_URL env variable is set
     file_data = {"name": name_job, "file": filename, "user": user}
-    response = requests.get(
-        urljoin(settings.API_URL, "/api/download"), params=file_data
-    )
-    file = response.content
 
     if not os.path.exists(full_path):
         os.makedirs(full_path)
-    with open(os.path.join(full_path, filename), "wb") as f:
-        f.write(file)
+    download_file(urljoin(settings.API_URL, "/api/download"), file_data, os.path.join(full_path, filename))
 
     # 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
+            os.path.join(full_path, filename), full_path, 0, RECURSION_DEPTH
         )
 
     self.update_state(state="PROGRESS", meta={"current": 1})
@@ -130,17 +152,19 @@ def ingest_worker(
         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,
+
+    chunker = Chunker(
+        chunking_strategy="classic_chunk",
+        max_tokens=MAX_TOKENS,
+        min_tokens=MIN_TOKENS,
+        duplicate_headers=False
     )
+    raw_docs = chunker.chunk(documents=raw_docs)
 
     docs = [Document.to_langchain_format(raw_doc) for raw_doc in raw_docs]
     id = ObjectId()
 
-    call_openai_api(docs, full_path, id, self)
+    embed_and_store_documents(docs, full_path, id, self)
     tokens = count_tokens_docs(docs)
     self.update_state(state="PROGRESS", meta={"current": 100})
 
@@ -148,28 +172,13 @@ def ingest_worker(
         for i in range(min(5, len(raw_docs))):
             logging.info(f"Sample document {i}: {raw_docs[i]}")
 
-    # get files from outputs/inputs/index.faiss and outputs/inputs/index.pkl
-    # and send them to the server (provide user and name in form)
-    file_data = {
-        "name": name_job,
-        "user": user,
+    file_data.update({
         "tokens": tokens,
         "retriever": retriever,
         "id": str(id),
         "type": "local",
-    }
-    if settings.VECTOR_STORE == "faiss":
-        files = {
-            "file_faiss": open(full_path + "/index.faiss", "rb"),
-            "file_pkl": open(full_path + "/index.pkl", "rb"),
-        }
-        response = requests.post(
-            urljoin(settings.API_URL, "/api/upload_index"), files=files, data=file_data
-        )
-    else:
-        response = requests.post(
-            urljoin(settings.API_URL, "/api/upload_index"), data=file_data
-        )
+    })
+    upload_index(full_path, file_data)
 
     # delete local
     shutil.rmtree(full_path)
@@ -183,7 +192,6 @@ def ingest_worker(
         "limited": False,
     }
 
-
 def remote_worker(
     self,
     source_data,
@@ -195,69 +203,63 @@ def remote_worker(
     sync_frequency="never",
     operation_mode="upload",
     doc_id=None,
-):
-    token_check = True
-    min_tokens = 150
-    max_tokens = 1250
-    full_path = directory + "/" + user + "/" + name_job
-
+):  
+    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)
-
-    docs = group_split(
-        documents=raw_docs,
-        min_tokens=min_tokens,
-        max_tokens=max_tokens,
-        token_check=token_check,
-    )
-    # docs = [Document.to_langchain_format(raw_doc) for raw_doc in raw_docs]
-    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})
-
-    # Proceed with uploading and cleaning as in the original function
-    file_data = {
-        "name": name_job,
-        "user": user,
-        "tokens": tokens,
-        "retriever": retriever,
-        "id": str(id),
-        "type": loader,
-        "remote_data": source_data,
-        "sync_frequency": sync_frequency,
-    }
-    if settings.VECTOR_STORE == "faiss":
-        files = {
-            "file_faiss": open(full_path + "/index.faiss", "rb"),
-            "file_pkl": open(full_path + "/index.pkl", "rb"),
-        }
-
-        requests.post(
-            urljoin(settings.API_URL, "/api/upload_index"), files=files, data=file_data
+        chunker = Chunker(
+            chunking_strategy="classic_chunk",
+            max_tokens=MAX_TOKENS,
+            min_tokens=MIN_TOKENS,
+            duplicate_headers=False
         )
-    else:
-        requests.post(urljoin(settings.API_URL, "/api/upload_index"), data=file_data)
+        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)
 
-    shutil.rmtree(full_path)
+        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)
 
+        self.update_state(state="PROGRESS", meta={"current": 100})
+
+        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,
@@ -283,10 +285,10 @@ def sync(
             doc_id,
         )
     except Exception as e:
+        logging.error(f"Error during sync: {e}")
         return {"status": "error", "error": str(e)}
     return {"status": "success"}
 
-
 def sync_worker(self, frequency):
     sync_counts = Counter()
     sources = sources_collection.find()

deployment/docker-compose-azure.yaml

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

deployment/docker-compose-dev.yaml

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

deployment/docker-compose-local.yaml

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

deployment/docker-compose.yaml

@@ -1,8 +1,8 @@
 services:
   frontend:
-    build: ./frontend
+    build: ../frontend
     volumes:
-      - ./frontend/src:/app/src
+      - ../frontend/src:/app/src
     environment:
       - VITE_API_HOST=http://localhost:7091
       - VITE_API_STREAMING=$VITE_API_STREAMING
@@ -12,7 +12,7 @@ services:
       - backend
 
   backend:
-    build: ./application
+    build: ../application
     environment:
       - API_KEY=$API_KEY
       - EMBEDDINGS_KEY=$API_KEY
@@ -20,19 +20,22 @@ services:
       - 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
+      - OPENAI_BASE_URL=$OPENAI_BASE_URL
+      - MODEL_NAME=$MODEL_NAME
     ports:
       - "7091:7091"
     volumes:
-      - ./application/indexes:/app/application/indexes
-      - ./application/inputs:/app/application/inputs
-      - ./application/vectors:/app/application/vectors
+      - ../application/indexes:/app/application/indexes
+      - ../application/inputs:/app/application/inputs
+      - ../application/vectors:/app/application/vectors
     depends_on:
       - redis
       - mongo
 
   worker:
-    build: ./application
-    command: celery -A application.app.celery worker -l INFO -B
+    build: ../application
+    command: celery -A application.app.celery worker -l INFO --pool=gevent -B
     environment:
       - API_KEY=$API_KEY
       - EMBEDDINGS_KEY=$API_KEY
@@ -41,6 +44,7 @@ services:
       - CELERY_RESULT_BACKEND=redis://redis:6379/1
       - MONGO_URI=mongodb://mongo:27017/docsgpt
       - API_URL=http://backend:7091
+      - CACHE_REDIS_URL=redis://redis:6379/2
     depends_on:
       - redis
       - mongo