parser functions change

token_func proposed change to chunking. open_ai_func proposed change to embedding_pipeline. Late chunking first  implementation requires further testing.

.github/dependabot.yml

@@ -0,0 +1,19 @@
+# To get started with Dependabot version updates, you'll need to specify which
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for all configuration options:
+# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
+
+version: 2
+updates:
+  - package-ecosystem: "pip" # See documentation for possible values
+    directory: "/application" # Location of package manifests
+    schedule:
+      interval: "weekly"
+  - package-ecosystem: "npm" # See documentation for possible values
+    directory: "/frontend" # Location of package manifests
+    schedule:
+      interval: "weekly"
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "weekly"

.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/ci.yml

@@ -1,10 +1,8 @@
 name: Build and push DocsGPT Docker image
 
 on:
-  workflow_dispatch:
-  push:
-    branches:
-      - main
+  release:
+    types: [published]
 
 jobs:
   deploy:
@@ -13,37 +11,37 @@ jobs:
     permissions:
       contents: read
       packages: write
-
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - name: Set up QEMU
-        uses: docker/setup-qemu-action@v1
+        uses: docker/setup-qemu-action@v3
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v1
+        uses: docker/setup-buildx-action@v3
 
       - 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
+        uses: docker/build-push-action@v6
         with:
           file: './application/Dockerfile'
           platforms: linux/amd64
           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 }},${{ secrets.DOCKER_USERNAME }}/docsgpt:latest
+            ghcr.io/${{ github.repository_owner }}/docsgpt:${{ github.event.release.tag_name }},ghcr.io/${{ github.repository_owner }}/docsgpt:latest
+          cache-from: type=registry,ref=${{ secrets.DOCKER_USERNAME }}/docsgpt:latest
+          cache-to: type=inline

.github/workflows/cife.yml

@@ -1,35 +1,33 @@
 name: Build and push DocsGPT-FE Docker image
 
 on:
-  workflow_dispatch:
-  push:
-    branches:
-      - main
+  release:
+    types: [published]
 
 jobs:
   deploy:
+    if: github.repository == 'arc53/DocsGPT'
     runs-on: ubuntu-latest
     permissions:
       contents: read
       packages: write
-
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - name: Set up QEMU
-        uses: docker/setup-qemu-action@v1
+        uses: docker/setup-qemu-action@v3
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v1
+        uses: docker/setup-buildx-action@v3
 
       - 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 }}
@@ -37,12 +35,14 @@ jobs:
 
       # 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
+        uses: docker/build-push-action@v6
         with:
           file: './frontend/Dockerfile'
-          platforms: linux/amd64
+          platforms: linux/amd64, linux/arm64
           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 }},${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:latest
+            ghcr.io/${{ github.repository_owner }}/docsgpt-fe:${{ github.event.release.tag_name }},ghcr.io/${{ github.repository_owner }}/docsgpt-fe:latest
+          cache-from: type=registry,ref=${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:latest
+          cache-to: type=inline

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

@@ -0,0 +1,49 @@
+name: Build and push DocsGPT Docker image for development
+
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+
+jobs:
+  deploy:
+    if: github.repository == 'arc53/DocsGPT'
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v3
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - 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 Docker images to docker.io and ghcr.io
+        uses: docker/build-push-action@v6
+        with:
+          file: './application/Dockerfile'
+          platforms: linux/amd64
+          context: ./application
+          push: true
+          tags: |
+            ${{ secrets.DOCKER_USERNAME }}/docsgpt:develop
+            ghcr.io/${{ github.repository_owner }}/docsgpt:develop
+          cache-from: type=registry,ref=${{ secrets.DOCKER_USERNAME }}/docsgpt:develop
+          cache-to: type=inline

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

@@ -0,0 +1,49 @@
+name: Build and push DocsGPT FE Docker image for development
+
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+
+jobs:
+  deploy:
+    if: github.repository == 'arc53/DocsGPT'
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v3
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - 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 Docker images to docker.io and ghcr.io
+        uses: docker/build-push-action@v6
+        with:
+          file: './frontend/Dockerfile'
+          platforms: linux/amd64
+          context: ./frontend
+          push: true
+          tags: |
+            ${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:develop
+            ghcr.io/${{ github.repository_owner }}/docsgpt-fe:develop
+          cache-from: type=registry,ref=${{ secrets.DOCKER_USERNAME }}/docsgpt-fe:develop
+          cache-to: type=inline

.github/workflows/labeler.yml

@@ -4,12 +4,13 @@ on:
   - pull_request_target
 jobs:
   triage:
+    if: github.repository == 'arc53/DocsGPT'
     permissions:
       contents: read
       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: ["3.9", "3.10", "3.11"]
+        python-version: ["3.11"]
     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
@@ -21,10 +21,10 @@ jobs:
           if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
       - name: Test with pytest and generate coverage report
         run: |
-          python -m pytest --cov=application --cov=scripts --cov=extensions --cov-report=xml
+          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
+        uses: codecov/codecov-action@v4
         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

.gitignore

@@ -172,5 +172,5 @@ application/vectors/
 
 node_modules/
 .vscode/settings.json
-models/
+/models/
 model/

.vscode/launch.json

@@ -0,0 +1,16 @@
+{
+  "version": "0.2.0",
+  "configurations": [
+    {
+      "name": "Docker Debug Frontend",
+      "request": "launch",
+      "type": "chrome",
+      "preLaunchTask": "docker-compose: debug:frontend",
+      "url": "http://127.0.0.1:5173",
+      "webRoot": "${workspaceFolder}/frontend",
+      "skipFiles": [
+        "<node_internals>/**"
+      ]
+    }
+  ]
+}

.vscode/tasks.json

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

CONTRIBUTING.md

@@ -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.
 
@@ -21,8 +21,9 @@ 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:
 
@@ -43,7 +44,7 @@ Please try to follow the guidelines.
 
 ### 🖥 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) or [`/scripts`](https://github.com/arc53/DocsGPT/tree/main/scripts) (please disregard old [`ingest_rst.py`](https://github.com/arc53/DocsGPT/blob/main/scripts/old/ingest_rst.py) [`ingest_rst_sphinx.py`](https://github.com/arc53/DocsGPT/blob/main/scripts/old/ingest_rst_sphinx.py) files; these will be deprecated soon).
 - 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.
   
@@ -125,4 +126,4 @@ 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

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

README.md

@@ -7,9 +7,9 @@
 </p>
 
 <p align="left">
-  <strong><a href="https://docsgpt.arc53.com/">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.
+  <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://docsgpt.arc53.com/">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.
+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.
 </p>
 
 <div align="center">
@@ -27,10 +27,12 @@ Say goodbye to time-consuming manual searches, and let <strong><a href="https://
 
 We're eager to provide personalized assistance when deploying your DocsGPT to a live environment.
 
-- [Book Demo :wave:](https://airtable.com/appdeaL0F1qV8Bl2C/shrrJF1Ll7btCJRbP)
-- [Send Email :email:](mailto:contact@arc53.com?subject=DocsGPT%20support%2Fsolutions)
+[Book a Meeting :wave:](https://cal.com/arc53/docsgpt-demo-b2b)⁠
 
-![video-example-of-docs-gpt](https://d3dg1063dc54p9.cloudfront.net/videos/demov3.gif)
+[Send Email :email:](mailto:contact@arc53.com?subject=DocsGPT%20support%2Fsolutions)
+
+
+<img src="https://github.com/user-attachments/assets/9a1f21de-7a15-4e42-9424-70d22ba5a913" alt="video-example-of-docs-gpt" width="1000" height="500">
 
 ## Roadmap
 
@@ -46,23 +48,23 @@ You can find our roadmap [here](https://github.com/orgs/arc53/projects/2). Pleas
 
 If you don't have enough resources to run it, you can use bitsnbytes to quantize.
 
-## Features
+## End to End AI Framework for Information Retrieval
 
-![Main features of DocsGPT showcasing six main features](https://user-images.githubusercontent.com/17906039/220427472-2644cff4-7666-46a5-819f-fc4a521f63c7.png)
+![Architecture chart](https://github.com/user-attachments/assets/fc6a7841-ddfc-45e6-b5a0-d05fe648cbe2)
 
 ## Useful Links
 
-- :mag: :fire: [Live preview](https://docsgpt.arc53.com/)
+- :mag: :fire: [Cloud Version](https://app.docsgpt.cloud/)
 
 - :speech_balloon: :tada: [Join our Discord](https://discord.gg/n5BX8dh8rU)
 
-- :books: :sunglasses: [Guides](https://docs.docsgpt.co.uk/)
+- :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.co.uk/Guides/How-to-train-on-other-documentation)
+- :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.co.uk/Guides/How-to-use-different-LLM)
+- :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
 
@@ -85,7 +87,7 @@ On Mac OS or Linux, write:
 
 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:
+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.
@@ -124,7 +126,7 @@ docker compose -f docker-compose-dev.yaml up -d
 > 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`.
+   - Copy [.env-template](https://github.com/arc53/DocsGPT/blob/main/application/.env-template) and create `.env`.
 
 (check out [`application/core/settings.py`](application/core/settings.py) if you want to see more config options.)
 
@@ -197,4 +199,9 @@ 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

@@ -1,31 +1,88 @@
-FROM python:3.11-slim-bullseye as builder
+# Builder Stage
+FROM ubuntu:24.04 as builder
 
-# Tiktoken requires Rust toolchain, so build it in a separate stage
-RUN apt-get update && apt-get install -y gcc curl
-RUN apt-get install -y wget unzip
-RUN wget https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip
-RUN unzip mpnet-base-v2.zip -d model
-RUN rm mpnet-base-v2.zip
-RUN curl https://sh.rustup.rs -sSf | sh -s -- -y && apt-get install --reinstall libc6-dev -y
-ENV PATH="/root/.cargo/bin:${PATH}"
-RUN pip install --upgrade pip && pip install tiktoken==0.5.2
+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 && \
+    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; \
+    else \
+        echo "Python 3.11 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 && \
+    rm mpnet-base-v2.zip
+
+# Install Rust
+RUN wget -q -O - https://sh.rustup.rs | sh -s -- -y
+
+# Clean up to reduce container size
+RUN apt-get remove --purge -y wget unzip && apt-get autoremove -y && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements.txt
 COPY requirements.txt .
-RUN pip install -r requirements.txt
 
+# Setup Python virtual environment
+RUN python3.11 -m venv /venv
 
+# Activate virtual environment and install Python packages
+ENV PATH="/venv/bin:$PATH"
 
-FROM python:3.11-slim-bullseye
+# Install Python packages
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir tiktoken && \
+    pip install --no-cache-dir -r requirements.txt
 
-# Copy pre-built packages and binaries from builder stage
-COPY --from=builder /usr/local/ /usr/local/
+# Final Stage
+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 && \
+    rm -rf /var/lib/apt/lists/*
+
+# Set working directory
 WORKDIR /app
+
+# Create a non-root user: `appuser` (Feel free to choose a name)
+RUN groupadd -r appuser && \
+    useradd -r -g appuser -d /app -s /sbin/nologin -c "Docker image user" appuser
+
+# Copy the virtual environment and model from the builder stage
+COPY --from=builder /venv /venv
 COPY --from=builder /model /app/model
 
+# Copy your application code
 COPY . /app/application
-ENV FLASK_APP=app.py
-ENV FLASK_DEBUG=true
 
+# Change the ownership of the /app directory to the appuser
+	
+RUN mkdir -p /app/application/inputs/local
+RUN chown -R appuser:appuser /app
+
+# Set environment variables
+ENV FLASK_APP=app.py \
+    FLASK_DEBUG=true \
+    PATH="/venv/bin:$PATH"
+
+# Expose the port the app runs on
 EXPOSE 7091
 
-CMD ["gunicorn", "-w", "2", "--timeout", "120", "--bind", "0.0.0.0:7091", "application.wsgi:app"]
+# Switch to non-root user
+USER appuser
+
+# Start Gunicorn
+CMD ["gunicorn", "-w", "2", "--timeout", "120", "--bind", "0.0.0.0:7091", "application.wsgi:app"]

application/api/answer/routes.py

@@ -1,30 +1,38 @@
 import asyncio
-import os
-from flask import Blueprint, request, Response
-import json
 import datetime
+import json
 import logging
+import os
+import sys
 import traceback
 
-from pymongo import MongoClient
+from bson.dbref import DBRef
 from bson.objectid import ObjectId
+from flask import Blueprint, current_app, make_response, request, Response
+from flask_restx import fields, Namespace, Resource
 
 
+from application.core.mongo_db import MongoDB
 from application.core.settings import settings
+from application.error import bad_request
+from application.extensions import api
 from application.llm.llm_creator import LLMCreator
 from application.retriever.retriever_creator import RetrieverCreator
-from application.error import bad_request
-
+from application.utils import check_required_fields
 
 logger = logging.getLogger(__name__)
 
-mongo = MongoClient(settings.MONGO_URI)
+mongo = MongoDB.get_client()
 db = mongo["docsgpt"]
 conversations_collection = db["conversations"]
-vectors_collection = db["vectors"]
+sources_collection = db["sources"]
 prompts_collection = db["prompts"]
 api_key_collection = db["api_keys"]
+user_logs_collection = db["user_logs"]
+
 answer = Blueprint("answer", __name__)
+answer_ns = Namespace("answer", description="Answer related operations", path="/")
+api.add_namespace(answer_ns)
 
 gpt_model = ""
 # to have some kind of default behaviour
@@ -32,6 +40,8 @@ if settings.LLM_NAME == "openai":
     gpt_model = "gpt-3.5-turbo"
 elif settings.LLM_NAME == "anthropic":
     gpt_model = "claude-2"
+elif settings.LLM_NAME == "groq":
+    gpt_model = "llama3-8b-8192"
 
 if settings.MODEL_NAME:  # in case there is particular model name configured
     gpt_model = settings.MODEL_NAME
@@ -75,25 +85,29 @@ def run_async_chain(chain, question, chat_history):
 
 def get_data_from_api_key(api_key):
     data = api_key_collection.find_one({"key": api_key})
+    # # Raise custom exception if the API key is not found
     if data is None:
-        return bad_request(401, "Invalid API key")
+        raise Exception("Invalid API Key, please generate new key", 401)
+
+    if "retriever" not in data:
+        data["retriever"] = None
+
+    if "source" in data and isinstance(data["source"], DBRef):
+        source_doc = db.dereference(data["source"])
+        data["source"] = str(source_doc["_id"])
+        if "retriever" in source_doc:
+            data["retriever"] = source_doc["retriever"]
+    else:
+        data["source"] = {}
     return data
 
 
-def get_vectorstore(data):
-    if "active_docs" in data:
-        if data["active_docs"].split("/")[0] == "default":
-            vectorstore = ""
-        elif data["active_docs"].split("/")[0] == "local":
-            vectorstore = "indexes/" + data["active_docs"]
-        else:
-            vectorstore = "vectors/" + data["active_docs"]
-        if data["active_docs"] == "default":
-            vectorstore = ""
-    else:
-        vectorstore = ""
-    vectorstore = os.path.join("application", vectorstore)
-    return vectorstore
+def get_retriever(source_id: str):
+    doc = sources_collection.find_one({"_id": ObjectId(source_id)})
+    if doc is None:
+        raise Exception("Source document does not exist", 404)
+    retriever_name = None if "retriever" not in doc else doc["retriever"]
+    return retriever_name
 
 
 def is_azure_configured():
@@ -127,17 +141,17 @@ 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,
             },
         ]
 
@@ -171,219 +185,435 @@ def get_prompt(prompt_id):
     return prompt
 
 
-def complete_stream(question, retriever, conversation_id, user_api_key):
+def complete_stream(
+    question, retriever, conversation_id, user_api_key, isNoneDoc=False
+):
 
-    response_full = ""
-    source_log_docs = []
-    answer = retriever.gen()
-    for line in answer:
-        if "answer" in line:
-            response_full += str(line["answer"])
-            data = json.dumps(line)
-            yield f"data: {data}\n\n"
-        elif "source" in line:
-            source_log_docs.append(line["source"])
-
-    llm = LLMCreator.create_llm(
-        settings.LLM_NAME, api_key=settings.API_KEY, user_api_key=user_api_key
-    )
-    conversation_id = save_conversation(
-        conversation_id, question, response_full, source_log_docs, llm
-    )
-
-    # send data.type = "end" to indicate that the stream has ended as json
-    data = json.dumps({"type": "id", "id": str(conversation_id)})
-    yield f"data: {data}\n\n"
-    data = json.dumps({"type": "end"})
-    yield f"data: {data}\n\n"
-
-
-@answer.route("/stream", methods=["POST"])
-def stream():
-    data = request.get_json()
-    # get parameter from url question
-    question = data["question"]
-    if "history" not in data:
-        history = []
-    else:
-        history = data["history"]
-        history = json.loads(history)
-    if "conversation_id" not in data:
-        conversation_id = None
-    else:
-        conversation_id = data["conversation_id"]
-    if "prompt_id" in data:
-        prompt_id = data["prompt_id"]
-    else:
-        prompt_id = "default"
-    if "selectedDocs" in data and data["selectedDocs"] is None:
-        chunks = 0
-    elif "chunks" in data:
-        chunks = int(data["chunks"])
-    else:
-        chunks = 2
-
-    prompt = get_prompt(prompt_id)
-
-    # check if active_docs is set
-
-    if "api_key" in data:
-        data_key = get_data_from_api_key(data["api_key"])
-        source = {"active_docs": data_key["source"]}
-        user_api_key = data["api_key"]
-    elif "active_docs" in data:
-        source = {"active_docs": data["active_docs"]}
-        user_api_key = None
-    else:
-        source = {}
-        user_api_key = None
-
-    if (
-        source["active_docs"].split("/")[0] == "default"
-        or source["active_docs"].split("/")[0] == "local"
-    ):
-        retriever_name = "classic"
-    else:
-        retriever_name = source["active_docs"]
-
-    retriever = RetrieverCreator.create_retriever(
-        retriever_name,
-        question=question,
-        source=source,
-        chat_history=history,
-        prompt=prompt,
-        chunks=chunks,
-        gpt_model=gpt_model,
-        user_api_key=user_api_key,
-    )
-
-    return Response(
-        complete_stream(
-            question=question,
-            retriever=retriever,
-            conversation_id=conversation_id,
-            user_api_key=user_api_key,
-        ),
-        mimetype="text/event-stream",
-    )
-
-
-@answer.route("/api/answer", methods=["POST"])
-def api_answer():
-    data = request.get_json()
-    question = data["question"]
-    if "history" not in data:
-        history = []
-    else:
-        history = data["history"]
-    if "conversation_id" not in data:
-        conversation_id = None
-    else:
-        conversation_id = data["conversation_id"]
-    print("-" * 5)
-    if "prompt_id" in data:
-        prompt_id = data["prompt_id"]
-    else:
-        prompt_id = "default"
-    if "chunks" in data:
-        chunks = int(data["chunks"])
-    else:
-        chunks = 2
-
-    prompt = get_prompt(prompt_id)
-
-    # use try and except  to check for exception
     try:
-        # check if the vectorstore is set
-        if "api_key" in data:
-            data_key = get_data_from_api_key(data["api_key"])
-            source = {"active_docs": data_key["source"]}
-            user_api_key = data["api_key"]
-        else:
-            source = {data}
-            user_api_key = None
-
-        if (
-            source["active_docs"].split("/")[0] == "default"
-            or source["active_docs"].split("/")[0] == "local"
-        ):
-            retriever_name = "classic"
-        else:
-            retriever_name = source["active_docs"]
-
-        retriever = RetrieverCreator.create_retriever(
-            retriever_name,
-            question=question,
-            source=source,
-            chat_history=history,
-            prompt=prompt,
-            chunks=chunks,
-            gpt_model=gpt_model,
-            user_api_key=user_api_key,
-        )
-        source_log_docs = []
         response_full = ""
-        for line in retriever.gen():
-            if "source" in line:
+        source_log_docs = []
+        answer = retriever.gen()
+        sources = retriever.search()
+        for source in sources:
+            if "text" in source:
+                source["text"] = source["text"][:100].strip() + "..."
+        if len(sources) > 0:
+            data = json.dumps({"type": "source", "source": sources})
+            yield f"data: {data}\n\n"
+        for line in answer:
+            if "answer" in line:
+                response_full += str(line["answer"])
+                data = json.dumps(line)
+                yield f"data: {data}\n\n"
+            elif "source" in line:
                 source_log_docs.append(line["source"])
-            elif "answer" in line:
-                response_full += line["answer"]
+
+        if isNoneDoc:
+            for doc in source_log_docs:
+                doc["source"] = "None"
 
         llm = LLMCreator.create_llm(
             settings.LLM_NAME, api_key=settings.API_KEY, user_api_key=user_api_key
         )
+        if user_api_key is None:
+            conversation_id = save_conversation(
+                conversation_id, question, response_full, source_log_docs, llm
+            )
+            # send data.type = "end" to indicate that the stream has ended as json
+            data = json.dumps({"type": "id", "id": str(conversation_id)})
+            yield f"data: {data}\n\n"
 
-        result = {"answer": response_full, "sources": source_log_docs}
-        result["conversation_id"] = save_conversation(
-            conversation_id, question, response_full, source_log_docs, llm
+        retriever_params = retriever.get_params()
+        user_logs_collection.insert_one(
+            {
+                "action": "stream_answer",
+                "level": "info",
+                "user": "local",
+                "api_key": user_api_key,
+                "question": question,
+                "response": response_full,
+                "sources": source_log_docs,
+                "retriever_params": retriever_params,
+                "timestamp": datetime.datetime.now(datetime.timezone.utc),
+            }
         )
-
-        return result
+        data = json.dumps({"type": "end"})
+        yield f"data: {data}\n\n"
     except Exception as e:
-        # print whole traceback
+        print("\033[91merr", str(e), file=sys.stderr)
         traceback.print_exc()
-        print(str(e))
-        return bad_request(500, str(e))
+        data = json.dumps(
+            {
+                "type": "error",
+                "error": "Please try again later. We apologize for any inconvenience.",
+                "error_exception": str(e),
+            }
+        )
+        yield f"data: {data}\n\n"
+        return
 
 
-@answer.route("/api/search", methods=["POST"])
-def api_search():
-    data = request.get_json()
-    # get parameter from url question
-    question = data["question"]
-
-    if "api_key" in data:
-        data_key = get_data_from_api_key(data["api_key"])
-        source = {"active_docs": data_key["source"]}
-        user_api_key = data["api_key"]
-    elif "active_docs" in data:
-        source = {"active_docs": data["active_docs"]}
-        user_api_key = None
-    else:
-        source = {}
-        user_api_key = None
-    if "chunks" in data:
-        chunks = int(data["chunks"])
-    else:
-        chunks = 2
-
-    if (
-        source["active_docs"].split("/")[0] == "default"
-        or source["active_docs"].split("/")[0] == "local"
-    ):
-        retriever_name = "classic"
-    else:
-        retriever_name = source["active_docs"]
-
-    retriever = RetrieverCreator.create_retriever(
-        retriever_name,
-        question=question,
-        source=source,
-        chat_history=[],
-        prompt="default",
-        chunks=chunks,
-        gpt_model=gpt_model,
-        user_api_key=user_api_key,
+@answer_ns.route("/stream")
+class Stream(Resource):
+    stream_model = api.model(
+        "StreamModel",
+        {
+            "question": fields.String(
+                required=True, description="Question to be asked"
+            ),
+            "history": fields.List(
+                fields.String, required=False, description="Chat history"
+            ),
+            "conversation_id": fields.String(
+                required=False, description="Conversation ID"
+            ),
+            "prompt_id": fields.String(
+                required=False, default="default", description="Prompt ID"
+            ),
+            "chunks": fields.Integer(
+                required=False, default=2, description="Number of chunks"
+            ),
+            "token_limit": fields.Integer(required=False, description="Token limit"),
+            "retriever": fields.String(required=False, description="Retriever type"),
+            "api_key": fields.String(required=False, description="API key"),
+            "active_docs": fields.String(
+                required=False, description="Active documents"
+            ),
+            "isNoneDoc": fields.Boolean(
+                required=False, description="Flag indicating if no document is used"
+            ),
+        },
     )
-    docs = retriever.search()
-    return docs
+
+    @api.expect(stream_model)
+    @api.doc(description="Stream a response based on the question and retriever")
+    def post(self):
+        data = request.get_json()
+        required_fields = ["question"]
+
+        missing_fields = check_required_fields(data, required_fields)
+        if missing_fields:
+            return missing_fields
+
+        try:
+            question = data["question"]
+            history = data.get("history", [])
+            history = json.loads(history)
+            conversation_id = data.get("conversation_id")
+            prompt_id = data.get("prompt_id", "default")
+            
+
+            chunks = int(data.get("chunks", 2))
+            token_limit = data.get("token_limit", settings.DEFAULT_MAX_HISTORY)
+            retriever_name = data.get("retriever", "classic")
+
+            if "api_key" in data:
+                data_key = get_data_from_api_key(data["api_key"])
+                chunks = int(data_key.get("chunks", 2))
+                prompt_id = data_key.get("prompt_id", "default")
+                source = {"active_docs": data_key.get("source")}
+                retriever_name = data_key.get("retriever", retriever_name)
+                user_api_key = data["api_key"]
+
+            elif "active_docs" in data:
+                source = {"active_docs": data["active_docs"]}
+                retriever_name = get_retriever(data["active_docs"]) or retriever_name
+                user_api_key = None
+
+            else:
+                source = {}
+                user_api_key = None
+
+            current_app.logger.info(
+                f"/stream - request_data: {data}, source: {source}",
+                extra={"data": json.dumps({"request_data": data, "source": source})},
+            )
+
+            prompt = get_prompt(prompt_id)
+            if "isNoneDoc" in data and data["isNoneDoc"] is True:
+                chunks = 0
+            retriever = RetrieverCreator.create_retriever(
+                retriever_name,
+                question=question,
+                source=source,
+                chat_history=history,
+                prompt=prompt,
+                chunks=chunks,
+                token_limit=token_limit,
+                gpt_model=gpt_model,
+                user_api_key=user_api_key,
+            )
+
+            return Response(
+                complete_stream(
+                    question=question,
+                    retriever=retriever,
+                    conversation_id=conversation_id,
+                    user_api_key=user_api_key,
+                    isNoneDoc=data.get("isNoneDoc"),
+                ),
+                mimetype="text/event-stream",
+            )
+
+        except ValueError:
+            message = "Malformed request body"
+            print("\033[91merr", str(message), file=sys.stderr)
+            return Response(
+                error_stream_generate(message),
+                status=400,
+                mimetype="text/event-stream",
+            )
+        except Exception as e:
+            current_app.logger.error(
+                f"/stream - error: {str(e)} - traceback: {traceback.format_exc()}",
+                extra={"error": str(e), "traceback": traceback.format_exc()},
+            )
+            message = e.args[0]
+            status_code = 400
+            # Custom exceptions with two arguments, index 1 as status code
+            if len(e.args) >= 2:
+                status_code = e.args[1]
+            return Response(
+                error_stream_generate(message),
+                status=status_code,
+                mimetype="text/event-stream",
+            )
+
+
+def error_stream_generate(err_response):
+    data = json.dumps({"type": "error", "error": err_response})
+    yield f"data: {data}\n\n"
+
+
+@answer_ns.route("/api/answer")
+class Answer(Resource):
+    answer_model = api.model(
+        "AnswerModel",
+        {
+            "question": fields.String(
+                required=True, description="The question to answer"
+            ),
+            "history": fields.List(
+                fields.String, required=False, description="Conversation history"
+            ),
+            "conversation_id": fields.String(
+                required=False, description="Conversation ID"
+            ),
+            "prompt_id": fields.String(
+                required=False, default="default", description="Prompt ID"
+            ),
+            "chunks": fields.Integer(
+                required=False, default=2, description="Number of chunks"
+            ),
+            "token_limit": fields.Integer(required=False, description="Token limit"),
+            "retriever": fields.String(required=False, description="Retriever type"),
+            "api_key": fields.String(required=False, description="API key"),
+            "active_docs": fields.String(
+                required=False, description="Active documents"
+            ),
+            "isNoneDoc": fields.Boolean(
+                required=False, description="Flag indicating if no document is used"
+            ),
+        },
+    )
+
+    @api.expect(answer_model)
+    @api.doc(description="Provide an answer based on the question and retriever")
+    def post(self):
+        data = request.get_json()
+        required_fields = ["question"]       
+        missing_fields = check_required_fields(data, required_fields)
+        if missing_fields:
+            return missing_fields
+
+        try:
+            question = data["question"]
+            history = data.get("history", [])
+            conversation_id = data.get("conversation_id")
+            prompt_id = data.get("prompt_id", "default")
+            chunks = int(data.get("chunks", 2))
+            token_limit = data.get("token_limit", settings.DEFAULT_MAX_HISTORY)
+            retriever_name = data.get("retriever", "classic")
+
+            if "api_key" in data:
+                data_key = get_data_from_api_key(data["api_key"])
+                chunks = int(data_key.get("chunks", 2))
+                prompt_id = data_key.get("prompt_id", "default")
+                source = {"active_docs": data_key.get("source")}
+                retriever_name = data_key.get("retriever", retriever_name)
+                user_api_key = data["api_key"]
+            elif "active_docs" in data:
+                source = {"active_docs": data["active_docs"]}
+                retriever_name = get_retriever(data["active_docs"]) or retriever_name
+                user_api_key = None
+            else:
+                source = {}
+                user_api_key = None
+
+            prompt = get_prompt(prompt_id)
+
+            current_app.logger.info(
+                f"/api/answer - request_data: {data}, source: {source}",
+                extra={"data": json.dumps({"request_data": data, "source": source})},
+            )
+
+            retriever = RetrieverCreator.create_retriever(
+                retriever_name,
+                question=question,
+                source=source,
+                chat_history=history,
+                prompt=prompt,
+                chunks=chunks,
+                token_limit=token_limit,
+                gpt_model=gpt_model,
+                user_api_key=user_api_key,
+            )
+
+            source_log_docs = []
+            response_full = ""
+            for line in retriever.gen():
+                if "source" in line:
+                    source_log_docs.append(line["source"])
+                elif "answer" in line:
+                    response_full += line["answer"]
+
+            if data.get("isNoneDoc"):
+                for doc in source_log_docs:
+                    doc["source"] = "None"
+
+            llm = LLMCreator.create_llm(
+                settings.LLM_NAME, api_key=settings.API_KEY, user_api_key=user_api_key
+            )
+
+            result = {"answer": response_full, "sources": source_log_docs}
+            result["conversation_id"] = str(
+                save_conversation(
+                    conversation_id, question, response_full, source_log_docs, llm
+                )
+            )
+            retriever_params = retriever.get_params()
+            user_logs_collection.insert_one(
+                {
+                    "action": "api_answer",
+                    "level": "info",
+                    "user": "local",
+                    "api_key": user_api_key,
+                    "question": question,
+                    "response": response_full,
+                    "sources": source_log_docs,
+                    "retriever_params": retriever_params,
+                    "timestamp": datetime.datetime.now(datetime.timezone.utc),
+                }
+            )
+
+        except Exception as e:
+            current_app.logger.error(
+                f"/api/answer - error: {str(e)} - traceback: {traceback.format_exc()}",
+                extra={"error": str(e), "traceback": traceback.format_exc()},
+            )
+            return bad_request(500, str(e))
+
+        return make_response(result, 200)
+
+
+@answer_ns.route("/api/search")
+class Search(Resource):
+    search_model = api.model(
+        "SearchModel",
+        {
+            "question": fields.String(
+                required=True, description="The question to search"
+            ),
+            "chunks": fields.Integer(
+                required=False, default=2, description="Number of chunks"
+            ),
+            "api_key": fields.String(
+                required=False, description="API key for authentication"
+            ),
+            "active_docs": fields.String(
+                required=False, description="Active documents for retrieval"
+            ),
+            "retriever": fields.String(required=False, description="Retriever type"),
+            "token_limit": fields.Integer(
+                required=False, description="Limit for tokens"
+            ),
+            "isNoneDoc": fields.Boolean(
+                required=False, description="Flag indicating if no document is used"
+            ),
+        },
+    )
+
+    @api.expect(search_model)
+    @api.doc(
+        description="Search for relevant documents based on the question and retriever"
+    )
+    def post(self):
+        data = request.get_json()
+        required_fields = ["question"]
+        missing_fields = check_required_fields(data, required_fields)
+        if missing_fields:
+            return missing_fields
+
+        try:
+            question = data["question"]
+            chunks = int(data.get("chunks", 2))
+            token_limit = data.get("token_limit", settings.DEFAULT_MAX_HISTORY)
+            retriever_name = data.get("retriever", "classic")
+
+            if "api_key" in data:
+                data_key = get_data_from_api_key(data["api_key"])
+                chunks = int(data_key.get("chunks", 2))
+                source = {"active_docs": data_key.get("source")}
+                user_api_key = data["api_key"]
+            elif "active_docs" in data:
+                source = {"active_docs": data["active_docs"]}
+                user_api_key = None
+            else:
+                source = {}
+                user_api_key = None
+
+            current_app.logger.info(
+                f"/api/answer - request_data: {data}, source: {source}",
+                extra={"data": json.dumps({"request_data": data, "source": source})},
+            )
+
+            retriever = RetrieverCreator.create_retriever(
+                retriever_name,
+                question=question,
+                source=source,
+                chat_history=[],
+                prompt="default",
+                chunks=chunks,
+                token_limit=token_limit,
+                gpt_model=gpt_model,
+                user_api_key=user_api_key,
+            )
+
+            docs = retriever.search()
+            retriever_params = retriever.get_params()
+
+            user_logs_collection.insert_one(
+                {
+                    "action": "api_search",
+                    "level": "info",
+                    "user": "local",
+                    "api_key": user_api_key,
+                    "question": question,
+                    "sources": docs,
+                    "retriever_params": retriever_params,
+                    "timestamp": datetime.datetime.now(datetime.timezone.utc),
+                }
+            )
+
+            if data.get("isNoneDoc"):
+                for doc in docs:
+                    doc["source"] = "None"
+
+        except Exception as e:
+            current_app.logger.error(
+                f"/api/search - error: {str(e)} - traceback: {traceback.format_exc()}",
+                extra={"error": str(e), "traceback": traceback.format_exc()},
+            )
+            return bad_request(500, str(e))
+
+        return make_response(docs, 200)

application/api/internal/routes.py

@@ -1,20 +1,25 @@
 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"]
-vectors_collection = db["vectors"]
+sources_collection = db["sources"]
 
-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__)))
+)
+
+
+internal = Blueprint("internal", __name__)
 
 
-internal = Blueprint('internal', __name__)
 @internal.route("/api/download", methods=["get"])
 def download_file():
     user = secure_filename(request.args.get("user"))
@@ -24,7 +29,6 @@ def download_file():
     return send_from_directory(save_dir, filename, as_attachment=True)
 
 
-
 @internal.route("/api/upload_index", methods=["POST"])
 def upload_index_files():
     """Upload two files(index.faiss, index.pkl) to the user's folder."""
@@ -34,7 +38,14 @@ def upload_index_files():
     if "name" not in request.form:
         return {"status": "no name"}
     job_name = secure_filename(request.form["name"])
-    save_dir = os.path.join(current_dir, "indexes", user, job_name)
+    tokens = secure_filename(request.form["tokens"])
+    retriever = secure_filename(request.form["retriever"])
+    id = secure_filename(request.form["id"])
+    type = secure_filename(request.form["type"])
+    remote_data = request.form["remote_data"] if "remote_data" in request.form else None
+    sync_frequency = secure_filename(request.form["sync_frequency"]) if "sync_frequency" in request.form else None
+
+    save_dir = os.path.join(current_dir, "indexes", str(id))
     if settings.VECTOR_STORE == "faiss":
         if "file_faiss" not in request.files:
             print("No file part")
@@ -49,21 +60,45 @@ def upload_index_files():
         if file_pkl.filename == "":
             return {"status": "no file name"}
         # saves index files
-        
+
         if not os.path.exists(save_dir):
             os.makedirs(save_dir)
         file_faiss.save(os.path.join(save_dir, "index.faiss"))
         file_pkl.save(os.path.join(save_dir, "index.pkl"))
-    # create entry in vectors_collection
-    vectors_collection.insert_one(
-        {
-            "user": user,
-            "name": job_name,
-            "language": job_name,
-            "location": save_dir,
-            "date": datetime.datetime.now().strftime("%d/%m/%Y %H:%M:%S"),
-            "model": settings.EMBEDDINGS_NAME,
-            "type": "local",
-        }
-    )
-    return {"status": "ok"}
+
+    existing_entry = sources_collection.find_one({"_id": ObjectId(id)})
+    if existing_entry:
+        sources_collection.update_one(
+            {"_id": ObjectId(id)},
+            {
+                "$set": {
+                    "user": user,
+                    "name": job_name,
+                    "language": job_name,
+                    "date": datetime.datetime.now(),
+                    "model": settings.EMBEDDINGS_NAME,
+                    "type": type,
+                    "tokens": tokens,
+                    "retriever": retriever,
+                    "remote_data": remote_data,
+                    "sync_frequency": sync_frequency,
+                }
+            },
+        )
+    else:
+        sources_collection.insert_one(
+            {
+                "_id": ObjectId(id),
+                "user": user,
+                "name": job_name,
+                "language": job_name,
+                "date": datetime.datetime.now(),
+                "model": settings.EMBEDDINGS_NAME,
+                "type": type,
+                "tokens": tokens,
+                "retriever": retriever,
+                "remote_data": remote_data,
+                "sync_frequency": sync_frequency,
+            }
+        )
+    return {"status": "ok"}

application/api/user/tasks.py

@@ -1,12 +1,38 @@
-from application.worker import ingest_worker, remote_worker
-from application.celery import celery
+from datetime import timedelta
+
+from application.celery_init import celery
+from application.worker import ingest_worker, remote_worker, sync_worker
+
 
 @celery.task(bind=True)
 def ingest(self, directory, formats, name_job, filename, user):
     resp = ingest_worker(self, directory, formats, name_job, filename, user)
     return resp
 
+
 @celery.task(bind=True)
 def ingest_remote(self, source_data, job_name, user, loader):
     resp = remote_worker(self, source_data, job_name, user, loader)
     return resp
+
+
+@celery.task(bind=True)
+def schedule_syncs(self, frequency):
+    resp = sync_worker(self, frequency)
+    return resp
+
+
+@celery.on_after_configure.connect
+def setup_periodic_tasks(sender, **kwargs):
+    sender.add_periodic_task(
+        timedelta(days=1),
+        schedule_syncs.s("daily"),
+    )
+    sender.add_periodic_task(
+        timedelta(weeks=1),
+        schedule_syncs.s("weekly"),
+    )
+    sender.add_periodic_task(
+        timedelta(days=30),
+        schedule_syncs.s("monthly"),
+    )

application/app.py

@@ -1,17 +1,23 @@
 import platform
+
 import dotenv
-from application.celery import celery
-from flask import Flask, request, redirect
-from application.core.settings import settings
-from application.api.user.routes import user
+from flask import Flask, redirect, request
+
 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
 
 if platform.system() == "Windows":
     import pathlib
+
     pathlib.PosixPath = pathlib.WindowsPath
 
 dotenv.load_dotenv()
+setup_logging()
 
 app = Flask(__name__)
 app.register_blueprint(user)
@@ -21,16 +27,19 @@ app.config.update(
     UPLOAD_FOLDER="inputs",
     CELERY_BROKER_URL=settings.CELERY_BROKER_URL,
     CELERY_RESULT_BACKEND=settings.CELERY_RESULT_BACKEND,
-    MONGO_URI=settings.MONGO_URI
+    MONGO_URI=settings.MONGO_URI,
 )
 celery.config_from_object("application.celeryconfig")
+api.init_app(app)
+
 
 @app.route("/")
 def home():
-    if request.remote_addr in ('0.0.0.0', '127.0.0.1', 'localhost', '172.18.0.1'):
-        return redirect('http://localhost:5173')
+    if request.remote_addr in ("0.0.0.0", "127.0.0.1", "localhost", "172.18.0.1"):
+        return redirect("http://localhost:5173")
     else:
-        return 'Welcome to DocsGPT Backend!'
+        return "Welcome to DocsGPT Backend!"
+
 
 @app.after_request
 def after_request(response):
@@ -39,6 +48,6 @@ def after_request(response):
     response.headers.add("Access-Control-Allow-Methods", "GET,PUT,POST,DELETE,OPTIONS")
     return response
 
+
 if __name__ == "__main__":
     app.run(debug=settings.FLASK_DEBUG_MODE, port=7091)
-

application/cache.py

@@ -0,0 +1,93 @@
+import redis
+import time
+import json
+import logging
+from threading import Lock
+from application.core.settings import settings
+from application.utils import get_hash
+
+logger = logging.getLogger(__name__)
+
+_redis_instance = None
+_instance_lock = Lock()
+
+def get_redis_instance():
+    global _redis_instance
+    if _redis_instance is None:
+        with _instance_lock:
+            if _redis_instance is None:
+                try:
+                    _redis_instance = redis.Redis.from_url(settings.CACHE_REDIS_URL, socket_connect_timeout=2)
+                except redis.ConnectionError as e:
+                    logger.error(f"Redis connection error: {e}")
+                    _redis_instance = None
+    return _redis_instance
+
+def gen_cache_key(*messages, model="docgpt"):
+    if not all(isinstance(msg, dict) for msg in messages):
+        raise ValueError("All messages must be dictionaries.")
+    messages_str = json.dumps(list(messages), sort_keys=True)
+    combined = f"{model}_{messages_str}"
+    cache_key = get_hash(combined)
+    return cache_key
+
+def gen_cache(func):
+    def wrapper(self, model, messages, *args, **kwargs):
+        try:
+            cache_key = gen_cache_key(*messages)
+            redis_client = get_redis_instance()
+            if redis_client:
+                try:
+                    cached_response = redis_client.get(cache_key)
+                    if cached_response:
+                        return cached_response.decode('utf-8')
+                except redis.ConnectionError as e:
+                    logger.error(f"Redis connection error: {e}")
+
+            result = func(self, model, messages, *args, **kwargs)
+            if redis_client:
+                try:
+                    redis_client.set(cache_key, result, ex=1800)
+                except redis.ConnectionError as e:
+                    logger.error(f"Redis connection error: {e}")
+
+            return result
+        except ValueError as e:
+            logger.error(e)
+            return "Error: No user message found in the conversation to generate a cache key."
+    return wrapper
+
+def stream_cache(func):
+    def wrapper(self, model, messages, stream, *args, **kwargs):
+        cache_key = gen_cache_key(*messages)
+        logger.info(f"Stream cache key: {cache_key}")
+        
+        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)
+                    return
+            except redis.ConnectionError as e:
+                logger.error(f"Redis connection error: {e}")
+
+        result = func(self, model, messages, stream, *args, **kwargs)
+        stream_cache_data = []
+        
+        for chunk in result:
+            stream_cache_data.append(chunk)
+            yield chunk
+        
+        if redis_client:
+            try:
+                redis_client.set(cache_key, json.dumps(stream_cache_data), ex=1800)
+                logger.info(f"Stream cache saved for key: {cache_key}")
+            except redis.ConnectionError as e:
+                logger.error(f"Redis connection error: {e}")
+        
+    return wrapper

application/celery_init.py

@@ -1,9 +1,15 @@
 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.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/logging_config.py

@@ -0,0 +1,22 @@
+from logging.config import dictConfig
+
+def setup_logging():
+    dictConfig({
+        'version': 1,
+        'formatters': {
+            'default': {
+                'format': '[%(asctime)s] %(levelname)s in %(module)s: %(message)s',
+            }
+        },
+        "handlers": {
+            "console": {
+                "class": "logging.StreamHandler",
+                "stream": "ext://sys.stdout",
+                "formatter": "default",
+            }
+        },
+        'root': {
+            'level': 'INFO',
+            'handlers': ['console'],
+        },
+    })

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

@@ -15,11 +15,15 @@ class Settings(BaseSettings):
     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")
-    TOKENS_MAX_HISTORY: int = 150
+    DEFAULT_MAX_HISTORY: int = 150
+    MODEL_TOKEN_LIMITS: dict = {"gpt-3.5-turbo": 4096, "claude-2": 1e5}
     UPLOAD_FOLDER: str = "inputs"
-    VECTOR_STORE: str = "faiss"  # "faiss" or "elasticsearch" or "qdrant"
+    VECTOR_STORE: str = "faiss" #  "faiss" or "elasticsearch" or "qdrant" or "milvus" or "lancedb"
     RETRIEVERS_ENABLED: list = ["classic_rag", "duckduck_search"] # also brave_search
 
+    # 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
@@ -28,6 +32,7 @@ class Settings(BaseSettings):
     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
 
     # elasticsearch
     ELASTIC_CLOUD_ID: Optional[str] = None  # cloud id for elasticsearch
@@ -60,6 +65,14 @@ class Settings(BaseSettings):
     QDRANT_PATH: Optional[str] = None
     QDRANT_DISTANCE_FUNC: str = "Cosine"
 
+    # Milvus vectorstore config
+    MILVUS_COLLECTION_NAME: Optional[str] = "docsgpt"
+    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

application/extensions.py

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

application/llm/base.py

@@ -1,28 +1,29 @@
 from abc import ABC, abstractmethod
 from application.usage import gen_token_usage, stream_token_usage
+from application.cache import stream_cache, gen_cache
 
 
 class BaseLLM(ABC):
     def __init__(self):
         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):
         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
-        )
+        decorators = [gen_token_usage, gen_cache]
+        return self._apply_decorator(self._raw_gen, decorators=decorators, model=model, messages=messages, stream=stream, *args, **kwargs)
 
     @abstractmethod
     def _raw_gen_stream(self, model, messages, stream, *args, **kwargs):
         pass
 
     def gen_stream(self, model, messages, stream=True, *args, **kwargs):
-        return self._apply_decorator(self._raw_gen_stream, stream_token_usage)(
-            self, model=model, messages=messages, stream=stream, *args, **kwargs
-        )
+        decorators = [stream_cache, stream_token_usage]
+        return self._apply_decorator(self._raw_gen_stream, decorators=decorators, model=model, messages=messages, stream=stream, *args, **kwargs)

application/llm/docsgpt_provider.py

@@ -9,35 +9,25 @@ class DocsGPTAPILLM(BaseLLM):
         super().__init__(*args, **kwargs)
         self.api_key = api_key
         self.user_api_key = user_api_key
-        self.endpoint = "https://llm.docsgpt.co.uk"
+        self.endpoint = "https://llm.arc53.com"
 
     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"
-
         response = requests.post(
-            f"{self.endpoint}/answer", json={"prompt": prompt, "max_new_tokens": 30}
+            f"{self.endpoint}/answer", json={"messages": messages, "max_new_tokens": 30}
         )
         response_clean = response.json()["a"].replace("###", "")
 
         return response_clean
 
     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"
-
-        # send prompt to endpoint /stream
         response = requests.post(
             f"{self.endpoint}/stream",
-            json={"prompt": prompt, "max_new_tokens": 256},
+            json={"messages": messages, "max_new_tokens": 256},
             stream=True,
         )
 
         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:])

application/llm/google_ai.py

@@ -0,0 +1,48 @@
+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):
+        return [
+            {
+                "role": "model" if message["role"] == "system" else message["role"],
+                "parts": [message["content"]],
+            }
+            for message in messages[1:]
+        ]
+
+    def _raw_gen(
+        self,
+        baseself,
+        model,
+        messages,
+        stream=False,
+        **kwargs
+    ):  
+        import google.generativeai as genai
+        genai.configure(api_key=self.api_key)
+        model = genai.GenerativeModel(model, system_instruction=messages[0]["content"])
+        response = model.generate_content(self._clean_messages_google(messages))
+        return response.text
+
+    def _raw_gen_stream(
+        self,
+        baseself,
+        model,
+        messages,
+        stream=True,
+        **kwargs
+    ):  
+        import google.generativeai as genai
+        genai.configure(api_key=self.api_key)
+        model = genai.GenerativeModel(model, system_instruction=messages[0]["content"])
+        response = model.generate_content(self._clean_messages_google(messages), stream=True)
+        for line in response:
+            if line.text is not None:
+                yield line.text

application/llm/groq.py

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

application/llm/llama_cpp.py

@@ -1,9 +1,30 @@
 from application.llm.base import BaseLLM
 from application.core.settings import settings
+import threading
+
+class LlamaSingleton:
+    _instances = {}
+    _lock = threading.Lock()  # Add a lock for thread synchronization
+
+    @classmethod
+    def get_instance(cls, llm_name):
+        if llm_name not in cls._instances:
+            try:
+                from llama_cpp import Llama
+            except ImportError:
+                raise ImportError(
+                    "Please install llama_cpp using pip install llama-cpp-python"
+                )
+            cls._instances[llm_name] = Llama(model_path=llm_name, n_ctx=2048)
+        return cls._instances[llm_name]
+
+    @classmethod
+    def query_model(cls, llm, prompt, **kwargs):
+        with cls._lock:
+            return llm(prompt, **kwargs)
 
 
 class LlamaCpp(BaseLLM):
-
     def __init__(
         self,
         api_key=None,
@@ -12,41 +33,23 @@ class LlamaCpp(BaseLLM):
         *args,
         **kwargs,
     ):
-        global llama
-        try:
-            from llama_cpp import Llama
-        except ImportError:
-            raise ImportError(
-                "Please install llama_cpp using pip install llama-cpp-python"
-            )
-
         super().__init__(*args, **kwargs)
         self.api_key = api_key
         self.user_api_key = user_api_key
-        llama = Llama(model_path=llm_name, n_ctx=2048)
+        self.llama = LlamaSingleton.get_instance(llm_name)
 
     def _raw_gen(self, baseself, model, messages, stream=False, **kwargs):
         context = messages[0]["content"]
         user_question = messages[-1]["content"]
         prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
-
-        result = llama(prompt, max_tokens=150, echo=False)
-
-        # import sys
-        # print(result['choices'][0]['text'].split('### Answer \n')[-1], file=sys.stderr)
-
+        result = LlamaSingleton.query_model(self.llama, prompt, max_tokens=150, echo=False)
         return result["choices"][0]["text"].split("### Answer \n")[-1]
 
     def _raw_gen_stream(self, baseself, model, messages, stream=True, **kwargs):
         context = messages[0]["content"]
         user_question = messages[-1]["content"]
         prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
-
-        result = llama(prompt, max_tokens=150, echo=False, stream=stream)
-
-        # import sys
-        # print(list(result), file=sys.stderr)
-
+        result = LlamaSingleton.query_model(self.llama, prompt, max_tokens=150, echo=False, stream=stream)
         for item in result:
             for choice in item["choices"]:
-                yield choice["text"]
+                yield choice["text"]

application/llm/llm_creator.py

@@ -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 +6,7 @@ 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
 
 
 class LLMCreator:
@@ -17,6 +19,8 @@ class LLMCreator:
         "anthropic": AnthropicLLM,
         "docsgpt": DocsGPTAPILLM,
         "premai": PremAILLM,
+        "groq": GroqLLM,
+        "google": GoogleLLM
     }
 
     @classmethod

application/llm/openai.py

@@ -2,25 +2,23 @@ from application.llm.base import BaseLLM
 from application.core.settings import settings
 
 
+
 class OpenAILLM(BaseLLM):
 
     def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
-        global openai
         from openai import OpenAI
 
         super().__init__(*args, **kwargs)
-        self.client = OpenAI(
-            api_key=api_key,
-        )
+        if 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 _get_openai(self):
-        # Import openai when needed
-        import openai
-
-        return openai
-
     def _raw_gen(
         self,
         baseself,
@@ -29,7 +27,7 @@ class OpenAILLM(BaseLLM):
         stream=False,
         engine=settings.AZURE_DEPLOYMENT_NAME,
         **kwargs
-    ):
+    ):  
         response = self.client.chat.completions.create(
             model=model, messages=messages, stream=stream, **kwargs
         )
@@ -44,7 +42,7 @@ class OpenAILLM(BaseLLM):
         stream=True,
         engine=settings.AZURE_DEPLOYMENT_NAME,
         **kwargs
-    ):
+    ):  
         response = self.client.chat.completions.create(
             model=model, messages=messages, stream=stream, **kwargs
         )
@@ -73,8 +71,3 @@ class AzureOpenAILLM(OpenAILLM):
             api_base=settings.OPENAI_API_BASE,
             deployment_name=settings.AZURE_DEPLOYMENT_NAME,
         )
-
-    def _get_openai(self):
-        openai = super()._get_openai()
-
-        return openai

application/parser/chunking.py

@@ -0,0 +1,118 @@
+import re
+from typing import List, Tuple, Union
+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(
+        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 / 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 +10,23 @@ 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.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(),
 }
 
 

application/parser/file/html_parser.py

@@ -3,7 +3,6 @@
 Contains parser for html files.
 
 """
-import re
 from pathlib import Path
 from typing import Dict, Union
 
@@ -18,66 +17,8 @@ class HTMLParser(BaseParser):
         return {}
 
     def parse_file(self, file: Path, errors: str = "ignore") -> Union[str, list[str]]:
-        """Parse file.
+        from langchain_community.document_loaders import BSHTMLLoader
 
-            Returns:
-            Union[str, List[str]]: a string or a List of strings.
-        """
-        try:
-            from unstructured.partition.html import partition_html
-            from unstructured.staging.base import convert_to_isd
-            from unstructured.cleaners.core import clean
-        except ImportError:
-            raise ValueError("unstructured package is required to parse HTML files.")
-
-        # Using the unstructured library to convert the html to isd format
-        # isd sample : isd = [
-        #   {"text": "My Title", "type": "Title"},
-        #   {"text": "My Narrative", "type": "NarrativeText"}
-        # ]
-        with open(file, "r", encoding="utf-8") as fp:
-            elements = partition_html(file=fp)
-            isd = convert_to_isd(elements)
-
-            # Removing non ascii charactwers from isd_el['text']
-        for isd_el in isd:
-            isd_el['text'] = isd_el['text'].encode("ascii", "ignore").decode()
-
-        # Removing all the \n characters from isd_el['text'] using regex and replace with single space
-        # Removing all the extra spaces  from isd_el['text'] using regex and replace with single space
-        for isd_el in isd:
-            isd_el['text'] = re.sub(r'\n', ' ', isd_el['text'], flags=re.MULTILINE | re.DOTALL)
-            isd_el['text'] = re.sub(r"\s{2,}", " ", isd_el['text'], flags=re.MULTILINE | re.DOTALL)
-
-        # more cleaning: extra_whitespaces, dashes, bullets, trailing_punctuation
-        for isd_el in isd:
-            clean(isd_el['text'], extra_whitespace=True, dashes=True, bullets=True, trailing_punctuation=True)
-
-        # Creating a list of all the indexes of isd_el['type'] = 'Title'
-        title_indexes = [i for i, isd_el in enumerate(isd) if isd_el['type'] == 'Title']
-
-        # Creating 'Chunks' - List of lists of strings 
-        # each list starting with isd_el['type'] = 'Title' and all the data till the next 'Title'
-        # Each Chunk can be thought of as an individual set of data, which can be sent to the model
-        # Where Each Title is grouped together with the data under it
-
-        Chunks = [[]]
-        final_chunks = list(list())
-
-        for i, isd_el in enumerate(isd):
-            if i in title_indexes:
-                Chunks.append([])
-            Chunks[-1].append(isd_el['text'])
-
-        # Removing all the chunks with sum of length of all the strings in the chunk < 25
-        # TODO: This value can be an user defined variable
-        for chunk in Chunks:
-            # sum of length of all the strings in the chunk
-            sum = 0
-            sum += len(str(chunk))
-            if sum < 25:
-                Chunks.remove(chunk)
-            else:
-                # appending all the approved chunks to final_chunks as a single string       
-                final_chunks.append(" ".join([str(item) for item in chunk]))
-        return final_chunks
+        loader = BSHTMLLoader(file)
+        data = loader.load()        
+        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/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/late_chunking.py

@@ -0,0 +1,94 @@
+from typing import List, Tuple, Union, Optional
+from transformers import AutoTokenizer, AutoModel
+from sentence_transformers import SentenceTransformer
+import torch
+import torch.nn as nn
+from application.parser.schema.base import Document
+
+
+class LateChunker:
+    def __init__(self, model_name: str, late_tokens: int = 1000, **model_kwargs):
+        """
+        Initialize the LateChunker with a model, tokenizer, and late_tokens limit.
+        Supports both transformers and sentence-transformers models.
+        """
+        self.late_tokens = late_tokens
+        self.model_name = model_name
+
+        # Load model based on type
+        if "sentence-transformers" in model_name:
+            self.model = SentenceTransformer(model_name, **model_kwargs)
+            self.tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+            self.wrapper_type = "sentence_transformers"
+        else:
+            self.model = AutoModel.from_pretrained(model_name, trust_remote_code=True, **model_kwargs)
+            self.tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+            self.wrapper_type = "transformers"
+
+    def tokenize_with_offsets(self, text: str):
+        """Tokenize text and return tokens with character offsets."""
+        tokens = self.tokenizer.encode_plus(
+            text, return_offsets_mapping=True, add_special_tokens=False
+        )
+        return tokens["input_ids"], tokens["offset_mapping"]
+
+    def late_chunk_with_embeddings(
+        self, documents: List[Document]
+    ) -> List[Tuple[str, List[Tuple[int, int]], List[float]]]:
+        """
+        Combines documents into 'super chunks' that fit within `late_tokens` limit.
+        Outputs each super chunk with span annotations and embeddings.
+        """
+        super_chunks = []
+        current_super_chunk_text = []
+        current_token_count = 0
+        span_annotations = []
+
+        for doc in documents:
+            doc_text = doc.text
+            input_ids, offsets = self.tokenize_with_offsets(doc_text)
+            doc_token_count = len(input_ids)
+
+            # Check if adding this document exceeds the late_tokens limit
+            if current_token_count + doc_token_count > self.late_tokens:
+                # Finalize the current super chunk
+                combined_text = " ".join(current_super_chunk_text)
+                embeddings = self.generate_embeddings(combined_text)
+
+                super_chunks.append((combined_text, span_annotations, embeddings))
+
+                # Reset for a new super chunk
+                current_super_chunk_text = []
+                span_annotations = []
+                current_token_count = 0
+
+            # Add document to the current super chunk
+            start_token = current_token_count
+            end_token = current_token_count + doc_token_count
+            span_annotations.append((start_token, end_token))
+            current_super_chunk_text.append(doc_text)
+            current_token_count = end_token
+
+        # Add the final super chunk if there are remaining documents
+        if current_super_chunk_text:
+            combined_text = " ".join(current_super_chunk_text)
+            embeddings = self.generate_embeddings(combined_text)
+            super_chunks.append((combined_text, span_annotations, embeddings))
+
+        return super_chunks
+
+    def generate_embeddings(self, text: str) -> List[float]:
+        """Generate embeddings for a given text using the loaded model."""
+        if self.wrapper_type == "sentence_transformers":
+            # Sentence-Transformers
+            embeddings = self.model.encode([text])
+            return embeddings[0].tolist()
+
+        elif self.wrapper_type == "transformers":
+            # Transformers models
+            inputs = self.tokenizer(text, return_tensors="pt")
+            model_output = self.model(**inputs)
+            return model_output.last_hidden_state.mean(dim=1).squeeze().tolist()
+
+        else:
+            raise ValueError("Unsupported model type for embedding generation.")

application/parser/open_ai_func.py

@@ -1,38 +1,33 @@
 import os
 
-import tiktoken
-from application.vectorstore.vector_creator import VectorCreator
-from application.core.settings import settings
 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
 
 
-def num_tokens_from_string(string: str, encoding_name: str) -> int:
-    # Function to convert string to tokens and estimate user cost.
-    encoding = tiktoken.get_encoding(encoding_name)
-    num_tokens = len(encoding.encode(string))
-    total_price = ((num_tokens / 1000) * 0.0004)
-    return num_tokens, total_price
-
-
 @retry(tries=10, delay=60)
-def store_add_texts_with_retry(store, i):
+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, task_status):
-    # Function to create a vector store from the documents and save it to disk.
+def call_openai_api(docs, folder_name, id, task_status):
+    # Function to create a vector store from the documents and save it to disk
 
-    # create output folder if it doesn't exist
     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]]
@@ -40,26 +35,34 @@ def call_openai_api(docs, folder_name, task_status):
 
         store = VectorCreator.create_vectorstore(
             settings.VECTOR_STORE,
-            docs_init = docs_init,
-            path=f"{folder_name}",
-            embeddings_key=os.getenv("EMBEDDINGS_KEY")
+            docs_init=docs_init,
+            source_id=f"{folder_name}",
+            embeddings_key=os.getenv("EMBEDDINGS_KEY"),
         )
     else:
         store = VectorCreator.create_vectorstore(
             settings.VECTOR_STORE,
-            path=f"{folder_name}",
-            embeddings_key=os.getenv("EMBEDDINGS_KEY")
+            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}'):
+    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)
+            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)
@@ -70,25 +73,3 @@ def call_openai_api(docs, folder_name, task_status):
         c1 += 1
     if settings.VECTOR_STORE == "faiss":
         store.save_local(f"{folder_name}")
-
-
-def get_user_permission(docs, folder_name):
-    # Function to ask user permission to call the OpenAI api and spend their OpenAI funds.
-    # Here we convert the docs list to a string and calculate the number of OpenAI tokens the string represents.
-    # docs_content = (" ".join(docs))
-    docs_content = ""
-    for doc in docs:
-        docs_content += doc.page_content
-
-    tokens, total_price = num_tokens_from_string(string=docs_content, encoding_name="cl100k_base")
-    # Here we print the number of tokens and the approx user cost with some visually appealing formatting.
-    print(f"Number of Tokens = {format(tokens, ',d')}")
-    print(f"Approx Cost = ${format(total_price, ',.2f')}")
-    # Here we check for user permission before calling the API.
-    user_input = input("Price Okay? (Y/N) \n").lower()
-    if user_input == "y":
-        call_openai_api(docs, folder_name)
-    elif user_input == "":
-        call_openai_api(docs, folder_name)
-    else:
-        print("The API was not called. No money was spent.")

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

@@ -5,7 +5,7 @@ from application.parser.remote.base import BaseRemote
 
 class CrawlerLoader(BaseRemote):
     def __init__(self, limit=10):
-        from langchain.document_loaders import WebBaseLoader
+        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
 

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/sitemap_loader.py

@@ -5,7 +5,7 @@ from application.parser.remote.base import BaseRemote
 
 class SitemapLoader(BaseRemote):
     def __init__(self, limit=20):
-        from langchain.document_loaders import WebBaseLoader
+        from langchain_community.document_loaders import WebBaseLoader
         self.loader = WebBaseLoader
         self.limit = limit  # Adding limit to control the number of URLs to process
 

application/parser/remote/web_loader.py

@@ -1,22 +1,32 @@
 from application.parser.remote.base import BaseRemote
+from langchain_community.document_loaders import WebBaseLoader
+
+headers = {
+    "User-Agent": "Mozilla/5.0",
+    "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*"
+    ";q=0.8",
+    "Accept-Language": "en-US,en;q=0.5",
+    "Referer": "https://www.google.com/",
+    "DNT": "1",
+    "Connection": "keep-alive",
+    "Upgrade-Insecure-Requests": "1",
+}
+
 
 class WebLoader(BaseRemote):
     def __init__(self):
-        from langchain.document_loaders import WebBaseLoader
         self.loader = WebBaseLoader
 
     def load_data(self, inputs):
         urls = inputs
-
         if isinstance(urls, str):
-            urls = [urls] # Convert string to list if a single URL is passed
-
+            urls = [urls]
         documents = []
         for url in urls:
             try:
-                loader = self.loader([url])  # Process URLs one by one
+                loader = self.loader([url], header_template=headers)
                 documents.extend(loader.load())
             except Exception as e:
                 print(f"Error processing URL {url}: {e}")
-                continue  # Continue with the next URL if an error occurs
-        return documents
+                continue
+        return documents

application/requirements.txt

@@ -1,35 +1,89 @@
-anthropic==0.12.0
-boto3==1.34.6
+anthropic==0.34.2
+boto3==1.34.153
+beautifulsoup4==4.12.3
 celery==5.3.6
-dataclasses_json==0.6.3
+dataclasses-json==0.6.7
 docx2txt==0.8
-duckduckgo-search==5.3.0
-EbookLib==0.18
-elasticsearch==8.12.0
+duckduckgo-search==6.3.0
+ebooklib==0.18
+elastic-transport==8.15.0
+elasticsearch==8.15.1
 escodegen==1.0.11
 esprima==4.0.1
-faiss-cpu==1.7.4
-Flask==3.0.1
-gunicorn==21.2.0
-html2text==2020.1.16
+esutils==1.0.1
+Flask==3.0.3
+faiss-cpu==1.8.0.post1
+flask-restx==1.3.0
+gTTS==2.3.2
+gunicorn==23.0.0
+html2text==2024.2.26
 javalang==0.13.0
-langchain==0.1.4
-langchain-openai==0.0.5
-nltk==3.8.1
-openapi3_parser==1.1.16
-pandas==2.2.0
-pydantic_settings==2.1.0
-pymongo==4.6.3
-PyPDF2==3.0.1
+jinja2==3.1.4
+jiter==0.5.0
+jmespath==1.0.1
+joblib==1.4.2
+jsonpatch==1.33
+jsonpointer==3.0.0
+jsonschema==4.23.0
+jsonschema-spec==0.2.4
+jsonschema-specifications==2023.7.1
+kombu==5.4.2
+langchain==0.3.0
+langchain-community==0.3.0
+langchain-core==0.3.2
+langchain-openai==0.2.0
+langchain-text-splitters==0.3.0
+langsmith==0.1.125
+lazy-object-proxy==1.10.0
+lxml==5.3.0
+markupsafe==2.1.5
+marshmallow==3.22.0
+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
+openapi-spec-validator==0.6.0
+openapi3-parser==1.1.18
+orjson==3.10.7
+packaging==24.1
+pandas==2.2.3
+openpyxl==3.1.5
+pathable==0.4.3
+pillow==10.4.0
+portalocker==2.10.1
+prance==23.6.21.0
+primp==0.6.3
+prompt-toolkit==3.0.47
+protobuf==5.28.2
+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
+python-dateutil==2.9.0.post0
 python-dotenv==1.0.1
-qdrant-client==1.8.2
+python-pptx==1.0.2
+qdrant-client==1.11.0
 redis==5.0.1
-Requests==2.31.0
+referencing==0.30.2
+regex==2024.9.11
+requests==2.32.3
 retry==0.9.2
-sentence-transformers
-tiktoken==0.5.2
-torch==2.1.2
-tqdm==4.66.1
-transformers==4.36.2
-unstructured==0.12.2
-Werkzeug==3.0.1
+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
+typing-extensions==4.12.2
+typing-inspect==0.9.0
+tzdata==2024.2
+urllib3==2.2.3
+vine==5.1.0
+wcwidth==0.2.13
+werkzeug==3.0.4
+yarl==1.11.1

application/retriever/base.py

@@ -12,3 +12,7 @@ class BaseRetriever(ABC):
     @abstractmethod
     def search(self, *args, **kwargs):
         pass
+
+    @abstractmethod
+    def get_params(self):
+        pass

application/retriever/brave_search.py

@@ -2,7 +2,7 @@ import json
 from application.retriever.base import BaseRetriever
 from application.core.settings import settings
 from application.llm.llm_creator import LLMCreator
-from application.utils import count_tokens
+from application.utils import num_tokens_from_string
 from langchain_community.tools import BraveSearch
 
 
@@ -15,6 +15,7 @@ class BraveRetSearch(BaseRetriever):
         chat_history,
         prompt,
         chunks=2,
+        token_limit=150,
         gpt_model="docsgpt",
         user_api_key=None,
     ):
@@ -24,6 +25,16 @@ class BraveRetSearch(BaseRetriever):
         self.prompt = prompt
         self.chunks = chunks
         self.gpt_model = gpt_model
+        self.token_limit = (
+            token_limit
+            if token_limit
+            < settings.MODEL_TOKEN_LIMITS.get(
+                self.gpt_model, settings.DEFAULT_MAX_HISTORY
+            )
+            else settings.MODEL_TOKEN_LIMITS.get(
+                self.gpt_model, settings.DEFAULT_MAX_HISTORY
+            )
+        )
         self.user_api_key = user_api_key
 
     def _get_data(self):
@@ -64,16 +75,12 @@ class BraveRetSearch(BaseRetriever):
         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 = count_tokens(i["prompt"]) + count_tokens(
+                    tokens_batch = num_tokens_from_string(i["prompt"]) + num_tokens_from_string(
                         i["response"]
                     )
-                    if (
-                        tokens_current_history + tokens_batch
-                        < settings.TOKENS_MAX_HISTORY
-                    ):
+                    if tokens_current_history + tokens_batch < self.token_limit:
                         tokens_current_history += tokens_batch
                         messages_combine.append(
                             {"role": "user", "content": i["prompt"]}
@@ -93,3 +100,15 @@ class BraveRetSearch(BaseRetriever):
 
     def search(self):
         return self._get_data()
+
+    def get_params(self):
+        return {
+            "question": self.question,
+            "source": self.source,
+            "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
+        }

application/retriever/classic_rag.py

@@ -1,10 +1,9 @@
-import os
 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.utils import count_tokens
+from application.utils import num_tokens_from_string
 
 
 class ClassicRAG(BaseRetriever):
@@ -16,32 +15,28 @@ class ClassicRAG(BaseRetriever):
         chat_history,
         prompt,
         chunks=2,
+        token_limit=150,
         gpt_model="docsgpt",
         user_api_key=None,
     ):
         self.question = question
-        self.vectorstore = self._get_vectorstore(source=source)
+        self.vectorstore = source['active_docs'] if 'active_docs' in source else None
         self.chat_history = chat_history
         self.prompt = prompt
         self.chunks = chunks
         self.gpt_model = gpt_model
+        self.token_limit = (
+            token_limit
+            if token_limit
+            < settings.MODEL_TOKEN_LIMITS.get(
+                self.gpt_model, settings.DEFAULT_MAX_HISTORY
+            )
+            else settings.MODEL_TOKEN_LIMITS.get(
+                self.gpt_model, settings.DEFAULT_MAX_HISTORY
+            )
+        )
         self.user_api_key = user_api_key
 
-    def _get_vectorstore(self, source):
-        if "active_docs" in source:
-            if source["active_docs"].split("/")[0] == "default":
-                vectorstore = ""
-            elif source["active_docs"].split("/")[0] == "local":
-                vectorstore = "indexes/" + source["active_docs"]
-            else:
-                vectorstore = "vectors/" + source["active_docs"]
-            if source["active_docs"] == "default":
-                vectorstore = ""
-        else:
-            vectorstore = ""
-        vectorstore = os.path.join("application", vectorstore)
-        return vectorstore
-
     def _get_data(self):
         if self.chunks == 0:
             docs = []
@@ -52,17 +47,18 @@ class ClassicRAG(BaseRetriever):
             docs_temp = docsearch.search(self.question, k=self.chunks)
             docs = [
                 {
-                    "title": (
-                        i.metadata["title"].split("/")[-1]
-                        if i.metadata
-                        else i.page_content
-                    ),
+                    "title": i.metadata.get(
+                        "title", i.metadata.get("post_title", i.page_content)
+                    ).split("/")[-1],
                     "text": i.page_content,
+                    "source": (
+                        i.metadata.get("source")
+                        if i.metadata.get("source")
+                        else "local"
+                    ),
                 }
                 for i in docs_temp
             ]
-        if settings.LLM_NAME == "llama.cpp":
-            docs = [docs[0]]
 
         return docs
 
@@ -79,16 +75,12 @@ class ClassicRAG(BaseRetriever):
         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 = count_tokens(i["prompt"]) + count_tokens(
+                    tokens_batch = num_tokens_from_string(i["prompt"]) + num_tokens_from_string(
                         i["response"]
                     )
-                    if (
-                        tokens_current_history + tokens_batch
-                        < settings.TOKENS_MAX_HISTORY
-                    ):
+                    if tokens_current_history + tokens_batch < self.token_limit:
                         tokens_current_history += tokens_batch
                         messages_combine.append(
                             {"role": "user", "content": i["prompt"]}
@@ -101,10 +93,21 @@ class ClassicRAG(BaseRetriever):
         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):
         return self._get_data()
+    
+    def get_params(self):
+        return {
+            "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
+        }

application/retriever/duckduck_search.py

@@ -1,7 +1,7 @@
 from application.retriever.base import BaseRetriever
 from application.core.settings import settings
 from application.llm.llm_creator import LLMCreator
-from application.utils import count_tokens
+from application.utils import num_tokens_from_string
 from langchain_community.tools import DuckDuckGoSearchResults
 from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
 
@@ -15,6 +15,7 @@ class DuckDuckSearch(BaseRetriever):
         chat_history,
         prompt,
         chunks=2,
+        token_limit=150,
         gpt_model="docsgpt",
         user_api_key=None,
     ):
@@ -24,6 +25,16 @@ class DuckDuckSearch(BaseRetriever):
         self.prompt = prompt
         self.chunks = chunks
         self.gpt_model = gpt_model
+        self.token_limit = (
+            token_limit
+            if token_limit
+            < settings.MODEL_TOKEN_LIMITS.get(
+                self.gpt_model, settings.DEFAULT_MAX_HISTORY
+            )
+            else settings.MODEL_TOKEN_LIMITS.get(
+                self.gpt_model, settings.DEFAULT_MAX_HISTORY
+            )
+        )
         self.user_api_key = user_api_key
 
     def _parse_lang_string(self, input_string):
@@ -81,16 +92,12 @@ class DuckDuckSearch(BaseRetriever):
         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 = count_tokens(i["prompt"]) + count_tokens(
+                    tokens_batch = num_tokens_from_string(i["prompt"]) + num_tokens_from_string(
                         i["response"]
                     )
-                    if (
-                        tokens_current_history + tokens_batch
-                        < settings.TOKENS_MAX_HISTORY
-                    ):
+                    if tokens_current_history + tokens_batch < self.token_limit:
                         tokens_current_history += tokens_batch
                         messages_combine.append(
                             {"role": "user", "content": i["prompt"]}
@@ -110,3 +117,15 @@ class DuckDuckSearch(BaseRetriever):
 
     def search(self):
         return self._get_data()
+    
+    def get_params(self):
+        return {
+            "question": self.question,
+            "source": self.source,
+            "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
+        }

application/retriever/retriever_creator.py

@@ -5,15 +5,16 @@ from application.retriever.brave_search import BraveRetSearch
 
 
 class RetrieverCreator:
-    retievers = {
+    retrievers = {
         'classic': ClassicRAG,
         'duckduck_search': DuckDuckSearch,
-        'brave_search': BraveRetSearch
+        'brave_search': BraveRetSearch,
+        'default': ClassicRAG
     }
 
     @classmethod
     def create_retriever(cls, type, *args, **kwargs):
-        retiever_class = cls.retievers.get(type.lower())
+        retiever_class = cls.retrievers.get(type.lower())
         if not retiever_class:
             raise ValueError(f"No retievers class found for type {type}")
         return retiever_class(*args, **kwargs)

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 +1,29 @@
+from io import BytesIO
+import base64
+from application.tts.base import BaseTTS
+
+
+class ElevenlabsTTS(BaseTTS):
+    def __init__(self):
+        from elevenlabs.client import ElevenLabs
+
+        self.client = ElevenLabs(
+            api_key="ELEVENLABS_API_KEY",
+            )
+    
+
+    def text_to_speech(self, text):
+        lang = "en"
+        audio = self.client.generate(
+            text=text,
+            model="eleven_multilingual_v2",
+            voice="Brian",
+        )
+        audio_data = BytesIO()
+        for chunk in audio:
+            audio_data.write(chunk)
+        audio_bytes = audio_data.getvalue()
+
+        # Encode to base64
+        audio_base64 = base64.b64encode(audio_bytes).decode("utf-8")
+        return audio_base64, lang

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

@@ -1,10 +1,9 @@
 import sys
-from pymongo import MongoClient
 from datetime import datetime
-from application.core.settings import settings
-from application.utils import count_tokens
+from application.core.mongo_db import MongoDB
+from application.utils import num_tokens_from_string
 
-mongo = MongoClient(settings.MONGO_URI)
+mongo = MongoDB.get_client()
 db = mongo["docsgpt"]
 usage_collection = db["token_usage"]
 
@@ -24,9 +23,9 @@ def update_token_usage(user_api_key, token_usage):
 def gen_token_usage(func):
     def wrapper(self, model, messages, stream, **kwargs):
         for message in messages:
-            self.token_usage["prompt_tokens"] += count_tokens(message["content"])
+            self.token_usage["prompt_tokens"] += num_tokens_from_string(message["content"])
         result = func(self, model, messages, stream, **kwargs)
-        self.token_usage["generated_tokens"] += count_tokens(result)
+        self.token_usage["generated_tokens"] += num_tokens_from_string(result)
         update_token_usage(self.user_api_key, self.token_usage)
         return result
 
@@ -36,14 +35,14 @@ def gen_token_usage(func):
 def stream_token_usage(func):
     def wrapper(self, model, messages, stream, **kwargs):
         for message in messages:
-            self.token_usage["prompt_tokens"] += count_tokens(message["content"])
+            self.token_usage["prompt_tokens"] += num_tokens_from_string(message["content"])
         batch = []
         result = func(self, model, messages, stream, **kwargs)
         for r in result:
             batch.append(r)
             yield r
         for line in batch:
-            self.token_usage["generated_tokens"] += count_tokens(line)
+            self.token_usage["generated_tokens"] += num_tokens_from_string(line)
         update_token_usage(self.user_api_key, self.token_usage)
 
     return wrapper

application/utils.py

@@ -1,6 +1,48 @@
-from transformers import GPT2TokenizerFast
+import tiktoken
+import hashlib
+from flask import jsonify, make_response
 
 
-def count_tokens(string):
-    tokenizer = GPT2TokenizerFast.from_pretrained('gpt2')
-    return len(tokenizer(string)['input_ids'])
+_encoding = None
+
+
+def get_encoding():
+    global _encoding
+    if _encoding is None:
+        _encoding = tiktoken.get_encoding("cl100k_base")
+    return _encoding
+
+
+def num_tokens_from_string(string: str) -> int:
+    encoding = get_encoding()
+    num_tokens = len(encoding.encode(string))
+    return num_tokens
+
+
+def count_tokens_docs(docs):
+    docs_content = ""
+    for doc in docs:
+        docs_content += doc.page_content
+
+    tokens = num_tokens_from_string(docs_content)
+    return tokens
+
+
+def check_required_fields(data, required_fields):
+    missing_fields = [field for field in required_fields if field not in data]
+    if missing_fields:
+        return make_response(
+            jsonify(
+                {
+                    "success": False,
+                    "message": f"Missing fields: {', '.join(missing_fields)}",
+                }
+            ),
+            400,
+        )
+    return None
+
+
+def get_hash(data):
+    return hashlib.md5(data.encode()).hexdigest()
+

application/vectorstore/base.py

@@ -1,13 +1,55 @@
 from abc import ABC, abstractmethod
 import os
-from langchain_community.embeddings import (
-    HuggingFaceEmbeddings,
-    CohereEmbeddings,
-    HuggingFaceInstructEmbeddings,
-)
+from sentence_transformers import SentenceTransformer
 from langchain_openai import OpenAIEmbeddings
 from application.core.settings import settings
 
+class EmbeddingsWrapper:
+    def __init__(self, model_name, *args, **kwargs):
+        self.model = SentenceTransformer(model_name, config_kwargs={'allow_dangerous_deserialization': True}, *args, **kwargs)
+        self.dimension = self.model.get_sentence_embedding_dimension()
+
+    def embed_query(self, query: str):
+        return self.model.encode(query).tolist()
+    
+    def embed_documents(self, documents: list):
+        return self.model.encode(documents).tolist()
+    
+    def __call__(self, text):
+        if isinstance(text, str):
+            return self.embed_query(text)
+        elif isinstance(text, list):
+            return self.embed_documents(text)
+        else:
+            raise ValueError("Input must be a string or a list of strings")
+
+
+
+class EmbeddingsSingleton:
+    _instances = {}
+
+    @staticmethod
+    def get_instance(embeddings_name, *args, **kwargs):
+        if embeddings_name not in EmbeddingsSingleton._instances:
+            EmbeddingsSingleton._instances[embeddings_name] = EmbeddingsSingleton._create_instance(
+                embeddings_name, *args, **kwargs
+            )
+        return EmbeddingsSingleton._instances[embeddings_name]
+
+    @staticmethod
+    def _create_instance(embeddings_name, *args, **kwargs):
+        embeddings_factory = {
+            "openai_text-embedding-ada-002": OpenAIEmbeddings,
+            "huggingface_sentence-transformers/all-mpnet-base-v2": lambda: EmbeddingsWrapper("sentence-transformers/all-mpnet-base-v2"),
+            "huggingface_sentence-transformers-all-mpnet-base-v2": lambda: EmbeddingsWrapper("sentence-transformers/all-mpnet-base-v2"),
+            "huggingface_hkunlp/instructor-large": lambda: EmbeddingsWrapper("hkunlp/instructor-large"),
+        }
+
+        if embeddings_name in embeddings_factory:
+            return embeddings_factory[embeddings_name](*args, **kwargs)
+        else:
+            return EmbeddingsWrapper(embeddings_name, *args, **kwargs)
+
 class BaseVectorStore(ABC):
     def __init__(self):
         pass
@@ -20,37 +62,28 @@ class BaseVectorStore(ABC):
         return settings.OPENAI_API_BASE and settings.OPENAI_API_VERSION and settings.AZURE_DEPLOYMENT_NAME
 
     def _get_embeddings(self, embeddings_name, embeddings_key=None):
-        embeddings_factory = {
-            "openai_text-embedding-ada-002": OpenAIEmbeddings,
-            "huggingface_sentence-transformers/all-mpnet-base-v2": HuggingFaceEmbeddings,
-            "huggingface_hkunlp/instructor-large": HuggingFaceInstructEmbeddings,
-            "cohere_medium": CohereEmbeddings
-        }
-        
-        if embeddings_name not in embeddings_factory:
-            raise ValueError(f"Invalid embeddings_name: {embeddings_name}")
-
         if embeddings_name == "openai_text-embedding-ada-002":
             if self.is_azure_configured():
                 os.environ["OPENAI_API_TYPE"] = "azure"
-                embedding_instance = embeddings_factory[embeddings_name](
+                embedding_instance = EmbeddingsSingleton.get_instance(
+                    embeddings_name,
                     model=settings.AZURE_EMBEDDINGS_DEPLOYMENT_NAME
                 )
             else:
-                embedding_instance = embeddings_factory[embeddings_name](
+                embedding_instance = EmbeddingsSingleton.get_instance(
+                    embeddings_name,
                     openai_api_key=embeddings_key
                 )
-        elif embeddings_name == "cohere_medium":
-            embedding_instance = embeddings_factory[embeddings_name](
-                cohere_api_key=embeddings_key
-            )
         elif embeddings_name == "huggingface_sentence-transformers/all-mpnet-base-v2":
-            embedding_instance = embeddings_factory[embeddings_name](
-                #model_name="./model/all-mpnet-base-v2",
-                model_kwargs={"device": "cpu"},
-            )
+            if os.path.exists("./model/all-mpnet-base-v2"):
+                embedding_instance = EmbeddingsSingleton.get_instance(
+                    embeddings_name="./model/all-mpnet-base-v2",
+                )
+            else:
+                embedding_instance = EmbeddingsSingleton.get_instance(
+                    embeddings_name,
+                )
         else:
-            embedding_instance = embeddings_factory[embeddings_name]()
-            
-        return embedding_instance
+            embedding_instance = EmbeddingsSingleton.get_instance(embeddings_name)
 
+        return embedding_instance

application/vectorstore/elasticsearch.py

@@ -9,9 +9,9 @@ import elasticsearch
 class ElasticsearchStore(BaseVectorStore):
     _es_connection = None  # Class attribute to hold the Elasticsearch connection
 
-    def __init__(self, path, embeddings_key, index_name=settings.ELASTIC_INDEX):
+    def __init__(self, source_id, embeddings_key, index_name=settings.ELASTIC_INDEX):
         super().__init__()
-        self.path = path.replace("application/indexes/", "").rstrip("/")
+        self.source_id = source_id.replace("application/indexes/", "").rstrip("/")
         self.embeddings_key = embeddings_key
         self.index_name = index_name
         
@@ -81,7 +81,7 @@ class ElasticsearchStore(BaseVectorStore):
         embeddings = self._get_embeddings(settings.EMBEDDINGS_NAME, self.embeddings_key)
         vector = embeddings.embed_query(question)
         knn = {
-            "filter": [{"match": {"metadata.store.keyword": self.path}}],
+            "filter": [{"match": {"metadata.source_id.keyword": self.source_id}}],
             "field": "vector",
             "k": k,
             "num_candidates": 100,
@@ -100,7 +100,7 @@ class ElasticsearchStore(BaseVectorStore):
                             }
                         }
                     ],
-                    "filter": [{"match": {"metadata.store.keyword": self.path}}],
+                    "filter": [{"match": {"metadata.source_id.keyword": self.source_id}}],
                 }
             },
             "rank": {"rrf": {}},
@@ -209,5 +209,4 @@ class ElasticsearchStore(BaseVectorStore):
 
     def delete_index(self):
         self._es_connection.delete_by_query(index=self.index_name, query={"match": {
-                                      "metadata.store.keyword": self.path}},)
-
+                                      "metadata.source_id.keyword": self.source_id}},)

application/vectorstore/faiss.py

@@ -1,21 +1,29 @@
 from langchain_community.vectorstores import FAISS
 from application.vectorstore.base import BaseVectorStore
 from application.core.settings import settings
+import os
+
+def get_vectorstore(path: str) -> str:
+    if path:
+        vectorstore = os.path.join("application", "indexes", path)
+    else:
+        vectorstore = os.path.join("application")
+    return vectorstore
 
 class FaissStore(BaseVectorStore):
-
-    def __init__(self, path, embeddings_key, docs_init=None):
+    def __init__(self, source_id: str, embeddings_key: str, docs_init=None):
         super().__init__()
-        self.path = path
+        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
-            )
+
+        try:
+            if docs_init:
+                self.docsearch = FAISS.from_documents(docs_init, embeddings)
+            else:
+                self.docsearch = FAISS.load_local(self.path, embeddings, allow_dangerous_deserialization=True)
+        except Exception:
+            raise
+
         self.assert_embedding_dimensions(embeddings)
 
     def search(self, *args, **kwargs):
@@ -31,16 +39,12 @@ 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 
-        """
+        """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.client[1].word_embedding_dimension
-            except AttributeError as e:
-                raise AttributeError("word_embedding_dimension not found in embeddings.client[1]") from e
+            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"word_embedding_dimension ({word_embedding_dimension}) " +
-                                 f"!= docsearch_index_word_embedding_dimension ({docsearch_index_dimension})")
+                raise ValueError(f"Embedding dimension mismatch: embeddings.dimension ({word_embedding_dimension}) != docsearch index dimension ({docsearch_index_dimension})")

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

@@ -0,0 +1,38 @@
+from typing import List, Optional
+from uuid import uuid4
+
+
+from application.core.settings import settings
+from application.vectorstore.base import BaseVectorStore
+
+
+class MilvusStore(BaseVectorStore):
+    def __init__(self, source_id: str = "", embeddings_key: str = "embeddings"):
+        super().__init__()
+        from langchain_milvus import Milvus
+
+        connection_args = {
+            "uri": settings.MILVUS_URI,
+            "token": settings.MILVUS_TOKEN,
+        }
+        self._docsearch = Milvus(
+            embedding_function=self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key),
+            collection_name=settings.MILVUS_COLLECTION_NAME,
+            connection_args=connection_args,
+        )
+        self._source_id = source_id
+
+    def search(self, question, k=2, *args, **kwargs):
+        expr = f"source_id == '{self._source_id}'"
+        return self._docsearch.similarity_search(query=question, k=k, expr=expr, *args, **kwargs)
+
+    def add_texts(self, texts: List[str], metadatas: Optional[List[dict]], *args, **kwargs):
+        ids = [str(uuid4()) for _ in range(len(texts))]
+
+        return self._docsearch.add_texts(texts=texts, metadatas=metadatas, ids=ids, *args, **kwargs)
+
+    def save_local(self, *args, **kwargs):
+        pass
+
+    def delete_index(self, *args, **kwargs):
+        pass

application/vectorstore/mongodb.py

@@ -1,11 +1,12 @@
-from application.vectorstore.base import BaseVectorStore
 from application.core.settings import settings
+from application.vectorstore.base import BaseVectorStore
 from application.vectorstore.document_class import Document
 
+
 class MongoDBVectorStore(BaseVectorStore):
     def __init__(
         self,
-        path: str = "",
+        source_id: str = "",
         embeddings_key: str = "embeddings",
         collection: str = "documents",
         index_name: str = "vector_search_index",
@@ -18,7 +19,7 @@ class MongoDBVectorStore(BaseVectorStore):
         self._embedding_key = embedding_key
         self._embeddings_key = embeddings_key
         self._mongo_uri = settings.MONGO_URI
-        self._path = path.replace("application/indexes/", "").rstrip("/")
+        self._source_id = source_id.replace("application/indexes/", "").rstrip("/")
         self._embedding = self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key)
 
         try:
@@ -33,27 +34,24 @@ class MongoDBVectorStore(BaseVectorStore):
         self._database = self._client[database]
         self._collection = self._database[collection]
 
-        
     def search(self, question, k=2, *args, **kwargs):
         query_vector = self._embedding.embed_query(question)
 
         pipeline = [
             {
                 "$vectorSearch": {
-                    "queryVector": query_vector, 
+                    "queryVector": query_vector,
                     "path": self._embedding_key,
-                    "limit": k, 
-                    "numCandidates": k * 10, 
+                    "limit": k,
+                    "numCandidates": k * 10,
                     "index": self._index_name,
-                    "filter": {
-                        "store": {"$eq": self._path}
-                    }
+                    "filter": {"source_id": {"$eq": self._source_id}},
                 }
             }
         ]
 
         cursor = self._collection.aggregate(pipeline)
-        
+
         results = []
         for doc in cursor:
             text = doc[self._text_key]
@@ -63,30 +61,32 @@ class MongoDBVectorStore(BaseVectorStore):
             metadata = doc
             results.append(Document(text, metadata))
         return results
-    
+
     def _insert_texts(self, texts, metadatas):
         if not texts:
             return []
         embeddings = self._embedding.embed_documents(texts)
+
         to_insert = [
             {self._text_key: t, self._embedding_key: embedding, **m}
             for t, m, embedding in zip(texts, metadatas, embeddings)
         ]
-        # insert the documents in MongoDB Atlas
+
         insert_result = self._collection.insert_many(to_insert)
         return insert_result.inserted_ids
-    
-    def add_texts(self,
+
+    def add_texts(
+        self,
         texts,
-        metadatas = None,
-        ids = None,
-        refresh_indices = True,
-        create_index_if_not_exists = True,
-        bulk_kwargs = None,
-        **kwargs,):
+        metadatas=None,
+        ids=None,
+        refresh_indices=True,
+        create_index_if_not_exists=True,
+        bulk_kwargs=None,
+        **kwargs,
+    ):
 
-
-        #dims = self._embedding.client[1].word_embedding_dimension
+        # dims = self._embedding.client[1].word_embedding_dimension
         # # check if index exists
         # if create_index_if_not_exists:
         #     # check if index exists
@@ -121,6 +121,6 @@ class MongoDBVectorStore(BaseVectorStore):
         if texts_batch:
             result_ids.extend(self._insert_texts(texts_batch, metadatas_batch))
         return result_ids
-    
+
     def delete_index(self, *args, **kwargs):
-        self._collection.delete_many({"store": self._path})
+        self._collection.delete_many({"source_id": self._source_id})

application/vectorstore/qdrant.py

@@ -5,12 +5,12 @@ from qdrant_client import models
 
 
 class QdrantStore(BaseVectorStore):
-    def __init__(self, path: str = "", embeddings_key: str = "embeddings"):
+    def __init__(self, source_id: str = "", embeddings_key: str = "embeddings"):
         self._filter = models.Filter(
             must=[
                 models.FieldCondition(
-                    key="metadata.store",
-                    match=models.MatchValue(value=path.replace("application/indexes/", "").rstrip("/")),
+                    key="metadata.source_id",
+                    match=models.MatchValue(value=source_id.replace("application/indexes/", "").rstrip("/")),
                 )
             ]
         )

application/vectorstore/vector_creator.py

@@ -1,5 +1,6 @@
 from application.vectorstore.faiss import FaissStore
 from application.vectorstore.elasticsearch import ElasticsearchStore
+from application.vectorstore.milvus import MilvusStore
 from application.vectorstore.mongodb import MongoDBVectorStore
 from application.vectorstore.qdrant import QdrantStore
 
@@ -10,6 +11,7 @@ class VectorCreator:
         "elasticsearch": ElasticsearchStore,
         "mongodb": MongoDBVectorStore,
         "qdrant": QdrantStore,
+        "milvus": MilvusStore,
     }
 
     @classmethod

application/worker.py

@@ -1,37 +1,40 @@
+import logging
 import os
 import shutil
 import string
 import zipfile
+from collections import Counter
 from urllib.parse import urljoin
 
-import nltk
 import requests
+from bson.objectid import ObjectId
 
+from application.core.mongo_db import MongoDB
 from application.core.settings import settings
 from application.parser.file.bulk import SimpleDirectoryReader
-from application.parser.remote.remote_creator import RemoteCreator
 from application.parser.open_ai_func import call_openai_api
+from application.parser.remote.remote_creator import RemoteCreator
 from application.parser.schema.base import Document
 from application.parser.token_func import group_split
+from application.utils import count_tokens_docs
 
-try:
-    nltk.download("punkt", quiet=True)
-    nltk.download("averaged_perceptron_tagger", quiet=True)
-except FileExistsError:
-    pass
+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):
-    store = "/".join(title.split("/")[1:3])
-    return {"title": title, "store": store}
-
+    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__)))
 )
@@ -47,12 +50,16 @@ def extract_zip_recursive(zip_path, extract_to, current_depth=0, max_depth=5):
         max_depth (int): Maximum allowed depth of recursion to prevent infinite loops.
     """
     if current_depth > max_depth:
-        print(f"Reached maximum recursion depth of {max_depth}")
+        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):
@@ -62,9 +69,43 @@ 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(self, directory, formats, name_job, filename, user):
+def ingest_worker(
+    self, directory, formats, name_job, filename, user, retriever="classic"
+):
     """
     Ingest and process documents.
 
@@ -75,46 +116,31 @@ def ingest_worker(self, directory, formats, name_job, filename, user):
         name_job (str): Name of the job for this ingestion task.
         filename (str): Name of the file to be ingested.
         user (str): Identifier for the user initiating the ingestion.
+        retriever (str): Type of retriever to use for processing the documents.
 
     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)
-    import sys
 
-
-    print(full_path, file=sys.stderr)
-    # check if API_URL env variable is set
+    logging.info(f"Ingest file: {full_path}", extra={"user": user, "job": name_job})
     file_data = {"name": name_job, "file": filename, "user": user}
-    response = requests.get(
-        urljoin(settings.API_URL, "/api/download"), params=file_data
-    )
-    # check if file is in the response
-    print(response, file=sys.stderr)
-    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)
+        extract_zip_recursive(
+            os.path.join(full_path, filename), full_path, 0, RECURSION_DEPTH
+        )
 
     self.update_state(state="PROGRESS", meta={"current": 1})
 
@@ -129,38 +155,29 @@ def ingest_worker(self, directory, formats, name_job, filename, user):
     ).load_data()
     raw_docs = group_split(
         documents=raw_docs,
-        min_tokens=min_tokens,
-        max_tokens=max_tokens,
+        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]
+    id = ObjectId()
 
-    call_openai_api(docs, full_path, self)
+    call_openai_api(docs, full_path, id, self)
+    tokens = count_tokens_docs(docs)
     self.update_state(state="PROGRESS", meta={"current": 100})
 
     if sample:
         for i in range(min(5, len(raw_docs))):
-            print(raw_docs[i].text)
+            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}
-    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
-        )
-        response = requests.get(
-            urljoin(settings.API_URL, "/api/delete_old?path=" + full_path)
-        )
-    else:
-        response = requests.post(
-            urljoin(settings.API_URL, "/api/upload_index"), data=file_data
-        )
+    file_data.update({
+        "tokens": tokens,
+        "retriever": retriever,
+        "id": str(id),
+        "type": "local",
+    })
+    upload_index(full_path, file_data)
 
     # delete local
     shutil.rmtree(full_path)
@@ -174,51 +191,114 @@ def ingest_worker(self, directory, formats, name_job, filename, user):
         "limited": False,
     }
 
-
-def remote_worker(self, source_data, name_job, user, loader, directory="temp"):
-    # sample = False
+def remote_worker(
+    self,
+    source_data,
+    name_job,
+    user,
+    loader,
+    directory="temp",
+    retriever="classic",
+    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},
+    )
 
-    # source_data {"data": [url]} for url type task just urls
-
-    # Use RemoteCreator to load data from URL
     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,
+        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]
-
-    call_openai_api(docs, full_path, self)
+    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}
-    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
-        )
-        requests.get(urljoin(settings.API_URL, "/api/delete_old?path=" + full_path))
-    else:
-        requests.post(urljoin(settings.API_URL, "/api/upload_index"), data=file_data)
+    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)
 
     shutil.rmtree(full_path)
 
     return {"urls": source_data, "name_job": name_job, "user": user, "limited": False}
+
+def sync(
+    self,
+    source_data,
+    name_job,
+    user,
+    loader,
+    sync_frequency,
+    retriever,
+    doc_id=None,
+    directory="temp",
+):
+    try:
+        remote_worker(
+            self,
+            source_data,
+            name_job,
+            user,
+            loader,
+            directory,
+            retriever,
+            sync_frequency,
+            "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()
+    for doc in sources:
+        if doc.get("sync_frequency") == frequency:
+            name = doc.get("name")
+            user = doc.get("user")
+            source_type = doc.get("type")
+            source_data = doc.get("remote_data")
+            retriever = doc.get("retriever")
+            doc_id = str(doc.get("_id"))
+            resp = sync(
+                self, source_data, name, user, source_type, frequency, retriever, doc_id
+            )
+            sync_counts["total_sync_count"] += 1
+            sync_counts[
+                "sync_success" if resp["status"] == "success" else "sync_failure"
+            ] += 1
+
+    return {
+        key: sync_counts[key]
+        for key in ["total_sync_count", "sync_success", "sync_failure"]
+    }

docker-compose-azure.yaml

@@ -1,5 +1,3 @@
-version: "3.9"
-
 services:
   frontend:
     build: ./frontend

docker-compose-dev.yaml

@@ -1,5 +1,3 @@
-version: "3.9"
-
 services:
 
   redis:

docker-compose-local.yaml

@@ -1,8 +1,8 @@
-version: "3.9"
-
 services:
   frontend:
     build: ./frontend
+    volumes:
+    - ./frontend/src:/app/src
     environment:
       - VITE_API_HOST=http://localhost:7091
       - VITE_API_STREAMING=$VITE_API_STREAMING

docker-compose-mock.yaml

@@ -1,5 +1,3 @@
-version: "3.9"
-
 services:
   frontend:
     build: ./frontend

docker-compose.yaml

@@ -1,8 +1,8 @@
-version: "3.9"
-
 services:
   frontend:
     build: ./frontend
+    volumes:
+      - ./frontend/src:/app/src
     environment:
       - VITE_API_HOST=http://localhost:7091
       - VITE_API_STREAMING=$VITE_API_STREAMING
@@ -20,6 +20,7 @@ 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
     ports:
       - "7091:7091"
     volumes:
@@ -32,7 +33,7 @@ services:
 
   worker:
     build: ./application
-    command: celery -A application.app.celery worker -l INFO
+    command: celery -A application.app.celery worker -l INFO -B
     environment:
       - API_KEY=$API_KEY
       - EMBEDDINGS_KEY=$API_KEY
@@ -41,6 +42,7 @@ 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

docs/README.md

@@ -46,6 +46,6 @@ yarn install
 yarn dev
 ```
 
-- Now, you should be able to view the docs on your local environment by visiting `http://localhost:5000`. You can explore the different markdown files and make changes as you see fit.
+- Now, you should be able to view the docs on your local environment by visiting `http://localhost:3000`. You can explore the different markdown files and make changes as you see fit.
 
 - **Footnotes:** This guide assumes you have Node.js and npm installed. The guide involves running a local server using yarn, and viewing the documentation offline. If you encounter any issues, it may be worth verifying your Node.js and npm installations and whether you have installed yarn correctly.

docs/package.json

@@ -7,8 +7,8 @@
   "license": "MIT",
   "dependencies": {
     "@vercel/analytics": "^1.1.1",
-    "docsgpt": "^0.3.7",
-    "next": "^14.0.4",
+    "docsgpt": "^0.4.7",
+    "next": "^14.2.12",
     "nextra": "^2.13.2",
     "nextra-theme-docs": "^2.13.2",
     "react": "^18.2.0",

docs/pages/API/_meta.json

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

docs/pages/API/api-key-guide.md

@@ -14,7 +14,7 @@ Before creating your first API key, you must upload the document that will be li
 After uploading your document, you can obtain an API key either through the graphical user interface or via an API call:
 
 - **Graphical User Interface:** Navigate to the Settings section of the DocsGPT web app, find the API Keys option, and press 'Create New' to generate your key.
-- **API Call:** Alternatively, you can use the `/api/create_api_key` endpoint to create a new API key. For detailed instructions, visit [DocsGPT API Documentation](https://docs.docsgpt.co.uk/Developing/API-docs#8-apicreate_api_key).
+- **API Call:** Alternatively, you can use the `/api/create_api_key` endpoint to create a new API key. For detailed instructions, visit [DocsGPT API Documentation](https://docs.docsgpt.cloud/API/API-docs#8-apicreate_api_key).
 
 ### Understanding Key Variables
 
@@ -27,4 +27,4 @@ Upon creating your API key, you will encounter several key variables. Each serve
 
 With your API key ready, you can now integrate DocsGPT into your application, such as the DocsGPT Widget or any other software, via `/api/answer` or `/stream` endpoints. The source document is preset with the API key, allowing you to bypass fields like `selectDocs` and `active_docs` during implementation.
 
-Congratulations on taking the first step towards enhancing your applications with DocsGPT! With this guide, you're now equipped to navigate the process of obtaining and understanding DocsGPT API keys.
+Congratulations on taking the first step towards enhancing your applications with DocsGPT! With this guide, you're now equipped to navigate the process of obtaining and understanding DocsGPT API keys.

docs/pages/Deploying/Kubernetes-Deploying.md

@@ -0,0 +1,100 @@
+# Self-hosting DocsGPT on Kubernetes
+
+This guide will walk you through deploying DocsGPT on Kubernetes.
+
+## Prerequisites
+
+Ensure you have the following installed before proceeding:
+
+- [kubectl](https://kubernetes.io/docs/tasks/tools/install-kubectl/)
+- Access to a Kubernetes cluster
+
+## Folder Structure
+
+The `k8s` folder contains the necessary deployment and service configuration files:
+
+- `deployments/`
+- `services/`
+- `docsgpt-secrets.yaml`
+
+## Deployment Instructions
+
+1. **Clone the Repository**
+
+   ```sh
+   git clone https://github.com/arc53/DocsGPT.git
+   cd docsgpt/k8s
+   ```
+
+2. **Configure Secrets (optional)**
+
+   Ensure that you have all the necessary secrets in `docsgpt-secrets.yaml`. Update it with your secrets before applying if you want. By default we will use qdrant as a vectorstore and public docsgpt llm as llm for inference.
+
+3. **Apply Kubernetes Deployments**
+
+   Deploy your DocsGPT resources using the following commands:
+
+   ```sh
+   kubectl apply -f deployments/
+   ```
+
+4. **Apply Kubernetes Services**
+
+   Set up your services using the following commands:
+
+   ```sh
+   kubectl apply -f services/
+   ```
+
+5. **Apply Secrets**
+
+   Apply the secret configurations:
+
+   ```sh
+   kubectl apply -f docsgpt-secrets.yaml
+   ```
+
+6. **Substitute API URL**
+
+   After deploying the services, you need to update the environment variable `VITE_API_HOST` in your deployment file `deployments/docsgpt-deploy.yaml` with the actual endpoint URL created by your `docsgpt-api-service`.
+
+    ```sh
+    kubectl get services/docsgpt-api-service -o jsonpath='{.status.loadBalancer.ingress[0].ip}' | xargs -I {} sed -i "s|<your-api-endpoint>|{}|g" deployments/docsgpt-deploy.yaml
+    ```
+
+7. **Rerun Deployment**
+
+   After making the changes, reapply the deployment configuration to update the environment variables:
+
+   ```sh
+   kubectl apply -f deployments/
+   ```
+
+## Verifying the Deployment
+
+To verify if everything is set up correctly, you can run the following:
+
+```sh
+kubectl get pods
+kubectl get services
+```
+
+Ensure that the pods are running and the services are available.
+
+## Accessing DocsGPT
+
+To access DocsGPT, you need to find the external IP address of the frontend service. You can do this by running:
+
+```sh
+kubectl get services/docsgpt-frontend-service | awk 'NR>1 {print "http://" $4}'
+```
+
+## Troubleshooting
+
+If you encounter any issues, you can check the logs of the pods for more details:
+
+```sh
+kubectl logs <pod-name>
+```
+
+Replace `<pod-name>` with the actual name of your DocsGPT pod.

docs/pages/Deploying/Quickstart.md

@@ -67,62 +67,3 @@ To run the setup on Windows, you have two options: using the Windows Subsystem f
 
 These steps should help you set up and run the project on Windows using either WSL or Git Bash/Command Prompt. 
 **Important:** Ensure that Docker is installed and properly configured on your Windows system for these steps to work.
-
-
-For WINDOWS:
-
-To run the given setup on Windows, you can use the Windows Subsystem for Linux (WSL) or a Git Bash terminal to execute similar commands. Here are the steps adapted for Windows:
-
-Option 1: Using Windows Subsystem for Linux (WSL):
-
-1. Install WSL if you haven't already. You can follow the official Microsoft documentation for installation: (https://learn.microsoft.com/en-us/windows/wsl/install).
-2. After setting up WSL, open the WSL terminal.
-3. Clone the repository and create the `.env` file:
-   ```bash
-   git clone https://github.com/arc53/DocsGPT.git
-   cd DocsGPT
-   echo "API_KEY=Yourkey" > .env
-   echo "VITE_API_STREAMING=true" >> .env
-   ```
-4. Run the following command to start the setup with Docker Compose:
-    ```bash
-    ./run-with-docker-compose.sh
-    ```
-5. Open your web browser and navigate to http://localhost:5173/.
-6. To stop the setup, just press **Ctrl + C** in the WSL terminal.
-
-Option 2: Using Git Bash or Command Prompt (CMD):
-
-1. Install Git for Windows if you haven't already. You can download it from the official website: (https://gitforwindows.org/).
-2. Open Git Bash or Command Prompt.
-3. Clone the repository and create the `.env` file:
-   ```bash
-   git clone https://github.com/arc53/DocsGPT.git
-   cd DocsGPT
-   echo "API_KEY=Yourkey" > .env
-   echo "VITE_API_STREAMING=true" >> .env
-   ```
-4. Run the following command to start the setup with Docker Compose:
-   ```bash
-   ./run-with-docker-compose.sh
-   ```
-5. Open your web browser and navigate to http://localhost:5173/.
-6. To stop the setup, just press **Ctrl + C** in the Git Bash or Command Prompt terminal.
-
-These steps should help you set up and run the project on Windows using either WSL or Git Bash/Command Prompt. Make sure you have Docker installed and properly configured on your Windows system for this to work.
-
-
-### Chrome Extension
-
-#### Installing the Chrome extension:
-To enhance your DocsGPT experience, you can install the DocsGPT Chrome extension. Here's how:
-
-1. In the DocsGPT GitHub repository, click on the **Code** button and select **Download ZIP**.
-2. Unzip the downloaded file to a location you can easily access.
-3. Open the Google Chrome browser and click on the three dots menu (upper right corner).
-4. Select **More Tools** and then **Extensions**.
-5. Turn on the **Developer mode** switch in the top right corner of the **Extensions page**.
-6. Click on the **Load unpacked** button.
-7. Select the **Chrome** folder where the DocsGPT files have been unzipped (docsgpt-main > extensions > chrome).
-8. The extension should now be added to Google Chrome and can be managed on the Extensions page.
-9. To disable or remove the extension, simply turn off the toggle switch on the extension card or click the **Remove** button.

docs/pages/Deploying/_meta.json

@@ -10,5 +10,9 @@
   "Railway-Deploying": {
     "title": "🚂Deploying on Railway",
     "href": "/Deploying/Railway-Deploying"
+  },
+  "Kubernetes-Deploying": {
+    "title": "☸️Deploying on Kubernetes",
+    "href": "/Deploying/Kubernetes-Deploying"
   }
-}
+}

docs/pages/Developing/_meta.json

@@ -1,6 +0,0 @@
-{
-  "API-docs": {
-    "title": "🗂️️ API-docs",
-    "href": "/Developing/API-docs"
-  }
-}

docs/pages/Extensions/Chrome-extension.mdx

@@ -0,0 +1,34 @@
+
+import {Steps} from 'nextra/components'
+import { Callout } from 'nextra/components'
+
+
+## Chrome  Extension Setup Guide
+
+To enhance your DocsGPT experience, you can install the DocsGPT Chrome extension. Here's how:
+<Steps >
+### Step 1
+
+
+
+In the DocsGPT GitHub repository, click on the **Code** button and select **Download ZIP**.
+### Step 2
+Unzip the downloaded file to a location you can easily access.
+### Step 3
+Open the Google Chrome browser and click on the three dots menu (upper right corner).
+### Step 4
+Select **More Tools** and then **Extensions**.
+### Step 5
+Turn on the **Developer mode** switch in the top right corner of the **Extensions page**.
+### Step 6
+Click on the **Load unpacked** button.
+### Step 7
+7. Select the **Chrome** folder where the DocsGPT files have been unzipped (docsgpt-main > extensions > chrome).
+### Step 8
+The extension should now be added to Google Chrome and can be managed on the Extensions page.
+### Step 9
+ To disable or remove the extension, simply turn off the toggle switch on the extension card or click the **Remove** button.
+</Steps>
+
+
+

docs/pages/Extensions/_meta.json

@@ -7,8 +7,8 @@
     "title": "🏗️ Widget setup",
     "href": "/Extensions/react-widget"
   },
-  "api-key-guide": {
-    "title": "🔐 API Keys guide",
-    "href": "/Extensions/api-key-guide"
+  "Chrome-extension": {
+    "title": "🌐 Chrome Extension",
+    "href": "/Extensions/Chrome-extension"
   }
 }

docs/pages/Extensions/react-widget.md

@@ -17,25 +17,31 @@ Now, you can use the widget in your component like this :
 ```jsx
 <DocsGPTWidget
   apiHost="https://your-docsgpt-api.com"
-  selectDocs="local/docs.zip"
   apiKey=""
-  avatar = "https://d3dg1063dc54p9.cloudfront.net/cute-docsgpt.png",
-  title = "Get AI assistance",
-  description = "DocsGPT's AI Chatbot is here to help",
-  heroTitle = "Welcome to DocsGPT !",
+  avatar = "https://d3dg1063dc54p9.cloudfront.net/cute-docsgpt.png"
+  title = "Get AI assistance"
+  description = "DocsGPT's AI Chatbot is here to help"
+  heroTitle = "Welcome to DocsGPT !"
   heroDescription="This chatbot is built with DocsGPT and utilises GenAI, 
   please review important information using sources."
+  theme = "dark"
+  buttonIcon = "https://your-icon"
+  buttonBg = "#222327"
 />
 ```
-DocsGPTWidget takes 8 **props** with default fallback values:
+To tailor the widget to your needs, you can configure the following props in your component:
 1. `apiHost` — The URL of your DocsGPT API.
-2. `selectDocs` — The documentation source that you want to use for your widget (e.g. `default` or `local/docs1.zip`).
+2. `theme` — Allows to select your specific theme (dark or light).
 3. `apiKey` — Usually, it's empty.
 4. `avatar`: Specifies the URL of the avatar or image representing the chatbot.
 5. `title`: Sets the title text displayed in the chatbot interface.
 6. `description`: Provides a brief description of the chatbot's purpose or functionality.
 7. `heroTitle`: Displays a welcome title when users interact with the chatbot.
 8. `heroDescription`: Provide additional introductory text or information about the chatbot's capabilities.
+9. `buttonIcon`: Specifies the url of the icon image for the widget.
+10. `buttonBg`: Allows to specify the Background color of the widget.
+11. `size`: Sets the size of the widget ( small, medium).
+
 
 ### How to use DocsGPTWidget with [Nextra](https://nextra.site/) (Next.js + MDX)
 Install your widget as described above and then go to your `pages/` folder and create a new file `_app.js` with the following content:
@@ -51,6 +57,69 @@ export default function MyApp({ Component, pageProps }) {
     )
 }
 ```  
+### How to use DocsGPTWidget with HTML
+```html
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <meta http-equiv="X-UA-Compatible" content="ie=edge" />
+    <title>HTML + CSS</title>
+    <link rel="stylesheet" href="styles.css" />
+  </head>
+  <body>
+    <h1>This is a simple HTML + CSS template!</h1>
+    <div id="app"></div>
+    <!-- Include the widget script from dist/modern or dist/legacy -->
+    <script
+      src="https://unpkg.com/docsgpt/dist/modern/main.js"
+      type="module"
+    ></script>
+    <script type="module">
+      window.onload = function () {
+        renderDocsGPTWidget("app", {
+          apiKey: "",
+          size: "medium",
+        });
+      };
+    </script>
+  </body>
+</html>
+```
+To link the widget to your api and your documents you can pass parameters to the renderDocsGPTWidget('div id', { parameters }).
+```html
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8" />
+  <meta http-equiv="X-UA-Compatible" content="IE=edge" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>DocsGPT Widget</title>
+  <script src="https://unpkg.com/docsgpt/dist/modern/main.js" type="module"></script>  
+</head>
+<body>
+  <div id="app"></div>
+  <!-- Include the widget script from dist/modern or dist/legacy -->
+  <script type="module">
+    window.onload = function() {
+      renderDocsGPTWidget('app', {
+        apiHost: 'http://localhost:7001',
+        apiKey:"",
+        avatar: 'https://d3dg1063dc54p9.cloudfront.net/cute-docsgpt.png',
+        title: 'Get AI assistance',
+        description: "DocsGPT's AI Chatbot is here to help",
+        heroTitle: 'Welcome to DocsGPT!',
+        heroDescription: 'This chatbot is built with DocsGPT and utilises GenAI, please review important information using sources.',
+        theme:"dark",
+        buttonIcon:"https://your-icon",
+        buttonBg:"#222327"
+      });
+    }
+  </script>
+</body>
+</html>
+```
 
 For more information about React, refer to this [link here](https://react.dev/learn)
 

docs/pages/Guides/Customising-prompts.mdx

@@ -1,10 +1,25 @@
+import Image from 'next/image'
+
 # Customizing the Main Prompt
 
 Customizing the main prompt for DocsGPT gives you the ability to tailor the AI's responses to your specific requirements. By modifying the prompt text, you can achieve more accurate and relevant answers. Here's how you can do it:
 
-1. Navigate to `/application/prompts/combine_prompt.txt`.
+1. Navigate to `SideBar -> Settings`.
+
+
+
+
+2.In Settings select the `Active Prompt` now you will be able to see various prompts style.x
+
+
+
+
+3.Click on the `edit icon`  on the prompt of your choice and you will be able to see the current prompt for it,you can now customise  the prompt as per your choice.
+
+### Video Demo
+<Image src="/prompts.gif" alt="prompts" width={800} height={500} />
+
 
-2. Open the `combine_prompt.txt` file and modify the prompt text to suit your needs. You can experiment with different phrasings and structures to observe how the model responds. The main prompt serves as guidance to the AI model on how to generate responses.
 
 ## Example Prompt Modification
 

docs/pages/Guides/How-to-train-on-other-documentation.md

@@ -1,63 +0,0 @@
-## How to train on other documentation
-
-This AI can utilize any documentation, but it requires preparation for similarity search. Follow these steps to get your documentation ready:
-
-**Step 1: Prepare Your Documentation**
-![video-example-of-how-to-do-it](https://d3dg1063dc54p9.cloudfront.net/videos/how-to-vectorise.gif)
-
-Start by going to `/scripts/` folder.
-
-If you open this file, you will see that it uses RST files from the folder to create a `index.faiss` and `index.pkl`. 
-
-It currently uses OPENAI to create the vector store, so make sure your documentation is not too large. Using Pandas cost me around $3-$4.
-
-You can typically find documentation on GitHub in the `docs/` folder for most open-source projects.
-
-### 1. Find documentation in .rst/.md format and create a folder with it in your scripts directory.
-- Name it `inputs/`.
-- Put all your .rst/.md files in there.  
-- The search is recursive, so you don't need to flatten them.
-
-If there are no .rst/.md files, convert whatever you find to a .txt file and feed it. (Don't forget to change the extension in the script).
-
-### Step 2: Configure Your OpenAI API Key
-1. Create a .env file in the scripts/ folder.
-  - Add your OpenAI API key inside: OPENAI_API_KEY=<your-api-key>.
-
-### Step 3: Run the Ingestion Script
-
-`python ingest.py ingest`
-
-It will provide you with the estimated cost.
-
-### Step 4: Move `index.faiss` and `index.pkl` generated in `scripts/output` to `application/` folder. 
-
-
-### Step 5: Run the Web App
-Once you run it, it will use new context relevant to your documentation.Make sure you select default in the dropdown in the UI.
-
-## Customization 
-You can learn more about options while running ingest.py by running:
-  - Make sure you select 'default' from the dropdown in the UI.
-
-## Customization
-You can learn more about options while running ingest.py by executing:
-`python ingest.py --help`
-|              Options             |                                                                                                                                |
-|:--------------------------------:|:------------------------------------------------------------------------------------------------------------------------------:|
-|            **ingest**            | Runs 'ingest' function, converting documentation to Faiss plus Index format                                                  |
-| --dir TEXT                       | List of paths to directory for index creation. E.g. --dir inputs --dir inputs2 [default: inputs]                               |
-| --file TEXT                      | File paths to use (Optional; overrides directory) E.g. --files inputs/1.md --files inputs/2.md                                 |
-| --recursive / --no-recursive     | Whether to recursively search in subdirectories [default: recursive]                                                           |
-| --limit INTEGER                  | Maximum number of files to read                                                                                                |
-| --formats TEXT                   | List of required extensions (list with .) Currently supported: .rst, .md, .pdf, .docx, .csv, .epub, .html [default: .rst, .md] |
-| --exclude / --no-exclude         | Whether to exclude hidden files (dotfiles) [default: exclude]                                                                  |
-| -y, --yes                        | Whether to skip price confirmation                                                                                             |
-| --sample / --no-sample           | Whether to output sample of the first 5 split documents. [default: no-sample]                                                  |
-| --token-check / --no-token-check | Whether to group small documents and split large. Improves semantics. [default: token-check]                                   |
-| --min_tokens INTEGER             | Minimum number of tokens to not group. [default: 150]                                                                          |
-| --max_tokens INTEGER             | Maximum number of tokens to not split. [default: 2000]                                                                         |
-|                                  |                                                                                                                                |
-|            **convert**           | Creates documentation in .md format from source code                                                                           |
-| --dir TEXT                       | Path to a directory with source code. E.g. --dir inputs [default: inputs]                                                      |
-| --formats TEXT                   | Source code language from which to create documentation. Supports py, js and java.  E.g. --formats py [default: py]            |

docs/pages/Guides/How-to-train-on-other-documentation.mdx

@@ -0,0 +1,44 @@
+
+import { Callout } from 'nextra/components'
+import Image from 'next/image'
+import { Steps } from 'nextra/components'
+
+## How to train on other documentation
+
+Training on other documentation sources can greatly enhance the versatility and depth of DocsGPT's knowledge. By incorporating diverse materials, you can broaden the AI's understanding and improve its ability to generate insightful responses across a range of topics. Here's a step-by-step guide on how to effectively train DocsGPT on additional documentation sources:
+
+**Get your document ready**:
+
+Make sure you have the document on which you want to train on  ready with you on the device which you are using .You can also use links to the documentation to train on.
+
+<Callout type="warning" emoji="⚠️">
+ Note: The document should be either of the given file formats .pdf, .txt, .rst, .docx, .md, .zip and  limited to 25mb.You can also train using the link of the documentation.
+
+</Callout>
+
+### Video Demo
+
+<Image src="/docs.gif" alt="prompts" width={800} height={500} />
+
+
+
+<Steps>
+### Step1
+Navigate to the sidebar where you will find `Source Docs` option,here you will find 3 options built in which are default,Web Search and None.
+
+
+### Step 2
+Click on the `Upload icon` just beside the source docs options,now browse and upload the document which you want to train on or select the `remote` option if you have to insert the link of the documentation.
+
+
+### Step 3
+Now you will be able to see the name of the file uploaded under the Uploaded Files ,now click on `Train`,once you click on train it might take some time to train on the document. You will be able to see the `Training progress` and once the training is completed you can click the `finish` button and there you go your document is uploaded.
+
+
+### Step 4
+Go to `New chat`  and from the side bar select the document  you uploaded  under the `Source Docs` and go ahead with your chat, now you can ask questions regarding the document you uploaded and you will get the effective answer based on it.
+
+</Steps>
+
+
+

docs/pages/Guides/How-to-use-different-LLM.md

@@ -1,48 +0,0 @@
-# Setting Up Local Language Models for Your App
-
-Your app relies on two essential models: Embeddings and Text Generation. While OpenAI's default models work seamlessly, you have the flexibility to switch providers or even run the models locally.
-
-## Step 1: Configure Environment Variables
-
-Navigate to the `.env` file or set the following environment variables:
-
-```env
-LLM_NAME=<your Text Generation model>
-API_KEY=<API key for Text Generation>
-EMBEDDINGS_NAME=<LLM for Embeddings>
-EMBEDDINGS_KEY=<API key for Embeddings>
-VITE_API_STREAMING=<true or false>
-```
-
-You can omit the keys if users provide their own. Ensure you set `LLM_NAME` and `EMBEDDINGS_NAME`.
-
-## Step 2: Choose Your Models
-
-**Options for `LLM_NAME`:**
-- openai ([More details](https://platform.openai.com/docs/models))
-- anthropic ([More details](https://docs.anthropic.com/claude/reference/selecting-a-model))
-- manifest ([More details](https://python.langchain.com/docs/integrations/llms/manifest))
-- cohere ([More details](https://docs.cohere.com/docs/llmu))
-- llama.cpp ([More details](https://python.langchain.com/docs/integrations/llms/llamacpp))
-- huggingface (Arc53/DocsGPT-7B by default)
-- sagemaker ([Mode details](https://aws.amazon.com/sagemaker/))
-
-
-Note: for huggingface you can choose any model inside application/llm/huggingface.py or pass llm_name on init, loads   
-
-**Options for `EMBEDDINGS_NAME`:**
-- openai_text-embedding-ada-002
-- huggingface_sentence-transformers/all-mpnet-base-v2
-- huggingface_hkunlp/instructor-large
-- cohere_medium
-
-If you want to be completely local, set `EMBEDDINGS_NAME` to `huggingface_sentence-transformers/all-mpnet-base-v2`. 
-
-For llama.cpp Download the required model and place it in the `models/` folder.
-
-Alternatively, for local Llama setup, run `setup.sh` and choose option 1. The script handles the DocsGPT model addition.
-
-## Step 3: Local Hosting for Privacy
-
-If working with sensitive data, host everything locally by setting `LLM_NAME`, llama.cpp or huggingface, use any model available on Hugging Face, for llama.cpp you need to convert it into gguf format.
-That's it! Your app is now configured for local and private hosting, ensuring optimal security for critical data.

docs/pages/Guides/How-to-use-different-LLM.mdx

@@ -0,0 +1,49 @@
+
+import { Callout } from 'nextra/components'
+import Image from 'next/image'
+import { Steps } from 'nextra/components'
+
+# Setting Up Local Language Models for Your App
+
+Setting up local language models for your app can significantly enhance its capabilities, enabling it to understand and generate text in multiple languages without relying on external APIs. By integrating local language models, you can improve privacy, reduce latency, and ensure continuous functionality even in offline environments. Here's a comprehensive guide on how to set up local language models for your application:
+
+## Steps:
+### For cloud version LLM change:
+<Steps >
+### Step 1
+Visit the chat screen and you will be to see the default LLM selected.
+### Step 2
+Click on it and you will get a drop down of various LLM's available to choose.
+### Step 3
+Choose the LLM of your choice.
+
+</Steps>
+
+
+
+
+### Video Demo
+<Image src="/llms.gif" alt="prompts" width={800} height={500} />
+
+### For Open source llm change:
+<Steps >
+### Step 1
+For open source you  have to edit .env file with LLM_NAME with their desired LLM name.
+### Step 2
+All the supported LLM providers are here application/llm and you  can check what env variable are needed for each
+List of latest supported LLMs are https://github.com/arc53/DocsGPT/blob/main/application/llm/llm_creator.py
+### Step 3
+Visit application/llm and select the file of your selected llm and there you will find the specific requirements needed to be filled in order to use it,i.e API key of that llm.
+</Steps>
+
+### For OpenAI-Compatible Endpoints:
+DocsGPT supports the use of OpenAI-compatible endpoints through base URL substitution. This feature allows you to use alternative AI models or services that implement the OpenAI API interface.
+
+
+Set the OPENAI_BASE_URL in your environment. You can change .env file with OPENAI_BASE_URL with the desired base URL or docker-compose.yml file and add the environment variable to the backend container.
+
+> Make sure you have the right API_KEY and correct LLM_NAME.
+
+
+
+