README.md

metadata

title: LC Gradio DocsAI
emoji: 🚀
colorFrom: gray
colorTo: gray
sdk: gradio
sdk_version: 4.2.0
app_file: app.py
pinned: false

LC Gradio DocsAI 🚀

Overview

LC-Gradio-DocAI is a demo project showcasing a privately hosted advanced Documentation AI helper, demonstrating a fine-tuned 7B model's capabilities in aiding users with software documentation. This application integrates technologies like Retrieval-Augmented Generation (RAG) using LangChain, a vector store using Chroma DB or and FAISS and Gradio for a model UI to offer insightful documentation assistance. It's designed to help users navigate and utilize software tools efficiently by retrieving relevant documentation pages and maintaining conversational flow.

Key Features

• AI-Powered Documentation Retrieval: Utilizes various fine-tuned 7B models for precise and context-aware responses.
• Rich User Interface: Features a user-friendly interface built with Gradio.
• Advanced Language Understanding: Employs LangChain for implementing RAG setups and sophisticated natural language processing.
• Efficient Data Handling: Leverages Chroma DB and FAISS for optimized data storage and retrieval.
• Retrieval Chain with Prompt Tuning: Includes a retrieval chain with a prompt template for prompt tuning.
• Conversation Memory: Incorporates BufferMemory for short-term conversation memory, enhancing conversational flow.

Models Used

This setup is tested with the following models:

• mistralai/Mistral-7B-v0.1
• mistralai/Mistral-7B-Instruct-v0.1
• HuggingFaceH4/zephyr-7b-beta
• HuggingFaceH4/zephyr-7b-alpha
• tiiuae/falcon-7b-instruct
• microsoft/Orca-2-7b
• teknium/OpenHermes-2.5-Mistral-7B

Prerequisites

• Python 3.8 or later
• [Additional prerequisites...]

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/Docs-QAchat.git
   

2. Navigate to the project directory:

   cd Docs-QAchat
   

3. Install required packages:

   pip install -r requirements.txt
   

Configuration

1. Create a .env file in the project root.
2. Add the following environment variables to the .env file:

   HUGGINGFACEHUB_API_TOKEN=""
   AWS_S3_LOCATION=""
   AWS_S3_FILE=""
   VS_DESTINATION=""
   

Usage

Start the application by running:

python app.py

[Include additional usage instructions and examples]

Contributing

Contributions to LC-Gradio-DocsAI are welcome. Here's how you can contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/YourFeature).
3. Make changes and commit (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/YourFeature).
5. Create a new Pull Request.

Support

For support, please open an issue here on Github.

Authors and Acknowledgement

• [Name]
• Thanks to contributors of all the awesome open-source LLMs, LangChain, HuggingFace, Chroma Vector Store, FAISS and Graido UI.

License

This project is licensed under the [License] - see the LICENSE file for details.