---
title: Docs Qachat
emoji: 🚀
colorFrom: gray
colorTo: gray
sdk: gradio
sdk_version: 4.2.0
app_file: app.py
pinned: false
---

# Docs QAchat 🚀

## Overview
Docs QAchat is an 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), LangChain, Gradio UI, Chroma DB, and FAISS 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 as needed]

## Installation
1. Clone the repository:
   ```bash
   git clone https://github.com/yourusername/Docs-QAchat.git
   ```
2. Navigate to the project directory:
   ```bash
   cd Docs-QAchat
   ```
3. Install required packages:
   ```bash
   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:
```bash
python app.py
```
[Include additional usage instructions and examples]

## Contributing
Contributions to Docs QAchat are welcome. [Include contribution guidelines]

## Support
For support, contact [Support Contact Information].

## Authors and Acknowledgement
- [Name]
- Acknowledgements to the contributors of the used models and technologies.

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