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title: LC Gradio DocsAI | |
emoji: π | |
colorFrom: gray | |
colorTo: gray | |
sdk: gradio | |
sdk_version: 4.2.0 | |
app_file: app.py | |
pinned: false | |
# LC Gradio DocsAI π | |
## 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: | |
```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 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. | |