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https://github.com/vinhnx/InkChatGPT

📕 Simple document Retrieval-augmented generation (RAG) LLM app
https://github.com/vinhnx/InkChatGPT

ai chromadb gpt langchain llm openai rag streamlit vector-database

Last synced: 2 months ago
JSON representation

📕 Simple document Retrieval-augmented generation (RAG) LLM app

Host: GitHub
URL: https://github.com/vinhnx/InkChatGPT
Owner: vinhnx
License: mit
Created: 2024-03-30T05:04:08.000Z (3 months ago)
Default Branch: main
Last Pushed: 2024-04-14T13:32:30.000Z (2 months ago)
Last Synced: 2024-04-14T13:58:19.980Z (2 months ago)
Topics: ai, chromadb, gpt, langchain, llm, openai, rag, streamlit, vector-database
Language: Python
Homepage: https://inkchatgpt.streamlit.app
Size: 864 KB
Stars: 6
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Lists

awesome-langchain - InkChatGPT - augmented generation (RAG) Chat app ![GitHub Repo stars](https://img.shields.io/github/stars/vinhnx/InkChatGPT?style=social) (Open Source Projects / Other / Chatbots)
awesome-langchain-zh - InkChatGPT

README

---
title: InkChatGPT
emoji: 📚
sdk: streamlit
sdk_version: 1.33.0 # The latest supported version
app_file: app.py
pinned: true
---

icon

📚 InkChatGPT - Chat with Documents

🤗 Hugging Face

Twitter
•
GitHub

# InkChatGPT

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

[![GitHub User's stars](https://img.shields.io/github/stars/vinhnx)](https://github.com/vinhnx)
[![HackerNews User Karma](https://img.shields.io/hackernews/user-karma/vinhnx)](https://news.ycombinator.com/user?id=vinhnx)
[![X (formerly Twitter) Follow](https://img.shields.io/twitter/follow/vinhnx)](https://x.com/vinhnx)

`InkChatGPT` is a `Streamlit` application that allows users to upload PDF documents and engage in a conversational Q&A with a language model (`LLM`) based on the content of those documents.

### Features

- Upload any documents and start asking key information about it, currently supports: PDF, TXT, DOCX, EPUB
- Limit 200MB per file
- Conversational Q&A with LLM (powered by `OpenAI`'s `gpt-3.5-turbo` model)
- `HuggingFace` embeddings to generate embeddings for the document chunks with `all-MiniLM-L6-v2` model.
- `VectorDB` for document vector retrieval storage

## Prerequisites

- Python 3.7 or later
- OpenAI API key (set as an environment variable: `OPENAI_API_KEY`)

## Installation

1. Clone the repository:

```sh
git clone https://github.com/vinhnx/InkChatGPT.git
cd InkChatGPT
```

2. Setup Virtual Environment
We recommend setting up a virtual environment to isolate Python dependencies, ensuring project-specific packages without conflicting with system-wide installations.

```sh
python3 -m venv venv
source venv/bin/activate
```

3. Install the required dependencies:

```sh
pip install -r requirements.txt
```

## Usage

1. Set the `OPENAI_API_KEY` environment variable with your OpenAI API key:

```sh
export OPENAI_API_KEY=YOUR_API_KEY
```

2. Run the Streamlit app:

```sh
streamlit run app.py
```

3. Upload PDF documents and start chatting with the LLM!

## Contributing

Contributions are welcome! Please open an issue or submit a pull request if you have any improvements or bug fixes.

## License

This project is licensed under the [MIT License](LICENSE).