docGPT

This is a Retrieval-Augmented Generation (RAG) system powered by Langchain, Langchain Community, FAISS document store, and GPT4All. It allows users to ask questions about uploaded documents and receive short, precise, and well-sourced answers.

Prerequisites

• Python 3.12
• Docker
• Docker Compose

Setup

1. Clone the repository:

git clone https://github.com/nshefeek/docGPT.git
cd docGPT

2. Build and run the Docker containers:

docker-compose up --build -d

4. Access the application:

• FastAPI backend: http://localhost:8000/api
• Streamlit frontend: http://localhost:8501

Usage

1. Upload documents using the /upload endpoint or the Streamlit frontend.
2. Process directories of documents using the /process-directory endpoint.
3. Ask questions about the uploaded documents using the /ask endpoint or the Streamlit frontend.
4. Retrieve task status and manage the document store using the corresponding API endpoints.

API Endpoints

• /ask-question (POST): Ask a question about the uploaded documents.
• /upload/file (POST): Upload a single document file.
• /upload/directory (POST): Process a directory of documents.
• /search-store (POST): Search for documents based on a query.
• /clear-store (POST): Clear the document store.
• /document-count (GET): Get the document counts

For detailed information on the request and response formats, refer to the API documentation.

Important Points

• If you are using docker compose instead of docker-compose the application startup would be done after the download of the LLM model into the data folder. So please wait for the backend application to start after the download is done.