This repository contains a FastAPI backend and Streamlit app for PrivateGPT, an application built by imartinez. The PrivateGPT App provides an interface to privateGPT, with options to embed and retrieve documents using a language model and an embeddings-based retrieval system. All data remains local.

• Python 3.11 or later
• Minimum 8GB of memory

1. Create a Python virtual environment using your preferred method.

2. Copy the environment variables from example.env to a new file named .env. Modify the values in the .env file to match your desired configuration. The variables to set are:

   • PERSIST_DIRECTORY: The directory where the app will persist data.
   • MODEL_TYPE: The type of the language model to use (e.g., "GPT4All", "LlamaCpp").
   • MODEL_PATH: The path to the language model file.
   • EMBEDDINGS_MODEL_NAME: The name of the embeddings model to use.
   • MODEL_N_CTX: The number of contexts to consider during model generation.
   • API_BASE_URL: The base API url for the FastAPI app, usually it's deployed to port:8000.

3. Install the required dependencies by running the following command:

   pip install -r requirements.txt
   

To run the FastAPI backend, execute the following command:

gunicorn app:app -k uvicorn.workers.UvicornWorker --reload --timeout 500

This command starts the backend server and automatically handles the necessary downloads for the language model and the embedding models. The --timeout 500 option ensures that sufficient time is allowed for proper model downloading.

Please update the API_BASE_URL to appropriate FastAPI url

To run the Streamlit app, use the following command:

streamlit run streamlit_app.py --server.address localhost

This command launches the Streamlit app and connects it to the backend server running at localhost.

• Embedding documents is a quick process, but retrieval may take a long time due to the language model generation step. Optimization efforts are required to improve retrieval performance.

• The FastAPI backend can be used with any front-end framework of your choice. Feel free to integrate it with your preferred user interface.

• Community contributions are welcome! We encourage you to contribute to make this app more robust and enhance its capabilities.

The supported extensions for documents are:

• .csv: CSV,
• .docx: Word Document,
• .enex: EverNote,
• .eml: Email,
• .epub: EPub,
• .html: HTML File,
• .md: Markdown,
• .msg: Outlook Message,
• .odt: Open Document Text,
• .pdf: Portable Document Format (PDF),
• .pptx : PowerPoint Document,
• .txt: Text file (UTF-8),

Certainly! Here are examples of how to call the API routes mentioned in the README:

• Endpoint: GET /
• Description: Get a simple greeting message to verify that the APIs are ready.
• Example Usage:

  curl -X GET http://localhost:8000/

  import requests
  
  response = requests.get("http://localhost:8000/")
  print(response.json())

• Endpoint: POST /embed
• Description: Embed files by uploading them to the server.
• Example Usage:

  curl -X POST -F "[email protected]" -F "[email protected]" -F "collection_name=my_collection" http://localhost:8000/embed

  import requests
  
  files = [("files", open("file1.txt", "rb")), ("files", open("file2.txt", "rb"))]
  data = {"collection_name": "my_collection"}
  
  response = requests.post("http://localhost:8000/embed", files=files, data=data)
  print(response.json())

• Endpoint: POST /retrieve
• Description: Retrieve documents based on a query.
• Example Usage:

  curl -X POST -H "Content-Type: application/json" -d '{"query": "sample query", "collection_name": "my_collection"}' http://localhost:8000/retrieve

  import requests
  
  data = {"query": "sample query", "collection_name": "my_collection"}
  
  response = requests.post("http://localhost:8000/retrieve", json=data)
  print(response.json())

Please note that the actual URL (http://localhost:8000/) and the request payloads should be adjusted based on your specific setup and requirements.