LocalGPT API - Serve Multiple Users At the Same time

---

---

AI Summary

Summary: Setting Up Local GPT for Multiple Clients

• Introduction
  • Discussing how to enable multiple clients to access a common knowledge base using Local GPT API.
  • Local GPT is a secure, local chat with documents project with 18,000+ GitHub stars.
  • Common business use case: multiple clients querying a shared knowledge base.
• Setup Process
  • Part 1: Ingestion
    • Documents (e.g., HR policies, financial documents) are chunked and vector embeddings are created.
    • Embeddings are stored in a vector store to serve as the knowledge base.
  • Part 2: Inference
    • Serving multiple clients through a Flask API server.
    • Implements a queuing mechanism to handle simultaneous client requests on a single GPU.
    • Multi-GPU systems can serve multiple Local GPT instances.
• Step-by-Step Setup Guide
  • Clone the Local GPT repository.
  • Optional: Use a preconfigured virtual machine with a discount code “prompt engineering”.
  • Create an account and select the “prompt engineering” category.
  • Change directory to desired location and clone the repo into a new folder.
  • Create a new virtual environment with the desired Python version.
  • If using the virtual machine, pull the latest changes from the repository.
  • Install all requirements to ensure up-to-date packages.
  • Choose between quantized or unquantized model versions based on GPU compatibility.
• Ingestion and API Server
  • Copy files to the source document folder.
  • Ingest documents to create a vector store using python ingest.py.
  • Start the API server with python run_local_gpt_api.py.
• User Interface (UI)
  • Move to the Local GPT UI folder and run python local_gpt_ui.py.
  • The UI allows for multiple instances to simulate different devices accessing the API.
  • Example queries demonstrate the API handling simultaneous requests.
• Responses and Customization
  • The server processes and responds to queries in the order received.
  • Responses are based on the context provided; irrelevant questions can be limited by adjusting system prompts.
• Conclusion
  • Tutorial demonstrates serving multiple clients with Local GPT API server.
  • More sophisticated methods like load balancing can be implemented.
  • Encourages contributions to the Local GPT project and participation in the Discord community.
  • Offers consulting services for products and startups.
  • Future content will highlight additional Local GPT features.
  • Subscribe to the channel for more videos.

For more details on the implementation and features of Local GPT, users can refer to the GitHub repository and the tutorial video.