AIE5-Demo / README.md
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metadata
title: Paul Graham Essay Bot
emoji: 🐠
colorFrom: pink
colorTo: pink
sdk: docker
pinned: false

πŸ” Open Source RAG with Hugging Face Enpoints

Open Source LangChain Chainlit HuggingFace

πŸ“ About

Welcome to this Paul Graham Essay Bot - a friendly AI-powered system that demonstrates the power of Retrieval Augmented Generation using completely open source models! This application leverages modern AI technology to provide intelligent answers to your questions based on a collection of essays by Paul Graham, covering topics such as programming languages, startup culture, spam filtering, design principles, and the philosophy of hacking and innovation.

✨ Features

  • Open Source Models: Powered by NousResearch/Meta-Llama-3.1-8B-Instruct for text generation and Snowflake/snowflake-arctic-embed-m for embeddings
  • HuggingFace Integration: Models deployed as serving endpoints on HuggingFace
  • Intelligent Retrieval: Utilizes RAG (Retrieval Augmented Generation) for accurate and contextual responses
  • Fast & Responsive: Async processing for quick responses even with large document collections
  • Content-Focused: Explore ideas and concepts from the essays, not just information about the author

πŸ”§ How It Works

Behind the scenes, this application:

  1. Loads and Processes Documents: Breaks down essay content into manageable chunks
  2. Creates Embeddings: Converts text into numerical representations using Snowflake/snowflake-arctic-embed-m
  3. Builds a Vector Database: Stores the embeddings in a FAISS vector store for efficient retrieval
  4. Retrieves Relevant Content: Finds the most relevant essay sections based on your questions
  5. Generates Thoughtful Responses: Uses Meta-Llama-3.1-8B-Instruct to craft helpful answers based on the retrieved content

πŸ€” Example Questions

  • "What are some key strategies for starting a successful startup?"
  • "Why is Silicon Valley considered a hub for tech innovation?"
  • "How can good design improve user experience in technology products?"

πŸ› οΈ Technical Details

This application uses:

  • LangChain: For document processing and orchestrating the RAG pipeline
  • FAISS: For efficient vector similarity search
  • HuggingFace Endpoints:
    • NousResearch/Meta-Llama-3.1-8B-Instruct for text generation
    • Snowflake/snowflake-arctic-embed-m for embeddings
  • Chainlit: For the interactive chat interface
  • Hugging Face Spaces: For deployment and hosting

Happy exploring the fascinating content with open source AI! πŸ“šβœ¨

HuggingFace Endpoint Usage

LLM Endpoint LLM Endpoint Usage

Embedding Endpoint Embedding Endpoint Usage