Chat with Documents is Now Crazy Fast thanks to Groq API and Streamlit



AI Summary

Summary: Building a RAG Pipeline with Grok API

  • Introduction
    • Demonstrated a sub-second response time in a previous video.
    • Current video focuses on building a RAG pipeline using Grok API.
    • Will package the pipeline in a Streamlit app.
  • Setup
    • Install necessary packages: Beautiful Soup 4, FAISS, AMA, Streamlit, Grok, Lang Chain, and a package for secret management.
    • Import required libraries and set up the environment.
  • RAG Pipeline Overview
    • Process:
      1. Chunk website content.
      2. Compute embeddings for each chunk.
      3. Store vectors.
      4. On user query, compute query embeddings.
      5. Perform similarity search.
      6. Send relevant chunks and query to LLM.
      7. Receive response.
  • Implementation Steps
    • Load Grok API key using a secret management package.
    • Download and load data from a website essay.
    • Chunk text using a recursive character text splitter.
    • Compute embeddings with AMA embeddings.
    • Create a vector store and load the MixM model from Grok.
    • Define a prompt template for the LLM.
    • Set up a document chain and retrieval chain.
    • Use Streamlit for the user interface.
  • Streamlit App
    • Start an AMA embedding server.
    • Load embedding model and data on app launch.
    • Store vector store in Streamlit session state.
    • Define LLM, prompt template, document chain, and retrieval chain.
    • Time API calls and retrieval process.
    • Display response and context chunks used.
  • Execution
    • Run Streamlit app and observe the time taken to compute embeddings and create the vector store.
    • Ask questions and receive responses in real-time.
    • Note the speed of the end-to-end RAG pipeline.
  • Conclusion
    • Highlighted the speed and efficiency of the pipeline.
    • Mentioned upcoming advanced series for improving RAG pipelines.
    • Offered consulting services for working with LLMs and RAG pipelines.

For more details, refer to the video description for links to resources and consulting services.