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A simple Streamlit application that helps visualize document chunks and queries in embedding space ๐Ÿ—บ๏ธ๐Ÿ”

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RAGmap ๐Ÿ—บ๏ธ๐Ÿ”

Overview

RAGmap is a simple RAG visualization tool for exploring document chunks and queries in embedding space.

Inspired by DeepLearning.ai's short course on Advanced Retrieval for AI with Chroma and Gabriel Chua's award-winning RAGxplorer.

What's inside?

RAGmap supports the following features:

โ˜๏ธโš ๏ธ Important notice: As of January 2024, chromadb's AmazonBedrockEmbeddingFunction only works with Titan models. Feel free to upvote this PR to add support for Cohere Embed models.

How to use

Prerequisites

Enable access to the embedding (Titan Embeddings, Cohere Embed) and text (Anthropic Claude) models via Amazon Bedrock.

For more information on how to request model access, please refer to the Amazon Bedrock User Guide (Set up > Model access)

Option 1 ๐Ÿ’ป

  1. Install dependencies

    pip install -r requirements.txt
  2. Run the application

    # ChromaDB
    streamlit run app.py
    
    # LanceDB (NEW!) ๐Ÿงช
    streamlit run app_lancedb.py
  3. Point your browser to http://localhost:8501

Option 2 ๐Ÿณ

  1. Run the following command to start the application

    docker-compose up
  2. Once the service is up and running, head over to http://localhost:8501

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A simple Streamlit application that helps visualize document chunks and queries in embedding space ๐Ÿ—บ๏ธ๐Ÿ”

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