Lecture on Building Contextual Retrieval with Anthropic and Pinecone

Presenters

• Arjun: Developer Advocate at Pinecone
• Alex: Leads Developer Relations at Anthropic

Agenda

1. Introduction to Retrieval Augmented Generation (RAG)
2. Case study on video presentations
3. Benefits and strengths of this approach
4. Implementing contextual retrieval using Pinecone, Anthropic, and AWS
5. Live demo
6. Q&A session

Introduction to Retrieval Augmented Generation (RAG)

• Goal: Enhance the accuracy and quality of responses from LLMs by integrating a knowledge base.
• Standard Workflow:
  • Source data embedding
  • Vector database (e.g., Pinecone) for storage
  • Query through chatbot application
  • Retrieve context and pass to LLM for response generation
• Benefits: Helps in situations where LLMs need access to proprietary or non-public data.

Challenges with Video Data

• Multi-modal data handling: audio, video, slides
• Contextual understanding over lengthy content
• Discrepancy between visual and spoken content

Solution Overview

• Use Pinecone, Anthropic, and AWS to process video data into image and text pairs
• Perform retrieval augmented generation over these pairs

Detailed Process

1. Pre-processing video data:
   • Extract frames and transcripts
   • Pair frames with transcript segments
2. Contextual retrieval:
   • Summarize entire transcript with Claude
   • Create contextual descriptions using image and transcript data
3. Embedding and Storage:
   • Use AWS Titan for text embedding
   • Store in Pinecone with metadata
4. Retrieval and Generation:
   • Query Pinecone
   • Use Claude for visual question answering

Live Demo Highlights

• Demonstrated with a video presentation on multilingual semantic search
• Showed how to retrieve relevant frames and context using Pinecone and Claude
• Discussed handling complex queries involving diagrams or specific content

Technologies Used

• Pinecone: Vector database for semantic search
• Claude (Anthropic): LLM with visual understanding capabilities
• AWS: For storage and compute using Bedrock, Sagemaker Notebooks

Additional Techniques

• Contextual Retrieval: Enhancing context by adding relevant information to each chunk before embedding
• Prompt Caching: Reducing costs and latency by caching repeated prompt elements

Q&A Highlights

• Benefits of Claude over other LLMs: Longer context windows, improved intelligence and planning abilities
• Scaling and Cost Considerations: Prompt caching and efficient processing for large datasets
• Re-ranking and Context Length: Techniques to improve retrieval quality

Conclusion

• The demo and techniques offer a scalable method to improve retrieval and response generation over multimedia content.
• Plans to release the demo as a public resource for further experimentation.