Lecture Notes: Building Contextual Retrieval with Anthropic and Pinecone

Introduction

• Presenters: Arjun from Pinecone and Alex from Anthropic.
• Focus: Using vector databases in retrieval augmented generation (RAG) applications.

Agenda

1. Introduction to RAG: Benefits and limitations.
2. Case study: Applying RAG in video presentations.
3. Introducing contextual retrieval with Pinecone, Anthropic, and AWS.
4. Live demo in a SageMaker notebook.
5. Q&A session.

What is Retrieval Augmented Generation (RAG)?

• Goal: Enhance the quality and accuracy of responses generated by LLMs.
• Method: Provide LLMs with semantically relevant content from a knowledge base.
• Typical Workflow:
  • Source data is embedded in a vector space.
  • Embedded data is stored in a vector database (e.g., Pinecone).
  • Queries are matched against the database, and relevant context is passed to an LLM.
  • The LLM generates context-aware responses.

Challenges with Video Data

• Video data involves multiple modalities (audio, video, slides).
• Difficulty embedding different modalities in the same vector space.
• Video context can be extensive and complex.
• Discrepancy between visual and spoken information.

Solution Approach

• Use Pinecone, Claude (Anthropic), and AWS to handle video data.
• Convert video to image and text pairs, then apply RAG techniques.

Case Study Example

• Scenario: Presentation slide with a table (pros and cons of full-text search and multilingual semantic search).
• Process: Annotate the slide, provide transcript, and summarize video content to create a comprehensive context.

Application Stack

• Pinecone: Vector database for indexing and fast semantic search.
• Claude (Anthropic): LLM with visual understanding for preprocessing and generating responses.
• AWS: For storage, compute, and development environment.

Demo Overview

• Preprocess video into frames and transcripts.
• Use Claude to create context descriptions for each frame.
• Embed contextual descriptions using AWS Titan text embedding model.
• Store embedded data in Pinecone for semantic search.
• Use Pinecone to retrieve context relevant to queries and generate responses with Claude.

Benefits of Pinecone

• Fast semantic search at scale.
• Serverless architecture; no resource provisioning required.
• Integrates with popular data sources and frameworks.

Anthropic's Claude

• Leading model capabilities in LLM.
• Supports long context windows up to 200k tokens.
• Efficient in multi-action planning and agentic workflows.
• Supports prompt caching to reduce costs and processing time.

Contextual Retrieval

• Adds relevant context to each document chunk before embedding.
• Uses Claude to generate explanatory context for each chunk.
• Reduces incorrect chunk retrieval by up to 67%.
• Enables efficient RAG by combining semantic search with contextual information.

Live Demo Highlights

• Use of SageMaker notebooks for demonstration of contextual retrieval.
• Example queries were demonstrated, such as "How much data does it take to make a multilingual LLM?"
• Ability to interpret slides and diagrams from video content.
• Real-time question answering with visual and transcript data.

Conclusion

• Contextual retrieval enhances RAG capabilities, especially for complex data like video.
• Demo will be made publicly available for further exploration.

Q&A

• Discussed the advantages of Claude for RAG compared to other models.
• Addressed queries on scaling, prompt caching, and combining semantic search with keyword search.
• Discussed potential future releases and improvements in the contextual retrieval process.