Lecture on Improving RAG Systems: Tackling Lost Context Problem

Introduction

• Issues with RAG Agents:
  • Inaccurate answers
  • Hallucinations due to lost context
• Goal: Improve retrieval accuracy and reduce hallucinations

Example of Lost Context Problem

• Document: Wikipedia article on Berlin
  • Chunked into sentences for demonstration
• Context Loss:
  • "Berlin" mentioned only in the first chunk
  • Subsequent chunks refer to "Berlin" as "its" or "city"
• Standard RAG System:
  • Chunks sent independently to embedding models
  • Loss of context affects retrieval accuracy

Techniques to Mitigate Context Loss

• Contextual Retrieval
• Late Chunking

What is RAG?

• Retrieval-Augmented Generation
• AI retrieves relevant information to answer questions

Chunking in RAG Systems

• Splitting documents into segments for processing
• Different strategies: token/character count, overlapping chunks

Late Chunking

• Developed by Gina AI
• Embedding whole document first, then chunking
• Benefits:
  • Long context embedding models maintain context
  • Pooling/aggregating vectors to preserve context

Long Context Embedding Models

• Examples: Mistl, Quinn
• Gina AI's V3 model supports ~8,000 tokens

Workflow for Late Chunking

• Fetch document, chunk it, embed it, and upsert in vector store
• Overcoming limitations in tools like N8N

Contextual Retrieval

• Introduced by Anthropic
• Uses LLMs for context instead of embedding models
• Process:
  • Document split into chunks
  • Each chunk analyzed by LLM for context
  • Descriptive blurb generated
  • Added to chunk for embedding

Implementation Challenges

• Cost and time constraints
• Managing rate limits and token limits

Evaluation and Results

• Late Chunking and Contextual Retrieval tested against simple RAG setup
• Contextual Retrieval improves accuracy significantly
• Issues with scalability and cost for large document sets

Conclusion

• Advanced RAG techniques improve retrieval accuracy
• Cost and scalability remain challenges
• Community resources available for further exploration and implementation