Two NEW n8n RAG Strategies (Anthropic’s Contextual Retrieval & Late Chunking)
AI Summary
Video Summary: Techniques to Improve RAG Agent Retrieval Accuracy
- Introduction to Context Loss Problem
- RAG agents often struggle with accuracy and hallucinations due to losing context.
- Example: A Wikipedia article about Berlin illustrating the importance of context in document chunking.
- Standard RAG Document Processing
- Traditional method involves chunking documents (e.g., by sentence) which leads to loss of contextual links.
- Independent chunk embeddings may result in irrelevant or incomplete answers during retrieval.
- New Techniques to Mitigate Context Loss
- Late Chunking
- Developed by Gina AI, it involves embedding the entire document before chunking.
- Provides a more accurate representation by maintaining contextual relationships across chunks.
- Long context embedding models (e.g., supporting thousands of tokens) are crucial for this approach.
- Contextual Retrieval
- Introduced by Entropic, leverages LLM capabilities by sending chunks along with the original document for context.
- Generates descriptive blurbs for each chunk to improve response accuracy.
- Employs caching to reduce processing costs for large documents.
- Implementation Insight
- Demonstration using N8N workflow for both techniques involved fetching documents, chunking, and embedding.
- Emphasis on batch processing to manage API rate limits and cost management.
- Performance Evaluation
- Results indicate improved retrieval accuracy when using late chunking and contextual retrieval, particularly with longer documents.
- Significant reductions in hallucination rates based on benchmark evaluations.
- Conclusion
- The video encourages viewers to explore these advanced RAG techniques to enhance AI systems by maintaining context, thereby improving retrieval performance.