Contextual Retrieval for RAG
Learn how combining contextual embeddings with BM25 and reranking significantly boosts retrieval quality in RAG systems.
Understand how Contextual Embeddings (chunk-level context prepended before embedding) and Contextual BM25 (indexing chunks with metadata) improve retrieval accuracy.
Follow a step-by-step guide (e.g. Anthropic’s cookbook) to implement a hybrid system that merges BM25 and dense embeddings, includes reranking, and enhances ranking precision https://www.anthropic.com/news/contextual-retrieval?utm_source=chatgpt.com
Try building and evaluating a contextual retrieval pipeline using tools like LlamaIndex or Anthropic notebooks https://docs.llamaindex.ai/en/stable/examples/cookbooks/contextual_retrieval/?utm_source=chatgpt.com
- Practical tutorial demonstrating how to build and optimize a contextual RAG system using Anthropic methods.
- Prepends chunk-specific summaries (“This chunk covers …”) before embedding & BM25 indexing
- Implements rank fusion between lexical (BM25) and semantic embeddings, followed by reranking.
- Includes example code and notebook from Anthropic’s Contextual Embeddings repo https://github.com/anthropics/anthropic-cookbook/blob/main/skills/contextual-embeddings/guide.ipynb?utm_source=chatgpt.com
- Microsoft guide on contextual retrieval within their Azure AI pipeline, combining semantic and lexical retrieval.
- Walks through using BM25 + embedding search, chunking documents, and merging results https://techcommunity.microsoft.com/blog/azure-ai-services-blog/building-a-contextual-retrieval-system-for-improving-rag-accuracy/4271924?utm_source=chatgpt.com
- Demonstrates contextual chunk creation before embedding to boost retrieval accuracy.
- Uses reranking to reduce retrieval failure rates https://medium.com/%40odhitom09/the-most-effective-rag-approach-to-date-anthropics-contextual-retrieval-and-hybrid-search-8dc2af5cb970
3.Notebook-based guide illustrating how to implement contextual retrieval using LlamaIndex tools.
- Provides code to evaluate retrieval with and without context https://docs.llamaindex.ai/en/stable/examples/cookbooks/contextual_retrieval/?utm_source=chatgpt.com
- Covers setting up embedding models, chunk generation, and reranking via Cohere.
- Includes performance comparison and best practices.
- Defines contextual retrieval vs hybrid search https://simonwillison.net/2024/Sep/20/introducing-contextual-retrieval/?utm_source=chatgpt.com