How does this skill reduce RAG costs?

It utilizes Anthropic's prompt caching feature to cache document content while generating contexts for multiple chunks, which can reduce indexing costs by approximately 90%.

Why is hybrid search important for this skill?

Hybrid search combines the strengths of BM25 (keyword matching) and vector embeddings (semantic meaning), which Anthropic's research shows is the most effective way to utilize contextualized chunks.

When should I use Contextual Retrieval over traditional RAG?

You should use it when your documents have important metadata like dates or names that are often lost during chunking, or when retrieval precision is critical for your application's success.

What is Contextual Retrieval?

Contextual Retrieval is a technique that prepends a short, situational context to document chunks before they are embedded to ensure document-level meaning is preserved during the retrieval process.

Contextual Retrieval for RAG

Name: Contextual Retrieval for RAG
Author: yonatangross

byyonatangross

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数据科学与机器学习

Implements Anthropic's Contextual Retrieval technique to reduce retrieval failures in RAG pipelines through contextualized chunking and hybrid search.

关于

This skill provides a production-ready implementation of Contextual Retrieval, a technique designed to solve the 'lost context' problem in traditional RAG. By prepending situational context to document chunks before embedding them, it ensures that metadata like company names, dates, and specific topics are preserved in every vector. The skill includes full patterns for automated context generation using Claude Sonnet, cost-efficient prompt caching, and a hybrid search retriever that combines BM25 keyword matching with semantic vector similarity to achieve up to a 67% reduction in retrieval failures.

主要功能

Prompt caching patterns for up to 90% cost reduction during indexing
Hybrid search implementation combining BM25 and Vector scores
Parallel processing with rate limiting for high-volume document ingest
29 GitHub stars
Standardized normalization for combining disparate search scores
Automated situational context generation for document chunks

使用场景

Improving search accuracy in dense enterprise knowledge bases and technical documentation
Optimizing RAG pipelines for financial or legal analysis where entities and dates are frequently lost in chunks
Building high-precision customer support bots where document context is critical

关于

主要功能

Prompt caching patterns for up to 90% cost reduction during indexing
Hybrid search implementation combining BM25 and Vector scores
Parallel processing with rate limiting for high-volume document ingest
29 GitHub stars
Standardized normalization for combining disparate search scores
Automated situational context generation for document chunks

使用场景

Improving search accuracy in dense enterprise knowledge bases and technical documentation
Optimizing RAG pipelines for financial or legal analysis where entities and dates are frequently lost in chunks
Building high-precision customer support bots where document context is critical