Langchain Retriever

LangChain retriever implementation with various retrieval strategies for RAG applications

a5c-ai Updated 1.7k repo stars

File contents

LangChain Retriever Skill

Capabilities

  • Implement various LangChain retriever types
  • Configure vector store retrievers
  • Set up multi-query retrievers for improved recall
  • Implement contextual compression retrievers
  • Design ensemble retrievers combining multiple strategies
  • Configure self-query retrievers for structured filtering

Target Processes

  • rag-pipeline-implementation
  • advanced-rag-patterns

Implementation Details

Retriever Types

  1. VectorStoreRetriever: Basic similarity search
  2. MultiQueryRetriever: Generates query variations
  3. ContextualCompressionRetriever: Filters and compresses results
  4. EnsembleRetriever: Combines multiple retrievers
  5. SelfQueryRetriever: Structured metadata filtering
  6. ParentDocumentRetriever: Returns parent chunks

Configuration Options

  • Search type (similarity, mmr, similarity_score_threshold)
  • Number of documents to retrieve (k)
  • Score thresholds
  • Metadata filtering
  • Compression settings

Dependencies

  • langchain
  • langchain-community
  • Vector store client

a5c-ai/babysitter/tree/main/library/specializations/ai-agents-conversational/skills/langchain-retriever commit 110ae76c5c

Frequently asked questions

npx skillmds@latest add a5c-ai/langchain-retriever