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name: langchain-retriever description: LangChain retriever implementation with various retrieval strategies for RAG applications allowed-tools:

• Read
• Write
• Edit
• Bash
• Glob
• Grep graph: domains: [domain:software-engineering] specializations: [specialization:ai-agents-conversational] skillAreas: [skill-area:retrieval-augmented-generation, skill-area:search-indexing] roles: [role:ml-engineer, role:backend-engineer] workflows: [workflow:ml-model-lifecycle, workflow:feature-development]

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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