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claude-codedevelopment

Babysitter langchain-retriever

LangChain retriever implementation with various retrieval strategies for RAG applications

install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/ai-agents-conversational/skills/langchain-retriever" ~/.claude/skills/a5c-ai-babysitter-langchain-retriever && rm -rf "$T"
manifest: library/specializations/ai-agents-conversational/skills/langchain-retriever/SKILL.md
tags
#rag-pipeline#retrieval-strategies#vector-store#langchain#document-retrieval#query-optimization
source content

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