Langchain Retriever

---
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]

---

# 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