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langchain-retriever skill

/plugins/babysitter/skills/babysit/process/specializations/ai-agents-conversational/skills/langchain-retriever

This skill enables building and optimizing LangChain retriever strategies for RAG workloads, improving recall and filtering across vector stores.

npx playbooks add skill a5c-ai/babysitter --skill langchain-retriever

Review the files below or copy the command above to add this skill to your agents.

Files (2)
SKILL.md
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---
name: langchain-retriever
description: LangChain retriever implementation with various retrieval strategies for RAG applications
allowed-tools:
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  - Glob
  - Grep
---

# 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

Overview

This skill provides a LangChain retriever implementation with multiple retrieval strategies tailored for RAG (retrieval-augmented generation) applications. It exposes configurable retriever types, vector store options, and ensemble patterns to improve recall and relevance. The goal is to make it straightforward to swap or combine retrievers to match different data and application needs.

How this skill works

The skill wires common LangChain retriever classes to your vector store client and exposes configuration for search type, k (number of docs), score thresholds, and metadata filters. It supports multi-query generation for broader recall, contextual compression to reduce noise, and ensemble patterns that merge results from multiple retrievers. Self-query and parent-document modes allow structured metadata filtering and returning original parent chunks for traceability.

When to use it

  • Building a RAG pipeline that needs flexible retrieval strategies.
  • When you need better recall by generating multiple query variants.
  • If you must compress or filter context before passing it to a model.
  • When combining signals from different retrievers (e.g., semantic + keyword).
  • When structured metadata filtering is required for legal, product, or domain constraints.

Best practices

  • Start with a VectorStoreRetriever and evaluate recall/precision before adding complexity.
  • Tune k and score thresholds with real user queries and a held-out test set.
  • Use MultiQueryRetriever when queries are short or ambiguous to improve coverage.
  • Apply ContextualCompressionRetriever to trim irrelevant content and reduce token cost.
  • Combine retrievers in an EnsembleRetriever only after measuring complementary value.

Example use cases

  • Customer support RAG system: combine semantic search with FAQ exact-match retriever for robust answers.
  • Enterprise search with metadata filters: use SelfQueryRetriever to restrict by department or confidentiality labels.
  • Summarization pipeline: use ContextualCompressionRetriever to feed only condensed, relevant passages to the model.
  • Academic literature assistant: MultiQueryRetriever improves recall across varied technical phrasing.

FAQ

Which retriever should I try first?

Begin with VectorStoreRetriever and tune k and search type, then add MultiQuery or compression if recall or noise is an issue.

How do I handle metadata filtering?

Use the SelfQueryRetriever to map structured filters to your vector store or apply metadata filters directly in the retriever config.