LangChain Retriever Skill

SkillAI & models

LangChain retriever implementation with various retrieval strategies for RAG applications

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the LangChain Retriever Skill skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/ai-agents-conversational/skills/langchain-retriever/SKILL.md and read by ahel’s review.

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

VectorStoreRetriever: Basic similarity search
MultiQueryRetriever: Generates query variations
ContextualCompressionRetriever: Filters and compresses results
EnsembleRetriever: Combines multiple retrievers
SelfQueryRetriever: Structured metadata filtering
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

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: langchain-retriever
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter