Which vector stores are supported by this skill?

The skill includes setup scripts and templates for popular vector databases including FAISS, Chroma, Pinecone, and Qdrant.

Can I use this for conversational search?

Absolutely. The skill includes specific examples for conversational retrieval systems that manage chat history and context-aware queries.

Does it support multi-step agent logic?

Yes, it includes LangGraph templates for creating stateful, multi-actor applications with conditional routing and human-in-the-loop workflows.

What is the LangChain Patterns skill?

It is a specialized capability for Claude Code that provides implementation patterns, scripts, and templates for building production-ready RAG pipelines and agent workflows using the LangChain ecosystem.

How does it help with production monitoring?

It provides built-in integration patterns for LangSmith, enabling automatic tracing, debugging, and performance evaluation for your AI chains.

LangChain RAG Patterns

Name: LangChain RAG Patterns
Author: vanman2024

byvanman2024

数据科学与机器学习

Implements production-ready RAG pipelines and multi-step agent workflows using LangChain, LangGraph, and LangSmith templates.

关于

This skill provides a comprehensive toolkit for building sophisticated Retrieval-Augmented Generation (RAG) applications within Claude Code. It includes standardized patterns for document loading, vector database integration, and conversational retrieval, alongside advanced LangGraph workflows for stateful agent orchestration. By offering pre-built scripts and templates, it streamlines the development of AI-driven search and conversational systems while ensuring production-level observability through LangSmith integration, making it ideal for developers moving from prototype to production.

主要功能

Integrated LangSmith tracing for performance monitoring and debugging
Multi-actor agent orchestration patterns using LangGraph
0 GitHub stars
Pre-configured scripts for vector store setup (FAISS, Chroma, Pinecone, Qdrant)
Standardized RAG pipeline templates for document ingestion and retrieval
Advanced retrieval strategies including multi-query and self-querying patterns

使用场景

Building conversational AI agents that query internal enterprise documentation
Implementing stateful multi-step workflows for complex reasoning and tool use
Setting up production-ready observability and evaluation for LLM applications

关于

主要功能

Integrated LangSmith tracing for performance monitoring and debugging
Multi-actor agent orchestration patterns using LangGraph
0 GitHub stars
Pre-configured scripts for vector store setup (FAISS, Chroma, Pinecone, Qdrant)
Standardized RAG pipeline templates for document ingestion and retrieval
Advanced retrieval strategies including multi-query and self-querying patterns

使用场景

Building conversational AI agents that query internal enterprise documentation
Implementing stateful multi-step workflows for complex reasoning and tool use
Setting up production-ready observability and evaluation for LLM applications