# Langchain RAG

> Builds advanced RAG (Retrieval-Augmented Generation) pipelines with LangChain, multiple retrievers, and document chains.

- Skill: `tomevault-io/langchain-rag` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/langchain-rag`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/langchain-rag/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/langchain-rag

---

# LangChain RAG

> Production RAG pipelines with LangChain — retrieval, generation, and evaluation.

## Quick Start
```python
from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate

# Vector store
db = Chroma(persist_directory="./chroma_db", embedding_function=OpenAIEmbeddings())

# RAG chain
llm = ChatOpenAI(model="gpt-4o")
prompt = ChatPromptTemplate.from_template("Answer based on context: {context}\nQuestion: {input}")
doc_chain = create_stuff_documents_chain(llm, prompt)
rag_chain = create_retrieval_chain(db.as_retriever(search_kwargs={"k": 4}), doc_chain)

result = rag_chain.invoke({"input": "What is RAG?"})
print(result["answer"])
```

## When to Use
- ✅ Question answering over documents
- ✅ Chat with your data (PDFs, wikis, databases)
- ❌ Not for simple Q&A without external knowledge

## Step-by-Step Instructions
1. Load and chunk documents
2. Create embeddings and vector store
3. Set up retriever with relevant parameters
4. Create RAG chain with custom prompt

## Dependencies
```bash
pip install langchain langchain-openai langchain-chroma
```

## Examples
Input: "Summarize the key findings" → Output: Answer grounded in retrieved documents

## Resources
- [LangChain RAG](https://python.langchain.com/docs/use_cases/question_answering/)
- [Examples](./examples/)

## Validation
1. Retriever returns relevant documents
2. LLM generates grounded answers
3. Citations are accurate

---
> Source: [ssrjkk/claude-skills](https://github.com/ssrjkk/claude-skills) — distributed by [TomeVault](https://tomevault.io).
<!-- tomevault:4.0:skill_md:2026-05-23 -->