langchain-rag

---
name: langchain-rag
description: "INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system. Covers document loaders, RecursiveCharacterTextSplitter, embeddings (OpenAI), and vector stores (Chroma, FAISS, Pinecone)."
---

<overview>
Retrieval Augmented Generation (RAG) enhances LLM responses by fetching relevant context from external knowledge sources.

**Pipeline:**
1. **Index**: Load → Split → Embed → Store
2. **Retrieve**: Query → Embed → Search → Return docs
3. **Generate**: Docs + Query → LLM → Response

**Key Components:**
- **Document Loaders**: Ingest data from files, web, databases
- **Text Splitters**: Break documents into chunks
- **Embeddings**: Convert text to vectors
- **Vector Stores**: Store and search embeddings
</overview>

<vectorstore-selection>

| Vector Store | Use Case | Persistence |
|--------------|----------|-------------|
| **InMemory** | Testing | Memory only |
| **FAISS** | Local, high performance | Disk |
| **Chroma** | Development | Disk |
| **Pinecone** | Production, managed | Cloud |

</vectorstore-selection>

---

## Complete RAG Pipeline

<ex-basic-rag-setup>
<python>
End-to-end RAG pipeline: load documents, split into ch