How

How to Build a RAG App with LangChain

RAG is the most practical pattern for building AI apps that need access to your own data. Here's how to build one from scratch.

Quick Answer

A RAG app combines a vector store (for retrieving relevant documents) with an LLM (for generating answers). LangChain provides document loaders, text splitters, embeddings, and retrieval chains to wire this together.

Step 1: Load Your Documents

Use LangChain's document loaders to ingest PDFs, web pages, or databases. The framework supports 80+ data sources out of the box.

Step 2: Create Embeddings

Split documents into chunks and generate vector embeddings using OpenAI, Cohere, or open-source models. Store these in a vector database like Pinecone, Chroma, or Weaviate.

Step 3: Build the Retrieval Chain

Connect your vector store to a retrieval chain that finds relevant documents and passes them to the LLM as context for generating answers.

Example Code

from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma

loader = PyPDFLoader("document.pdf")
docs = loader.load()

splitter = RecursiveCharacterTextSplitter(chunk_size=1000)
chunks = splitter.split_documents(docs)

vectorstore = Chroma.from_documents(chunks, OpenAIEmbeddings())

Use Cases

  • Internal knowledge base search
  • Customer support bots with product documentation
  • Legal document analysis
  • Research paper Q&A systems

When Not to Use

  • When your data fits entirely in the LLM context window
  • Real-time data that changes every second
  • When exact keyword search is more appropriate than semantic search

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