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

    Build this properly → Start the LangChain Course

    Go from concepts to production-ready AI applications with our structured, hands-on course.

    Start the Course