How to Build a RAG System with LangChain

If you’re building AI apps that need to give smart, context-aware answers, Retrieval-Augmented Generation (RAG) should be on your radar. In this guide, we’ll walk through how to set up a production-grade RAG system using LangChain—a powerful toolset for developing language model-powered apps.

So, What Exactly Is RAG?

RAG is like giving your language model a memory boost. Instead of making it guess everything from scratch, it helps the model pull in relevant info from a knowledge base before answering. Here’s how it works:

Find useful documents related to the query
Stuff the prompt with that context
Generate a response using the combined input

It’s like giving your LLM a cheat sheet before every test.

Step 1: Get Your Environment Ready

Let’s get our tools installed:

pip install langchain chromadb sentence-transformers

That gives us everything we need—LangChain to orchestrate, ChromaDB to store vectors, and a solid transformer model for embeddings.

Step 2: Build the RAG Pipeline

Load and Chunk Your Documents

First, we bring in our data and break it down into manageable pieces:

from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

loader = TextLoader("data.txt")
documents = loader.load()

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200
)
chunks = text_splitter.split_documents(documents)

Why chunks? Because smaller, overlapping chunks help keep context tight without overwhelming the model.

Create Embeddings for Each Chunk

Now we translate those chunks into embeddings—numeric representations the model can understand:

from langchain.embeddings import HuggingFaceEmbeddings

embeddings = HuggingFaceEmbeddings(
    model_name="sentence-transformers/all-MiniLM-L6-v2"
)

This model is fast, lightweight, and great for general-purpose tasks.

Store Them in a Vector Database

We’ll store these embeddings in Chroma so we can search through them later:

from langchain.vectorstores import Chroma

vectorstore = Chroma.from_documents(
    documents=chunks,
    embedding=embeddings,
    persist_directory="./data/chroma"
)

Build Your Retrieval Chain

Next, we connect everything to an LLM:

from langchain.chains import RetrievalQA
from langchain.llms import OpenAI

llm = OpenAI(temperature=0)

qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever()
)

Now when you send a question, it’ll find the relevant info, plug it into the prompt, and send back a smart response.

Best Practices for Going Live

Here are some real-world tips for making your RAG setup shine in production:

Tune Your Chunking

Don’t just go with default sizes—try different chunk sizes depending on your content.
Preserve structure where possible (e.g., paragraphs, bullet points).
More overlap helps with continuity, but slows things down—find the sweet spot.

Pick the Right Embeddings

Match your model to the domain—some work better for legal, medical, or multilingual text.
Benchmark a few if you’re unsure.
Balance speed vs. accuracy based on your app’s needs.

Be Smart About Retrieval

Hybrid search (e.g., combining keyword + vector) can boost recall.
Filter by metadata when possible (like date or author).
Use re-ranking for higher precision.

Always Monitor

Track which documents are retrieved and whether they actually help.
Collect feedback from users and tune accordingly.
Build feedback loops to auto-improve results.

Example: A Conversational RAG Bot

Here’s a simple conversational setup with memory support:

from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory

memory = ConversationBufferMemory(
    memory_key="chat_history",
    return_messages=True
)

qa = ConversationalRetrievalChain.from_llm(
    llm=llm,
    retriever=vectorstore.as_retriever(),
    memory=memory
)

response = qa({"question": "What are the key components of a RAG system?"})
print(response["answer"])

This lets your bot remember past chats, making it feel more natural and helpful.

Key Deployment Tips

Once you’re ready to go live, think beyond the code:

Scalability

Use distributed vector stores if your dataset grows
Cache repeated queries
Load balance across multiple LLM instances

Security

Sanitize all user inputs
Watch out for prompt injection attacks
Rate-limit and monitor access

Performance

Use async functions wherever you can
Cache embeddings so you don’t recompute
Batch LLM calls if possible

Wrapping Up

LangChain gives you a flexible, powerful toolkit for building smart, context-aware AI apps. With a solid RAG setup, your app can pull in the right knowledge at the right time—and answer with confidence.

Here’s your quick checklist before launching:

Understand your users and use case
Pick embedding and LLM models that match your needs
Optimize retrieval, chunking, and memory
Monitor quality and scale wisely

Go build something awesome!

Building RAG Systems