RAG with LangChain — Document Loaders, Vector Stores, Retrieval

Build a RAG pipeline with LangChain. Load documents, split into chunks, embed into a vector store, and retrieve relevant context to answer questions from your own data.

~1 hour Hands-on Precision AI Academy

Today's Objective

A document Q&A system — load a PDF or text file, index it into a vector store, and ask natural language questions that get answered from the document's content. This is the most deployed LangChain pattern in production.

code

pip install langchain langchain-openai langchain-community
pip install chromadb pypdf tiktoken

The RAG Pipeline — 4 Steps

1. Load documents from files, URLs, or databases. 2. Split them into small chunks (LLMs have context limits). 3. Embed chunks into vectors and store in a vector database. 4. Retrieve the most relevant chunks at query time, inject them into the prompt.

Complete RAG Pipeline

rag_pipeline.py

python

from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_community.vectorstores import Chroma
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough

# 1. Load a document
loader = TextLoader("my_document.txt")
docs = loader.load()

# 2. Split into chunks
splitter = RecursiveCharacterTextSplitter(
    chunk_size=500,
    chunk_overlap=50
)
chunks = splitter.split_documents(docs)
print(f"Created {len(chunks)} chunks")

# 3. Embed and store in Chroma vector DB
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(chunks, embeddings)
retriever = vectorstore.as_retriever(search_kwargs={"k": 4})

# 4. Build the RAG chain
model = ChatOpenAI(model="gpt-4o-mini")

prompt = ChatPromptTemplate.from_template("""
Answer the question using only the provided context.
If the answer isn't in the context, say "I don't have that information."

Context:
{context}

Question: {question}
""")

def format_docs(docs):
    return "\n\n".join(d.page_content for d in docs)

rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt | model | StrOutputParser()
)

# Ask questions
answer = rag_chain.invoke("What are the main topics in this document?")
print(answer)

Day 3 Checkpoint

Before moving on, confirm understanding of these key concepts:

What is the core concept introduced in this lesson?
How does the main technique or tool work in practice?
What common mistakes should be avoided?
How would this apply in a real-world project?
What is the next logical step to build on this knowledge?

RAG with LangChain — Document Loaders, Vector Stores, Retrieval

Today's Objective

The RAG Pipeline — 4 Steps

Complete RAG Pipeline

Tomorrow: agents and tools

Supporting References & Reading

Go deeper with these external resources.

Day 3 Checkpoint