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In this guide, you’ll learn how to connect PDF loaders, chunk your documents, generate embeddings, and query a vector database to build a Retrieval-Augmented Generation (RAG) pipeline using LangChain.
Make sure your OPENAI_API_KEY is set in the environment before running any of the code snippets.

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1. Install Dependencies

pip install langchain langchain-community langchain-openai langchain-chroma

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2. Import Required Modules

We’ll use: 
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_chroma import Chroma
from langchain.prompts import PromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser

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3. Load and Chunk the PDF

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3.1 Load the PDF

loader = PyPDFLoader("data/handbook.pdf")
pages = loader.load_and_split()

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3.2 Split into Overlapping Chunks

Adjust chunk_size and chunk_overlap based on your document length and the LLM’s context window.
text_splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=50)
chunks = text_splitter.split_documents(pages)

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4. Generate Embeddings & Build Vector Store

embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings)

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5. Configure Retriever & Formatter

Convert the vector store to a retriever and define a helper to merge relevant chunks: 
retriever = vectorstore.as_retriever()

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

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6. Define the LLM and Prompt Template

We’ll create a system prompt that forces the model to answer based only on retrieved context: 
llm = ChatOpenAI()

template = """
SYSTEM: You are a question-answering assistant.
Use only the provided context to answer.
If you don’t know, respond with “I don’t know.”
QUESTION: {question}
CONTEXT:
{context}
"""
prompt = PromptTemplate.from_template(template)

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7. Assemble the RAG Chain

Chain steps:
  1. Retrieve relevant chunks
  2. Format them into a context string
  3. Inject into the prompt template
  4. Generate a factual answer
  5. Parse output to string
chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

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8. Query the Chain

chain.invoke("How many sick leaves are allowed in a year?")
chain.invoke("How many unpaid personal leaves are allowed in a year?")
chain.invoke("What's the sick leave policy?")
# 'You are eligible for 10 days of paid sick leave per year, which can be used for any illness or injury that prevents you from working.'

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Summary of Steps

StepActionDescription
1Install & ImportInstall langchain, import loaders, splitters, embeddings, etc.
2Load PDFUse PyPDFLoader
3Chunk DocumentsSplit pages with RecursiveCharacterTextSplitter
4Embed & Store VectorsGenerate embeddings and index with Chroma.from_documents
5Create RetrieverConvert vector store into a retriever
6Format ContextDefine format_docs helper
7Define Prompt & LLMSet up PromptTemplate and ChatOpenAI
8Build & Invoke ChainAssemble LCEL chain and query with chain.invoke()

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Further Reading & References

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