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Python / Python Modern Generative AI and Agents Interview Questions

How do you build a complete RAG pipeline using LangChain?

LangChain provides composable abstractions for every component of a RAG pipeline — document loaders, text splitters, embedding models, vector stores, retrievers, and LLM chains — making it straightforward to assemble a production-quality system without boilerplate.

The pipeline follows the standard RAG pattern: load and split documents into chunks, embed and index the chunks, then at query time retrieve the top-k relevant chunks and pass them with the question to an LLM for answer generation. LangChain's LCEL (LangChain Expression Language) uses the pipe operator | to compose these steps into a clean, readable chain.

# pip install langchain langchain-openai langchain-chroma
from langchain_community.document_loaders import WebBaseLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_chroma import Chroma
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough

# ── Step 1: Load and chunk documents
loader   = WebBaseLoader('https://lilianweng.github.io/posts/2023-06-23-agent/')
docs     = loader.load()
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks   = splitter.split_documents(docs)
print(f'Created {len(chunks)} chunks')

# ── Step 2: Embed and index
embeddings = OpenAIEmbeddings(model='text-embedding-3-small')
vectorstore = Chroma.from_documents(chunks, embeddings)
retriever   = vectorstore.as_retriever(search_kwargs={'k': 4})

# ── Step 3: Define the RAG prompt and chain
prompt = ChatPromptTemplate.from_template("""
Answer the question using ONLY the following context.
If the answer is not in the context, say 'I don't know'.

Context:
{context}

Question: {question}
""")

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

llm = ChatOpenAI(model='gpt-4o-mini', temperature=0)

# LCEL chain: retriever | format | prompt | llm | parse
rag_chain = (
    {'context': retriever | format_docs, 'question': RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

answer = rag_chain.invoke('What are the main components of an AI agent?')
print(answer)

What does RecursiveCharacterTextSplitter's chunk_overlap parameter do?
In a LangChain LCEL chain, what does the pipe operator (|) represent?

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