AI / LangGraph LangChain Interview questions II
How do you build RAG pipelines with LangChain?
A RAG (Retrieval-Augmented Generation) pipeline enriches LLM responses with external knowledge by retrieving relevant documents at query time and injecting them into the prompt. A complete LangChain RAG pipeline has five stages:
- Load — ingest source documents with a DocumentLoader
- Split — chunk documents with a TextSplitter for efficient retrieval
- Embed & Store — embed chunks and store in a vector store
- Retrieve — at query time, fetch the most relevant chunks
- Generate — inject retrieved context into the prompt and generate an answer
from langchain_community.document_loaders import WebBaseLoader from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_community.vectorstores import Chroma from langchain_openai import OpenAIEmbeddings, ChatOpenAI from langchain import hub from langchain_core.output_parsers import StrOutputParser from langchain_core.runnables import RunnablePassthrough # 1. Load loader = WebBaseLoader("https://python.langchain.com/docs/get_started/introduction") docs = loader.load() # 2. Split splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) chunks = splitter.split_documents(docs) # 3. Embed & Store vectorstore = Chroma.from_documents(chunks, OpenAIEmbeddings()) # 4 & 5. Retrieve + Generate retriever = vectorstore.as_retriever() rag_prompt = hub.pull("rlm/rag-prompt") rag_chain = ( {"context": retriever | (lambda docs: "\n\n".join(d.page_content for d in docs)), "question": RunnablePassthrough()} | rag_prompt | ChatOpenAI() | StrOutputParser() ) answer = rag_chain.invoke("What is LangChain?")
What is the purpose of chunk_overlap in RecursiveCharacterTextSplitter?
In a RAG LCEL chain, why is RunnablePassthrough used for the 'question' key?
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