RAG combines retrieval and generation: retrieve relevant documents, then generate answers conditioned on them. This post builds a complete pipeline from raw documents to question-answering.
Complete RAG Pipeline
Code Snippet
from langchain.document_loaders import PDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
# 1. Load documents
loader = PDFLoader("document.pdf")
docs = loader.load()
# 2. Split documents
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = splitter.split_documents(docs)
# 3. Create embeddings and vector store
embeddings = OpenAIEmbeddings()
vector_store = FAISS.from_documents(chunks, embeddings)
# 4. Create retrieval QA chain
llm = ChatOpenAI(model="gpt-4")
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=vector_store.as_retriever()
)
# 5. Query
result = qa_chain.run("What is the main topic?")
print(result)
Output:
Code Snippet
The main topic is...
Conclusion
RAG pipelines combine retrieval and generation to ground answers in data. Building end-to-end systems requires understanding data loading, chunking, embeddings, retrieval, and generation. This pipeline is the foundation of many production LLM applications. Next: advanced retrieval strategies (MMR, self-query) that improve answer quality.