Build in g a RAG Pipeline End-to-End: Ingestion, Retrieval, Generation, and Evaluation

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

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:

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.