Building Real-World RAG Pipelines with LangChain

RAG (Retrieval-Augmented Generation) is the most practical pattern for building AI applications that need access to custom knowledge. Here's how I build production RAG pipelines.

The Architecture

User Query → Embeddings → Vector Search → Context Retrieval → LLM → Response

Step 1: Document Ingestion

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import DirectoryLoader

loader = DirectoryLoader("./docs", glob="**/*.md")
documents = loader.load()

splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200,
)
chunks = splitter.split_documents(documents)

Step 2: Vector Storage

I use Turso (libSQL with vector search) for production:

from langchain_community.vectorstores import Turso
from langchain_openai import OpenAIEmbeddings

vectorstore = Turso.from_documents(
    chunks,
    OpenAIEmbeddings(),
    connection_string=os.environ["TURSO_URL"],
)

Step 3: Retrieval + Generation

from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI

chain = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(model="gpt-4o"),
    retriever=vectorstore.as_retriever(search_kwargs={"k": 5}),
)

Production Considerations

1. Chunking strategy matters more than the LLM
2. Hybrid search (vector + keyword) outperforms pure vector search
3. Re-ranking with Cohere or cross-encoders improves relevance
4. Caching repeated queries saves money

NexusAI

These patterns power NexusAI, my multi-agent RAG platform that orchestrates multiple AI agents for complex research tasks.