A step-by-step guide to creating your own Retrieval-Augmented Generation system for querying private documentation.

Building a RAG Pipeline with LangChain

Retrieval-Augmented Generation (RAG) is transforming how we build AI applications that need to work with private or dynamic data. In this comprehensive tutorial, you’ll learn how to build your own RAG system from scratch using LangChain.

What is RAG?

RAG combines the power of large language models with the ability to retrieve relevant information from your own data sources. This approach addresses the key limitations of vanilla LLMs:

• Hallucination reduction: Grounds responses in your actual data
• Up-to-date information: No need to retrain models
• Domain-specific knowledge: Perfect for enterprise applications

Prerequisites

pip install langchain openai chromadb

Step 1: Document Loading

First, we’ll load and chunk our documents:

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

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

# Split into chunks
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200
)
chunks = text_splitter.split_documents(documents)

Step 2: Creating Embeddings

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma

embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(
    documents=chunks,
    embedding=embeddings,
    persist_directory="./chroma_db"
)

Step 3: Building the RAG Chain

from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI

llm = ChatOpenAI(model_name="gpt-4", temperature=0)

qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever()
)

Step 4: Querying Your Data

query = "How do I configure the authentication system?"
result = qa_chain.run(query)
print(result)

Advanced Optimizations

1. Hybrid Search

Combine semantic search with traditional keyword search for better recall.

2. Re-ranking

Use a separate model to re-rank retrieved documents for improved relevance.

3. Metadata Filtering

Add metadata to your documents for more precise retrieval.

Production Considerations

• Monitoring: Track query latency and retrieval quality
• Caching: Implement caching for frequently asked questions
• Security: Ensure proper access controls on your vector database

Conclusion

You now have a working RAG pipeline! This architecture is production-ready and can scale to handle millions of documents. Experiment with different chunk sizes, embedding models, and retrieval strategies to optimize for your specific use case.

Questions? Drop them in the comments or reach out on Twitter @mikeross