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Getting Started with RAG: A Practical Guide

·8 min read

Retrieval-Augmented Generation (RAG) has become one of the most practical applications of Large Language Models. By combining the power of LLMs with external knowledge bases, we can build systems that provide accurate, up-to-date, and contextually relevant responses.

What is RAG?

RAG is an architecture pattern that enhances LLM responses by retrieving relevant information from a knowledge base before generating an answer. This approach solves several key limitations of pure LLMs:

  • Knowledge cutoff: LLMs only know what they learned during training
  • Hallucinations: Without grounding, LLMs may generate plausible-sounding but incorrect information
  • Domain specificity: General-purpose LLMs lack deep knowledge of your specific domain

The RAG Pipeline

A typical RAG system consists of three main components:

1. Document Ingestion

First, we need to process and store our documents:

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
 
# Split documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200
)
chunks = text_splitter.split_documents(documents)
 
# Create embeddings and store in vector database
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(chunks, embeddings)

2. Retrieval

When a user asks a question, we retrieve relevant chunks:

# Retrieve relevant documents
retriever = vectorstore.as_retriever(
    search_type="similarity",
    search_kwargs={"k": 4}
)
relevant_docs = retriever.get_relevant_documents(query)

3. Generation

Finally, we pass the retrieved context to the LLM:

from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA
 
llm = ChatOpenAI(model="gpt-4")
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=retriever,
    return_source_documents=True
)
 
response = qa_chain({"query": "What is our refund policy?"})

Best Practices

Here are some lessons learned from building RAG systems in production:

  1. Chunk size matters: Experiment with different chunk sizes. Too small and you lose context; too large and you dilute relevance.

  2. Hybrid search: Combine semantic search with keyword matching for better results.

  3. Reranking: Use a reranker to improve the order of retrieved documents before passing to the LLM.

  4. Evaluation: Build a test set of questions and expected answers to measure system quality.

Conclusion

RAG systems offer a practical path to building LLM-powered applications that are grounded in your specific data. Start simple, measure everything, and iterate based on real user feedback.

In future posts, we'll explore advanced topics like query expansion, multi-step reasoning, and evaluation frameworks.

RAGLLMPythonLangChain
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