Introduction
Retrieval-Augmented Generation (RAG) is a technique that combines the power of LLMs with external knowledge retrieval. This tutorial will guide you through building your first RAG application.
What is RAG?
RAG addresses a key limitation of LLMs: they only know what they were trained on. By retrieving relevant information from a knowledge base before generating a response, RAG enables LLMs to access up-to-date, domain-specific information.
Architecture
Documents → Chunking → Embedding → Vector Store ↓ Query → Embedding → Similarity Search → Retrieved Chunks ↓ Query + Context → LLM → Response
Prerequisites
pip install langchain openai chromadb sentence-transformers
Step 1: Load and Chunk Documents
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import TextLoader
loader = TextLoader("your_document.txt")
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200
)
chunks = text_splitter.split_documents(documents)
Step 2: Create Embeddings and Vector Store
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
embeddings = HuggingFaceEmbeddings()
vectorstore = Chroma.from_documents(chunks, embeddings)
retriever = vectorstore.as_retriever()
Step 3: Build the RAG Chain
from langchain.chains import RetrievalQA
from langchain_community.llms import OpenAI
llm = OpenAI(temperature=0)
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever
)
Step 4: Query Your Data
response = qa_chain.run("What is the main topic of the document?")
print(response)
Best Practices
- Use appropriate chunk sizes (500-1500 tokens)
- Include overlap between chunks
- Choose the right embedding model
- Implement metadata filtering
- Add citation to sources
Resources
Source: JackAI Hub