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AI lessons (28/37)

AI — RAG

What is RAG?

Combining retrieval from external knowledge with LLM generation.

RAG Pipeline

  1. Embed documents
  2. Store in vector DB
  3. Retrieve relevant docs
  4. Generate with context

Basic RAG

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA

# Embed documents
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(docs, embeddings)

# Create QA chain
qa_chain = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(),
    retriever=vectorstore.as_retriever()
)

# Query
answer = qa_chain.run("What is AI?")

Mini Practice

  1. Build RAG pipeline
  2. Embed documents
  3. Query with context
  4. Evaluate results

Up Next

Continue with Fine-Tuning — fine-tuning models.

Related Topics

Fine-TuningEthicsBiasSafety

Frequently Asked Questions about RAG

What is RAG in AI?

RAG is a fundamental concept in AI. This lesson explains it step by step with clear examples, making it easy for beginners to understand.

How do I learn RAG?

Start by reading the explanation above, then try the code examples. Practice by modifying the examples and experimenting with different values. Hands-on practice is the best way to learn RAG.

Why is RAG important in AI?

RAG is essential for AI development. Understanding this concept will help you write better code and solve real-world problems more effectively.