RAG Guide for Building AI Applications

Table of Contents

1. RAG Overview

What is RAG?

Retrieval-Augmented Generation (RAG) combines retrieval (searching a knowledge base) with generation (LLM) to provide accurate, up-to-date answers.

Why RAG?

• Reduces hallucinations
• Provides source citations
• Works with custom data
• Cost-effective compared to fine-tuning

2. RAG Pipeline

1. Document Loading: Load PDFs, websites, databases
2. Text Splitting: Split documents into chunks
3. Embedding: Convert text to vectors
4. Vector Store: Store vectors in database
5. Retrieval: Search for relevant chunks
6. Generation: Generate answer using retrieved context

3. Embeddings

from langchain.embeddings import OpenAIEmbeddings

# Create embeddings
embeddings = OpenAIEmbeddings()

# Embed text
text = "What is data science?"
vector = embeddings.embed_query(text)
print(vector)  # [0.0023, -0.0045, ...]

4. Vector Store

from langchain.vectorstores import FAISS

# Create vector store
vectorstore = FAISS.from_documents(chunks, embeddings)

# Search
results = vectorstore.similarity_search("What is data science?", k=3)
for result in results:
    print(result.page_content)

5. Retrieval

from langchain.chains import RetrievalQA

# Create retrieval chain
qa_chain = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(model="gpt-4"),
    retriever=vectorstore.as_retriever()
)

# Ask question
answer = qa_chain.run("What is data science?")
print(answer)

6. RAG Projects

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