Retrieval-Augmented Generation

What is RAG?

Retrieval-Augmented Generation (RAG) combines the power of pre-trained language models with external knowledge retrieval. It retrieves relevant documents from a knowledge base and uses them to generate more accurate, up-to-date responses.

Why RAG?

• Reduced Hallucination: Grounds responses in retrieved facts
• Up-to-date Information: Access to current data
• Domain Specialization: Can use domain-specific knowledge bases
• Transparency: Sources can be cited

RAG Implementation

from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA

class RAGSystem:
    def __init__(self, documents):
        self.embeddings = OpenAIEmbeddings()
        self.vectorstore = FAISS.from_documents(documents, self.embeddings)
        self.llm = OpenAI(temperature=0)

    def query(self, question, k=3):
        # Retrieve relevant documents
        retriever = self.vectorstore.as_retriever(search_kwargs={"k": k})
        relevant_docs = retriever.get_relevant_documents(question)

        # Build context
        context = "\n\n".join([doc.page_content for doc in relevant_docs])

        # Generate response
        prompt = f"""Answer the question based on the context below.

Context: {context}

Question: {question}

Answer:"""

        response = self.llm(prompt)
        return response, relevant_docs

Advanced RAG Techniques

Chunking Strategies

# Different ways to chunk documents
from langchain.text_splitter import (
    RecursiveCharacterTextSplitter,
    CharacterTextSplitter,
    MarkdownHeaderTextSplitter
)

# Strategy 1: Fixed-size chunks
fixed_splitter = CharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200,
    separator="\n"
)

# Strategy 2: Recursive character splitting
recursive_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200,
    separators=["\n\n", "\n", ". ", " ", ""]
)

# Strategy 3: Markdown-aware splitting
headers_to_split = [
    ("#", "Header 1"),
    ("##", "Header 2"),
    ("###", "Header 3"),
]
md_splitter = MarkdownHeaderTextSplitter(headers_to_split_on=headers_to_split)

Evaluation Metrics

Summary

RAG combines retrieval and generation for accurate, grounded responses. It's essential for building AI systems that need current or domain-specific knowledge.

Next: We'll explore vector databases and embeddings.