RAG Systems Explained: From Theory to Practice

Understand and implement Retrieval-Augmented Generation systems for enhanced AI applications.

Introduction

Large Language Models (LLMs) have a knowledge cutoff and can hallucinate. Retrieval-Augmented Generation (RAG) solves this by retrieving relevant information from an external knowledge base and feeding it to the LLM along with the prompt.

How RAG Works

1. Ingestion: Documents are split into chunks, embedded into vectors, and stored in a Vector Database.
2. Retrieval: When a user asks a question, it is embedded into a vector. The system searches the Vector DB for the most similar chunks.
3. Generation: The retrieved chunks are combined with the user's question into a prompt for the LLM.

Implementation

We'll use LangChain, OpenAI, and ChromaDB (a local vector store).

pip install langchain langchain-openai chromadb tiktoken

1. Ingestion

from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma

# Load documents
loader = TextLoader("knowledge_base.txt")
documents = loader.load()

# Split text
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = text_splitter.split_documents(documents)

# Embed and store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings)

2. Retrieval and Generation

from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)

qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever()
)

query = "What are the company's remote work policies?"
response = qa_chain.run(query)
print(response)

Advanced RAG Techniques

• Hybrid Search: Combining keyword search (BM25) with semantic search.
• Re-ranking: Using a cross-encoder model to re-rank retrieved documents for better relevance.
• Parent Document Retriever: Retrieving small chunks for search but passing larger parent chunks to the LLM for context.

Conclusion

RAG is the standard architecture for building "Chat with your Data" applications. It bridges the gap between the general knowledge of LLMs and your proprietary data.