Introduction to RAG

Retrieval-Augmented Generation (RAG) combines information retrieval with text generation to create more accurate and contextual AI systems.

This guide covers the three core components required to build a production RAG system.

Core Components

1. Embeddings

Embeddings are vector representations of your documents that enable semantic search. They transform text into numerical vectors that capture meaning.

DEVELOPERpython
from sentence_transformers import SentenceTransformer

# Load embedding model
model = SentenceTransformer('all-MiniLM-L6-v2')

# Create embedding
document = "RAG combines retrieval and generation"
embedding = model.encode(document)

Why it matters: Embeddings allow semantic comparison between documents, enabling the system to find contextually relevant information even when exact keywords don't match.

2. Vector Database

Once embeddings are created, they need to be stored in a vector database optimized for similarity search.

Popular options:

• Pinecone - Managed cloud solution
• Weaviate - Open-source, feature-rich
• ChromaDB - Lightweight, easy to start

Example with ChromaDB:

DEVELOPERpython
import chromadb

client = chromadb.Client()
collection = client.create_collection("documents")

# Store embeddings
collection.add(
    embeddings=[embedding],
    documents=[document],
    ids=["doc1"]
)

3. Retrieval and Generation

The retrieval component searches for relevant documents, which are then provided as context to a language model.

DEVELOPERpython
# Search for similar documents
query = "How does RAG work?"
query_embedding = model.encode(query)

results = collection.query(
    query_embeddings=[query_embedding],
    n_results=5
)

# Use results as context for generation
context = "\n".join(results['documents'][0])
prompt = f"Context: {context}\n\nQuestion: {query}\n\nAnswer:"

Implementation Steps

1. Indexing Phase: Transform documents into embeddings and store them
2. Retrieval Phase: Convert user query to embedding and search for relevant documents
3. Generation Phase: Provide retrieved context to LLM for response generation

Performance Considerations

• Embedding Model Selection: Balance between quality and speed
• Chunk Size: Optimal size depends on your use case (typically 256-512 tokens)
• Number of Retrieved Documents: More context isn't always better (3-5 is often optimal)

Next Steps

Once you understand these fundamentals:

• Explore chunking strategies for better retrieval
• Learn about reranking techniques
• Implement hybrid search (combining keyword and semantic search)