RAG • Chapter 3

Vector Databases Architecture

RAG engineering module on Vector Databases Architecture.

6 note blocks4 exam topics

🎯 Exam Focus Areas

Evaluate chunking and embedding strategies.Understand Vector DB indexing architectures like HNSW.Analyze RAG prompts for injection vulnerabilities.Calculate and utilize RAGAS evaluation metrics.

Vector databases are specialized storage systems designed to handle the high-dimensional vectors generated by embedding models. They allow for rapid similarity searches across millions or billions of records.

Advanced System Mechanics

Unlike relational databases that use B-Trees for exact matching, vector databases (like Pinecone, Milvus, ChromaDB, and Qdrant) use Approximate Nearest Neighbor (ANN) algorithms. The most common index type is HNSW (Hierarchical Navigable Small World) graphs, which sacrifice a tiny bit of accuracy for massive speed gains in calculating cosine similarity or Euclidean distance.

1Understand the vector space implications of this concept.
2Identify potential hallucination risks.
3Optimize for low latency and high relevance.
4Ensure robust system prompts.

Implementation Blueprint

# Using ChromaDB (an open-source vector database)
import chromadb

chroma_client = chromadb.Client()
collection = chroma_client.create_collection(name="my_rag_data")

collection.add(
    documents=["This is a document about RAG", "This is a document about cars"],
    metadatas=[{"source": "wiki"}, {"source": "manual"}],
    ids=["id1", "id2"]
)

results = collection.query(
    query_texts=["Tell me about Retrieval Augmented Generation"],
    n_results=1
)
print(results['documents'])

📝 Quick Revision Points

1Review the differences between similarity metrics.
2Practice the LangChain/LlamaIndex code snippets.
3Understand the HyDE architecture deeply.
4Memorize the security guardrail implementations.

← PreviousText Embeddings & Embedding Models Next →Document Chunking Strategies

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# Using ChromaDB (an open-source vector database) import chromadb chroma_client = chromadb.Client() collection = chroma_client.create_collection(name="my_rag_data") collection.add( documents=["This is a document about RAG", "This is a document about cars"], metadatas=[{"source": "wiki"}, {"source": "manual"}], ids=["id1", "id2"] ) results = collection.query( query_texts=["Tell me about Retrieval Augmented Generation"], n_results=1 ) print(results['documents'])