With the rise of LLMs, I see everyone talking about Vector Databases like Pinecone or Milvus. Why can't I just store embeddings as blobs in my SQL database? What makes the indexing and retrieval (like HNSW or IVF) in a dedicated vector store so much faster for similarity searches?
3 answers
Traditional SQL databases are optimized for exact matches and range queries. Vector databases are built for "Approximate Nearest Neighbor" (ANN) searches. When you store embeddings, you aren't looking for an exact ID; you're looking for semantic similarity in high-dimensional space. Dedicated vector stores use algorithms like HNSW (Hierarchical Navigable Small World) to navigate these dimensions in milliseconds. While pgvector for Postgres is a great middle ground, a dedicated store handles the memory-intensive nature of high-dimensional indexing much better when you scale to millions of vectors for RAG applications.
If you use a dedicated vector database, how do you keep your metadata in sync with your primary relational database without significant latency or data drift?
You can use pgvector for small scales, but once you hit millions of high-dimensional vectors, the specialized indexing of a dedicated vector DB becomes mandatory for performance.
Exactly, Nancy. Latency is the killer in AI apps. If your similarity search takes 2 seconds, the user experience for your chatbot will be completely ruined.
Charles, most teams use Change Data Capture (CDC) tools. Whenever a row updates in the SQL DB, a trigger sends the update to a Lambda function to re-embed the text and update the Vector store.