The Secret Sauce Behind Semantic Search

Vector databases have become essential infrastructure for AI applications, enabling semantic search, recommendations, and RAG systems. Here's how they work.

What Are Vector Databases?

Key concepts to understand:

• Embeddings: Dense numerical representations of data

• Similarity Search: Find nearest neighbors by distance

• ANN Algorithms: Approximate nearest neighbor for speed

• Indexing: Organize vectors for efficient retrieval

Leading Solutions

The vector database landscape:

• Pinecone: Fully managed, enterprise-focused

• Weaviate: Open-source with hybrid search

• Milvus: High-performance, scalable

• Chroma: Developer-friendly, easy to start

• pgvector: PostgreSQL extension for existing stacks

"Vector search transformed our product recommendations. Conversion increased 35% overnight." — E-commerce CTO

Use Cases

Where vector databases shine:

• Semantic document search

• Image and video similarity

• Recommendation engines

• Anomaly detection

• RAG applications

Choosing the Right Solution

Consider: managed vs self-hosted, scale requirements, hybrid search needs, existing tech stack, and budget constraints.