Vector Databases Explained: Building AI Apps That Remember
pgvector, Pinecone, and Weaviate compared. Learn how vector databases power RAG, semantic search, and AI memory in modern applications.
Vector databases are the backbone of modern AI applications. They store embeddings and enable semantic search.
What Are Embeddings?
Embeddings are numerical representations of text, images, or other data. Similar items have similar embeddings.
The Options
pgvector
A PostgreSQL extension. Use it with Supabase or Neon for a simple, integrated solution.
Pinecone
A dedicated vector database. Best for large-scale production use.
Weaviate
Open-source with built-in ML models. Great for self-hosted setups.
RAG Pattern
1. Convert documents to embeddings
2. Store in vector database
3. When user asks a question, convert to embedding
4. Find similar documents
5. Feed to LLM as context
Best Practices
Choose pgvector if you already use PostgreSQL. Choose Pinecone for scale. Choose Weaviate for self-hosting.