Vector Databases for AI Agents
The foundation for agent memory, RAG systems, and semantic search. Learn how vector databases work and which one to choose for your AI application.
Think of a vector database like a magical library where books are organized by meaning instead of alphabetically. If you ask for a book about "dogs," the librarian doesn't just find books with the word "dog" in the title - they also bring you books about "puppies," "pets," and "animals" because they understand those concepts are related.
Vector databases convert everything (text, images, sounds) into lists of numbers called "vectors." Similar things get similar numbers, so the database can find related items instantly - even if the words are completely different.
Example: You search for "happy" → The vector database also finds "joyful," "cheerful," "excited" because their vector numbers are close together in "meaning space."
Why Vector Databases Matter
Vector databases are essential infrastructure for AI agents that need memory and context. They enable:
When to Use Vector Databases
- • Building chatbots that need knowledge retrieval (RAG)
- • Creating agents with long-term memory across sessions
- • Implementing semantic search (finding by meaning)
- • Building recommendation systems
- • Storing and searching multimodal embeddings (text, images, audio)
- • Your context fits in the LLM's context window (<200K tokens)
- • You only need exact keyword search (use traditional search)
- • Your data changes constantly (vectors need re-embedding)
- • You have <1000 documents (simple in-memory search works)
Key Decision Factors
Vector Database Comparison
| Database | Best For | Deployment | Free Tier | Key Feature |
|---|---|---|---|---|
| Pinecone | Production, managed | Cloud | Yes (limited) | Serverless, auto-scaling |
| Weaviate | Flexibility, open-source | Self Cloud | Yes | GraphQL API, hybrid search |
| Milvus | Scale, performance | Self Cloud | Yes | GPU acceleration, billions of vectors |
| Qdrant | Speed, hybrid search | Self Cloud | Yes (1GB) | Rust-based, advanced filtering |
| ChromaDB | Prototyping, simplicity | Embedded | Yes | In-memory, developer-friendly |
| FAISS | Research, custom deployments | Local | Yes (open-source) | Meta-scale proven, CPU/GPU |
💡 Quick tip: Start with ChromaDB for prototyping, then move to Pinecone (managed) or Qdrant (open-source) for production.
Choosing the Right Vector Database
Choose managed if you want:
- No infrastructure management
- Auto-scaling and high availability
- Fast time to production
Choose self-hosted if you need:
- Full control and customization
- Data privacy and compliance
- Lower costs at very high scale
How Vector Databases Work
Convert text, images, or audio into high-dimensional vectors (typically 768-1536 dimensions) using embedding models like OpenAI's text-embedding-3-small.
Store vectors in specialized data structures (HNSW, IVF, etc.) optimized for similarity search. Metadata is stored alongside for filtering.
Query with a vector and find the k-nearest neighbors using distance metrics (cosine similarity, Euclidean distance, dot product). Sub-100ms retrieval even with millions of vectors.