Glossary · ai
Vector Database
Definition
A vector database stores text/images/audio as high-dimensional vectors (1024-3072 dim) and runs similarity search. It's the backbone of modern AI applications like RAG, semantic search, recommendations, and AI agents. Pinecone, Qdrant, Weaviate, pgvector are leading choices.
Detailed explanation
Classic DBs do exact match (WHERE name = 'X'); vector DBs do semantic similarity. A query for 'dog' returns high similarity for 'cat' because their embeddings are close. This is the foundation of semantic search.
Indexing algorithms: HNSW (Hierarchical Navigable Small World — most common), IVF (Inverted File), FAISS. Trade-offs: speed vs accuracy vs memory.
2026 options: Pinecone (managed, most mature, $70+/month), Qdrant (Rust-fast, open source), Weaviate (built-in modules), pgvector (Postgres extension — leverages existing DB), Chroma (POC).
Use cases
→RAG (document retrieval for LLM context)
→Semantic search (meaning, not keywords)
→Recommendation engines (user + product similarity)
→Image / video / audio search
→Anomaly detection (outlier vectors)
Pros
- +Strong semantic search
- +Multi-modal (text + image + audio)
- +Horizontally scalable
- +Core of modern AI infrastructure
Cons
- −Embedding cost (OpenAI $0.13/1M tokens)
- −Storage: 1M chunks × 3072 dim = ~12 GB
- −Re-embedding needed (new model)
- −Complexity (chunk + embed + retrieve + re-rank)
Related terms
Related services
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