Vector Databases Explained
Overview Vector databases store and search high-dimensional embeddings. They’re essential for semantic search, recommendation systems, and RAG applications. How They Work Text → Embedding Model → Vector [0.1, -0.3, 0.8, ...] → Store → ANN Search Database Comparison Database Hosting Open Source Best For Pinecone Managed No Production, scale Weaviate Both Yes Hybrid search Chroma Self-host Yes Local dev, RAG Milvus Both Yes Large scale Qdrant Both Yes Filtering pgvector Self-host Yes Postgres users Chroma (Local Development) import chromadb from chromadb.utils import embedding_functions client = chromadb.Client() ef = embedding_functions.SentenceTransformerEmbeddingFunction() collection = client.create_collection("docs", embedding_function=ef) collection.add( documents=["Machine learning is...", "Deep learning uses..."], ids=["doc1", "doc2"] ) results = collection.query(query_texts=["What is ML?"], n_results=2) Pinecone (Production) from pinecone import Pinecone pc = Pinecone(api_key="your-key") index = pc.Index("my-index") index.upsert(vectors=[ {"id": "doc1", "values": [0.1, 0.2, ...], "metadata": {"source": "wiki"}} ]) results = index.query(vector=[0.1, 0.2, ...], top_k=5, include_metadata=True) Qdrant (Filtering) from qdrant_client import QdrantClient from qdrant_client.models import Filter, FieldCondition, MatchValue client = QdrantClient("localhost", port=6333) # Search with filters results = client.search( collection_name="docs", query_vector=[0.1, 0.2, ...], query_filter=Filter( must=[FieldCondition(key="category", match=MatchValue(value="tech"))] ), limit=10 ) Choosing a Database Prototyping: Chroma (no setup) Production SaaS: Pinecone (managed) Self-hosted scale: Milvus or Qdrant Existing Postgres: pgvector Hybrid search: Weaviate Key Resources Chroma Pinecone Qdrant