Data Infrastructure
Vector Databases Explained
Understanding vector embeddings, semantic search, and choosing the right vector database.
What are Vector Databases?
Vector databases store and query high-dimensional vector embeddings—numerical representations of text, images, or other data. Unlike traditional databases that match exact keywords, vector databases enable semantic search by finding conceptually similar content.
How They Work
- Embedding Generation: Content is converted to vectors using models like OpenAI text-embedding-3, Cohere, or open-source alternatives.
- Indexing: Vectors are stored with optimized indexing (e.g., HNSW, IVF) for fast retrieval.
- Similarity Search: Queries are embedded and compared using cosine similarity or Euclidean distance.
- Results: Most similar vectors (and their associated metadata) are returned.
Popular Vector Databases
- Pinecone: Managed, scalable, easy to integrate—ideal for production.
- Weaviate: Open-source with GraphQL, good for hybrid search.
- Milvus: Open-source, high performance, supports multiple indexes.
- pgvector: PostgreSQL extension—great for existing Postgres users.
Use Cases
- Semantic search for documents, products, or knowledge bases
- RAG systems for AI chatbots and assistants
- Recommendation engines (similar items, content)
- Anomaly detection and fraud prevention
Choosing the Right Database
Selection depends on scale, latency requirements, budget, and team expertise. Managed services (Pinecone) simplify operations; open-source options (Weaviate, Milvus) offer flexibility. We help clients evaluate trade-offs and design architectures aligned with their roadmap.
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