What is a Vector Database?
A vector database is a type of database optimized for storing and querying high-dimensional vectors (embeddings). Unlike traditional databases that search for exact matches, vector databases excel at finding similar items based on mathematical distance between vectors.
How Vector Databases Work
- Data is converted to embeddings (vectors)
- Vectors are indexed for efficient search
- Queries are also converted to vectors
- Database finds nearest neighbors
- Results ranked by similarity
Key Concepts
Embeddings Dense numerical representations of data (text, images, etc.)
Similarity Metrics
- Cosine similarity
- Euclidean distance
- Dot product
Indexing Algorithms
- HNSW (Hierarchical Navigable Small World)
- IVF (Inverted File Index)
- LSH (Locality-Sensitive Hashing)
Use Cases
- Semantic search
- Recommendation systems
- RAG (Retrieval-Augmented Generation)
- Image similarity search
- Anomaly detection
- Clustering and classification
Popular Vector Databases
Purpose-Built
- Pinecone
- Weaviate
- Milvus
- Qdrant
- Chroma
Extensions
- pgvector (PostgreSQL)
- Elasticsearch vector search
- Redis Vector Similarity
Considerations
- Embedding model selection
- Index type for your use case
- Scaling and performance
- Metadata filtering needs
- Hybrid search requirements