Vector Database
A specialized database designed to store and efficiently query high-dimensional vector embeddings for similarity search.
A vector database is a specialized type of database optimized for storing and querying vector embeddings. Unlike traditional databases that store structured data, vector databases are designed to handle high-dimensional numerical vectors and perform efficient similarity searches.
Key Features
Vector Storage
Vector databases store embeddings as arrays of floating-point numbers, typically ranging from hundreds to thousands of dimensions. Each vector represents a semantic encoding of data like text, images, or audio.
Similarity Search
The primary operation in vector databases is finding vectors that are "close" to a query vector. Similarity is typically measured using:
- Cosine similarity: Measures the angle between vectors
- Euclidean distance: Measures straight-line distance
- Dot product: Measures vector alignment
Indexing Strategies
Vector databases use specialized indexing techniques for fast search:
- HNSW (Hierarchical Navigable Small World): Graph-based index
- IVF (Inverted File Index): Clustering-based approach
- LSH (Locality-Sensitive Hashing): Hash-based approximation
Why Vector Databases Matter
Traditional databases aren't optimized for high-dimensional data:
- SQL databases struggle with similarity queries
- Standard indexes (B-trees) don't work well for vectors
- Vector operations require specialized algorithms
Vector databases solve these problems with purpose-built architectures.
Popular Vector Databases
- Pinecone: Managed cloud service
- Weaviate: Open-source with GraphQL
- Qdrant: Rust-based, high performance
- Milvus: Scalable, open-source
- Chroma: Lightweight, developer-friendly
Use Cases
- Semantic search: Find documents by meaning, not just keywords
- Recommendation engines: Suggest similar items to users
- Anomaly detection: Identify outliers in data
- RAG systems: Retrieve relevant context for language models
- Image search: Find visually similar images
Performance Considerations
- Indexing time: Building indexes can be time-consuming
- Query latency: Trade-off between speed and accuracy
- Memory usage: Large vector datasets require significant RAM
- Scalability: Sharding and replication strategies
Examples
- •Storing document embeddings for fast semantic search
- •Finding similar images based on visual features
- •Recommendation systems based on user preference vectors