Vector Database
A specialised database optimised for storing and searching vector embeddings. Essential for RAG and semantic search applications.
In-Depth Explanation
A vector database is a specialised data store optimised for storing, indexing, and querying high-dimensional vector data. Unlike traditional databases that search by exact values, vector databases find similar items based on mathematical distance.
Vector database capabilities:
- Vector storage: Efficiently store millions/billions of embeddings
- Similarity search: Find nearest neighbors quickly
- Metadata filtering: Combine semantic with attribute filters
- Scalability: Handle growing data volumes
- Real-time updates: Add new vectors instantly
Key technologies:
- Indexing algorithms: HNSW, IVF, PQ for fast search
- Distance metrics: Cosine, dot product, Euclidean
- Approximate nearest neighbor: Trade accuracy for speed
- Hybrid search: Combine vector and keyword search
Popular vector databases:
- Managed: Pinecone, Weaviate Cloud, Qdrant Cloud
- Self-hosted: Milvus, Weaviate, Qdrant, Chroma
- Extensions: pgvector (PostgreSQL), Elasticsearch vector
Use cases:
- RAG knowledge bases
- Semantic search engines
- Recommendation systems
- Image/audio similarity
- Anomaly detection
Business Context
Vector databases like Pinecone, Weaviate, and Qdrant enable fast similarity search over millions of embeddings for real-time AI applications.
How Clever Ops Uses This
We implement and optimise vector databases for Australian businesses, choosing the right solution based on scale, budget, and operational requirements.
Example Use Case
"Storing and searching millions of product embeddings for instant recommendations based on what users are viewing."