Microsoft Azure Vector Database
Microsoft Azure provides vector search capabilities as part of its Azure AI Search service, supporting advanced information retrieval across various content types and scenarios.
Features
- Similarity Search: Retrieve documents by encoding queries and documents into vectors using embedding models (e.g., OpenAI, SBERT).
- Multimodal Search: Search across different content types, including text and images, using multimodal embeddings (e.g., OpenAI CLIP, GPT-4 Turbo with Vision).
- Hybrid Search: Supports combined vector and keyword search in a single query, merging results for improved relevance.
- Multilingual Search: Integrate with embedding and chat models trained in multiple languages; supports multi-language capabilities for both vector and non-vector content.
- Filtered Vector Search: Combine vector queries with metadata filters on text or numeric fields.
- Integration with Azure Services: Works with Azure AI Foundry, Azure OpenAI (for embeddings), Azure AI Services (image vectorization), and Azure data platforms (Blob Storage, Cosmos DB, SQL, OneLake) for indexing and ingestion.
- Flexible Embedding Options: Use Azure OpenAI models, bring your own models, or fine-tune general-purpose models for generating embeddings.
- Integrated Data Chunking and Vectorization: Supports both integrated and external vectorization workflows.
- Vector Store: Use as a pure vector store for long-term memory, knowledge bases, RAG architectures, and more.
- Scalable Vector Indexing: Supports large-scale vector workloads with quotas depending on service tier and creation date.
- Nearest Neighbor Search Algorithms:
- HNSW (Hierarchical Navigable Small World): Optimized for high-recall, low-latency applications; scalable and fast.
- Exhaustive K-Nearest Neighbors (KNN): Suitable for smaller datasets, finds global nearest neighbors.
- Approximate Nearest Neighbor (ANN) Search: Uses HNSW for scalable, efficient retrieval with tunable parameters for recall, latency, and resource usage.
- Open Source Compatibility: Commonly used with frameworks like LangChain.
Pricing
- Vector search is included at no extra charge with all Azure AI Search tiers in all regions.
- Newer services (created after April 3, 2024) support higher quotas for vector indexes.
Category
- cloud-native
- vector-search
- enterprise
- scalable
Source
Microsoft Azure Vector Search Overview
