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

• Vector Database Engines

Tags

• cloud-native
• vector-search
• enterprise
• scalable

Source

Microsoft Azure Vector Search Overview