Aliyun Milvus Searchv1.0.0

Store document embeddings in AliCloud Milvus and enable natural language queries across a knowledge base. Filters allow scoping by source or chunk index, making it suitable for enterprise FAQ or internal documentation retrieval.

Use vector embeddings from product images (e.g., ResNet) to find visually similar items. Quick insert and filtered search support partitioning by category or brand, enhancing recommendation systems.

Match patient records across facilities by embedding demographic and medical history vectors. Filter by source or date range improves accuracy while maintaining compliance with data scoping.

Detect duplicate or near-duplicate articles, videos, or audio clips by comparing embeddings. With async insert and filters, the pipeline can index large media libraries and run periodic dedup checks.

Support multiple tenants in a single Milvus collection by using tenant IDs as filter fields. Each tenant's vectors remain isolated, and dimension alignment ensures consistent performance across tenants.

Offer Milvus search as a managed API for customers, charging based on number of vectors, queries, or storage usage. Companies pay a recurring fee for high-scale similarity search without managing infrastructure.

Charge per vector insertion and per search query, similar to cloud AI services. This model suits applications with variable load, allowing customers to pay only for what they consume.

Provide a free tier with limited vector capacity (e.g., 10K vectors) and basic filters. Premium tiers unlock larger dimensions, higher limits, and advanced filter capabilities, converting power users.