2 .Vector Search Engine for AI applications
An Open Source Approach
3 . Background
– Deep learning has been proven to be an effective way to process unstructured data like image, video, sound, text, etc.
– The data management service and similarity search service of feature vectors are general components in many AI applications.
– To build a vector similarity search engine will help people to put their AI applications in production much easier.
4 .Content-based Retrieval
5 .Vectors Are Different
6 . How Milvus Helps AI Developers
Common requirements of
Challenge Benefit of Milvus
vector search
To call library like Faiss, SPTAG needs additional effort on Provide user friendly SDK and integrated data management
Ease of use
programing, performance tuning and data management capability.
Multiple Similarity Metrics In different scenarios, people may need different metrics Support Euclidean distance, dot product and more on roadmap.
High performance is the key if an AI application is doable Milvus is designed for similarity search over billions of
High Performance
in the real world vectors.
The vector similarity search is a computing intensive task.
If it requires a large number of servers to perform this Not only provide CPU option, we also try to adopt ASIC like
Cost Effective
task, then the AI applications will be less likely to be put GPU to reduce the hardware cost.
into production
Unstructured data are explosive growing. The amount of On single node, Milvus could support up to billions of
Scalability
vector data would continuously increasing in next decade vectors. Milvus also provide distributed scale out solution.
7 .Milvus Overview
8 .Vector Data Management
Sharding by growth
Searching across multi shards
Easy to append new vectors
9 .Flexible Distributed Policy
Scale Out for Capacity Scale Out for HA
10 . Indexes for Different Scenarios
IVF-CPU VS GRAPH IVF-CPU VS IVF-GPU
Index Build Search Accuracy Accuracy
IVF • CPU
3 3
• CPU
2.5
• GPU
2.5
• GPU
2 2
• CPU + GPU
Memory… Query time Memory… Query time
1.5 1.5
Graph • CPU
1 1
• CPU + GPU • CPU
0.5 0.5
0 0
– For CPU model Computing power Build time
• IVF index: better for scale-up and batch processing
Computing power Build time
(n:N)
IVF-CPU Graph-NSG IVF-CPU IVF-GPU
Batch support Batch support
• Graph index: fast response time for single query (1:N)
IVF-CPU VS IVF-HYBRID GRAPH VS IVF-HYBRID
– For CPU + GPU model, more indexes available
• IVF GPU index: optimized for large batch size (n:N)
Accuracy Accuracy
3 3
2.5 2.5
• IVF Hybrid index: optimized for most scenarios, but
2 2
Memory… Query time Memory… Query time
1.5 1.5
requires both CPU and GPU (still in experiment)
1 1
0.5 0.5
0 0
Best practice tip for IVF index: Computing power Build time Computing power Build time
• Build index with GPU IVF-CPU IVF-Hybrid IVF-Hybrid Graph-NSG
• Query with CPU
Batch support Batch support
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