Vespa.ai's core functionality is designed around efficient search and recommendations, and its technical features include:
- Hybrid query capability: Support vector search, text search and mixed query of structured data to meet the needs of complex scenarios (e.g., matching product images and descriptions simultaneously in e-commerce search).
- Real-time machine learning inference: Integrate TensorFlow or ONNX models to enable dynamic result sorting optimization directly during the search process.
- Ultra-large scale data processing: Handles hundreds of millions of data and supports thousands of queries per second with latency controlled within 100 milliseconds.
- Streaming Search Mode: Process only a subset of relevant data, reducing costs by a factor of 20 for privacy-sensitive scenarios.
- High Performance Indexing: Integrate HNSW (Hierarchical Navigation Small World) algorithm to optimize nearest neighbor search efficiency.
- multivector representation: Allow documents to contain multiple vector fields to improve search precision (e.g., matching both the title and body content of a paper).
In addition, Vespa provides both open source code and cloud services for deployment, and supports developers to customize extended functionality through Java.
This answer comes from the articleVespa.ai: an open source platform for building efficient AI search and recommendation systemsThe
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