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Managed Vector Search using Vespa Cloud

There is a growing interest in AI-powered vector representations of unstructured multimodal data and searching efficiently over these representations. This blog post describes how your organization can unlock the full potential of multimodal AI-powered vector representations using Vespa — the industry-leading open-source big data serving engine.


Vespa — Serving Engine

  • Scale elastically with data volume — handling billion scale datasets efficiently without pre-provisioning resources up-front.
  • Scale update and ingestion rates to handle evolving real-time data.
  • Scale with query volume using state-of-the-art retrieval and index structures and fully use modern hardware stacks.
  • CRUD operations at scale. Dataset sizes vary across organizations and use cases. Handling fast-paced evolving datasets is one of Vespa’s core strengths. Returning to our in-cart recommendation system for a moment, handling in-stock status updates, price changes, or real-time click feedback can dramatically improve the experience — imagine recommending an item out of stock? A lost revenue opportunity and a negative user experience.
  • Document Model. Vespa’s document model supports structured and unstructured field types, including tensor fields representing single-order dense vectors. Vespa’s tensor storage and compute engine is built from the ground up. The document model with tensor also enables feature-store functionality, accessing real-time features close to the data. Features stored as Vespa attributes support in place real-time updates at scale (50K updates/s per tensor field per compute node).
  • A feature-rich query language. Vespa’s SQL-like query language enables efficient online selection over potentially billions of rows, combining structured and unstructured data in the same query.
  • Machine Learning frameworks and accelerator integrations. Vespa integrates with the most popular machine learning frameworks like Tensorflow, PyTorch, XGboost, and LightGBM. In addition, Vespa integrates with ONNX-Runtime for accelerated inference with large deep neural network models that accelerate powerful data-to-vector models. Vespa handles model versioning, distribution, and auto-scaling of online inference computations. These framework integrations complement Vespa’s native support for tensor storage and calculations over tensors.
  • Efficient Vector Search. AI-powered vector representations are at the core of the unstructured data revolution. Vespa implements a real-time version of the HNSW algorithm for efficient Vector search, an implementation that is vetted and verified with multiple vector datasets on Vespa supports combining vector search with structured query filters at scale.

Get Started Today with Vector Search using Vespa Cloud.

  • Deployment to Vespa Cloud environments (dev, perf, and production) and how to perform safe deployments to production using CI/CD
  • Vespa Cloud’s security model
  • Vespa Cloud Auto-Scaling and pricing, optimizing the deployment cost by auto-scaling by resource usage
  • Interacting with Vespa Cloud — indexing your vector data and searching it at scale.
  • State-of-the-art text ranking: Vector search with AI-powered representations built on NLP Transformer models for candidate retrieval. The application has multi-vector representations for re-ranking, using Vespa’s phased retrieval and ranking pipelines. Furthermore, the application shows how embedding models, which map the text data to vector representation, can be deployed to Vespa for run-time inference during document and query processing.
  • State-of-the-art image search: AI-powered multi-modal vector representations to retrieve images for a text query.
  • State-of-the-art open-domain question answering: AI-powered vector representations to retrieve passages from Wikipedia, which are fed into an NLP reader model which extracts the answer. End-to-end represented using Vespa.



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