What is Vespa?
Vespa.ai is an open-source AI search platform built for real-time, large-scale applications that blend vector search, text retrieval, machine learning, and structured data. Whether you're building a RAG (Retrieval-Augmented Generation) system, a personalized recommendation engine, or an intelligent e-commerce search, Vespa handles billions of constantly changing data items with sub-100ms latency—even under thousands of queries per second.
Unlike generic vector databases or traditional search engines, Vespa natively supports tensors, hybrid search, and on-the-fly model inference, letting developers combine semantic relevance with business logic seamlessly. It’s trusted by global innovators like Spotify, Yahoo, and Farfetch to power mission-critical AI features at enterprise scale—without sacrificing speed, accuracy, or flexibility.
What are the features of Vespa?
- Native Tensor & Vector Search: Store and query dense/sparse vectors and multi-dimensional tensors directly in your data model.
- Hybrid Search: Combine keyword (BM25), vector similarity, and learned ranking in a single query for superior relevance.
- Real-Time Inference: Run machine-learned models (ONNX, TensorFlow, etc.) during search or ranking without external services.
- Streaming Search Mode: Efficiently handle personal or private data (e.g., user-specific content) without full indexing—up to 20x cheaper.
- Automatic Scalability: Scale horizontally across clusters with zero downtime; handles petabytes and millions of writes per second.
- Unified Data Model: Index and query text, vectors, structured fields, and images in one system—no need for multiple databases.
- Fully Managed Cloud Option: Deploy on Vespa Cloud or AWS with built-in security, monitoring, and CI/CD support.
What are the use cases of Vespa?
- Powering RAG applications with high-quality, context-aware document retrieval for generative AI.
- Delivering personalized product recommendations in e-commerce with real-time user behavior integration.
- Enabling semantic + faceted search in digital commerce (e.g., “red running shoes under $100” + visual similarity).
- Building ad targeting systems that combine user profiles, content embeddings, and business rules.
- Supporting private search for user-specific data (e.g., email, documents) using streaming mode for cost efficiency.
- Accelerating AI research platforms like Elicit, where precision and speed in retrieving scientific papers are critical.
How to use Vespa?
- Start with a free trial on Vespa Cloud or deploy locally using Docker.
- Define your data schema with fields for text, vectors (as tensors), and structured attributes.
- Feed data via JSON APIs—Vespa handles real-time updates without reindexing delays.
- Write queries using YQL (Yahoo Query Language) to combine filters, ranking, and vector similarity.
- Integrate ONNX models for custom ranking or use built-in BM25 + ML-Rerank pipelines.
- Monitor performance via the built-in metrics dashboard and scale resources as needed.
