Vespa

Execute searches that combine vector embeddings, keyword matching, and structured data filters in a single query. Vespa processes all three search modalities simultaneously and ranks results using unified scoring.

Serve machine learning models (ONNX, XGBoost, TensorFlow) directly within ranking pipelines to score and re-rank search results without external inference services. Models execute on indexed data during query time.

Process and index large batches of documents efficiently, supporting bulk updates, deletions, and insertions with optimized throughput.

Execute complex queries using Vespa's YQL (Vespa Query Language) to specify search logic, filtering, grouping, and result processing in a single declarative statement.

Build recommendation systems by combining collaborative filtering, content-based filtering, and ML models within Vespa's ranking pipeline to generate personalized recommendations.

Execute multi-phase ranking pipelines where initial phases use fast approximate ranking to reduce candidate set, and later phases apply expensive ML models to final candidates.

Filter search results using structured data conditions on fields like dates, numbers, categories, and enums. Combine multiple filter conditions with boolean logic.

Index new documents and updates to existing documents in real-time with immediate searchability. Supports both streaming updates and batch ingestion while maintaining index consistency.

Perform mathematical operations on multi-dimensional tensors during ranking and query processing. Enables complex feature interactions, embeddings operations, and custom scoring functions.

Automatically distribute indexes across multiple nodes and partitions to handle large datasets and high query throughput. Supports replication for fault tolerance and query load balancing.

Define custom ranking functions using Vespa's expression language to implement arbitrary scoring logic combining multiple signals, features, and mathematical operations.

Search documents using vector embeddings to find semantically similar content regardless of exact keyword matches. Supports approximate nearest neighbor search for efficient retrieval at scale.

Apply user-specific or context-specific ranking adjustments to search results based on user features, history, or preferences. Re-rank results differently for different users without separate queries.

Generate facet counts and enable drill-down navigation through search results by categories, attributes, or dimensions. Users can refine searches by selecting facet values.

Define document schemas that specify fields, data types, indexing strategies, and ranking features. Schemas control how data is stored, indexed, and made available for ranking.