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xgboost.html.md
# Ranking with XGBoost Models Vespa supports importing Gradient Boosting Decision Tree (GBDT) models trained with XGBoost. ## Exporting models from XGBoost Vespa supports importing XGBoost's JSON m...docs.vespa.ai/en/xgboost.html.mdRegistered: Wed Nov 05 06:36:42 UTC 2025 - Last Modified: Tue Nov 04 13:00:40 UTC 2025 - 9.2K bytes - Viewed (0) -
batch-delete.html.md
# Batch delete Options for batch deleting documents: 1. Use [vespa feed](../vespa-cli.html#documents): ``` $ vespa feed -t my-endpoint deletes.json ``` 2. Find documents using a query, delete, repe...docs.vespa.ai/en/operations/batch-delete.html.mdRegistered: Wed Nov 05 06:35:57 UTC 2025 - Last Modified: Tue Nov 04 13:00:40 UTC 2025 - 4.5K bytes - Viewed (0) -
query-api.html.md
# Query API Use the Vespa Query API to query, rank and organize data. Example: ``` $ vespa query "select * from music where year > 2001" \ "ranking=rank_albums" \ "input.query(user_profile)={{cat:p...docs.vespa.ai/en/query-api.html.mdRegistered: Wed Nov 05 06:36:00 UTC 2025 - Last Modified: Tue Nov 04 13:00:40 UTC 2025 - 35.9K bytes - Viewed (0) -
features.html.md
# Features ## What is Vespa? Vespa is a platform for applications which need low-latency computation over large data sets. It allows you to write and persist any amount of data, and execute high vo...docs.vespa.ai/en/features.html.mdRegistered: Wed Nov 05 06:35:34 UTC 2025 - Last Modified: Tue Nov 04 13:00:40 UTC 2025 - 7.9K bytes - Viewed (0) -
A tensor formalism for computer science
A tensor formalism for computer science Jon Bratseth bratseth@verizonmedia.com Verizon Media Trondheim, Norway Håvard Pettersen havard.pettersen@verizonmedia.com Verizon Media Trondheim, Norway Les...docs.vespa.ai/en/a_tensor_formalism_for_computer_science.pdfRegistered: Wed Nov 05 06:37:46 UTC 2025 - Last Modified: Tue Nov 04 13:00:40 UTC 2025 - 567.1K bytes - Viewed (0) -
hybrid-search.html.md
# Hybrid Text Search Tutorial Hybrid search combines different retrieval methods to improve search quality. This tutorial distinguishes between two core components of search: - **Retrieval**: Ident...docs.vespa.ai/en/tutorials/hybrid-search.html.mdRegistered: Wed Nov 05 06:37:19 UTC 2025 - Last Modified: Tue Nov 04 13:00:40 UTC 2025 - 47.5K bytes - Viewed (0) -
ranking-expressions-features.html.md
# Ranking Expressions and Features Read the [ranking introduction](ranking.html) first. This guide is about [ranking expressions](reference/ranking-expressions.html)and [rank features](reference/ra...docs.vespa.ai/en/ranking-expressions-features.html.mdRegistered: Wed Nov 05 06:37:41 UTC 2025 - Last Modified: Tue Nov 04 13:00:40 UTC 2025 - 17.1K bytes - Viewed (0) -
exposing-schema-information.html.md
# Exposing schema information Some applications need to expose information about schemas to data plane clients. This document explains how to add an API for that to your application. You need to kn...docs.vespa.ai/en/exposing-schema-information.html.mdRegistered: Wed Nov 05 06:39:24 UTC 2025 - Last Modified: Tue Nov 04 13:00:40 UTC 2025 - 3.8K bytes - Viewed (0) -
approximate-nn-hnsw.html.md
# Approximate Nearest Neighbor Search using HNSW Index For an introduction to nearest neighbor search, see [nearest neighbor search](nearest-neighbor-search.html) documentation, for practical usage...docs.vespa.ai/en/approximate-nn-hnsw.html.mdRegistered: Wed Nov 05 06:38:25 UTC 2025 - Last Modified: Tue Nov 04 13:00:40 UTC 2025 - 14.3K bytes - Viewed (0) -
lightgbm.html.md
# Ranking with LightGBM Models [LightGBM](https://github.com/microsoft/LightGBM) is a gradient boosting framework, similar to [XGBoost](xgboost.html). Among other[advantages](https://github.com/mic...docs.vespa.ai/en/lightgbm.html.mdRegistered: Wed Nov 05 06:38:46 UTC 2025 - Last Modified: Tue Nov 04 13:00:40 UTC 2025 - 10.4K bytes - Viewed (0)