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  1. embedding.html.md

    # Embedding A common technique is to map unstructured data - say, text or images - to points in an abstract vector space and then do the computation in that space. For example, retrieve similar dat...
    docs.vespa.ai/en/embedding.html.md
    Registered: Mon Sep 15 06:16:56 UTC 2025
    - Last Modified: Thu Sep 11 07:41:56 UTC 2025
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  2. rate-limiting-searcher.html.md

    # Rate Limiting Search Requests To avoid overloading a Vespa content cluster or to limit query load from e.g. certain clients, the bundled [Vespa Rate Limiting Searcher](https://github.com/vespa-en...
    docs.vespa.ai/en/performance/rate-limiting-searcher.html.md
    Registered: Mon Sep 15 06:15:36 UTC 2025
    - Last Modified: Thu Sep 11 07:41:56 UTC 2025
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  3. llms-external.html.md

    # External LLMs in Vespa Please refer to [Large Language Models in Vespa](llms-in-vespa.html) for an introduction to using LLMs in Vespa. Vespa provides a client for integration with OpenAI compati...
    docs.vespa.ai/en/llms-external.html.md
    Registered: Mon Sep 15 06:17:18 UTC 2025
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  4. sizing-feeding.html.md

    # Vespa Feed Sizing Guide Vespa is optimized to sustain a high feed load while serving - also during planned and unplanned changes to the instance. This guide provides an overview of how to optimiz...
    docs.vespa.ai/en/performance/sizing-feeding.html.md
    Registered: Mon Sep 15 06:17:26 UTC 2025
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  5. benchmarking.html.md

    # Benchmarking This is a step-by-step guide to get started with benchmarking on Vespa Cloud, based on the [Vespa benchmarking guide](/en/performance/vespa-benchmarking.html), using the [sample app]...
    docs.vespa.ai/en/cloud/benchmarking.html.md
    Registered: Mon Sep 15 06:17:37 UTC 2025
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  6. linguistics.html.md

    # Linguistics in Vespa Vespa uses a _linguistics_ module to process text in queries and documents during indexing and searching. The goal of linguistic processing is to increase _recall_ (how many ...
    docs.vespa.ai/en/linguistics.html.md
    Registered: Mon Sep 15 06:17:49 UTC 2025
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  7. nativerank.html.md

    # Ranking With nativeRank The _nativeRank_ text match score is a reasonably good text feature score which is computed at an acceptable performance by Vespa. It computes a normalized rank score whic...
    docs.vespa.ai/en/nativerank.html.md
    Registered: Mon Sep 15 06:15:46 UTC 2025
    - Last Modified: Thu Sep 11 07:41:56 UTC 2025
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  8. 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.md
    Registered: Mon Sep 15 06:19:19 UTC 2025
    - Last Modified: Thu Sep 11 07:41:56 UTC 2025
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  9. nativerank.html.md

    # nativeRank Reference The _nativeRank_ feature produces a reasonable text ranking score which is computed at an acceptable performance, and is a good candidate for [first phase](../phased-ranking....
    docs.vespa.ai/en/reference/nativerank.html.md
    Registered: Mon Sep 15 06:19:44 UTC 2025
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  10. page-templates-syntax.html.md

    # Page Templates Syntax This document is a reference to the elements of the Page Template XML format. Refer to the [Introduction to Page Templates](../page-templates.html). A page template describe...
    docs.vespa.ai/en/reference/page-templates-syntax.html.md
    Registered: Mon Sep 15 06:20:49 UTC 2025
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