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* Add multimodal search documentation Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Text image embedding processor Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add prerequisite Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Change query text Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Added bedrock connector tutorial and renamed ML TOC Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Name changes and rewording Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Change connector link Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Change link Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Implemented tech review comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Link fix and field name fix Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add default text embedding preprocessing and post-processing functions Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add sparse search documentation Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Fix links Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Pre/post processing function tech review comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Fix link Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Sparse search tech review comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Apply suggestions from code review Co-authored-by: Melissa Vagi <vagimeli@amazon.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> * Implemented doc review comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add actual test sparse pipeline response Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Added tested examples Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Added model choice for sparse search Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Remove Bedrock connector Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Implemented tech review feedback Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add that the model must be deployed to neural search Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Apply suggestions from code review Co-authored-by: Nathan Bower <nbower@amazon.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> * Link fix Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add session token to sagemaker blueprint Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Formatted bullet points the same way Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Specified both model types in neural sparse query Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Added more explanation for default pre/post-processing functions Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Remove framework and extensibility references Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Minor rewording Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> --------- Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> Co-authored-by: Melissa Vagi <vagimeli@amazon.com> Co-authored-by: Nathan Bower <nbower@amazon.com>
30 lines
1.3 KiB
Markdown
30 lines
1.3 KiB
Markdown
---
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layout: default
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title: Specialized queries
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has_children: true
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nav_order: 65
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has_toc: false
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---
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# Specialized queries
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OpenSearch supports the following specialized queries:
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- `distance_feature`: Calculates document scores based on the dynamically calculated distance between the origin and a document's `date`, `date_nanos`, or `geo_point` fields. This query can skip non-competitive hits.
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- `more_like_this`: Finds documents similar to the provided text, document, or collection of documents.
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- [`neural`]({{site.url}}{{site.baseurl}}/query-dsl/specialized/neural/): Used for vector field search in [neural search]({{site.url}}{{site.baseurl}}/search-plugins/neural-search/).
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- [`neural_sparse`]({{site.url}}{{site.baseurl}}/query-dsl/specialized/neural-sparse/): Used for vector field search in [sparse neural search]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-search/).
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- `percolate`: Finds queries (stored as documents) that match the provided document.
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- `rank_feature`: Calculates scores based on the values of numeric features. This query can skip non-competitive hits.
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- `script`: Uses a script as a filter.
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- [`script_score`]({{site.url}}{{site.baseurl}}/query-dsl/specialized/script-score/): Calculates a custom score for matching documents using a script.
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- `wrapper`: Accepts other queries as JSON or YAML strings.
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