Modified: sites/solr/guide/solr/9_8/query-guide/dense-vector-search.html URL: http://svn.apache.org/viewvc/sites/solr/guide/solr/9_8/query-guide/dense-vector-search.html?rev=1088329&r1=1088328&r2=1088329&view=diff ============================================================================== --- sites/solr/guide/solr/9_8/query-guide/dense-vector-search.html (original) +++ sites/solr/guide/solr/9_8/query-guide/dense-vector-search.html Sun Dec 22 15:25:07 2024 @@ -1523,10 +1523,10 @@ client.add(Arrays.asList(d1, d2));</code <h2 id="query-time"><a class="anchor" href="#query-time"></a>Query Time</h2> <div class="sectionbody"> <div class="paragraph"> -<p>Apache Solr provides two query parsers that work with dense vector fields, that each support different ways of matching documents based on vector similarity: The <code>knn</code> query parser, and the <code>vectorSimilarity</code> query parser.</p> +<p>Apache Solr provides three query parsers that work with dense vector fields, that each support different ways of matching documents based on vector similarity: The <code>knn</code> query parser, the <code>vectorSimilarity</code> query parser and the <code>knn_text_to_vector</code> query parser.</p> </div> <div class="paragraph"> -<p>Both parsers return scores for retrieved documents that are the approximate distance to the target vector (defined by the similarityFunction configured at indexing time) and both support "Pre-Filtering" the document graph to reduce the number of candidate vectors evaluated (with out needing to compute their vector similarity distances).</p> +<p>All parsers return scores for retrieved documents that are the approximate distance to the target vector (defined by the similarityFunction configured at indexing time) and both support "Pre-Filtering" the document graph to reduce the number of candidate vectors evaluated (without needing to compute their vector similarity distances).</p> </div> <div class="paragraph"> <p>Common parameters for both query parsers are:</p> @@ -1650,6 +1650,69 @@ client.add(Arrays.asList(d1, d2));</code </div> </div> <div class="sect2"> +<h3 id="knn_text_to_vector-query-parser"><a class="anchor" href="#knn_text_to_vector-query-parser"></a>knn_text_to_vector Query Parser</h3> +<div class="paragraph"> +<p>The <code>knn_text_to_vector</code> query parser encode a textual query to a vector using a dedicated Large Language Model(fine tuned for the task of encoding text to vector for sentence similarity) and matches k-nearest neighbours documents to such query vector.</p> +</div> +<div class="paragraph"> +<p>In addition to the parameters in common with the other dense-retrieval query parsers, it takes the following:</p> +</div> +<div class="dlist"> +<dl> +<dt class="hdlist1"><code>model</code></dt> +<dd> +<table class="tableblock frame-none grid-all fit-content"> +<colgroup> +<col> +<col> +</colgroup> +<tbody> +<tr> +<td class="tableblock halign-left valign-top"><p class="tableblock"><strong>Required</strong></p></td> +<td class="tableblock halign-left valign-top"><p class="tableblock">Default: none</p></td> +</tr> +</tbody> +</table> +<div class="paragraph"> +<p>The model to use to encode the text to a vector. Must reference an existing model loaded into the <code>/schema/text-to-vector-model-store</code>.</p> +</div> +</dd> +<dt class="hdlist1"><code>topK</code></dt> +<dd> +<table class="tableblock frame-none grid-all fit-content"> +<colgroup> +<col> +<col> +</colgroup> +<tbody> +<tr> +<td class="tableblock halign-left valign-top"><p class="tableblock">Optional</p></td> +<td class="tableblock halign-left valign-top"><p class="tableblock">Default: 10</p></td> +</tr> +</tbody> +</table> +<div class="paragraph"> +<p>How many k-nearest results to return.</p> +</div> +</dd> +</dl> +</div> +<div class="paragraph"> +<p>Here’s an example of a simple <code>knn_text_to_vector</code> search:</p> +</div> +<div class="listingblock"> +<div class="content"> +<pre class="highlightjs highlight"><code class="language-text hljs" data-lang="text">?q={!knn_text_to_vector model=a-model f=vector topK=10}hello world query</code></pre> +</div> +</div> +<div class="paragraph"> +<p>The search results retrieved are the k=10 nearest documents to the vector encoded from the query <code>hello world query</code>, using the model <code>a-model</code>.</p> +</div> +<div class="paragraph"> +<p>For more details on how to work with vectorise text in Apache Solr, please refer to the dedicated page: <a href="text-to-vector.html" class="xref page">Text to Vector</a></p> +</div> +</div> +<div class="sect2"> <h3 id="vectorsimilarity-query-parser"><a class="anchor" href="#vectorsimilarity-query-parser"></a>vectorSimilarity Query Parser</h3> <div class="paragraph"> <p>The <code>vectorSimilarity</code> vector similarity query parser matches documents whose similarity with the target vector is a above a minimum threshold.</p> @@ -1710,6 +1773,90 @@ client.add(Arrays.asList(d1, d2));</code </div> </div> <div class="sect2"> +<h3 id="which-one-to-use"><a class="anchor" href="#which-one-to-use"></a>Which one to use?</h3> +<div class="paragraph"> +<p>Let’s see when to use each of the dense retrieval query parsers available:</p> +</div> +</div> +</div> +</div> +<div class="sect1"> +<h2 id="knn-query-parser-2"><a class="anchor" href="#knn-query-parser-2"></a>knn Query Parser</h2> +<div class="sectionbody"> +<div class="paragraph"> +<p>You should use the <code>knn</code> query parser when:</p> +</div> +<div class="ulist"> +<ul> +<li> +<p>you search for the top-K closest vectors to a query vector</p> +</li> +<li> +<p>you work directly with vectors (no text encoding is involved)</p> +</li> +<li> +<p>you want to a have a fine-grained control over the way you encode text to vector and prefer to do it outside of Apache Solr</p> +</li> +</ul> +</div> +</div> +</div> +<div class="sect1"> +<h2 id="knn_text_to_vector-query-parser-2"><a class="anchor" href="#knn_text_to_vector-query-parser-2"></a>knn_text_to_vector Query Parser</h2> +<div class="sectionbody"> +<div class="paragraph"> +<p>You should use the <code>knn_text_to_vector</code> query parser when:</p> +</div> +<div class="ulist"> +<ul> +<li> +<p>you search for the top-K closest vectors to a query text</p> +</li> +<li> +<p>you work directly with text and want Solr to handle the encoding to vector behind the scenes</p> +</li> +<li> +<p>you are building demos/prototypes</p> +</li> +</ul> +</div> +<div class="admonitionblock important"> +<table> +<tr> +<td class="icon"> +<i class="fa icon-important" title="Important"></i> +</td> +<td class="content"> +<div class="paragraph"> +<p>Apache Solr uses <a href="https://github.com/langchain4j/langchain4j";>LangChain4j</a> to interact with Large Language Models. +The integration is experimental and we are going to improve our stress-test and benchmarking coverage of this query parser in future iterations: if you care about raw performance you may prefer to encode the text outside of Solr</p> +</div> +</td> +</tr> +</table> +</div> +</div> +</div> +<div class="sect1"> +<h2 id="vectorsimilarity-query-parser-2"><a class="anchor" href="#vectorsimilarity-query-parser-2"></a>vectorSimilarity Query Parser</h2> +<div class="sectionbody"> +<div class="paragraph"> +<p>You should use the <code>vectorSimilarity</code> query parser when:</p> +</div> +<div class="ulist"> +<ul> +<li> +<p>you search for the closest vectors to a query vector within a similarity threshold</p> +</li> +<li> +<p>you work directly with vectors (no text encoding is involved)</p> +</li> +<li> +<p>you want to a have a fine-grained control over the way you encode text to vector and prefer to do it outside of Apache Solr</p> +</li> +</ul> +</div> +<div class="sect2"> <h3 id="graph-pre-filtering"><a class="anchor" href="#graph-pre-filtering"></a>Graph Pre-Filtering</h3> <div class="paragraph"> <p>Pre-Filtering the set of candidate documents considered when walking the graph can be specified either explicitly, or implicitly (based on existing <code>fq</code> params) depending on how and when these dense vector query parsers are used.</p>
Added: sites/solr/guide/solr/9_8/query-guide/text-to-vector.html URL: http://svn.apache.org/viewvc/sites/solr/guide/solr/9_8/query-guide/text-to-vector.html?rev=1088329&view=auto ============================================================================== --- sites/solr/guide/solr/9_8/query-guide/text-to-vector.html (added) +++ sites/solr/guide/solr/9_8/query-guide/text-to-vector.html Sun Dec 22 15:25:07 2024 @@ -0,0 +1,1522 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> + <meta name="viewport" content="width=device-width,initial-scale=1"> + <title>Text to Vector :: Apache Solr Reference Guide</title> + <meta name="generator" content="Antora 3.1.4"> + <link rel="stylesheet" href="../../../_/css/site.css"> + <script>var uiRootPath = '../../../_'</script> +<!-- Solr Changes - Start --> + <link rel="icon" href="../../../_/img/favicon.ico" type="image/x-icon"> +<!-- Solr Changes - End --> + </head> + <body class="article"> +<header class="header"> + <nav 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role="navigation"> +<button class="nav-toggle"></button> + <a href="../../latest/index.html" class="home-link"></a> +<nav class="breadcrumbs" aria-label="breadcrumbs"> + <ul> + <li><a href="../index.html">Solr Reference Guide</a></li> + <li><a href="text-to-vector.html">Text to Vector</a></li> + </ul> +</nav> +<div class="page-versions"> + <button class="version-menu-toggle" title="Show other versions of page">9.8-beta</button> + <div class="version-menu"> + <a class="version is-current" href="text-to-vector.html">9.8-beta</a> + <a class="version is-missing" href="../../latest/index.html">9.7</a> + <a class="version is-missing" href="../../9_6/index.html">9.6</a> + <a class="version is-missing" href="../../9_5/index.html">9.5</a> + <a class="version is-missing" href="../../9_4/index.html">9.4</a> + <a class="version is-missing" href="../../9_3/index.html">9.3</a> + <a class="version is-missing" href="../../9_2/index.html">9.2</a> + <a class="version is-missing" href="../../9_1/index.html">9.1</a> + <a class="version is-missing" href="../../9_0/index.html">9.0</a> + </div> +</div> +<!-- Solr Additions - Start --> +<!-- Solr New File --> +<span class="perma-link-copy" version="9_8"> +</span><!-- Solr Additions - End --> + <div class="edit-this-page"><a href="https://github.com/apache/solr/tree/main/solr/solr-ref-guide/modules/query-guide/pages/text-to-vector.adoc";>Edit this Page</a></div> + </div> + <div class="content"> +<!-- Solr Changes - Start --> +<nav class="toc sidebar" data-title="On This Page" data-levels="2"> + <div class="toc-menu"></div> +</nav> +<!-- Solr Changes - End --><article class="doc"> +<h1 class="page">Text to Vector</h1> +<div id="preamble"> +<div class="sectionbody"> +<div class="paragraph"> +<p>This module brings the power of <strong>Large Language Models</strong> (*LLM*s) to Solr. More specifically, it provides a text-to-vector capability, used on documents or queries, via integrating with popular external services that do this. The state-of-the-art of such services use an LLM, hence the name of this module. +<em>Without</em> this module, vectors must be supplied <em>to</em> Solr for indexing & searching, possibly coordinating with such services.</p> +</div> +</div> +</div> +<div class="sect1"> +<h2 id="main-concepts"><a class="anchor" href="#main-concepts"></a>Main Concepts</h2> +<div class="sectionbody"> +<div class="sect2"> +<h3 id="from-text-to-vector"><a class="anchor" href="#from-text-to-vector"></a>From Text to Vector</h3> +<div class="paragraph"> +<p>The aim of encoding text to numerical vectors is to represent text in a way that semantically similar sentences are encoded to vectors close in a vector space. +Vector distance metrics (algorithms) can then be used to compute a pairwise similarity, producing a score.</p> +</div> +</div> +<div class="sect2"> +<h3 id="large-language-models"><a class="anchor" href="#large-language-models"></a>Large Language Models</h3> +<div class="paragraph"> +<p>Specific Large Language Models are able to encode text to a numerical vector.</p> +</div> +<div class="paragraph"> +<p>For additional information you can refer to this <a href="https://sease.io/2021/12/using-bert-to-improve-search-relevance.html";>blog post</a>.</p> +</div> +<div class="sect3"> +<h4 id="text-to-vector-online-services"><a class="anchor" href="#text-to-vector-online-services"></a>Text to Vector Online Services</h4> +<div class="paragraph"> +<p>Training, fine-tuning and operating such Large Language Models is expensive.</p> +</div> +<div class="paragraph"> +<p>Many companies focus on this aspect and let users access APIs to encode the text (at the price of a license fee).</p> +</div> +<div class="paragraph"> +<p>Apache Solr uses <a href="https://github.com/langchain4j/langchain4j";>LangChain4j</a> to connect to such apis.</p> +</div> +<div class="admonitionblock important"> +<table> +<tr> +<td class="icon"> +<i class="fa icon-important" title="Important"></i> +</td> +<td class="content"> +<div class="paragraph"> +<p>This module sends your documents and queries off to some hosted service on the internet. +There are cost, privacy, performance, and service availability implications on such a strong dependency that should be diligently examined before employing this module in a serious way.</p> +</div> +</td> +</tr> +</table> +</div> +<div class="paragraph"> +<p>At the moment a subset of the text vectorisation services supported by LangChain4j is supported by Solr.</p> +</div> +<div class="paragraph"> +<p><strong>Disclaimer</strong>: Apache Solr is <strong>in no way</strong> affiliated to any of these corporations or services.</p> +</div> +<div class="paragraph"> +<p>If you want to add support for additional services or improve the support for the existing ones, feel free to contribute:</p> +</div> +<div class="ulist"> +<ul> +<li> +<p><a href="https://github.com/apache/solr/blob/main/CONTRIBUTING.md";>Contributing to Solr</a></p> +</li> +</ul> +</div> +</div> +</div> +</div> +</div> +<div class="sect1"> +<h2 id="module"><a class="anchor" href="#module"></a>Module</h2> +<div class="sectionbody"> +<div class="paragraph"> +<p>This is provided via the <code>llm</code> <a href="../configuration-guide/solr-modules.html" class="xref page">Solr Module</a> that needs to be enabled before use.</p> +</div> +</div> +</div> +<div class="sect1"> +<h2 id="llm-configuration"><a class="anchor" href="#llm-configuration"></a>LLM Configuration</h2> +<div class="sectionbody"> +<div class="paragraph"> +<p>You need to register / configure the plugins provided by the LLM module that you want to use. This is done in <code>solrconfig.xml</code>.</p> +</div> +<div class="ulist"> +<ul> +<li> +<p>Declaration of the <code>knn_text_to_vector</code> query parser.</p> +<div class="listingblock"> +<div class="content"> +<pre class="highlightjs highlight"><code class="language-xml hljs" data-lang="xml"><queryParser name="knn_text_to_vector" class="org.apache.solr.llm.texttovector.search.TextToVectorQParserPlugin"/></code></pre> +</div> +</div> +</li> +</ul> +</div> +</div> +</div> +<div class="sect1"> +<h2 id="text-to-vector-lifecycle"><a class="anchor" href="#text-to-vector-lifecycle"></a>Text to Vector Lifecycle</h2> +<div class="sectionbody"> +<div class="sect2"> +<h3 id="models"><a class="anchor" href="#models"></a>Models</h3> +<div class="ulist"> +<ul> +<li> +<p>A model encodes text to a vector.</p> +</li> +<li> +<p>A model in Solr is a reference to an external API that runs the Large Language Model responsible for text vectorisation.</p> +</li> +</ul> +</div> +<div class="admonitionblock important"> +<table> +<tr> +<td class="icon"> +<i class="fa icon-important" title="Important"></i> +</td> +<td class="content"> +<div class="paragraph"> +<p>the Solr vectorisation model specifies the parameters to access the APIs, the model doesn’t run internally in Solr</p> +</div> +</td> +</tr> +</table> +</div> +<div class="paragraph"> +<p>A model is described by these parameters:</p> +</div> +<div class="dlist"> +<dl> +<dt class="hdlist1"><code>class</code></dt> +<dd> +<table class="tableblock frame-none grid-all fit-content"> +<colgroup> +<col> +<col> +</colgroup> +<tbody> +<tr> +<td class="tableblock halign-left valign-top"><p class="tableblock"><strong>Required</strong></p></td> +<td class="tableblock halign-left valign-top"><p class="tableblock">Default: none</p></td> +</tr> +</tbody> +</table> +<div class="paragraph"> +<p>The model implementation. +Accepted values:</p> +</div> +<div class="ulist"> +<ul> +<li> +<p><code>dev.langchain4j.model.huggingface.HuggingFaceEmbeddingModel</code>.</p> +</li> +<li> +<p><code>dev.langchain4j.model.mistralai.MistralAiEmbeddingModel</code>.</p> +</li> +<li> +<p><code>dev.langchain4j.model.openai.OpenAiEmbeddingModel</code>.</p> +</li> +<li> +<p><code>dev.langchain4j.model.cohere.CohereEmbeddingModel</code>.</p> +</li> +</ul> +</div> +</dd> +<dt class="hdlist1"><code>name</code></dt> +<dd> +<table class="tableblock frame-none grid-all fit-content"> +<colgroup> +<col> +<col> +</colgroup> +<tbody> +<tr> +<td class="tableblock halign-left valign-top"><p class="tableblock"><strong>Required</strong></p></td> +<td class="tableblock halign-left valign-top"><p class="tableblock">Default: none</p></td> +</tr> +</tbody> +</table> +<div class="paragraph"> +<p>The identifier of your model, this is used by any component that intends to use the model (<code>knn_text_to_vector</code> query parser).</p> +</div> +</dd> +<dt class="hdlist1"><code>params</code></dt> +<dd> +<table class="tableblock frame-none grid-all fit-content"> +<colgroup> +<col> +<col> +</colgroup> +<tbody> +<tr> +<td class="tableblock halign-left valign-top"><p class="tableblock">Optional</p></td> +<td class="tableblock halign-left valign-top"><p class="tableblock">Default: none</p></td> +</tr> +</tbody> +</table> +<div class="paragraph"> +<p>Each model class has potentially different params. +Many are shared but for the full set of parameters of the model you are interested in please refer to the official documentation of the LangChain4j version included in Solr: <a href="https://docs.langchain4j.dev/category/embedding-models";>Vectorisationm Models in LangChain4j</a>.</p> +</div> +</dd> +</dl> +</div> +</div> +<div class="sect2"> +<h3 id="supported-models"><a class="anchor" href="#supported-models"></a>Supported Models</h3> +<div class="paragraph"> +<p>Apache Solr uses <a href="https://github.com/langchain4j/langchain4j";>LangChain4j</a> to support text vectorisation. +The models currently supported are:</p> +</div> +<div id="supported-models" class="openblock tabs is-loading"> +<div class="content"> +<div class="ulist tablist"> +<ul> +<li id="supported-models-hugging-face" class="tab"> +<p>Hugging Face</p> +</li> +<li id="supported-models-mistralai" class="tab"> +<p>MistralAI</p> +</li> +<li id="supported-models-openai" class="tab"> +<p>OpenAI</p> +</li> +<li id="supported-models-cohere" class="tab"> +<p>Cohere</p> +</li> +</ul> +</div> +<div id="supported-models-hugging-face--panel" class="tabpanel" aria-labelledby="supported-models-hugging-face"> +<div class="exampleblock"> +<div class="content"> +<div class="listingblock"> +<div class="content"> +<pre class="highlightjs highlight"><code class="language-json hljs" data-lang="json">{ + "class": "dev.langchain4j.model.huggingface.HuggingFaceEmbeddingModel", + "name": "<a-name-for-your-model>", + "params": { + "accessToken": "<your-huggingface-api-key>", + "modelId": "<a-huggingface-vectorisation-model>" + } +}</code></pre> +</div> +</div> +</div> +</div> +</div> +<div id="supported-models-mistralai--panel" class="tabpanel" aria-labelledby="supported-models-mistralai"> +<div class="exampleblock"> +<div class="content"> +<div class="listingblock"> +<div class="content"> +<pre class="highlightjs highlight"><code class="language-json hljs" data-lang="json">{ + "class": "dev.langchain4j.model.mistralai.MistralAiEmbeddingModel", + "name": "<a-name-for-your-model>", + "params": { + "baseUrl": "https://api.mistral.ai/v1";, + "apiKey": "<your-mistralAI-api-key>", + "modelName": "<a-mistralAI-vectorisation-model>", + "timeout": 60, + "logRequests": true, + "logResponses": true, + "maxRetries": 5 + } +}</code></pre> +</div> +</div> +</div> +</div> +</div> +<div id="supported-models-openai--panel" class="tabpanel" aria-labelledby="supported-models-openai"> +<div class="exampleblock"> +<div class="content"> +<div class="listingblock"> +<div class="content"> +<pre class="highlightjs highlight"><code class="language-json hljs" data-lang="json">{ + "class": "dev.langchain4j.model.openai.OpenAiEmbeddingModel", + "name": "<a-name-for-your-model>", + "params": { + "baseUrl": "https://api.openai.com/v1";, + "apiKey": "<your-openAI-api-key>", + "modelName": "<a-openAI-vectorisation-model>", + "timeout": 60, + "logRequests": true, + "logResponses": true, + "maxRetries": 5 + } +}</code></pre> +</div> +</div> +</div> +</div> +</div> +<div id="supported-models-cohere--panel" class="tabpanel" aria-labelledby="supported-models-cohere"> +<div class="exampleblock"> +<div class="content"> +<div class="listingblock"> +<div class="content"> +<pre class="highlightjs highlight"><code class="language-json hljs" data-lang="json">{ + "class": "dev.langchain4j.model.cohere.CohereEmbeddingModel", + "name": "<a-name-for-your-model>", + "params": { + "baseUrl": "https://api.cohere.ai/v1/";, + "apiKey": "<your-cohere-api-key>", + "modelName": "<a-cohere-vectorisation-model>", + "inputType": "search_document", + "timeout": 60, + "logRequests": true, + "logResponses": true + } +}</code></pre> +</div> +</div> +</div> +</div> +</div> +</div> +</div> +</div> +<div class="sect2"> +<h3 id="uploading-a-model"><a class="anchor" href="#uploading-a-model"></a>Uploading a Model</h3> +<div class="paragraph"> +<p>To upload the model in a <code>/path/myModel.json</code> file, please run:</p> +</div> +<div class="listingblock"> +<div class="content"> +<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">curl -XPUT 'http://localhost:8983/solr/techproducts/schema/vectorisation-model-store' --data-binary "@/path/myModel.json" -H 'Content-type:application/json'</code></pre> +</div> +</div> +<div class="paragraph"> +<p>To view all models:</p> +</div> +<div class="listingblock"> +<div class="content"> +<pre class="highlightjs highlight"><code class="language-text hljs" data-lang="text">http://localhost:8983/solr/techproducts/schema/vectorisation-model-store</code></pre> +</div> +</div> +<div class="paragraph"> +<p>To delete the <code>currentModel</code> model:</p> +</div> +<div class="listingblock"> +<div class="content"> +<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">curl -XDELETE 'http://localhost:8983/solr/techproducts/schema/vectorisation-model-store/currentModel'</code></pre> +</div> +</div> +<div class="paragraph"> +<p>To view the model you just uploaded please open the following URL in a browser:</p> +</div> +<div class="listingblock"> +<div class="content"> +<pre class="highlightjs highlight"><code class="language-text hljs" data-lang="text">http://localhost:8983/solr/techproducts/schema/vectorisation-model-store</code></pre> +</div> +</div> +<div class="listingblock"> +<div class="title">Example: /path/myModel.json</div> +<div class="content"> +<pre class="highlightjs highlight"><code class="language-json hljs" data-lang="json">{ + "class": "dev.langchain4j.model.openai.OpenAiEmbeddingModel", + "name": "openai-1", + "params": { + "baseUrl": "https://api.openai.com/v1";, + "apiKey": "apiKey-openAI", + "modelName": "text-embedding-3-small", + "timeout": 60, + "logRequests": true, + "logResponses": true, + "maxRetries": 5 + } +}</code></pre> +</div> +</div> +</div> +<div class="sect2"> +<h3 id="running-a-text-to-vector-query"><a class="anchor" href="#running-a-text-to-vector-query"></a>Running a Text-to-Vector Query</h3> +<div class="paragraph"> +<p>To run a query that embeds your query text, using a model you previously uploaded is simple:</p> +</div> +<div class="listingblock"> +<div class="content"> +<pre class="highlightjs highlight"><code class="language-text hljs" data-lang="text">?q={!knn_text_to_vector model=a-model f=vector topK=10}hello world query</code></pre> +</div> +</div> +<div class="paragraph"> +<p>The search results retrieved are the k=10 nearest documents to the vector encoded from the query <code>hello world query</code>, using the model <code>a-model</code>.</p> +</div> +<div class="paragraph"> +<p>For more details on how to work with vector search query parsers in Apache Solr, please refer to the dedicated page: <a href="dense-vector-search.html" class="xref page">Dense Vector Search</a></p> +</div> +</div> +</div> +</div> +</article> + </div> +</main> +</div> +<footer class="footer"> + <!-- Solr Changes - Start --> + <p> © Apache Software Foundation. All rights reserved.</p> + <script async src="../../../_/js/vendor/tabs.js"></script> + <!-- Solr Changes - End --> +</footer> +<script src="../../../_/js/site.js"></script> +<script async src="../../../_/js/vendor/highlight.js"></script> +<script src="../../../_/js/vendor/lunr.js"></script> +<script src="../../../_/js/search-ui.js" id="search-ui-script" data-site-root-path="../../.." data-snippet-length="100" data-stylesheet="../../../_/css/search.css"></script> +<script async src="../../../search-index.js"></script> + </body> +</html>
