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77</head><body class="body-none theme-line header-line header-bg-size-auto header-bg-pos-tl header-overlay-triangles reference-layout-row lumosity-dark is-hub-themed"><div style="display:none" aria-hidden="true">For AI agents: visit https://docs.voyageai.com/llms.txt for an index of all pages formatted in Markdown and endpoints in OpenAPI. 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class="Sidebar-listWrapper6Q9_yUrG906C Sidebar-listWrapper_expandedvCieTVZJcoub rm-Sidebar-section"><h2 class="Sidebar-headingTRQyOa2pk0gh rm-Sidebar-heading">GET STARTED</h2><ul class="Sidebar-list_sidebarLayout3RaX72iQNOEI Sidebar-list3cZWQLaBf9k8 Sidebar-list_expandedWy9fQM8vmtOc rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a aria-current="page" class="Sidebar-link2Dsha-r-GKh2 childless text-wrap rm-Sidebar-link active" target="_self" href="/docs/introduction"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Introduction</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless text-wrap rm-Sidebar-link" target="_self" href="/docs/api-key-and-installation"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">API Key and Python Client</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless 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77001Z"></path></svg></a></div></div><button aria-label="Hide sidebar navigation" class="Nav-toggle-collapse39KxgTH727KL" type="button"></button></nav><article class="SuperHubDoc-article3ArTrEavUTKg rm-Article rm-Guides-SuperHub"><header id="content-head"><div class="row clearfix"><div class="col-xs-9"><h1>Introduction</h1></div></div></header><div class="grid-container-fluid" id="content-container"><section class="content-body grid-75 "><div style="display:contents"><div class="rm-Markdown markdown-body rm-Markdown markdown-body ng-non-bindable" data-testid="RDMD" style="margin-left:31px"><style>/*! tailwindcss v4.1.17 | MIT License | https://tailwindcss.com */ 78@layer theme, base, components, utilities; 79@layer utilities; 80</style><p>Voyage AI provides cutting-edge embedding models and rerankers.</p> 81<p>Embedding models are neural net models (e.g., transformers) that convert unstructured and complex data, such as documents, images, audios, videos, or tabular data, into dense numerical vectors (i.e. embeddings) that capture their semantic meanings. These vectors serve as representations/indices for datapoints and are essential building blocks for semantic search and retrieval-augmented generation (RAG), which is the predominant approach for domain-specific or company-specific chatbots and other AI applications.</p> 82<p>Rerankers are neural nets that output relevance scores between a query and multiple documents. It is common practice to use the relevance scores to rerank the documents initially retrieved with embedding-based methods (or with lexical search algorithms such as <a href="https://en.wikipedia.org/wiki/Okapi_BM25" target="_blank" title="">BM25</a> and <a href="https://en.wikipedia.org/wiki/Tfâidf" target="_blank" title="">TF-IDF</a>). Selecting the highest-scored documents refines the retrieval results into a more relevant subset.</p> 83<p>Voyage AI provides API endpoints for embedding and reranking models that take in your data (e.g., documents, queries, or query-document pairs) and return their embeddings or relevance scores. Embedding models and rerankers, as modular components, seamlessly integrate with other parts of a RAG stack, including vector stores and generative Large Language Models (LLMs).</p> 84<p>Voyage AIâs embedding models and rerankers are <strong>state-of-the-art</strong> in retrieval accuracy. Please read our announcing <a href="https://blog.voyageai.com/2023/10/29/voyage-embeddings/" target="_blank" title="">blog post</a> for details. Please also check out a high-level <a href="https://www.mongodb.com/resources/basics/artificial-intelligence/retrieval-augmented-generation" target="_blank" title="">introduction</a> of embedding models, semantic search, and RAG, and our step-by-step <a href="/docs/quickstart-tutorial" target="" title="" class="doc-link" data-sidebar="quickstart-tutorial">quickstart tutorial</a> on implementing a minimalist RAG chatbot using Voyage model endpoints.</p></div></div><div align="center" class="Flex Flex_row " justify="between" style="--flex-gap:var(--md)"><div class="UpdatedAt"><p class="DateLine "><i class="icon icon-watch"></i>Updated<!-- --> <!-- -->9 months ago<!-- --> </p></div></div><hr class="NextStepsDivider"/><nav aria-label="Pagination Controls" class="PaginationControlsjDYuqu8pBMUy rm-Pagination"><a class="PaginationControls-link254uPEbaP92i PaginationControls-link_right5lsfuICZqXYk" aria-label="Next Page: API Key and Python Client" href="/docs/api-key-and-installation"><div class="PaginationControls-text3qEXHZU00znD PaginationControls-text_right3EIfWubgRVVP">API Key and Python Client</div><span class="PaginationControls-iconZbaZ3tgyhNmI icon-arrow-right2"></span></a></nav><div class="rm-PageThumbs PageThumbs"><div class="PageThumbs-helpful">Did this page help you?<div class="PageThumbs-cta"><div class="Dropdown Dropdown_closed" data-testid="dropdown-container"><button aria-label="Yes, this page helped me" aria-haspopup="dialog" class="Button Button_sm PageThumbs-button Dropdown-toggle Button_secondary Button_secondary_text" type="button"><i class="icon icon-thumbs-up-2"></i><span>Yes</span></button></div><div class="Dropdown Dropdown_closed" data-testid="dropdown-container">
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84<script id="ssr-props" type="application/x-ssr-props">{"sidebars":{},"apiBaseUrl":"/","baseUrl":"/","search":{"appId":"","searchApiKey":"","indexName":"readme_search_v2","projectsMeta":[{"modules":{"landing":false,"docs":true,"examples":false,"reference":true,"graphql":false,"changelog":false,"discuss":false,"suggested_edits":false,"custompages":false,"tutorials":false},"id":"6580bea00965e90023e85436","name":"Voyage AI","subdomain":"voyage-ai","subpath":"","nav_names":{"docs":"","reference":"","changelog":"","discuss":"","recipes":"","tutorials":""}}],"urlManagerOpts":{"lang":"en","parent":{"childrenProjects":[]},"project":{"subdomain":"voyage-ai"},"version":"1.1"}},"customBlocks":[],"document":{"allow_crawlers":"enabled","processing_status":null,"renderable":{"status":true},"slug":"introduction","state":"current","title":"Introduction","type":"basic","updated_at":"2025-12-23T19:07:01.000Z","uri":"/branches/1.1/guides/introduction","appearance":{"icon":{"name":null,"type":null},"table_of_contents":"default"},"category":{"uri":"/branches/1.1/categories/guides/GET%20STARTED"},"href":{"dash":"https://dash.readme.com/project/voyage-ai/v1.1/docs/introduction","hub":"https://docs.voyageai.com/docs/introduction","github_url":null},"links":{"project":"/projects/me"},"parent":{"uri":null},"privacy":{"view":"public"},"project":{"name":"Voyage AI","subdomain":"voyage-ai","uri":"/projects/me"},"content":{"body":"Voyage AI provides cutting-edge embedding models and rerankers.\n\nEmbedding models are neural net models (e.g., transformers) that convert unstructured and complex data, such as documents, images, audios, videos, or tabular data, into dense numerical vectors (i.e. embeddings) that capture their semantic meanings. These vectors serve as representations/indices for datapoints and are essential building blocks for semantic search and retrieval-augmented generation (RAG), which is the predominant approach for domain-specific or company-specific chatbots and other AI applications.\n\nRerankers are neural nets that output relevance scores between a query and multiple documents. It is common practice to use the relevance scores to rerank the documents initially retrieved with embedding-based methods (or with lexical search algorithms such as \u003ca href=\"https://en.wikipedia.org/wiki/Okapi_BM25\" target=\"_blank\">BM25\u003c/a> and \u003ca href=\"https://en.wikipedia.org/wiki/Tfâidf\" target=\"_blank\">TF-IDF\u003c/a>). Selecting the highest-scored documents refines the retrieval results into a more relevant subset.\n\nVoyage AI provides API endpoints for embedding and reranking models that take in your data (e.g., documents, queries, or query-document pairs) and return their embeddings or relevance scores. Embedding models and rerankers, as modular components, seamlessly integrate with other parts of a RAG stack, including vector stores and generative Large Language Models (LLMs).\n\nVoyage AIâs embedding models and rerankers are **state-of-the-art** in retrieval accuracy. Please read our announcing \u003ca href=\"https://blog.voyageai.com/2023/10/29/voyage-embeddings/\" target=\"_blank\">blog post\u003c/a> for details. Please also check out a high-level \u003ca href=\"https://www.mongodb.com/resources/basics/artificial-intelligence/retrieval-augmented-generation\" target=\"_blank\">introduction\u003c/a> of embedding models, semantic search, and RAG, and our step-by-step [quickstart tutorial](doc:quickstart-tutorial) on implementing a minimalist RAG chatbot using Voyage model endpoints.","excerpt":null,"link":{"url":null,"new_tab":false},"next":{"description":null,"pages":[]}},"metadata":{"description":null,"keywords":null,"title":null,"x_import":null,"image":{"uri":null,"url":null}}},"meta":{"baseUrl":"/","description":"Voyage AI provides cutting-edge embedding models and rerankers. Embedding models are neural net models (e.g., transformers) that convert unstructured and complex data, such as documents, images, audios, videos, or tabular data, into dense numerical vectors (i.e. embeddings) that capture their semantâ¦","hidden":false,"image":[],"metaTitle":"Introduction","robots":"index","slug":"introduction","title":"Introduction","type":"docs","updatedAt":"2025-12-23T19:07:01.000Z"},"rdmd":{"dehydrated":{"toc":"","body":"\u003cstyle>/*! tailwindcss v4.1.17 | MIT License | https://tailwindcss.com */\n@layer theme, base, components, utilities;\n@layer utilities;\n\u003c/style>\u003cp>Voyage AI provides cutting-edge embedding models and rerankers.\u003c/p>\n\u003cp>Embedding models are neural net models (e.g., transformers) that convert unstructured and complex data, such as documents, images, audios, videos, or tabular data, into dense numerical vectors (i.e. embeddings) that capture their semantic meanings. These vectors serve as representations/indices for datapoints and are essential building blocks for semantic search and retrieval-augmented generation (RAG), which is the predominant approach for domain-specific or company-specific chatbots and other AI applications.\u003c/p>\n\u003cp>Rerankers are neural nets that output relevance scores between a query and multiple documents. It is common practice to use the relevance scores to rerank the documents initially retrieved with embedding-based methods (or with lexical search algorithms such as \u003ca href=\"https://en.wikipedia.org/wiki/Okapi_BM25\" target=\"_blank\" title=\"\">BM25\u003c/a> and \u003ca href=\"https://en.wikipedia.org/wiki/Tfâidf\" target=\"_blank\" title=\"\">TF-IDF\u003c/a>). Selecting the highest-scored documents refines the retrieval results into a more relevant subset.\u003c/p>\n\u003cp>Voyage AI provides API endpoints for embedding and reranking models that take in your data (e.g., documents, queries, or query-document pairs) and return their embeddings or relevance scores. Embedding models and rerankers, as modular components, seamlessly integrate with other parts of a RAG stack, including vector stores and generative Large Language Models (LLMs).\u003c/p>\n\u003cp>Voyage AIâs embedding models and rerankers are \u003cstrong>state-of-the-art\u003c/strong> in retrieval accuracy. Please read our announcing \u003ca href=\"https://blog.voyageai.com/2023/10/29/voyage-embeddings/\" target=\"_blank\" title=\"\">blog post\u003c/a> for details. Please also check out a high-level \u003ca href=\"https://www.mongodb.com/resources/basics/artificial-intelligence/retrieval-augmented-generation\" target=\"_blank\" title=\"\">introduction\u003c/a> of embedding models, semantic search, and RAG, and our step-by-step \u003ca href=\"/docs/quickstart-tutorial\" target=\"\" title=\"\" class=\"doc-link\" data-sidebar=\"quickstart-tutorial\">quickstart tutorial\u003c/a> on implementing a minimalist RAG chatbot using Voyage model endpoints.\u003c/p>","css":"/*! tailwindcss v4.1.17 | MIT License | https://tailwindcss.com */\n@layer theme, base, components, utilities;\n@layer utilities;\n"},"opts":{"hardBreaks":true,"alwaysThrow":false,"compatibilityMode":false,"copyButtons":true,"correctnewlines":false,"markdownOptions":{"fences":true,"commonmark":true,"gfm":true,"ruleSpaces":false,"listItemIndent":"1","spacedTable":true,"paddedTable":true},"normalize":true,"lazyImages":true,"reusableContent":{"tags":{},"disabled":false,"serialize":true,"wrap":true},"safeMode":false,"settings":{"position":true},"theme":"light","customBlocks":{},"componentTypes":{},"resourceID":"/branches/1.1/guides/introduction","resourceType":"page","components":{},"baseUrl":"/","terms":[{"_id":"6580bea00965e90023e85437","term":"parliament","definition":"Owls are generally solitary, but when seen together the group is called a 'parliament'!"}],"variables":{"user":{},"defaults":[{"source":"security","type":"http","_id":"65c2c8831b9ef2006a655c52","name":"HTTPBearer","scheme":"bearer","apiSetting":"65c2c8811b9ef2006a655c50"},{"apiSetting":"65efa6d9d4215d005e853469","name":"Authorization: Bearer","default":"$VOYAGE_API_KEY","source":"security","type":"apiKey","_id":"68881112a13723edeabf3ca6"}
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Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.