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itemprop="name">Components</span></a><meta itemprop="position" content="1"></li><li itemscope="" itemprop="itemListElement" itemtype="https://schema.org/ListItem" class="breadcrumbs__item breadcrumbs__item--active"><span class="breadcrumbs__link" itemprop="name">Embedding Models</span><meta itemprop="position" content="2"></li></ul></nav><div class="tocCollapsible_ETCw theme-doc-toc-mobile tocMobile_ITEo"><button type="button" class="clean-btn tocCollapsibleButton_TO0P">On this page</button></div><div class="theme-doc-markdown markdown"><header><h1>Embedding Models</h1></header><h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="4ddcfde8c1664e358d3f16d718e944d8">Amazon Bedrock Embeddings<a class="hash-link" aria-label="Direct link to Amazon Bedrock Embeddings" title="Direct link to Amazon Bedrock Embeddings" href="/components-embedding-models#4ddcfde8c1664e358d3f16d718e944d8">​</a></h2>
<p>Used to load embedding models from <a href="https://aws.amazon.com/bedrock/" target="_blank" rel="noopener noreferrer">Amazon Bedrock</a>.</p>
<table><thead><tr><th><strong>Parameter</strong></th><th><strong>Type</strong></th><th><strong>Description</strong></th><th><strong>Default</strong></th></tr></thead><tbody><tr><td><code>credentials_profile_name</code></td><td><code>str</code></td><td>Name of the AWS credentials profile in ~/.aws/credentials or ~/.aws/config, which has access keys or role information.</td><td></td></tr><tr><td><code>model_id</code></td><td><code>str</code></td><td>ID of the model to call, e.g., <code>amazon.titan-embed-text-v1</code>. This is equivalent to the <code>modelId</code> property in the <code>list-foundation-models</code> API.</td><td></td></tr><tr><td><code>endpoint_url</code></td><td><code>str</code></td><td>URL to set a specific service endpoint other than the default AWS endpoint.</td><td></td></tr><tr><td><code>region_name</code></td><td><code>str</code></td><td>AWS region to use, e.g., <code>us-west-2</code>. Falls back to <code>AWS_DEFAULT_REGION</code> environment variable or region specified in ~/.aws/config if not provided.</td><td></td></tr></tbody></table>
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="c1e6d1373824424ea130e052ba0f46af">Astra vectorize<a class="hash-link" aria-label="Direct link to Astra vectorize" title="Direct link to Astra vectorize" href="/components-embedding-models#c1e6d1373824424ea130e052ba0f46af">​</a></h2>
<p>Used to generate server-side embeddings using <a href="https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html" target="_blank" rel="noopener noreferrer">DataStax Astra</a>.</p>
<table><thead><tr><th><strong>Parameter</strong></th><th><strong>Type</strong></th><th><strong>Description</strong></th><th><strong>Default</strong></th></tr></thead><tbody><tr><td><code>provider</code></td><td><code>str</code></td><td>The embedding provider to use.</td><td></td></tr><tr><td><code>model_name</code></td><td><code>str</code></td><td>The embedding model to use.</td><td></td></tr><tr><td><code>authentication</code></td><td><code>dict</code></td><td>Authentication parameters. Use the Astra Portal to add the embedding provider integration to your Astra organization.</td><td></td></tr><tr><td><code>provider_api_key</code></td><td><code>str</code></td><td>An alternative to the Astra Authentication that let you use directly the API key of the provider.</td><td></td></tr><tr><td><code>model_parameters</code></td><td><code>dict</code></td><td>Additional model parameters.</td><td></td></tr></tbody></table>
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="0c5b7b8790da448fabd4c5ddba1fcbde">Cohere Embeddings<a class="hash-link" aria-label="Direct link to Cohere Embeddings" title="Direct link to Cohere Embeddings" href="/components-embedding-models#0c5b7b8790da448fabd4c5ddba1fcbde">​</a></h2>
<p>Used to load embedding models from <a href="https://cohere.com/" target="_blank" rel="noopener noreferrer">Cohere</a>.</p>
<table><thead><tr><th><strong>Parameter</strong></th><th><strong>Type</strong></th><th><strong>Description</strong></th><th><strong>Default</strong></th></tr></thead><tbody><tr><td><code>cohere_api_key</code></td><td><code>str</code></td><td>API key required to authenticate with the Cohere service.</td><td></td></tr><tr><td><code>model</code></td><td><code>str</code></td><td>Language model used for embedding text documents and performing queries.</td><td><code>embed-english-v2.0</code></td></tr><tr><td><code>truncate</code></td><td><code>bool</code></td><td>Whether to truncate the input text to fit within the model&#x27;s constraints.</td><td><code>False</code></td></tr></tbody></table>
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="8ffb790d5a6c484dab3fe6c777638a44">Azure OpenAI Embeddings<a class="hash-link" aria-label="Direct link to Azure OpenAI Embeddings" title="Direct link to Azure OpenAI Embeddings" href="/components-embedding-models#8ffb790d5a6c484dab3fe6c777638a44">​</a></h2>
<p>Generate embeddings using Azure OpenAI models.</p>
<table><thead><tr><th><strong>Parameter</strong></th><th><strong>Type</strong></th><th><strong>Description</strong></th><th><strong>Default</strong></th></tr></thead><tbody><tr><td><code>Azure Endpoint</code></td><td><code>str</code></td><td>Your Azure endpoint, including the resource. Example: <code>https://example-resource.azure.openai.com/</code></td><td></td></tr><tr><td><code>Deployment Name</code></td><td><code>str</code></td><td>The name of the deployment.</td><td></td></tr><tr><td><code>API Version</code></td><td><code>str</code></td><td>The API version to use, options include various dates.</td><td></td></tr><tr><td><code>API Key</code></td><td><code>str</code></td><td>The API key to access the Azure OpenAI service.</td><td></td></tr></tbody></table>
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="8536e4ee907b48688e603ae9bf7822cb">Hugging Face API Embeddings<a class="hash-link" aria-label="Direct link to Hugging Face API Embeddings" title="Direct link to Hugging Face API Embeddings" href="/components-embedding-models#8536e4ee907b48688e603ae9bf7822cb">​</a></h2>
<p>Generate embeddings using Hugging Face Inference API models.</p>
<table><thead><tr><th><strong>Parameter</strong></th><th><strong>Type</strong></th><th><strong>Description</strong></th><th><strong>Default</strong></th></tr></thead><tbody><tr><td><code>API Key</code></td><td><code>str</code></td><td>API key for accessing the Hugging Face Inference API.</td><td></td></tr><tr><td><code>API URL</code></td><td><code>str</code></td><td>URL of the Hugging Face Inference API.</td><td><code>http://localhost:8080</code></td></tr><tr><td><code>Model Name</code></td><td><code>str</code></td><td>Name of the model to use for embeddings.</td><td><code>BAAI/bge-large-en-v1.5</code></td></tr><tr><td><code>Cache Folder</code></td><td><code>str</code></td><td>Folder path to cache Hugging Face models.</td><td></td></tr><tr><td><code>Encode Kwargs</code></td><td><code>dict</code></td><td>Additional arguments for the encoding process.</td><td></td></tr><tr><td><code>Model Kwargs</code></td><td><code>dict</code></td><td>Additional arguments for the model.</td><td></td></tr><tr><td><code>Multi Process</code></td><td><code>bool</code></td><td>Whether to use multiple processes.</td><td><code>False</code></td></tr></tbody></table>
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="b2b74732874743d3be6fdf8aae049e74">Hugging Face Embeddings<a class="hash-link" aria-label="Direct link to Hugging Face Embeddings" title="Direct link to Hugging Face Embeddings" href="/components-embedding-models#b2b74732874743d3be6fdf8aae049e74">​</a></h2>
<p>Used to load embedding models from <a href="https://huggingface.co/" target="_blank" rel="noopener noreferrer">HuggingFace</a>.</p>
<table><thead><tr><th><strong>Parameter</strong></th><th><strong>Type</strong></th><th><strong>Description</strong></th><th><strong>Default</strong></th></tr></thead><tbody><tr><td><code>Cache Folder</code></td><td><code>str</code></td><td>Folder path to cache HuggingFace models.</td><td></td></tr><tr><td><code>Encode Kwargs</code></td><td><code>dict</code></td><td>Additional arguments for the encoding process.</td><td></td></tr><tr><td><code>Model Kwargs</code></td><td><code>dict</code></td><td>Additional arguments for the model.</td><td></td></tr><tr><td><code>Model Name</code></td><td><code>str</code></td><td>Name of the HuggingFace model to use.</td><td><code>sentence-transformers/all-mpnet-base-v2</code></td></tr><tr><td><code>Multi Process</code></td><td><code>bool</code></td><td>Whether to use multiple processes.</td><td><code>False</code></td></tr></tbody></table>
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="af7630df05a245d1a632e1bf6db2a4c5">OpenAI Embeddings<a class="hash-link" aria-label="Direct link to OpenAI Embeddings" title="Direct link to OpenAI Embeddings" href="/components-embedding-models#af7630df05a245d1a632e1bf6db2a4c5">​</a></h2>
<p>Used to load embedding models from <a href="https://openai.com/" target="_blank" rel="noopener noreferrer">OpenAI</a>.</p>
<table><thead><tr><th><strong>Parameter</strong></th><th><strong>Type</strong></th><th><strong>Description</strong></th><th><strong>Default</strong></th></tr></thead><tbody><tr><td><code>OpenAI API Key</code></td><td><code>str</code></td><td>The API key to use for accessing the OpenAI API.</td><td></td></tr><tr><td><code>Default Headers</code></td><td><code>Dict[str, str]</code></td><td>Default headers for the HTTP requests.</td><td></td></tr><tr><td><code>Default Query</code></td><td><code>NestedDict</code></td><td>Default query parameters for the HTTP requests.</td><td></td></tr><tr><td><code>Allowed Special</code></td><td><code>List[str]</code></td><td>Special tokens allowed for processing.</td><td><code>[]</code></td></tr><tr><td><code>Disallowed Special</code></td><td><code>List[str]</code></td><td>Special tokens disallowed for processing.</td><td><code>[&quot;all&quot;]</code></td></tr><tr><td><code>Chunk Size</code></td><td><code>int</code></td><td>Chunk size for processing.</td><td><code>1000</code></td></tr><tr><td><code>Client</code></td><td><code>Any</code></td><td>HTTP client for making requests.</td><td></td></tr><tr><td><code>Deployment</code></td><td><code>str</code></td><td>Deployment name for the model.</td><td><code>text-embedding-3-small</code></td></tr><tr><td><code>Embedding Context Length</code></td><td><code>int</code></td><td>Length of embedding context.</td><td><code>8191</code></td></tr><tr><td><code>Max Retries</code></td><td><code>int</code></td><td>Maximum number of retries for failed requests.</td><td><code>6</code></td></tr><tr><td><code>Model</code></td><td><code>str</code></td><td>Name of the model to use.</td><td><code>text-embedding-3-small</code></td></tr><tr><td><code>Model Kwargs</code></td><td><code>NestedDict</code></td><td>Additional keyword arguments for the model.</td><td></td></tr><tr><td><code>OpenAI API Base</code></td><td><code>str</code></td><td>Base URL of the OpenAI API.</td><td></td></tr><tr><td><code>OpenAI API Type</code></td><td><code>str</code></td><td>Type of the OpenAI API.</td><td></td></tr><tr><td><code>OpenAI API Version</code></td><td><code>str</code></td><td>Version of the OpenAI API.</td><td></td></tr><tr><td><code>OpenAI Organization</code></td><td><code>str</code></td><td>Organization associated with the API key.</td><td></td></tr><tr><td><code>OpenAI Proxy</code></td><td><code>str</code></td><td>Proxy server for the requests.</td><td></td></tr><tr><td><code>Request Timeout</code></td><td><code>float</code></td><td>Timeout for the HTTP requests.</td><td></td></tr><tr><td><code>Show Progress Bar</code></td><td><code>bool</code></td><td>Whether to show a progress bar for processing.</td><td><code>False</code></td></tr><tr><td><code>Skip Empty</code></td><td><code>bool</code></td><td>Whether to skip empty inputs.</td><td><code>False</code></td></tr><tr><td><code>TikToken Enable</code></td><td><code>bool</code></td><td>Whether to enable TikToken.</td><td><code>True</code></td></tr><tr><td><code>TikToken Model Name</code></td><td><code>str</code></td><td>Name of the TikToken model.</td><td></td></tr></tbody></table>
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="a26d2cb92e6d44669c2cfff71a5e9431">Ollama Embeddings<a class="hash-link" aria-label="Direct link to Ollama Embeddings" title="Direct link to Ollama Embeddings" href="/components-embedding-models#a26d2cb92e6d44669c2cfff71a5e9431">​</a></h2>
<p>Generate embeddings using Ollama models.</p>
<table><thead><tr><th><strong>Parameter</strong></th><th><strong>Type</strong></th><th><strong>Description</strong></th><th><strong>Default</strong></th></tr></thead><tbody><tr><td><code>Ollama Model</code></td><td><code>str</code></td><td>Name of the Ollama model to use.</td><td><code>llama2</code></td></tr><tr><td><code>Ollama Base URL</code></td><td><code>str</code></td><td>Base URL of the Ollama API.</td><td><code>http://localhost:11434</code></td></tr><tr><td><code>Model Temperature</code></td><td><code>float</code></td><td>Temperature parameter for the model. Adjusts the randomness in the generated embeddings.</td><td></td></tr></tbody></table>
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="707b38c23cb9413fbbaab1ae7b872311">VertexAI Embeddings<a class="hash-link" aria-label="Direct link to VertexAI Embeddings" title="Direct link to VertexAI Embeddings" href="/components-embedding-models#707b38c23cb9413fbbaab1ae7b872311">​</a></h2>
<p>Wrapper around <a href="https://cloud.google.com/vertex-ai" target="_blank" rel="noopener noreferrer">Google Vertex AI</a> <a href="https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings" target="_blank" rel="noopener noreferrer">Embeddings API</a>.</p>
<table><thead><tr><th><strong>Parameter</strong></th><th><strong>Type</strong></th><th><strong>Description</strong></th><th><strong>Default</strong></th></tr></thead><tbody><tr><td><code>credentials</code></td><td><code>Credentials</code></td><td>The default custom credentials to use.</td><td></td></tr><tr><td><code>location</code></td><td><code>str</code></td><td>The default location to use when making API calls.</td><td><code>us-central1</code></td></tr><tr><td><code>max_output_tokens</code></td><td><code>int</code></td><td>Token limit determines the maximum amount of text output from one prompt.</td><td><code>128</code></td></tr><tr><td><code>model_name</code></td><td><code>str</code></td><td>The name of the Vertex AI large language model.</td><td><code>text-bison</code></td></tr><tr><td><code>project</code></td><td><code>str</code></td><td>The default GCP project to use when making Vertex API calls.</td><td></td></tr><tr><td><code>request_parallelism</code></td><td><code>int</code></td><td>The amount of parallelism allowed for requests issued to VertexAI models.</td><td><code>5</code></td></tr><tr><td><code>temperature</code></td><td><code>float</code></td><td>Tunes the degree of randomness in text generations. Should be a non-negative value.</td><td><code>0</code></td></tr><tr><td><code>top_k</code></td><td><code>int</code></td><td>How the model selects tokens for output, the next token is selected from the top <code>k</code> tokens.</td><td><code>40</code></td></tr><tr><td><code>top_p</code></td><td><code>float</code></td><td>Tokens are selected from the most probable to least until the sum of their probabilities exceeds the top <code>p</code> value.</td><td><code>0.95</code></td></tr><tr><td><code>tuned_model_name</code></td><td><code>str</code></td><td>The name of a tuned model. If provided, <code>model_name</code> is ignored.</td><td></td></tr><tr><td><code>verbose</code></td><td><code>bool</code></td><td>This parameter controls the level of detail in the output. When set to <code>True</code>, it prints internal states of the chain to help debug.</td><td><code>False</code></td></tr></tbody></table>
<p><a href="/components-vector-stores">Previous Vector Stores</a></p></div></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages"><a class="pagination-nav__link pagination-nav__link--prev" href="/components-models"><div class="pagination-nav__sublabel">Previous</div><div class="pagination-nav__label">Models</div></a><a class="pagination-nav__link pagination-nav__link--next" href="/components-vector-stores"><div class="pagination-nav__sublabel">Next</div><div class="pagination-nav__label">Vector Stores</div></a></nav></div></div><div class="col col--3"><div class="tableOfContents_bqdL thin-scrollbar theme-doc-toc-desktop"><ul class="table-of-contents table-of-contents__left-border"><li><a class="table-of-contents__link toc-highlight" href="/components-embedding-models#4ddcfde8c1664e358d3f16d718e944d8">Amazon Bedrock Embeddings</a></li><li><a class="table-of-contents__link toc-highlight" href="/components-embedding-models#c1e6d1373824424ea130e052ba0f46af">Astra vectorize</a></li><li><a class="table-of-contents__link toc-highlight" href="/components-embedding-models#0c5b7b8790da448fabd4c5ddba1fcbde">Cohere Embeddings</a></li><li><a class="table-of-contents__link toc-highlight" href="/components-embedding-models#8ffb790d5a6c484dab3fe6c777638a44">Azure OpenAI Embeddings</a></li><li><a class="table-of-contents__link toc-highlight" href="/components-embedding-models#8536e4ee907b48688e603ae9bf7822cb">Hugging Face API Embeddings</a></li><li><a class="table-of-contents__link toc-highlight" href="/components-embedding-models#b2b74732874743d3be6fdf8aae049e74">Hugging Face Embeddings</a></li><li><a class="table-of-contents__link toc-highlight" href="/components-embedding-models#af7630df05a245d1a632e1bf6db2a4c5">OpenAI Embeddings</a></li><li><a class="table-of-contents__link toc-highlight" href="/components-embedding-models#a26d2cb92e6d44669c2cfff71a5e9431">Ollama Embeddings</a></li><li><a class="table-of-contents__link toc-highlight" href="/components-embedding-models#707b38c23cb9413fbbaab1ae7b872311">VertexAI Embeddings</a></li></ul></div></div></div></div></main></div></div></div><div id="ms-floating-button" class="ms-fixed ms-bottom-4 ms-right-8 ms-z-[100] ms-flex ms-flex-col ms-items-end hover:ms-cursor-pointer"><div class="ms-relative ms-mb-4"><div style="background-color:#f6f6f6" class="ms-absolute -ms-bottom-1 ms-right-6 -ms-z-10 ms-h-4 ms-w-4 ms-rotate-45"></div><div style="background-color:#f6f6f6" class="ms-z-10 ms-flex ms-items-center ms-justify-center ms-rounded-lg ms-px-3 ms-py-2"><p style="color:#000000;margin:0" class="ms-text-sm">Hi, how can I help you?</p></div></div><div style="background-color:#f6f6f6" class="ms-flex ms-h-16 ms-w-16 ms-flex-col ms-items-center ms-justify-center ms-self-end ms-rounded-full ms-p-2 ms-transition"><div class="ms-rotate-0 ms-flex ms-h-full ms-w-full ms-transform ms-cursor-pointer ms-items-center ms-justify-center ms-rounded-full ms-transition-all ms-delay-75 ms-duration-100 ms-ease-in-out"><div style="color:#000000;font-size:22px;width:48px;height:48px;margin:0px;padding:0px;display:flex;align-items:center;justify-content:center;text-align:center"><img src="/img/chain.png" style="width:40px"></div></div></div><div></div></div></div>
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