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61 lines
14 KiB
Plaintext
---
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title: "Embedders"
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id: embedders
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slug: "/embedders"
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description: "Embedders in Haystack transform texts or documents into vector representations using pre-trained models. You can then use the embedding for tasks like question answering, information retrieval, and more."
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---
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# Embedders
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Embedders in Haystack transform texts or documents into vector representations using pre-trained models. You can then use the embedding for tasks like question answering, information retrieval, and more.
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:::note
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For general guidance on how to choose an Embedder that would be right for you, read our [Choosing the Right Embedder](doc:choosing-the-right-embedder) page.
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:::
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These are the Embedders available in Haystack:
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| Embedder | Description |
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| :------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| [AmazonBedrockTextEmbedder](/docs/amazonbedrocktextembedder) | Computes embeddings for text (such as a query) using models through Amazon Bedrock API. |
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| [AmazonBedrockDocumentEmbedder](/docs/amazonbedrockdocumentembedder) | Computes embeddings for documents using models through Amazon Bedrock API. |
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| [AmazonBedrockDocumentImageEmbedder](doc:amazonbedrockdocumentimageembedder) | Computes image embeddings for a document. |
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| [AzureOpenAITextEmbedder](/docs/azureopenaitextembedder) | Computes embeddings for text (such as a query) using OpenAI models deployed through Azure. |
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| [AzureOpenAIDocumentEmbedder](/docs/azureopenaidocumentembedder) | Computes embeddings for documents using OpenAI models deployed through Azure. |
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| [CohereTextEmbedder](/docs/coheretextembedder) | Embeds a simple string (such as a query) with a Cohere model. Requires an API key from Cohere |
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| [CohereDocumentEmbedder](/docs/coheredocumentembedder) | Embeds a list of documents with a Cohere model. Requires an API key from Cohere. |
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| [CohereDocumentImageEmbedder](doc:coheredocumentimageembedder) | Computes the image embeddings of a list of documents and stores the obtained vectors in the embedding field of each document. |
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| [FastembedTextEmbedder](/docs/fastembedtextembedder) | Computes the embeddings of a string using embedding models supported by Fastembed. |
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| [FastembedDocumentEmbedder](/docs/fastembeddocumentembedder) | Computes the embeddings of a list of documents using the models supported by Fastembed. |
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| [FastembedSparseTextEmbedder](/docs/fastembedsparsetextembedder) | Embeds a simple string (such as a query) into a sparse vector using the models supported by Fastembed. |
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| [FastembedSparseDocumentEmbedder](/docs/fastembedsparsedocumentembedder) | Enriches a list of documents with their sparse embeddings using the models supported by Fastembed. |
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| [GoogleGenAITextEmbedder](doc:googlegenaitextembedder) | Embeds a simple string (such as a query) with a Google AI model. Requires an API key from Google. |
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| [GoogleGenAIDocumentEmbedder](doc:googlegenaidocumentembedder) | Embeds a list of documents with a Google AI model. Requires an API key from Google. |
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| [HuggingFaceAPIDocumentEmbedder](/docs/huggingfaceapidocumentembedder) | Computes document embeddings using various Hugging Face APIs. |
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| [HuggingFaceAPITextEmbedder](/docs/huggingfaceapitextembedder) | Embeds strings using various Hugging Face APIs. |
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| [JinaTextEmbedder](/docs/jinatextembedder) | Embeds a simple string (such as a query) with a Jina AI Embeddings model. Requires an API key from Jina AI. |
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| [JinaDocumentEmbedder](/docs/jinadocumentembedder) | Embeds a list of documents with a Jina AI Embeddings model. Requires an API key from Jina AI. |
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| [JinaDocumentImageEmbedder](doc:jinadocumentimageembedder) | Computes the image embeddings of a list of documents and stores the obtained vectors in the embedding field of each document. |
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| [MistralTextEmbedder](/docs/mistraltextembedder) | Transforms a string into a vector using the Mistral API and models. |
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| [MistralDocumentEmbedder](/docs/mistraldocumentembedder) | Computes the embeddings of a list of documents using the Mistral API and models. |
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| [NvidiaTextEmbedder](/docs/nvidiatextembedder) | Embeds a simple string (such as a query) into a vector. |
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| [NvidiaDocumentEmbedder](/docs/nvidiadocumentembedder) | Enriches the metadata of documents with an embedding of their content. |
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| [OllamaTextEmbedder](/docs/ollamatextembedder) | Computes the embeddings of a string using embedding models compatible with the Ollama Library. |
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| [OllamaDocumentEmbedder](/docs/ollamadocumentembedder) | Computes the embeddings of a list of documents using embedding models compatible with the Ollama Library. |
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| [OpenAIDocumentEmbedder](/docs/openaidocumentembedder) | Embeds a list of documents with an OpenAI embedding model. Requires an API key from an active OpenAI account. |
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| [OpenAITextEmbedder](/docs/openaitextembedder) | Embeds a simple string (such as a query) with an OpenAI embedding model. Requires an API key from an active OpenAI account. |
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| [OptimumTextEmbedder](/docs/optimumtextembedder) | Embeds text using models loaded with the Hugging Face Optimum library. |
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| [OptimumDocumentEmbedder](/docs/optimumdocumentembedder) | Computes documents’ embeddings using models loaded with the Hugging Face Optimum library. |
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| [SentenceTransformersTextEmbedder](/docs/sentencetransformerstextembedder) | Embeds a simple string (such as a query) using a Sentence Transformer model. |
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| [SentenceTransformersDocumentEmbedder](/docs/sentencetransformersdocumentembedder) | Embeds a list of documents with a Sentence Transformer model. |
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| [SentenceTransformersDocumentImageEmbedder](doc:sentencetransformersdocumentimageembedder) | Computes the image embeddings of a list of documents and stores the obtained vectors in the embedding field of each document. |
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| [SentenceTransformersSparseTextEmbedder](doc:sentencetransformerssparsetextembedder) | Embeds a simple string (such as a query) into a sparse vector using Sentence Transformers models. |
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| [SentenceTransformersSparseDocumentEmbedder](doc:sentencetransformerssparsedocumentembedder) | Enriches a list of documents with their sparse embeddings using Sentence Transformers models. |
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| [STACKITTextEmbedder](doc:stackittextembedder) | Enables text embedding using the STACKIT API. |
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| [STACKITDocumentEmbedder](doc:stackitdocumentembedder) | Enables document embedding using the STACKIT API. |
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| [VertexAITextEmbedder](doc:vertexaitextembedder) | Computes embeddings for text (such as a query) using models through VertexAI Embeddings API. **_This integration will be deprecated soon. We recommend using [GoogleGenAITextEmbedder](doc:googlegenaitextembedder) integration instead._** |
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| [VertexAIDocumentEmbedder](doc:vertexaidocumentembedder) | Computes embeddings for documents using models through VertexAI Embeddings API. **_This integration will be deprecated soon. We recommend using [GoogleGenAIDocumentEmbedder](doc:googlegenaidocumentembedder) integration instead._** |
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| [WatsonxTextEmbedder](doc:watsonxtextembedder) | Computes embeddings for text (such as a query) using IBM Watsonx models. |
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| [WatsonxDocumentEmbedder](doc:watsonxdocumentembedder) | Computes embeddings for documents using IBM Watsonx models. |
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