| title | Embedders |
|---|---|
| id | embedders |
| slug | /embedders |
| 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. |
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.
:::info For general guidance on how to choose an Embedder that would be right for you, read our Choosing the Right Embedder page. :::
These are the Embedders available in Haystack:
| Embedder | Description |
|---|---|
| AmazonBedrockTextEmbedder | Computes embeddings for text (such as a query) using models through Amazon Bedrock API. |
| AmazonBedrockDocumentEmbedder | Computes embeddings for documents using models through Amazon Bedrock API. |
| AmazonBedrockDocumentImageEmbedder | Computes image embeddings for a document. |
| AzureOpenAITextEmbedder | Computes embeddings for text (such as a query) using OpenAI models deployed through Azure. |
| AzureOpenAIDocumentEmbedder | Computes embeddings for documents using OpenAI models deployed through Azure. |
| CohereTextEmbedder | Embeds a simple string (such as a query) with a Cohere model. Requires an API key from Cohere |
| CohereDocumentEmbedder | Embeds a list of documents with a Cohere model. Requires an API key from Cohere. |
| CohereDocumentImageEmbedder | Computes the image embeddings of a list of documents and stores the obtained vectors in the embedding field of each document. |
| FastembedTextEmbedder | Computes the embeddings of a string using embedding models supported by Fastembed. |
| FastembedDocumentEmbedder | Computes the embeddings of a list of documents using the models supported by Fastembed. |
| FastembedSparseTextEmbedder | Embeds a simple string (such as a query) into a sparse vector using the models supported by Fastembed. |
| FastembedSparseDocumentEmbedder | Enriches a list of documents with their sparse embeddings using the models supported by Fastembed. |
| GoogleGenAITextEmbedder | Embeds a simple string (such as a query) with a Google AI model. Requires an API key from Google. |
| GoogleGenAIDocumentEmbedder | Embeds a list of documents with a Google AI model. Requires an API key from Google. |
| GoogleGenAIMultimodalDocumentEmbedder | Embeds a list of non-textual documents with a Google AI model. Requires an API key from Google. |
| HuggingFaceAPIDocumentEmbedder | Computes document embeddings using various Hugging Face APIs. |
| HuggingFaceAPITextEmbedder | Embeds strings using various Hugging Face APIs. |
| JinaTextEmbedder | Embeds a simple string (such as a query) with a Jina AI Embeddings model. Requires an API key from Jina AI. |
| JinaDocumentEmbedder | Embeds a list of documents with a Jina AI Embeddings model. Requires an API key from Jina AI. |
| JinaDocumentImageEmbedder | Computes the image embeddings of a list of documents and stores the obtained vectors in the embedding field of each document. |
| MistralTextEmbedder | Transforms a string into a vector using the Mistral API and models. |
| MistralDocumentEmbedder | Computes the embeddings of a list of documents using the Mistral API and models. |
| NvidiaTextEmbedder | Embeds a simple string (such as a query) into a vector. |
| NvidiaDocumentEmbedder | Enriches the metadata of documents with an embedding of their content. |
| OllamaTextEmbedder | Computes the embeddings of a string using embedding models compatible with the Ollama Library. |
| OllamaDocumentEmbedder | Computes the embeddings of a list of documents using embedding models compatible with the Ollama Library. |
| OpenAIDocumentEmbedder | Embeds a list of documents with an OpenAI embedding model. Requires an API key from an active OpenAI account. |
| OpenAITextEmbedder | Embeds a simple string (such as a query) with an OpenAI embedding model. Requires an API key from an active OpenAI account. |
| OptimumTextEmbedder | Embeds text using models loaded with the Hugging Face Optimum library. |
| OptimumDocumentEmbedder | Computes documents’ embeddings using models loaded with the Hugging Face Optimum library. |
| SentenceTransformersTextEmbedder | Embeds a simple string (such as a query) using a Sentence Transformer model. |
| SentenceTransformersDocumentEmbedder | Embeds a list of documents with a Sentence Transformer model. |
| SentenceTransformersDocumentImageEmbedder | Computes the image embeddings of a list of documents and stores the obtained vectors in the embedding field of each document. |
| SentenceTransformersSparseTextEmbedder | Embeds a simple string (such as a query) into a sparse vector using Sentence Transformers models. |
| SentenceTransformersSparseDocumentEmbedder | Enriches a list of documents with their sparse embeddings using Sentence Transformers models. |
| STACKITTextEmbedder | Enables text embedding using the STACKIT API. |
| STACKITDocumentEmbedder | Enables document embedding using the STACKIT API. |
| 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 integration instead. |
| VertexAIDocumentEmbedder | Computes embeddings for documents using models through VertexAI Embeddings API. This integration will be deprecated soon. We recommend using GoogleGenAIDocumentEmbedder integration instead. |
| VLLMTextEmbedder | Computes the embeddings of a string using models served with vLLM. |
| VLLMDocumentEmbedder | Computes the embeddings of a list of documents using models served with vLLM. |
| WatsonxTextEmbedder | Computes embeddings for text (such as a query) using IBM Watsonx models. |
| WatsonxDocumentEmbedder | Computes embeddings for documents using IBM Watsonx models. |