Embedders
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.
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. |
| 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. |
| 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. |
| 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. |
| 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. |
Updated about 2 months ago