Embedders
Embedders in Haystack transform texts or Documents into vector representations using pre-trained models. You can then use the embeddings in your pipeline for tasks like question answering, information retrieval, and more.
The embeddings produced by Haystack embedders are fixed-length vectors. They capture contextual information and semantic relationships within the text. The primary goal of embeddings is to transform text into a format the language model can understand and process in a more nuanced and contextually aware way.
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. |
| GradientTextEmbedder | Computes embeddings for text (such as a query) using models deployed through the Gradient AI platform. |
| GradientDocumentEmbedder | Computes embeddings for documents using models deployed through the Gradient AI platform. |
| HuggingFaceAPIDocumentEmbedder | Computes document embeddings using various Hugging Face APIs. |
| HuggingFaceAPITextEmbedder | Embeds strings using various Hugging Face APIs. |
| HuggingFaceTEIDocumentEmbedder | Computes embeddings for documents using the TEI library. |
| HuggingFaceTEITextEmbedder | Cmputes embeddings for text using the TEI library. |
| 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 6 months ago