Embedders
Embedders convert text strings or Document objects into vector representations (embeddings). They use pre-trained models to do that.
Embeddings are vector representations of text that capture the context and meaning of words, rather than just relying on keywords. In LLM apps, they speed up processing and improve the model's ability to understand complex linguistic nuances and semantic understanding of text.
Text and Document Embedders
There are two types of embedders: text and document. Text embedders work with text strings and are most often used at the beginning of query pipelines to convert query text into vectors and send it to a retriever. Document embedders embed Document objects and are most often used in indexing pipelines, after converters, and before DocumentWriter.
You must use the same embedding model for text and documents. This means that if you use CohereDocumentEmbedder in your indexing pipeline, you must then use CohereTextEmbedder with the same model in your query pipeline.
Embedders by Model Provider
Amazon Bedrock API
These embedders use models through Amazon Bedrock API:
OpenAI Models on Azure
Cohere
Nvidia
OpenAI
Sentence Transformers
Updated 4 months ago