📄️ Amazon Bedrock Embedding
Amazon Bedrock provides managed access to foundation embedding models from leading providers — Amazon Titan and Cohere — that convert text into dense numerical vectors for semantic search, classification, and retrieval-augmented generation. With Unstract's Bedrock embedding adapter you can use these models in your prompt and indexing pipelines without provisioning any embedding infrastructure yourself.
📄️ Azure OpenAI Embedding
Azure OpenAI Embeddings are a way to convert text data into numerical representations that capture semantic meaning. This allows you to find similar text content or compare documents based on their ideas, even if the wording differs.
📄️ Gemini (Google AI Studio)
Gemini Embeddings convert text into numerical vectors that capture meaning and context, accessible through Google AI Studio. Similar text snippets produce close vectors, enabling tasks like semantic search and classification.
📄️ OpenAI Embeddings
OpenAI embedding is used for converting text into numerical representations, enabling machines to understand and process language efficiently in various natural language processing tasks such as sentiment analysis, text classification, and language translation.
📄️ Palm Embeddings
Palm embedding is a technique that converts text into numerical vectors, capturing meaning and context. Similar text snippets have close embeddings, allowing for tasks like semantic search and text classification.