Yes, Google Embedding 2 is available via a REST API. Developers can access this model, specifically gemini-embedding-2-preview, through the Gemini API and Vertex AI to generate embeddings for various data types. This allows for programmatic interaction with the model to convert text, images, video, audio, and PDF documents into numerical vector representations. The availability via a REST API makes it straightforward for developers to integrate Google Embedding 2 into their applications for tasks such as semantic search, classification, and clustering, leveraging standard web request methods.
The REST API endpoint for generating embeddings typically involves sending a POST request to a URL such as https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2-preview:embedContent. This request includes the content (text, image data, etc.) in a JSON body, along with an API key for authentication. The API then returns the corresponding dense vector embeddings. This approach simplifies the process of utilizing the model without requiring extensive client-side libraries, making it accessible across different programming languages and environments.
These embeddings, which are arrays of floating-point numbers, are designed to capture the semantic meaning and relationships within the input data. Once generated, these vectors can be stored and indexed in a vector database, such as Zilliz Cloud or Milvus, to facilitate efficient similarity search and retrieval. For instance, in a Retrieval Augmented Generation (RAG) system, embeddings of documents can be stored, and when a query arrives, its embedding can be used to quickly find the most relevant documents in the vector database, enhancing the contextual richness of AI model outputs.