Batch processing with Google Embedding 2 involves submitting multiple input items—such as text, images, video, or audio—in a single request to the Gemini Batch API, rather than sending them individually. This approach is specifically designed for handling large volumes of data efficiently, offering higher throughput and reduced costs, often 50% less than standard real-time API calls. Instead of waiting for a synchronous response for each item, the batch API processes these requests asynchronously. This makes it ideal for non-urgent tasks like data pre-processing or re-embedding an entire corpus, where immediate, low-latency responses are not critical. The Gemini Embedding 2 model is notable for its multimodal capabilities, allowing a single API call to embed a combination of text (up to 8,192 tokens), images (up to 6 per request), video (up to 120 seconds), audio, and PDF documents, mapping them into a unified semantic space.
Technically, implementing batch processing with Google Embedding 2 typically involves using the Google Cloud Vertex AI Embeddings API or the Gemini Batch API directly through client libraries. Developers can submit batch jobs in two primary ways: either through inline requests, where a list of GenerateContentRequest objects is included directly in the batch creation request (suitable for smaller batches under 20MB), or by providing an input file, such as a JSON Lines (JSONL) file, for larger datasets. Each line in the JSONL file contains a GenerateContentRequest object. Once the batch job is submitted, the system processes the inputs and stores the resulting embeddings in a specified output location, often a Google Cloud Storage bucket. The process is asynchronous, meaning you initiate the job and then retrieve the results once it's completed, with a typical turnaround time up to 24 hours, though often faster.
After batch processing, the generated embeddings are crucial for various downstream AI applications. These high-dimensional vectors, which capture the semantic meaning of the original data, are commonly stored in vector databases. A vector database, such as Milvus or Zilliz Cloud, is optimized for efficient storage and similarity search of these embeddings. By ingesting the batched embeddings into a vector database, applications can perform tasks like semantic search, recommendation systems, or clustering across large multimodal datasets. For instance, after embedding a vast collection of documents and images using Google Embedding 2, a vector database enables rapid retrieval of semantically similar content, even if the query is in a different modality than the stored data, thereby powering sophisticated retrieval-augmented generation (RAG) systems and other AI-driven experiences.