Vertex AI integrates with Gemini and other large models by exposing them as managed APIs for text, code, image, multimodal, and embedding tasks, and by wiring them into tooling for prompts, safety, tuning, and deployment. In practice, you use the Vertex AI SDK or REST/gRPC to call Gemini for generation or embeddings, while Vertex AI handles authentication, quotas, and model routing under the hood. You can ground prompts with retrieved context, attach safety settings, and stream tokens back to your application. For production, endpoints, quotas, and monitoring live in the same control plane you use for your custom models, so you don’t need a separate operational stack.
A typical architecture places Gemini behind a lightweight service that orchestrates retrieval and tooling. Your app sends a request with user input and optional tool definitions. The service performs retrieval (for example, from Milvus if you need semantic memory), composes a structured prompt or function/tool call, and invokes Gemini. On response, you can parse function-call outputs, fetch external data, and iterate until the task is complete. For fine-grained control, you can parameterize temperature, output length, system instructions, and tool schemas, and you can log prompts and responses to your observability pipeline for audits and evaluations.
For developers, this integration enables concrete workflows: RAG (retrieve in Milvus → ground Gemini), multimodal search (embed images/text with a Gemini embedding model → search in Milvus), and agentic tool use (Gemini calls functions you register to fetch data, trigger actions, or run SQL). You keep the heavy lifting—rate limits, model updates, and security—inside Vertex AI, while your application code focuses on retrieval, business logic, and post-processing. This split is practical at scale: you can A/B test prompts, apply canary traffic, and observe token usage and latencies without changing your app’s external interface.