Part 3: Developing and Configuring the Gemini Agent
This is the core of our project where we bring the AI to life. We will use the Vertex AI Gemini API with the Java SDK to create our agent, provide it with the function declarations from Part 2, and process its responses.
Initializing the Gemini Model
First, we need to initialize the generative model and define the tools it is allowed to use. The tool definition must match the API specification we created in our Spring Boot backend.
Java SDK Example
Below is a conceptual example of how to configure the model with tools. This code would typically live in a backend service that communicates with the Gemini API.
// Define the tool (function) the model can call
FunctionDeclaration getSlots = FunctionDeclaration.newBuilder()
.setName("get_available_slots")
.setDescription("Get a list of available appointment slots for a given date.")
.setParameters(
Schema.newBuilder()
.setType(Type.OBJECT)
.putProperties("date", Schema.newBuilder().setType(Type.STRING).setDescription("Date in YYYY-MM-DD format").build())
.addRequired("date")
.build())
.build();
// Create the model with the tool configured
GenerativeModel model = new GenerativeModel.Builder()
.setModelName("gemini-1.5-pro")
.setTools(List.of(Tool.newBuilder().addFunctionDeclarations(getSlots).build()))
.build();
Once the model responds with a function call, our backend code will execute that call against our Spring Boot API, get the result, and send it back to the model so it can formulate a natural language response for the user.