Feat/ai samples function calling

ai/ai-samples/src/features/function-calling/service.ts

@@ -1 +1,109 @@
-// Service for function-calling
+import { FunctionDeclarationsTool, Schema } from 'firebase/ai';
+import { getAiModel } from '../../services/firebaseAIService';
+
+/**
+ * Step 1: Write the local function.
+ * This interacts with your internal/external API or service.
+ */
+
+// This function calls a hypothetical external API that returns
+// a collection of weather information for a given location on a given date.
+// `location` is an object of the form { city: string, state: string }
+async function fetchWeather({ location, date }: { location: { city: string, state: string }; date: string }) {
+
+    // TODO(developer): Write a standard function that would call to an external weather API.
+
+    console.log(`Fetching mock weather for ${location.city}, ${location.state} on ${date}`)
+
+    // For demo purposes, this hypothetical response is hardcoded here in the expected format.
+    return {
+        temperature: 38,
+        chancePrecipitation: "56%",
+        cloudConditions: "partlyCloudy",
+    };
+}
+/**
+ * Step 2: Create a function declaration.
+ * This defines the schema and description that Gemini uses to decide when to call the tool.
+ */
+const fetchWeatherTool: FunctionDeclarationsTool = {
+    functionDeclarations: [
+        {
+            name: "fetchWeather",
+            description:
+                "Get the weather conditions for a specific city on a specific date",
+            parameters: Schema.object({
+                properties: {
+                    location: Schema.object({
+                        description:
+                            "The name of the city and its state for which to get " +
+                            "the weather. Only cities in the USA are supported.",
+                        properties: {
+                            city: Schema.string({
+                                description: "The city of the location."
+                            }),
+                            state: Schema.string({
+                                description: "The US state of the location."
+                            }),
+                        },
+                    }),
+                    date: Schema.string({
+                        description:
+                            "The date for which to get the weather. Date must be in the" +
+                            " format: YYYY-MM-DD.",
+                    }),
+                },
+            }),
+        },
+    ],
+};
+
+/**
+ * Step 3: Provide the function declaration during model initialization.
+ */
+
+const model = getAiModel('gemini-3.6-flash', {
+    model: "gemini-3.6-flash",
+    // Provide the function declaration to the model.
+    tools: fetchWeatherTool
+});
+
+/**
+ * Step 4: Call the function to invoke the external API.
+ */
+const chat = model.startChat();
+const prompt = "What was the weather in Boston on October 17, 2024?";
+
+// Send the user's question (the prompt) to the model using multi-turn chat.
+let result = await chat.sendMessage(prompt);
+const functionCalls = result.response.functionCalls() ?? [];
+let functionCall;
+let functionResult;
+// When the model responds with one or more function calls, invoke the function(s).
+if (functionCalls.length > 0) {
+    for (const call of functionCalls) {
+        if (call.name === "fetchWeather") {
+            // Forward the structured input data prepared by the model
+            // to the hypothetical external API.
+            functionResult = await fetchWeather(call.args as { location: { city: string; state: string }; date: string });
+            functionCall = call;
+        }
+    }
+}
+
+/**
+ * Step 5: Send the response from the function back to the model
+ * so that the model can use it to generate its final response.
+ */
+
+if (functionCall && functionResult) {
+    result = await chat.sendMessage([
+        {
+            functionResponse: {
+                name: functionCall.name, // "fetchWeather"
+                response: functionResult,
+            },
+        },
+    ]);
+}
+console.log(result.response.text());