Add more samples in standardized format (#199)

samples/node/README.md

@@ -1,9 +1,9 @@
 # Google Generative AI Sample for Node.js (Javascript)
 
-This sample demonstrates how to use state-of-the-art
+These samples demonstrate how to use state-of-the-art
 generative AI models (like Gemini) to build AI-powered features and applications.
 
-To try out this sample, you'll need Node.js v18+.
+To try out these samples, you'll need Node.js v18+.
 
 ## Requirements
 
@@ -13,6 +13,18 @@ It’s strongly recommended that you do not check an API key into your version c
 
 This sample assumes that you're providing an `API_KEY` environment variable.
 
+## Instructions
+
+Each of these sample files can be run in Node.js from the command line, for
+example:
+
+```
+node function_calling.js
+```
+
+Some of these files run multiple example cases sequentially, and you may want
+to comment out cases you do not want to run.
+
 ## Documentation
 
 - [Quickstart: Get started with the Gemini API in Node.js applications](https://ai.google.dev/tutorials/node_quickstart)

samples/node/controlled_generation.js

@@ -0,0 +1,81 @@
+/**
+ * @license
+ * Copyright 2024 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+import {
+  FunctionDeclarationSchemaType,
+  GoogleGenerativeAI,
+} from "@google/generative-ai";
+
+async function jsonControlledGeneration() {
+  // [START json_controlled_generation]
+  const genAI = new GoogleGenerativeAI(process.env.API_KEY);
+
+  const schema = {
+    description: "List of recipes",
+    type: FunctionDeclarationSchemaType.ARRAY,
+    items: {
+      type: FunctionDeclarationSchemaType.OBJECT,
+      properties: {
+        recipeName: {
+          type: FunctionDeclarationSchemaType.STRING,
+          description: "Name of the recipe",
+          nullable: false,
+        },
+      },
+      required: ["recipeName"],
+    },
+  };
+
+  const model = genAI.getGenerativeModel({
+    model: "gemini-1.5-pro",
+    generationConfig: {
+      responseMimeType: "application/json",
+      responseSchema: schema,
+    },
+  });
+
+  const result = await model.generateContent(
+    "List a few popular cookie recipes.",
+  );
+  console.log(result.response.text());
+  // [END json_controlled_generation]
+}
+
+async function jsonNoSchema() {
+  // [START json_no_schema]
+  const genAI = new GoogleGenerativeAI(process.env.API_KEY);
+
+  const model = genAI.getGenerativeModel({
+    model: "gemini-1.5-flash",
+  });
+
+  const prompt = `List a few popular cookie recipes using this JSON schema:
+
+  Recipe = {'recipeName': string}
+  Return: Array<Recipe>`;
+
+  const result = await model.generateContent(prompt);
+  console.log(result.response.text());
+  // [END json_no_schema]
+}
+
+async function run() {
+  await jsonControlledGeneration();
+  await jsonNoSchema();
+}
+
+run();

samples/node/function_calling.js

@@ -0,0 +1,96 @@
+/**
+ * @license
+ * Copyright 2024 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+import { GoogleGenerativeAI } from "@google/generative-ai";
+
+async function functionCalling() {
+  // [START function_calling]
+  async function setLightValues(brightness, colorTemperature) {
+    // This mock API returns the requested lighting values
+    return {
+      brightness,
+      colorTemperature,
+    };
+  }
+
+  const controlLightFunctionDeclaration = {
+    name: "controlLight",
+    parameters: {
+      type: "OBJECT",
+      description: "Set the brightness and color temperature of a room light.",
+      properties: {
+        brightness: {
+          type: "NUMBER",
+          description:
+            "Light level from 0 to 100. Zero is off and 100 is full brightness.",
+        },
+        colorTemperature: {
+          type: "STRING",
+          description:
+            "Color temperature of the light fixture which can be `daylight`, `cool` or `warm`.",
+        },
+      },
+      required: ["brightness", "colorTemperature"],
+    },
+  };
+
+  // Executable function code. Put it in a map keyed by the function name
+  // so that you can call it once you get the name string from the model.
+  const functions = {
+    controlLight: ({ brightness, colorTemperature }) => {
+      return setLightValues(brightness, colorTemperature);
+    },
+  };
+
+  const genAI = new GoogleGenerativeAI(process.env.API_KEY);
+  const model = genAI.getGenerativeModel({
+    model: "gemini-1.5-flash",
+    tools: { functionDeclarations: [controlLightFunctionDeclaration] },
+  });
+  const chat = model.startChat();
+  const prompt = "Dim the lights so the room feels cozy and warm.";
+
+  // Send the message to the model.
+  const result = await chat.sendMessage(prompt);
+
+  // For simplicity, this uses the first function call found.
+  const call = result.response.functionCalls()[0];
+
+  if (call) {
+    // Call the executable function named in the function call
+    // with the arguments specified in the function call and
+    // let it call the hypothetical API.
+    const apiResponse = await functions[call.name](call.args);
+
+    // Send the API response back to the model so it can generate
+    // a text response that can be displayed to the user.
+    const result2 = await chat.sendMessage([
+      {
+        functionResponse: {
+          name: "controlLight",
+          response: apiResponse,
+        },
+      },
+    ]);
+
+    // Log the text response.
+    console.log(result2.response.text());
+  }
+  // [END function_calling]
+}
+
+functionCalling();