feat(generative-ai): migrate text and multimodal inference samples to @google/genai

Author: angelcaamal

generative-ai/snippets/inference/nonStreamMultiModalityBasic.js

@@ -13,51 +13,52 @@
 // limitations under the License.
 
 // [START generativeaionvertexai_non_stream_multimodality_basic]
-const {VertexAI} = require('@google-cloud/vertexai');
-
+const {GoogleGenAI} = require('@google/genai');
 /**
  * TODO(developer): Update these variables before running the sample.
  */
-const PROJECT_ID = process.env.CAIP_PROJECT_ID;
-const LOCATION = 'us-central1';
-const MODEL = 'gemini-2.0-flash-001';
-
-async function generateContent() {
-  // Initialize Vertex AI
-  const vertexAI = new VertexAI({project: PROJECT_ID, location: LOCATION});
-  const generativeModel = vertexAI.getGenerativeModel({model: MODEL});
+async function generateContent(
+  projectId,
+  location = 'us-central1',
+  model = 'gemini-2.0-flash-001'
+) {
+  // Initialize client
+  const client = new GoogleGenAI({
+    vertexai: true,
+    project: projectId,
+    location: location,
+  });
 
-  const request = {
+  const result = await client.models.generateContent({
+    model: model,
     contents: [
       {
         role: 'user',
         parts: [
           {
-            file_data: {
-              file_uri: 'gs://cloud-samples-data/video/animals.mp4',
-              mime_type: 'video/mp4',
+            fileData: {
+              fileUri: 'gs://cloud-samples-data/video/animals.mp4',
+              mimeType: 'video/mp4',
             },
           },
           {
-            file_data: {
-              file_uri:
+            fileData: {
+              fileUri:
                 'gs://cloud-samples-data/generative-ai/image/character.jpg',
-              mime_type: 'image/jpeg',
+              mimeType: 'image/jpeg',
             },
           },
           {text: 'Are this video and image correlated?'},
         ],
       },
     ],
-  };
-
-  const result = await generativeModel.generateContent(request);
+  });
 
-  console.log(result.response.candidates[0].content.parts[0].text);
+  console.log(result.text);
 }
 // [END generativeaionvertexai_non_stream_multimodality_basic]
 
-generateContent().catch(err => {
+generateContent(...process.argv.slice(2)).catch(err => {
   console.error(err.message);
   process.exitCode = 1;
 });

generative-ai/snippets/inference/nonStreamTextBasic.js

@@ -13,25 +13,25 @@
 // limitations under the License.
 
 // [START generativeaionvertexai_non_stream_text_basic]
-const {VertexAI} = require('@google-cloud/vertexai');
-
+const {GoogleGenAI} = require('@google/genai');
 /**
  * TODO(developer): Update these variables before running the sample.
  */
-const PROJECT_ID = process.env.CAIP_PROJECT_ID;
-const LOCATION = process.env.LOCATION;
-const MODEL = 'gemini-2.0-flash-001';
-
-async function generateContent() {
-  // Initialize Vertex with your Cloud project and location
-  const vertexAI = new VertexAI({project: PROJECT_ID, location: LOCATION});
 
-  // Instantiate the model
-  const generativeModel = vertexAI.getGenerativeModel({
-    model: MODEL,
+async function generateContent(
+  projectId,
+  location = 'us-central1',
+  model = 'gemini-2.0-flash-001'
+) {
+  // Initialize client with your Cloud project and location
+  const client = new GoogleGenAI({
+    vertexai: true,
+    project: projectId,
+    location: location,
   });
 
   const request = {
+    model: model,
     contents: [
       {
         role: 'user',
@@ -46,13 +46,13 @@ async function generateContent() {
 
   console.log(JSON.stringify(request));
 
-  const result = await generativeModel.generateContent(request);
+  const response = await client.models.generateContent(request);
 
-  console.log(result.response.candidates[0].content.parts[0].text);
+  console.log(response.text);
 }
 // [END generativeaionvertexai_non_stream_text_basic]
 
-generateContent().catch(err => {
+generateContent(...process.argv.slice(2)).catch(err => {
   console.error(err.message);
   process.exitCode = 1;
 });

generative-ai/snippets/inference/streamMultiModalityBasic.js

@@ -13,36 +13,41 @@
 // limitations under the License.
 
 // [START generativeaionvertexai_stream_multimodality_basic]
-const {VertexAI} = require('@google-cloud/vertexai');
+const {GoogleGenAI} = require('@google/genai');
 
 /**
  * TODO(developer): Update these variables before running the sample.
  */
-const PROJECT_ID = process.env.CAIP_PROJECT_ID;
-const LOCATION = process.env.LOCATION;
-const MODEL = 'gemini-2.0-flash-001';
 
-async function generateContent() {
-  // Initialize Vertex AI
-  const vertexAI = new VertexAI({project: PROJECT_ID, location: LOCATION});
-  const generativeModel = vertexAI.getGenerativeModel({model: MODEL});
+async function generateContent(
+  projectId,
+  location = 'us-central1',
+  model = 'gemini-2.0-flash-001'
+) {
+  // Initialize client
+  const client = new GoogleGenAI({
+    vertexai: true,
+    project: projectId,
+    location: location,
+  });
 
   const request = {
+    model: model,
     contents: [
       {
         role: 'user',
         parts: [
           {
-            file_data: {
-              file_uri: 'gs://cloud-samples-data/video/animals.mp4',
-              mime_type: 'video/mp4',
+            fileData: {
+              fileUri: 'gs://cloud-samples-data/video/animals.mp4',
+              mimeType: 'video/mp4',
             },
           },
           {
-            file_data: {
-              file_uri:
+            fileData: {
+              fileUri:
                 'gs://cloud-samples-data/generative-ai/image/character.jpg',
-              mime_type: 'image/jpeg',
+              mimeType: 'image/jpeg',
             },
           },
           {text: 'Are this video and image correlated?'},
@@ -51,15 +56,15 @@ async function generateContent() {
     ],
   };
 
-  const result = await generativeModel.generateContentStream(request);
+  const responseStream = await client.models.generateContentStream(request);
 
-  for await (const item of result.stream) {
-    console.log(item.candidates[0].content.parts[0].text);
+  for await (const chunk of responseStream) {
+    console.log(chunk.text);
   }
 }
 // [END generativeaionvertexai_stream_multimodality_basic]
 
-generateContent().catch(err => {
+generateContent(...process.argv.slice(2)).catch(err => {
   console.error(err.message);
   process.exitCode = 1;
 });

generative-ai/snippets/inference/streamTextBasic.js

@@ -13,25 +13,26 @@
 // limitations under the License.
 
 // [START generativeaionvertexai_stream_text_basic]
-const {VertexAI} = require('@google-cloud/vertexai');
+const {GoogleGenAI} = require('@google/genai');
 
 /**
  * TODO(developer): Update these variables before running the sample.
  */
-const PROJECT_ID = process.env.CAIP_PROJECT_ID;
-const LOCATION = process.env.LOCATION;
-const MODEL = 'gemini-2.0-flash-001';
 
-async function generateContent() {
-  // Initialize Vertex with your Cloud project and location
-  const vertexAI = new VertexAI({project: PROJECT_ID, location: LOCATION});
-
-  // Instantiate the model
-  const generativeModel = vertexAI.getGenerativeModel({
-    model: MODEL,
+async function generateContent(
+  projectId,
+  location = 'us-central1',
+  model = 'gemini-2.0-flash-001'
+) {
+  // Initialize client with your Cloud project and location
+  const client = new GoogleGenAI({
+    vertexai: true,
+    project: projectId,
+    location: location,
   });
 
   const request = {
+    model: model,
     contents: [
       {
         role: 'user',
@@ -43,17 +44,17 @@ async function generateContent() {
       },
     ],
   };
-
   console.log(JSON.stringify(request));
 
-  const result = await generativeModel.generateContentStream(request);
-  for await (const item of result.stream) {
-    console.log(item.candidates[0].content.parts[0].text);
+  const responseStream = await client.models.generateContentStream(request);
+
+  for await (const chunk of responseStream) {
+    console.log(chunk.text);
   }
 }
 // [END generativeaionvertexai_stream_text_basic]
 
-generateContent().catch(err => {
+generateContent(...process.argv.slice(2)).catch(err => {
   console.error(err.message);
   process.exitCode = 1;
 });

generative-ai/snippets/test/inference/nonStreamMultiModalityBasic.test.js

@@ -19,9 +19,15 @@ const {describe, it} = require('mocha');
 const cp = require('child_process');
 const execSync = cmd => cp.execSync(cmd, {encoding: 'utf-8'});
 
+const projectId = process.env.GOOGLE_SAMPLES_PROJECT;
+const location = process.env.LOCATION;
+const model = 'gemini-2.0-flash-001';
+
 describe('Generative AI Multimodal Text Inference', () => {
   it('should generate text based on a prompt containing text, a video, and an image', async () => {
-    const output = execSync('node ./inference/nonStreamMultiModalityBasic.js');
+    const output = execSync(
+      `node ./inference/nonStreamMultiModalityBasic.js ${projectId} ${location} ${model}`
+    );
     assert(output.length > 0);
   });
 });

generative-ai/snippets/test/inference/nonStreamTextBasic.test.js

@@ -19,9 +19,15 @@ const {describe, it} = require('mocha');
 const cp = require('child_process');
 const execSync = cmd => cp.execSync(cmd, {encoding: 'utf-8'});
 
+const projectId = process.env.GOOGLE_SAMPLES_PROJECT;
+const location = process.env.LOCATION;
+const model = 'gemini-2.0-flash-001';
+
 describe('Generative AI Basic Text Inference', () => {
   it('should create a generative text model and infer text from a prompt', async () => {
-    const output = execSync('node ./inference/nonStreamTextBasic.js');
+    const output = execSync(
+      `node ./inference/nonStreamTextBasic.js ${projectId} ${location} ${model}`
+    );
 
     // Assert that the correct prompt was issued
     assert(output.match(/Write a story about a magic backpack/));

generative-ai/snippets/test/inference/streamMultiModalityBasic.test.js

@@ -19,9 +19,15 @@ const {describe, it} = require('mocha');
 const cp = require('child_process');
 const execSync = cmd => cp.execSync(cmd, {encoding: 'utf-8'});
 
+const projectId = process.env.GOOGLE_SAMPLES_PROJECT;
+const location = process.env.LOCATION;
+const model = 'gemini-2.0-flash-001';
+
 describe('Generative AI Basic Multimodal Text Inference Streaming', () => {
   it('should create a generative text model and infer text from a prompt, streaming the results', async () => {
-    const output = execSync('node ./inference/streamMultiModalityBasic.js');
+    const output = execSync(
+      `node ./inference/streamMultiModalityBasic.js ${projectId} ${location} ${model}`
+    );
     assert(output.length > 0);
   });
 });

generative-ai/snippets/test/inference/streamTextBasic.test.js

@@ -19,9 +19,15 @@ const {describe, it} = require('mocha');
 const cp = require('child_process');
 const execSync = cmd => cp.execSync(cmd, {encoding: 'utf-8'});
 
+const projectId = process.env.GOOGLE_SAMPLES_PROJECT;
+const location = process.env.LOCATION;
+const model = 'gemini-2.0-flash-001';
+
 describe('Generative AI Basic Text Inference Streaming', () => {
   it('should create a generative text model and infer text from a prompt, streaming the results', async () => {
-    const output = execSync('node ./inference/streamTextBasic.js');
+    const output = execSync(
+      `node ./inference/streamTextBasic.js ${projectId} ${location} ${model}`
+    );
     assert(output.length > 0);
   });
 });