chore: attempt to fix linting errors

Author: Cloud Shell

generative-ai/snippets/functionCallingStreamChat.js

@@ -14,8 +14,8 @@
 
 // [START aiplatform_gemini_function_calling_chat]
 const {
-    VertexAI,
-    FunctionDeclarationSchemaType
+  VertexAI,
+  FunctionDeclarationSchemaType
 } = require('@google-cloud/vertexai');
 
 const functionDeclarations = [
@@ -27,27 +27,27 @@ const functionDeclarations = [
         parameters: {
           type: FunctionDeclarationSchemaType.OBJECT,
           properties: {
-            location: { type: FunctionDeclarationSchemaType.STRING },
+            location: {type: FunctionDeclarationSchemaType.STRING},
             unit: {
               type: FunctionDeclarationSchemaType.STRING,
               enum: ['celsius', 'fahrenheit'],
-            },
-          },
+           },
+         },
           required: ['location'],
-        },
-      },
+       },
+     },
     ],
-  },
+ },
 ];
 
 const functionResponseParts = [
   {
     functionResponse: {
       name: 'get_current_weather',
       response:
-        { name: 'get_current_weather', content: { weather: 'super nice' } },
-    },
-  },
+        {name: 'get_current_weather', content: {weather: 'super nice'}},
+   },
+ },
 ];
 
 /**
@@ -59,32 +59,32 @@ async function functionCallingStreamChat(
   model = 'gemini-pro'
 ) {
   // Initialize Vertex with your Cloud project and location
-  const vertexAI = new VertexAI({ project: projectId, location: location });
+  const vertexAI = new VertexAI({project: projectId, location: location});
 
   // Instantiate the model
   const generativeModel = vertexAI.preview.getGenerativeModel({
     model: model,
-  });
+ });
 
   // Create a chat session and pass your function declarations
   const chat = generativeModel.startChat({
     tools: functionDeclarations,
-  });
+ });
 
   const chatInput1 = 'What is the weather in Boston?';
 
   // This should include a functionCall response from the model
   const result1 = await chat.sendMessageStream(chatInput1);
   for await (const item of result1.stream) {
     console.log(item.candidates[0]);
-  }
+ }
   const response1 = await result1.response;
 
   // Send a follow up message with a FunctionResponse
   const result2 = await chat.sendMessageStream(functionResponseParts);
   for await (const item of result2.stream) {
     console.log(item.candidates[0]);
-  }
+ }
 
   // This should include a text response from the model using the response content
   // provided above

generative-ai/snippets/functionCallingStreamContent.js

@@ -13,73 +13,73 @@
 // limitations under the License.
 
 // [START aiplatform_gemini_function_calling_content]
-const { VertexAI, FunctionDeclarationSchemaType } = require('@google-cloud/vertexai');
+const {VertexAI, FunctionDeclarationSchemaType} = require('@google-cloud/vertexai');
 
 const functionDeclarations = [
-    {
-      function_declarations: [
-        {
-          name: 'get_current_weather',
-          description: 'get weather in a given location',
-          parameters: {
-            type: FunctionDeclarationSchemaType.OBJECT,
-            properties: {
-              location: {type: FunctionDeclarationSchemaType.STRING},
-              unit: {
-                type: FunctionDeclarationSchemaType.STRING,
-                enum: ['celsius', 'fahrenheit'],
-              },
-            },
-            required: ['location'],
-          },
-        },
-      ],
-    },
-  ];
-  
-  const functionResponseParts = [
-    {
-      functionResponse: {
+  {
+    function_declarations: [
+      {
         name: 'get_current_weather',
-        response:
-            {name: 'get_current_weather', content: {weather: 'super nice'}},
-      },
-    },
-  ];
+        description: 'get weather in a given location',
+        parameters: {
+          type: FunctionDeclarationSchemaType.OBJECT,
+          properties: {
+            location: {type: FunctionDeclarationSchemaType.STRING},
+            unit: {
+              type: FunctionDeclarationSchemaType.STRING,
+              enum: ['celsius', 'fahrenheit'],
+           },
+         },
+          required: ['location'],
+       },
+     },
+    ],
+ },
+];
+
+const functionResponseParts = [
+  {
+    functionResponse: {
+      name: 'get_current_weather',
+      response:
+        {name: 'get_current_weather', content: {weather: 'super nice'}},
+   },
+ },
+];
 
 /**
  * TODO(developer): Update these variables before running the sample.
  */
 async function functionCallingStreamChat(
-    projectId = 'PROJECT_ID',
-    location = 'us-central1',
-    model = 'gemini-pro'
+  projectId = 'PROJECT_ID',
+  location = 'us-central1',
+  model = 'gemini-pro'
 ) {
-    // Initialize Vertex with your Cloud project and location
-    const vertexAI = new VertexAI({ project: projectId, location: location });
+  // Initialize Vertex with your Cloud project and location
+  const vertexAI = new VertexAI({project: projectId, location: location});
 
-    // Instantiate the model
-    const generativeModel = vertexAI.preview.getGenerativeModel({
-        model: model,
-    });
+  // Instantiate the model
+  const generativeModel = vertexAI.preview.getGenerativeModel({
+    model: model,
+ });
 
-    const request = {
-        contents: [
-          {role: 'user', parts: [{text: 'What is the weather in Boston?'}]},
-          {role: 'model', parts: [{functionCall: {name: 'get_current_weather', args: {'location': 'Boston'}}}]},
-          {role: 'function', parts: functionResponseParts}
-        ],
-        tools: functionDeclarations,
-      };
-      const streamingResp =
-          await generativeModel.generateContentStream(request);
-      for await (const item of streamingResp.stream) {
-        console.log(item.candidates[0].content.parts[0].text);
-      }    
+  const request = {
+    contents: [
+      {role: 'user', parts: [{text: 'What is the weather in Boston?'}]},
+      {role: 'model', parts: [{functionCall: {name: 'get_current_weather', args: {'location': 'Boston'}}}]},
+      {role: 'function', parts: functionResponseParts}
+    ],
+    tools: functionDeclarations,
+ };
+  const streamingResp =
+    await generativeModel.generateContentStream(request);
+  for await (const item of streamingResp.stream) {
+    console.log(item.candidates[0].content.parts[0].text);
+ }
 }
 // [END aiplatform_gemini_function_calling_content]
 
 functionCallingStreamChat(...process.argv.slice(2)).catch(err => {
-    console.error(err.message);
-    process.exitCode = 1;
+  console.error(err.message);
+  process.exitCode = 1;
 });