feat(js): Vertex AI plugin JSON mode

js/plugins/vertexai/package.json

@@ -38,8 +38,8 @@
     "@anthropic-ai/sdk": "^0.24.3",
     "@anthropic-ai/vertex-sdk": "^0.4.0",
     "@google-cloud/aiplatform": "^3.23.0",
-    "@google-cloud/vertexai": "^1.1.0",
-    "google-auth-library": "^9.6.3",
+    "@google-cloud/vertexai": "^1.7.0",
+    "google-auth-library": "^9.14.1",
     "googleapis": "^140.0.1",
     "node-fetch": "^3.3.2",
     "openai": "^4.52.7",
@@ -51,8 +51,8 @@
     "@genkit-ai/flow": "workspace:*"
   },
   "optionalDependencies": {
-    "firebase-admin": "^12.1.0",
-    "@google-cloud/bigquery": "^7.8.0"
+    "@google-cloud/bigquery": "^7.8.0",
+    "firebase-admin": "^12.1.0"
   },
   "devDependencies": {
     "@types/node": "^20.11.16",

js/plugins/vertexai/src/gemini.ts

@@ -399,7 +399,10 @@ function fromGeminiFunctionResponsePart(part: GeminiPart): Part {
 }
 
 // Converts vertex part to genkit part
-function fromGeminiPart(part: GeminiPart): Part {
+function fromGeminiPart(part: GeminiPart, jsonMode: boolean): Part {
+  if (jsonMode && part.text !== undefined) {
+    return { data: JSON.parse(part.text) };
+  }
   if (part.text !== undefined) return { text: part.text };
   if (part.functionCall) return fromGeminiFunctionCallPart(part);
   if (part.functionResponse) return fromGeminiFunctionResponsePart(part);
@@ -411,14 +414,15 @@ function fromGeminiPart(part: GeminiPart): Part {
 }
 
 export function fromGeminiCandidate(
-  candidate: GenerateContentCandidate
+  candidate: GenerateContentCandidate,
+  jsonMode: boolean
 ): CandidateData {
   const parts = candidate.content.parts || [];
   const genkitCandidate: CandidateData = {
     index: candidate.index || 0, // reasonable default?
     message: {
       role: 'model',
-      content: parts.map(fromGeminiPart),
+      content: parts.map((p) => fromGeminiPart(p, jsonMode)),
     },
     finishReason: fromGeminiFinishReason(candidate.finishReason),
     finishMessage: candidate.finishMessage,
@@ -518,11 +522,18 @@ export function geminiModel(
         }
       }
 
+      const tools = request.tools?.length
+        ? [{ functionDeclarations: request.tools?.map(toGeminiTool) }]
+        : [];
+
+      // Cannot use tools and function calling at the same time
+      const jsonMode =
+        (request.output?.format === 'json' || !!request.output?.schema) &&
+        tools.length === 0;
+
       const chatRequest: StartChatParams = {
         systemInstruction,
-        tools: request.tools?.length
-          ? [{ functionDeclarations: request.tools?.map(toGeminiTool) }]
-          : [],
+        tools,
         history: messages
           .slice(0, -1)
           .map((message) => toGeminiMessage(message, model)),
@@ -532,6 +543,7 @@ export function geminiModel(
           maxOutputTokens: request.config?.maxOutputTokens,
           topK: request.config?.topK,
           topP: request.config?.topP,
+          responseMimeType: jsonMode ? 'application/json' : undefined,
           stopSequences: request.config?.stopSequences,
         },
         safetySettings: request.config?.safetySettings,
@@ -566,7 +578,7 @@ export function geminiModel(
           .sendMessageStream(msg.parts);
         for await (const item of result.stream) {
           (item as GenerateContentResponse).candidates?.forEach((candidate) => {
-            const c = fromGeminiCandidate(candidate);
+            const c = fromGeminiCandidate(candidate, jsonMode);
             streamingCallback({
               index: c.index,
               content: c.message.content,
@@ -578,7 +590,9 @@ export function geminiModel(
           throw new Error('No valid candidates returned.');
         }
         return {
-          candidates: response.candidates?.map(fromGeminiCandidate) || [],
+          candidates:
+            response.candidates?.map((c) => fromGeminiCandidate(c, jsonMode)) ||
+            [],
           custom: response,
         };
       } else {
@@ -592,7 +606,9 @@ export function geminiModel(
           throw new Error('No valid candidates returned.');
         }
         const responseCandidates =
-          result.response.candidates?.map(fromGeminiCandidate) || [];
+          result.response.candidates?.map((c) =>
+            fromGeminiCandidate(c, jsonMode)
+          ) || [];
         return {
           candidates: responseCandidates,
           custom: result.response,

js/testapps/basic-gemini/package.json

@@ -18,6 +18,7 @@
     "@genkit-ai/dotprompt": "workspace:*",
     "@genkit-ai/flow": "workspace:*",
     "@genkit-ai/googleai": "workspace:*",
+    "@genkit-ai/vertexai": "workspace:*",
     "express": "^4.20.0",
     "zod": "^3.22.4"
   },

js/testapps/basic-gemini/src/index.ts

@@ -20,20 +20,24 @@ import * as z from 'zod';
 import { defineTool, generate } from '@genkit-ai/ai';
 import { configureGenkit } from '@genkit-ai/core';
 import { defineFlow, startFlowsServer } from '@genkit-ai/flow';
-import { googleAI } from '@genkit-ai/googleai';
+import {
+  gemini15Flash as gemini15FlashGoogleAi,
+  googleAI,
+} from '@genkit-ai/googleai';
 
 // Import models from the Google AI plugin. The Google AI API provides access to
-// several generative models. Here, we import Gemini 1.5 Flash.
-import { gemini15Flash } from '@genkit-ai/googleai';
+
+import {
+  gemini15Flash as gemini15FlashVertexAi,
+  vertexAI,
+} from '@genkit-ai/vertexai';
+
+const provider = process.env.PROVIDER || 'vertexai';
+
+const plugin = provider === 'vertexai' ? vertexAI : googleAI;
 
 configureGenkit({
-  plugins: [
-    // Load the Google AI plugin. You can optionally specify your API key
-    // by passing in a config object; if you don't, the Google AI plugin uses
-    // the value from the GOOGLE_GENAI_API_KEY environment variable, which is
-    // the recommended practice.
-    googleAI(),
-  ],
+  plugins: [plugin()],
   // Log debug output to tbe console.
   logLevel: 'debug',
   // Perform OpenTelemetry instrumentation and enable trace collection.
@@ -62,23 +66,37 @@ export const jokeFlow = defineFlow(
   },
   async () => {
     // Construct a request and send it to the model API.
+    if (provider === 'vertexai') {
+      const llmResponse = await generate({
+        model: gemini15FlashVertexAi,
+        config: {
+          temperature: 2,
+        },
+        output: {
+          schema: z.object({ jokeSubject: z.string() }),
+        },
+        tools: [jokeSubjectGenerator],
+        prompt: `come up with a subject to joke about (using the function provided)`,
+      });
 
-    const llmResponse = await generate({
-      model: gemini15Flash,
-      config: {
-        temperature: 2,
-      },
-      output: {
-        schema: z.object({ jokeSubject: z.string() }),
-      },
-      tools: [jokeSubjectGenerator],
-      prompt: `come up with a subject to joke about (using the function provided)`,
-    });
+      return llmResponse.output();
+    } else {
+      const llmResponse = await generate({
+        model: gemini15FlashGoogleAi,
+        config: {
+          temperature: 2,
+        },
+        output: {
+          schema: z.object({ jokeSubject: z.string() }),
+        },
+        tools: [jokeSubjectGenerator],
+        prompt: `come up with a subject to joke about (using the function provided)`,
+      });
+      return llmResponse.output();
+    }
 
     // Handle the response from the model API. In this sample, we just convert
     // it to a string, but more complicated flows might coerce the response into
-    // structured output or chain the response into another LLM call, etc.
-    return llmResponse.output();
   }
 );