feat(testapps): add anthropic vision testapp

js/plugins/anthropic/README.md

@@ -56,11 +56,6 @@ const response = await ai.generate({
     { text: 'What animal is in the photo?' },
     { media: { url: imageUrl } },
   ],
-  config: {
-    // control of the level of visual detail when processing image embeddings
-    // Low detail level also decreases the token usage
-    visualDetailLevel: 'low',
-  },
 });
 console.log(response.text);
 ```

js/testapps/anthropic/README.md

@@ -11,7 +11,9 @@ src/
     text-plain.ts     - Text/plain error handling demonstration
     webp.ts           - WEBP image handling demonstration
     pdf.ts            - PDF document processing examples
+    vision.ts         - Image/vision analysis examples
     attention-first-page.pdf - Sample PDF file for testing
+    sample-image.png  - Sample image file for vision demo
   beta/
     basic.ts          - Basic beta API examples
 ```
@@ -39,6 +41,7 @@ src/
 - `pnpm run dev:stable:text-plain` – Start Dev UI for text/plain error handling demo.
 - `pnpm run dev:stable:webp` – Start Dev UI for WEBP image handling demo.
 - `pnpm run dev:stable:pdf` – Start Dev UI for PDF document processing demo.
+- `pnpm run dev:stable:vision` – Start Dev UI for image/vision analysis demo.
 
 ## Flows
 
@@ -64,4 +67,9 @@ Each source file defines flows that can be invoked from the Dev UI or the Genkit
 - `stable-pdf-url` – Process a PDF from a publicly accessible URL
 - `stable-pdf-analysis` – Analyze a PDF document for key topics, concepts, and visual elements
 
+### Vision/Image Analysis
+- `stable-vision-url` – Analyze an image from a public URL
+- `stable-vision-base64` – Analyze an image from a local file (base64 encoded)
+- `stable-vision-conversation` – Multi-turn conversation about an image
+
 Example: `genkit flow:run anthropic-stable-hello`

js/testapps/anthropic/package.json

@@ -13,6 +13,7 @@
     "dev:stable:text-plain": "genkit start -- npx tsx --watch src/stable/text-plain.ts",
     "dev:stable:webp": "genkit start -- npx tsx --watch src/stable/webp.ts",
     "dev:stable:pdf": "genkit start -- npx tsx --watch src/stable/pdf.ts",
+    "dev:stable:vision": "genkit start -- npx tsx --watch src/stable/vision.ts",
     "genkit:dev": "cross-env GENKIT_ENV=dev npm run dev:stable",
     "genkit:start": "cross-env GENKIT_ENV=dev genkit start -- tsx --watch src/stable/basic.ts",
     "dev": "export GENKIT_RUNTIME_ID=$(openssl rand -hex 8) && node lib/stable/basic.js 2>&1"

js/testapps/anthropic/src/stable/vision.ts

@@ -0,0 +1,132 @@
+/**
+ * Copyright 2025 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 { anthropic } from '@genkit-ai/anthropic';
+import * as fs from 'fs';
+import { genkit } from 'genkit';
+import * as path from 'path';
+
+const ai = genkit({
+  plugins: [anthropic()],
+});
+
+/**
+ * This flow demonstrates image analysis using a publicly accessible URL.
+ * Claude will describe what it sees in the image.
+ */
+ai.defineFlow('stable-vision-url', async () => {
+  // Using a Wikipedia Commons image (public domain)
+  const imageUrl =
+    'https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg';
+
+  const { text } = await ai.generate({
+    model: anthropic.model('claude-sonnet-4-5'),
+    messages: [
+      {
+        role: 'user',
+        content: [
+          { text: 'What do you see in this image? Describe it in detail.' },
+          {
+            media: {
+              url: imageUrl,
+            },
+          },
+        ],
+      },
+    ],
+  });
+
+  return text;
+});
+
+/**
+ * This flow demonstrates image analysis using a local file.
+ * The image is read from disk and sent as a base64 data URL.
+ */
+ai.defineFlow('stable-vision-base64', async () => {
+  // Read image file from the same directory as this source file
+  const imagePath = path.join(__dirname, 'sample-image.png');
+  const imageBuffer = fs.readFileSync(imagePath);
+  const imageBase64 = imageBuffer.toString('base64');
+
+  const { text } = await ai.generate({
+    model: anthropic.model('claude-sonnet-4-5'),
+    messages: [
+      {
+        role: 'user',
+        content: [
+          {
+            text: 'Describe this image. What objects, colors, and scenes do you observe?',
+          },
+          {
+            media: {
+              url: `data:image/png;base64,${imageBase64}`,
+              contentType: 'image/png',
+            },
+          },
+        ],
+      },
+    ],
+  });
+
+  return text;
+});
+
+/**
+ * This flow demonstrates multi-turn conversation about an image.
+ * Claude can answer follow-up questions about images it has seen.
+ */
+ai.defineFlow('stable-vision-conversation', async () => {
+  const imagePath = path.join(__dirname, 'sample-image.png');
+  const imageBuffer = fs.readFileSync(imagePath);
+  const imageBase64 = imageBuffer.toString('base64');
+
+  const { text } = await ai.generate({
+    model: anthropic.model('claude-sonnet-4-5'),
+    messages: [
+      {
+        role: 'user',
+        content: [
+          { text: 'What do you see in this image?' },
+          {
+            media: {
+              url: `data:image/png;base64,${imageBase64}`,
+              contentType: 'image/png',
+            },
+          },
+        ],
+      },
+      {
+        role: 'model',
+        content: [
+          {
+            text: 'I see a beautiful mountain landscape with a fjord or lake, green hills, and dramatic peaks under a blue sky with wispy clouds.',
+          },
+        ],
+      },
+      {
+        role: 'user',
+        content: [
+          {
+            text: 'What time of day do you think this photo was taken, and what season might it be?',
+          },
+        ],
+      },
+    ],
+  });
+
+  return text;
+});