feat(js/plugins/google-genai): Added Veo and Lyria

js/ai/src/formats/types.ts

@@ -15,7 +15,7 @@
  */
 
 import type { JSONSchema } from '@genkit-ai/core';
-import type { GenerateResponseChunk } from '../generate.js';
+import type { GenerateResponseChunk } from '../generate/chunk.js';
 import type { Message } from '../message.js';
 import type { ModelRequest } from '../model.js';
 

js/ai/src/generate/action.ts

@@ -33,10 +33,10 @@ import {
 import type { Formatter } from '../formats/types.js';
 import {
   GenerateResponse,
-  GenerateResponseChunk,
   GenerationResponseError,
   tagAsPreamble,
 } from '../generate.js';
+import { GenerateResponseChunk } from '../generate/chunk.js';
 import {
   GenerateActionOptionsSchema,
   GenerateResponseChunkSchema,

js/plugins/google-genai/src/common/types.ts

@@ -40,7 +40,7 @@ export enum FunctionCallingMode {
 }
 
 /**
- * The reason why the reponse is blocked.
+ * The reason why the response is blocked.
  */
 export enum BlockReason {
   /** Unspecified block reason. */
@@ -156,7 +156,7 @@ export declare interface GroundingSupport {
   /** Optional. Segment of the content this support belongs to. */
   segment?: GroundingSupportSegment;
   /**
-   * Optional. A arrau of indices (into {@link GroundingChunk}) specifying the
+   * Optional. A array of indices (into {@link GroundingChunk}) specifying the
    * citations associated with the claim. For instance [1,3,4] means
    * that grounding_chunk[1], grounding_chunk[3],
    * grounding_chunk[4] are the retrieved content attributed to the claim.
@@ -441,7 +441,7 @@ export declare interface GoogleDate {
   year?: number;
   /**
    * Month of the date. Must be from 1 to 12, or 0 to specify a year without a
-   * monthi and day.
+   * month and day.
    */
   month?: number;
   /**
@@ -983,7 +983,7 @@ export declare interface GenerateContentRequest {
 
 /**
  * Result from calling generateContentStream.
- * It constains both the stream and the final aggregated response.
+ * It contains both the stream and the final aggregated response.
  * @public
  */
 export declare interface GenerateContentStreamResult {

js/plugins/google-genai/src/common/utils.ts

@@ -18,11 +18,12 @@ import {
   EmbedderReference,
   GenkitError,
   JSONSchema,
+  MediaPart,
   ModelReference,
+  Part,
   z,
 } from 'genkit';
 import { GenerateRequest } from 'genkit/model';
-import { ImagenInstance } from './types';
 
 /**
  * Safely extracts the error message from the error.
@@ -64,8 +65,9 @@ export function extractVersion(
 export function modelName(name?: string): string | undefined {
   if (!name) return name;
 
-  // Remove any of these prefixes: (but keep tunedModels e.g.)
-  const prefixesToRemove = /models\/|embedders\/|googleai\/|vertexai\//g;
+  // Remove any of these prefixes:
+  const prefixesToRemove =
+    /background-model\/|model\/|models\/|embedders\/|googleai\/|vertexai\//g;
   return name.replace(prefixesToRemove, '');
 }
 
@@ -95,20 +97,114 @@ export function extractText(request: GenerateRequest) {
   );
 }
 
-export function extractImagenImage(
-  request: GenerateRequest
-): ImagenInstance['image'] | undefined {
-  const image = request.messages
-    .at(-1)
-    ?.content.find(
-      (p) => !!p.media && (!p.metadata?.type || p.metadata?.type === 'base')
-    )
-    ?.media?.url.split(',')[1];
-
-  if (image) {
-    return { bytesBase64Encoded: image };
+const KNOWN_MIME_TYPES = {
+  jpg: 'image/jpeg',
+  jpeg: 'image/jpeg',
+  png: 'image/png',
+  mp4: 'video/mp4',
+  pdf: 'application/pdf',
+};
+
+export function extractMimeType(url?: string): string {
+  if (!url) {
+    return '';
+  }
+
+  const dataPrefix = 'data:';
+  if (!url.startsWith(dataPrefix)) {
+    // Not a data url, try suffix
+    url.lastIndexOf('.');
+    const key = url.substring(url.lastIndexOf('.') + 1);
+    if (Object.keys(KNOWN_MIME_TYPES).includes(key)) {
+      return KNOWN_MIME_TYPES[key];
+    }
+    return '';
+  }
+
+  const commaIndex = url.indexOf(',');
+  if (commaIndex == -1) {
+    // Invalid - missing separator
+    return '';
+  }
+
+  // The part between 'data:' and the comma
+  let mimeType = url.substring(dataPrefix.length, commaIndex);
+  const base64Marker = ';base64';
+  if (mimeType.endsWith(base64Marker)) {
+    mimeType = mimeType.substring(0, mimeType.length - base64Marker.length);
+  }
+
+  return mimeType.trim();
+}
+
+export function checkSupportedMimeType(
+  media: MediaPart['media'],
+  supportedTypes: string[]
+) {
+  if (!supportedTypes.includes(media.contentType ?? '')) {
+    throw new GenkitError({
+      status: 'INVALID_ARGUMENT',
+      message: `Invalid mimeType for ${displayUrl(media.url)}: "${media.contentType}". Supported mimeTypes: ${supportedTypes.join(', ')}`,
+    });
+  }
+}
+
+/**
+ *
+ * @param url The url to show (e.g. in an error message)
+ * @returns The appropriately  sized url
+ */
+export function displayUrl(url: string): string {
+  if (url.length <= 50) {
+    return url;
   }
-  return undefined;
+
+  return url.substring(0, 25) + '...' + url.substring(url.length - 25);
+}
+
+/**
+ *
+ * @param request A generate request to extract from
+ * @param metadataType The media must have metadata matching this type if isDefault is false
+ * @param isDefault 'true' allows missing metadata type to match as well.
+ * @returns
+ */
+export function extractMedia(
+  request: GenerateRequest,
+  params: {
+    metadataType?: string;
+    /* Is there is no metadata type, it will match if isDefault is true */
+    isDefault?: boolean;
+  }
+): MediaPart['media'] | undefined {
+  const predicate = (part: Part) => {
+    const media = part.media;
+    if (!media) {
+      return false;
+    }
+    if (params.metadataType || params.isDefault) {
+      // We need to check the metadata type
+      const metadata = part.metadata;
+      if (!metadata?.type) {
+        return !!params.isDefault;
+      } else {
+        return metadata.type == params.metadataType;
+      }
+    }
+    return true;
+  };
+
+  const media = request.messages.at(-1)?.content.find(predicate)?.media;
+
+  // Add the mimeType
+  if (media && !media?.contentType) {
+    return {
+      url: media.url,
+      contentType: extractMimeType(media.url),
+    };
+  }
+
+  return media;
 }
 
 /**

js/plugins/google-genai/src/googleai/utils.ts

@@ -16,12 +16,12 @@
 
 import { GenerateRequest, GenkitError } from 'genkit';
 import process from 'process';
-import { VeoImage } from './types.js';
+import { extractMedia } from '../common/utils.js';
+import { ImagenInstance, VeoImage } from './types.js';
 
 export {
   checkModelName,
   cleanSchema,
-  extractImagenImage,
   extractText,
   extractVersion,
   modelName,
@@ -143,3 +143,17 @@ export function extractVeoImage(
   }
   return undefined;
 }
+
+export function extractImagenImage(
+  request: GenerateRequest
+): ImagenInstance['image'] | undefined {
+  const image = extractMedia(request, {
+    metadataType: 'base',
+    isDefault: true,
+  })?.url.split(',')[1];
+
+  if (image) {
+    return { bytesBase64Encoded: image };
+  }
+  return undefined;
+}

js/plugins/google-genai/src/vertexai/client.ts

@@ -31,9 +31,14 @@ import {
   ImagenPredictRequest,
   ImagenPredictResponse,
   ListModelsResponse,
+  LyriaPredictRequest,
+  LyriaPredictResponse,
   Model,
+  VeoOperation,
+  VeoOperationRequest,
+  VeoPredictRequest,
 } from './types';
-import { calculateApiKey, checkIsSupported } from './utils';
+import { calculateApiKey, checkSupportedResourceMethod } from './utils';
 
 export async function listModels(
   clientOptions: ClientOptions
@@ -94,25 +99,37 @@ export async function generateContentStream(
   return processStream(response);
 }
 
-export async function embedContent(
+async function internalPredict(
   model: string,
-  embedContentRequest: EmbedContentRequest,
+  body: string,
   clientOptions: ClientOptions
-): Promise<EmbedContentResponse> {
+): Promise<Response> {
   const url = getVertexAIUrl({
     includeProjectAndLocation: true,
     resourcePath: `publishers/google/models/${model}`,
-    resourceMethod: 'predict', // embedContent is a Vertex API predict call
+    resourceMethod: 'predict',
     clientOptions,
   });
 
   const fetchOptions = await getFetchOptions({
     method: 'POST',
     clientOptions,
-    body: JSON.stringify(embedContentRequest),
+    body,
   });
 
-  const response = await makeRequest(url, fetchOptions);
+  return await makeRequest(url, fetchOptions);
+}
+
+export async function embedContent(
+  model: string,
+  embedContentRequest: EmbedContentRequest,
+  clientOptions: ClientOptions
+): Promise<EmbedContentResponse> {
+  const response = await internalPredict(
+    model,
+    JSON.stringify(embedContentRequest),
+    clientOptions
+  );
   return response.json() as Promise<EmbedContentResponse>;
 }
 
@@ -121,31 +138,78 @@ export async function imagenPredict(
   imagenPredictRequest: ImagenPredictRequest,
   clientOptions: ClientOptions
 ): Promise<ImagenPredictResponse> {
+  const response = await internalPredict(
+    model,
+    JSON.stringify(imagenPredictRequest),
+    clientOptions
+  );
+  return response.json() as Promise<ImagenPredictResponse>;
+}
+
+export async function lyriaPredict(
+  model: string,
+  lyriaPredictRequest: LyriaPredictRequest,
+  clientOptions: ClientOptions
+): Promise<LyriaPredictResponse> {
+  const response = await internalPredict(
+    model,
+    JSON.stringify(lyriaPredictRequest),
+    clientOptions
+  );
+  return response.json() as Promise<LyriaPredictResponse>;
+}
+
+export async function veoPredict(
+  model: string,
+  veoPredictRequest: VeoPredictRequest,
+  clientOptions: ClientOptions
+): Promise<VeoOperation> {
   const url = getVertexAIUrl({
     includeProjectAndLocation: true,
     resourcePath: `publishers/google/models/${model}`,
-    resourceMethod: 'predict',
+    resourceMethod: 'predictLongRunning',
     clientOptions,
   });
 
   const fetchOptions = await getFetchOptions({
     method: 'POST',
     clientOptions,
-    body: JSON.stringify(imagenPredictRequest),
+    body: JSON.stringify(veoPredictRequest),
   });
 
   const response = await makeRequest(url, fetchOptions);
-  return response.json() as Promise<ImagenPredictResponse>;
+  return response.json() as Promise<VeoOperation>;
+}
+
+export async function veoCheckOperation(
+  model: string,
+  veoOperationRequest: VeoOperationRequest,
+  clientOptions: ClientOptions
+): Promise<VeoOperation> {
+  const url = getVertexAIUrl({
+    includeProjectAndLocation: true,
+    resourcePath: `publishers/google/models/${model}`,
+    resourceMethod: 'fetchPredictOperation',
+    clientOptions,
+  });
+  const fetchOptions = await getFetchOptions({
+    method: 'POST',
+    clientOptions,
+    body: JSON.stringify(veoOperationRequest),
+  });
+
+  const response = await makeRequest(url, fetchOptions);
+  return response.json() as Promise<VeoOperation>;
 }
 
 export function getVertexAIUrl(params: {
   includeProjectAndLocation: boolean; // False for listModels, true for most others
   resourcePath: string;
-  resourceMethod?: 'streamGenerateContent' | 'generateContent' | 'predict';
+  resourceMethod?: string;
   queryParams?: string;
   clientOptions: ClientOptions;
 }): string {
-  checkIsSupported(params);
+  checkSupportedResourceMethod(params);
 
   const DEFAULT_API_VERSION = 'v1beta1';
   const API_BASE_PATH = 'aiplatform.googleapis.com';