{"version":3,"sources":["../../src/vertexai/veo.ts"],"sourcesContent":["/**\n * Copyright 2025 Google LLC\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\nimport {\n ActionMetadata,\n ModelReference,\n modelActionMetadata,\n modelRef,\n z,\n} from 'genkit';\nimport { BackgroundModelAction, ModelInfo } from 'genkit/model';\nimport { backgroundModel as pluginBackgroundModel } from 'genkit/plugin';\nimport { isKnownKey } from '../common/utils.mjs';\nimport { veoCheckOperation, veoPredict } from './client.mjs';\nimport {\n fromVeoOperation,\n toVeoModel,\n toVeoOperationRequest,\n toVeoPredictRequest,\n} from './converters.mjs';\nimport { ClientOptions, Model, VertexPluginOptions } from './types.mjs';\nimport {\n applyContextOverrides,\n calculateRequestOptions,\n checkModelName,\n extractVersion,\n} from './utils.mjs';\n\nexport const VeoConfigSchema = z\n .object({\n sampleCount: z.number().optional().describe('Number of output videos'),\n storageUri: z\n .string()\n .optional()\n .describe('The gcs bucket where to save the generated videos'),\n fps: z\n .number()\n .optional()\n .describe('Frames per second for video generation'),\n durationSeconds: z\n .number()\n .optional()\n .describe('Duration of the clip for video generation in seconds'),\n seed: z\n .number()\n .optional()\n .describe(\n 'The RNG seed. If RNG seed is exactly same for each request with unchanged ' +\n 'inputs, the prediction results will be consistent. Otherwise, a random RNG ' +\n 'seed will be used each time to produce a different result. If the sample ' +\n 'count is greater than 1, random seeds will be used for each sample.'\n ),\n aspectRatio: z\n .enum(['9:16', '16:9'])\n .optional()\n .describe('The aspect ratio for the generated video'),\n resolution: z\n .enum(['720p', '1080p'])\n .optional()\n .describe('The resolution for the generated video'),\n personGeneration: z\n .enum(['dont_allow', 'allow_adult', 'allow_all'])\n .optional()\n .describe(\n 'Specifies the policy for generating persons in videos, including age restrictions'\n ),\n pubsubTopic: z\n .string()\n .optional()\n .describe('The pubsub topic to publish the video generation progress to'),\n negativePrompt: z\n .string()\n .optional()\n .describe(\n 'In addition to the text context, negative prompts can be explicitly stated here to help generate the video'\n ),\n enhancePrompt: z\n .boolean()\n .optional()\n .describe(\n 'If true, the prompt will be improved before it is used to generate videos. ' +\n 'The RNG seed, if provided, will not result in consistent results if prompts are enhanced.'\n ),\n generateAudio: z\n .boolean()\n .optional()\n .describe('If true, audio will be generated along with the video'),\n compressionQuality: z\n .enum(['optimized', 'lossless'])\n .default('optimized')\n .optional()\n .describe('Compression quality of the generated video'),\n resizeMode: z\n .enum(['pad', 'crop'])\n .default('pad')\n .optional()\n .describe(\n 'Veo 3 only. The resize mode that the model uses to resize the video'\n ),\n location: z\n .string()\n .describe('Google Cloud region e.g. us-central1. or global')\n .optional(),\n })\n .passthrough();\nexport type VeoConfigSchemaType = typeof VeoConfigSchema;\nexport type VeoConfig = z.infer;\n\n// This contains all the Veo config schema types\ntype ConfigSchemaType = VeoConfigSchemaType;\n\nfunction commonRef(\n name: string,\n info?: ModelInfo,\n configSchema: ConfigSchemaType = VeoConfigSchema\n): ModelReference {\n return modelRef({\n name: `vertexai/${name}`,\n configSchema,\n info:\n info ??\n ({\n supports: {\n media: true,\n multiturn: false,\n tools: false,\n systemRole: false,\n output: ['media'],\n longRunning: true,\n },\n } as ModelInfo), // TODO(ifielker): Remove this cast if we fix longRunning\n });\n}\n\nconst GENERIC_MODEL = commonRef('veo');\n\nconst KNOWN_MODELS = {\n 'veo-3.1-lite-generate-001': commonRef('veo-3.1-lite-generate-001'),\n 'veo-3.1-fast-generate-001': commonRef('veo-3.1-fast-generate-001'),\n 'veo-3.1-generate-001': commonRef('veo-3.1-generate-001'),\n} as const;\nexport type KnownModels = keyof typeof KNOWN_MODELS; // For autocomplete\n\n// Models we no longer suggest, but that still work: they still resolve and\n// are still registered as actions, but aren't in the KnownModels type, so\n// they don't show up in autocomplete. Set `stage` on each one (keeping the\n// default `supports`). Remove a model once the server returns 404 for it.\nconst HIDDEN_MODELS = {} as const;\n\nconst ALL_MODELS = {\n ...HIDDEN_MODELS,\n ...KNOWN_MODELS,\n};\n\nexport type VeoModelName = `veo-${string}`;\nexport function isVeoModelName(value?: string): value is VeoModelName {\n return !!value?.startsWith('veo-');\n}\n\nexport function model(\n version: string,\n config: VeoConfig = {}\n): ModelReference {\n const name = checkModelName(version);\n\n if (isKnownKey(name, ALL_MODELS)) {\n const known = ALL_MODELS[name];\n return modelRef({\n name: known.name,\n info: known.info,\n configSchema: known.configSchema,\n config,\n });\n }\n\n return modelRef({\n name: `vertexai/${name}`,\n config,\n configSchema: VeoConfigSchema,\n info: { ...GENERIC_MODEL.info },\n });\n}\n\n// Takes a full list of models, filters for current Veo models only\n// and returns a modelActionMetadata for each.\nexport function listActions(models: Model[]): ActionMetadata[] {\n return models\n .filter((m: Model) => isVeoModelName(m.name))\n .map((m: Model) => {\n const ref = model(m.name);\n return modelActionMetadata({\n name: ref.name,\n info: ref.info,\n configSchema: ref.configSchema,\n });\n });\n}\n\nexport function listKnownModels(\n clientOptions: ClientOptions,\n pluginOptions?: VertexPluginOptions\n) {\n return Object.keys(ALL_MODELS).map((name: string) =>\n defineModel(name, clientOptions, pluginOptions)\n );\n}\n\nexport function defineModel(\n name: string,\n clientOptions: ClientOptions,\n pluginOptions?: VertexPluginOptions\n): BackgroundModelAction {\n const ref = model(name);\n\n return pluginBackgroundModel({\n name: ref.name,\n ...ref.info,\n configSchema: ref.configSchema,\n async start(request, options) {\n const clientOpt = applyContextOverrides(\n calculateRequestOptions(clientOptions, request.config),\n options?.context\n );\n const veoPredictRequest = toVeoPredictRequest(request);\n\n const response = await veoPredict(\n extractVersion(ref),\n veoPredictRequest,\n clientOpt\n );\n\n return fromVeoOperation(response);\n },\n async check(operation, options) {\n const checkClientOptions = applyContextOverrides(\n clientOptions,\n options?.context\n );\n const response = await veoCheckOperation(\n toVeoModel(operation),\n toVeoOperationRequest(operation),\n checkClientOptions\n );\n return fromVeoOperation(response);\n },\n });\n}\n\nexport const TEST_ONLY = {\n GENERIC_MODEL,\n KNOWN_MODELS,\n HIDDEN_MODELS,\n 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