{"version":3,"sources":["../../src/vertexai/gemini.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 GENKIT_UI_METADATA,\n GENKIT_UI_WIDGETS,\n GenkitError,\n annotateSchema,\n modelActionMetadata,\n z,\n} from 'genkit';\nimport { logger } from 'genkit/logging';\nimport {\n CandidateData,\n GenerateRequest,\n GenerateResponseChunkData,\n GenerateResponseData,\n GenerationCommonConfigDescriptions,\n GenerationCommonConfigSchema,\n MessageData,\n ModelAction,\n ModelInfo,\n ModelMiddleware,\n ModelReference,\n getBasicUsageStats,\n modelRef,\n} from 'genkit/model';\nimport { downloadRequestMedia } from 'genkit/model/middleware';\nimport { model as pluginModel } from 'genkit/plugin';\nimport {\n fromGeminiCandidate,\n toGeminiFunctionModeEnum,\n toGeminiMessage,\n toGeminiSystemInstruction,\n toGeminiTool,\n} from '../common/converters.mjs';\nimport {\n applyLatLngToGoogleMapsTools,\n fromInteractionDelta,\n fromInteractionSync,\n mergeStreamedAudio,\n selectInteractionInput,\n toInteractionConfigTool,\n toInteractionGenerationConfig,\n toInteractionGoogleSearch,\n toInteractionResponseModalities,\n toInteractionSafetySettings,\n toInteractionSteps,\n toInteractionTool,\n toInteractionToolChoice,\n toInteractionVideoResponseFormat,\n} from '../common/interaction-converters.mjs';\nimport {\n CreateInteractionRequest,\n InteractionTool,\n} from '../common/interaction-types.mjs';\nimport { isObject } from '../common/types.mjs';\nimport {\n isKnownKey,\n isStringArray,\n warnDroppedConfigField,\n} from '../common/utils.mjs';\nimport {\n createInteraction,\n createInteractionStream,\n generateContent,\n generateContentStream,\n} from './client.mjs';\nimport { toGeminiLabels, toGeminiSafetySettings } from './converters.mjs';\nimport {\n ClientOptions,\n Content,\n GenerateContentRequest,\n GenerateContentResponse,\n GoogleSearchRetrieval,\n Model,\n Tool,\n ToolConfig,\n VertexPluginOptions,\n} from './types.mjs';\nimport {\n calculateRequestOptions,\n checkModelName,\n cleanSchema,\n extractVersion,\n modelName,\n toInteractionsClientOptions,\n} from './utils.mjs';\n\nexport const SafetySettingsSchema = z\n .object({\n category: z.enum([\n /** The harm category is unspecified. */\n 'HARM_CATEGORY_UNSPECIFIED',\n /** The harm category is hate speech. */\n 'HARM_CATEGORY_HATE_SPEECH',\n /** The harm category is dangerous content. */\n 'HARM_CATEGORY_DANGEROUS_CONTENT',\n /** The harm category is harassment. */\n 'HARM_CATEGORY_HARASSMENT',\n /** The harm category is sexually explicit content. */\n 'HARM_CATEGORY_SEXUALLY_EXPLICIT',\n ]),\n threshold: z.enum([\n 'BLOCK_LOW_AND_ABOVE',\n 'BLOCK_MEDIUM_AND_ABOVE',\n 'BLOCK_ONLY_HIGH',\n 'BLOCK_NONE',\n ]),\n })\n .passthrough();\n\nconst VertexRetrievalSchema = z\n .object({\n datastore: z\n .object({\n projectId: z.string().describe('Google Cloud Project ID.').optional(),\n location: z\n .string()\n .describe('Google Cloud region e.g. us-central1.')\n .optional(),\n dataStoreId: z\n .string()\n .describe(\n 'The data store id, when project id and location are provided as ' +\n 'separate options. Alternatively, the full path to the data ' +\n 'store should be provided in the form: \"projects/{project}/' +\n 'locations/{location}/collections/default_collection/dataStores/{data_store}\".'\n ),\n })\n .describe('Vertex AI Search data store details')\n .passthrough(),\n disableAttribution: z\n .boolean()\n .describe(\n 'Disable using the search data in detecting grounding attribution. This ' +\n 'does not affect how the result is given to the model for generation.'\n )\n .optional(),\n })\n .passthrough();\n\nconst GoogleSearchRetrievalSchema = z\n .object({\n disableAttribution: z\n .boolean()\n .describe(\n 'Disable using the search data in detecting grounding attribution. This ' +\n 'does not affect how the result is given to the model for generation.'\n )\n .optional(),\n })\n .passthrough();\n\n/**\n * Zod schema of Gemini model options.\n * Please refer to: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference#generationconfig, for further information.\n */\nexport const GeminiConfigSchema = GenerationCommonConfigSchema.extend({\n apiKey: z\n .string()\n .describe('Overrides the plugin-configured API key, if specified.')\n .optional(),\n apiVersion: z\n .enum(['v1', 'v1beta1'])\n .describe(\n 'Overrides the plugin-configured API version to use, if specified.'\n )\n .optional(),\n labels: z\n .record(z.string())\n .optional()\n .describe('Key-value labels to attach to the request for cost tracking.'),\n temperature: z\n .number()\n .min(0.0)\n .max(2.0)\n .describe(\n GenerationCommonConfigDescriptions.temperature +\n ' The default value is 1.0.'\n )\n .optional(),\n topP: z\n .number()\n .min(0)\n .max(1.0)\n .describe(\n GenerationCommonConfigDescriptions.topP + ' The default value is 0.95.'\n )\n .optional(),\n location: z\n .string()\n .describe('Google Cloud region e.g. us-central1.')\n .optional(),\n store: z\n .boolean()\n .describe(\n 'Whether to store the interaction for later retrieval. Defaults to ' +\n 'false. Only used by models on the Interactions API.'\n )\n .optional(),\n previousInteractionId: z\n .string()\n .describe(\n 'The ID of the previous interaction to continue. Requires store. ' +\n 'Only used by models on the Interactions API.'\n )\n .optional(),\n\n /**\n * Safety filter settings. See: https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/configure-safety-filters#configurable-filters\n *\n * E.g.\n *\n * ```js\n * config: {\n * safetySettings: [\n * {\n * category: 'HARM_CATEGORY_HATE_SPEECH',\n * threshold: 'BLOCK_LOW_AND_ABOVE',\n * },\n * {\n * category: 'HARM_CATEGORY_DANGEROUS_CONTENT',\n * threshold: 'BLOCK_MEDIUM_AND_ABOVE',\n * },\n * {\n * category: 'HARM_CATEGORY_HARASSMENT',\n * threshold: 'BLOCK_ONLY_HIGH',\n * },\n * {\n * category: 'HARM_CATEGORY_SEXUALLY_EXPLICIT',\n * threshold: 'BLOCK_NONE',\n * },\n * ],\n * }\n * ```\n */\n safetySettings: annotateSchema(\n z\n .array(SafetySettingsSchema)\n .describe(\n 'Adjust how likely you are to see responses that could be harmful. ' +\n 'Content is blocked based on the probability that it is harmful.'\n )\n .optional(),\n { [GENKIT_UI_METADATA.WIDGET]: GENKIT_UI_WIDGETS.SAFETY_SETTINGS }\n ),\n\n /**\n * Vertex retrieval options.\n *\n * E.g.\n *\n * ```js\n * config: {\n * vertexRetrieval: {\n * datastore: {\n * projectId: 'your-cloud-project',\n * location: 'us-central1',\n * collection: 'your-collection',\n * },\n * disableAttribution: true,\n * }\n * }\n * ```\n */\n vertexRetrieval: VertexRetrievalSchema.describe(\n 'Retrieve from Vertex AI Search data store for grounding ' +\n 'generative responses.'\n ).optional(),\n\n /**\n * Google Search retrieval options.\n *\n * ```js\n * config: {\n * googleSearchRetrieval: {\n * disableAttribution: true,\n * }\n * }\n * ```\n */\n googleSearchRetrieval: GoogleSearchRetrievalSchema.describe(\n 'Retrieve public web data for grounding, powered by Google Search.'\n ).optional(),\n\n /**\n * Function calling options.\n *\n * E.g. forced tool call:\n *\n * ```js\n * config: {\n * functionCallingConfig: {\n * mode: 'ANY',\n * }\n * }\n * ```\n */\n functionCallingConfig: z\n .object({\n mode: z.enum(['MODE_UNSPECIFIED', 'AUTO', 'ANY', 'NONE']).optional(),\n allowedFunctionNames: z.array(z.string()).optional(),\n /**\n * When set to true, arguments of a single function call will be streamed out in\n * multiple parts/contents/responses. Partial parameter results will be returned in the\n * [FunctionCall.partial_args] field. This field is not supported in Gemini API.\n */\n streamFunctionCallArguments: z.boolean().optional(),\n })\n .describe(\n 'Controls how the model uses the provided tools (function declarations). ' +\n 'With AUTO (Default) mode, the model decides whether to generate a ' +\n 'natural language response or suggest a function call based on the ' +\n 'prompt and context. With ANY, the model is constrained to always ' +\n 'predict a function call and guarantee function schema adherence. ' +\n 'With NONE, the model is prohibited from making function calls.'\n )\n .passthrough()\n .optional(),\n /**\n * Retrieval config for search grounding and maps grounding\n */\n retrievalConfig: z\n .object({\n /**\n * User location for search grounding or\n * place location for maps grounding.\n */\n latLng: z\n .object({\n latitude: z.number().optional(),\n longitude: z.number().optional(),\n })\n .describe('User location for Google search or Google maps grounding.')\n .optional(),\n /**\n * Language code for the request. e.g. 'en-us'\n */\n languageCode: z.string().optional(),\n })\n .passthrough()\n .optional(),\n payGo: z\n .enum(['priority', 'priority-only', 'flex', 'flex-only'])\n .describe(\n 'PayGo consumption options. Priority provides more consistent performance than Standard PayGo. ' +\n 'Flex is a cost-effective option for non-critical workloads. ' +\n 'The \"-only\" options use only PayGo and no Provisioned Throughput.'\n )\n .optional(),\n thinkingConfig: z\n .object({\n includeThoughts: z\n .boolean()\n .describe(\n 'Indicates whether to include thoughts in the response.' +\n 'If true, thoughts are returned only if the model supports ' +\n 'thought and thoughts are available.'\n )\n .optional(),\n thinkingBudget: z\n .number()\n .min(0)\n .max(24576)\n .describe(\n 'For Gemini 2.5 - Indicates the thinking budget in tokens. 0 is DISABLED. ' +\n '-1 is AUTOMATIC. The default values and allowed ranges are model ' +\n 'dependent. The thinking budget parameter gives the model guidance ' +\n 'on the number of thinking tokens it can use when generating a ' +\n 'response. A greater number of tokens is typically associated with ' +\n 'more detailed thinking, which is needed for solving more complex ' +\n 'tasks. '\n )\n .optional(),\n thinkingLevel: z\n .enum(['MINIMAL', 'LOW', 'MEDIUM', 'HIGH'])\n .describe(\n 'For Gemini 3.0 - Indicates the thinking level. A higher level ' +\n 'is associated with more detailed thinking, which is needed for solving ' +\n 'more complex tasks.'\n )\n .optional(),\n })\n .passthrough()\n .optional(),\n}).passthrough();\nexport type GeminiConfigSchemaType = typeof GeminiConfigSchema;\n/**\n * Gemini model configuration options.\n *\n * E.g.\n * ```js\n * config: {\n * temperature: 0.9,\n * maxOutputTokens: 300,\n * safetySettings: [\n * {\n * category: 'HARM_CATEGORY_HATE_SPEECH',\n * threshold: 'BLOCK_LOW_AND_ABOVE',\n * },\n * {\n * category: 'HARM_CATEGORY_DANGEROUS_CONTENT',\n * threshold: 'BLOCK_MEDIUM_AND_ABOVE',\n * },\n * {\n * category: 'HARM_CATEGORY_HARASSMENT',\n * threshold: 'BLOCK_ONLY_HIGH',\n * },\n * {\n * category: 'HARM_CATEGORY_SEXUALLY_EXPLICIT',\n * threshold: 'BLOCK_NONE',\n * },\n * ],\n * functionCallingConfig: {\n * mode: 'ANY',\n * }\n * }\n * ```\n */\nexport type GeminiConfig = z.infer;\n\nexport const GeminiImageConfigSchema = GeminiConfigSchema.extend({\n imageConfig: z\n .object({\n aspectRatio: z\n .enum([\n '1:1',\n '2:3',\n '3:2',\n '3:4',\n '4:3',\n '4:5',\n '5:4',\n '9:16',\n '16:9',\n '21:9',\n ])\n .optional(),\n imageSize: z.enum(['512', '1K', '2K', '4K']).optional(),\n })\n .passthrough()\n .optional(),\n}).passthrough();\nexport type GeminiImageConfigSchemaType = typeof GeminiImageConfigSchema;\nexport type GeminiImageConfig = z.infer;\n\nconst VoiceConfigSchema = z\n .object({\n voice: z\n .union([\n z.enum([\n 'Zephyr',\n 'Puck',\n 'Charon',\n 'Kore',\n 'Fenrir',\n 'Leda',\n 'Orus',\n 'Aoede',\n 'Callirrhoe',\n 'Autonoe',\n 'Enceladus',\n 'Iapetus',\n 'Umbriel',\n 'Algieba',\n 'Despina',\n 'Erinome',\n 'Algenib',\n 'Rasalgethi',\n 'Laomedeia',\n 'Achernar',\n 'Alnilam',\n 'Schedar',\n 'Gacrux',\n 'Pulcherrima',\n 'Achird',\n 'Zubenelgenubi',\n 'Vindemiatrix',\n 'Sadachbia',\n 'Sadaltager',\n 'Sulafat',\n ]),\n // Voice Library and custom voice IDs (e.g. `voice_...`). `string & {}`\n // keeps the prebuilt names in TypeScript autocomplete.\n z.custom((val) => typeof val === 'string'),\n ])\n .describe(\n 'A prebuilt voice name, an Extended Voice Library voice ID, or a ' +\n 'custom voice ID.'\n )\n .optional(),\n // The googleAI shape, also accepted by the platform, so TTS code can move\n // between the two plugins.\n prebuiltVoiceConfig: z\n .object({\n voiceName: z.string().describe('Name of the preset voice to use'),\n })\n .passthrough()\n .optional(),\n })\n .describe(\n 'Configuration for the voice to use: `voice`, or `prebuiltVoiceConfig` ' +\n '(the googleAI shape)'\n )\n .passthrough();\n\nexport const GeminiTtsConfigSchema = GeminiConfigSchema.extend({\n speechConfig: z\n .object({\n voiceConfig: VoiceConfigSchema.optional(),\n multiSpeakerVoiceConfig: z\n .object({\n speakerVoiceConfigs: z\n .array(\n z\n .object({\n speaker: z.string().describe('Name of the speaker to use'),\n voiceConfig: VoiceConfigSchema,\n })\n .describe(\n 'Configuration for a single speaker in a multi speaker setup'\n )\n .passthrough()\n )\n .describe(\n 'Configuration for the two speaker voices. Each text part must ' +\n 'name its speaker in `metadata.speechMetadata.speaker`.'\n ),\n })\n .describe('Configuration for multi-speaker setup')\n .passthrough()\n .optional(),\n })\n .describe('Speech generation config')\n .passthrough()\n .optional(),\n audioFormat: z\n .object({\n mimeType: z\n .union([\n z.enum(['audio/wav', 'audio/l16', 'audio/mulaw', 'audio/alaw']),\n z.custom((val) => typeof val === 'string'),\n ])\n .describe(\n 'Audio encoding. Defaults to audio/wav (with a header) for ' +\n 'non-streamed requests and audio/l16 (raw PCM, no header) for ' +\n 'streamed requests.'\n )\n .optional(),\n })\n .describe('Format of the generated audio.')\n .passthrough()\n .optional(),\n}).passthrough();\nexport type GeminiTtsConfigSchemaType = typeof GeminiTtsConfigSchema;\nexport type GeminiTtsConfig = z.infer;\n\n// Gemini Omni (video generation). The fields follow the platform's\n// Interactions `VideoResponseFormat` and the Omni guide's `video_config`.\nexport const GeminiVideoConfigSchema = GeminiConfigSchema.extend({\n videoFormat: z\n .object({\n aspectRatio: z\n .union([\n z.enum(['16:9', '9:16']),\n // Allows other values. `string & {}` (rather than z.string()) keeps\n // the listed ones in TypeScript autocomplete.\n z.custom((val) => typeof val === 'string'),\n ])\n .describe(\n 'Aspect ratio of the generated video. Defaults to 16:9 (landscape).'\n )\n .optional(),\n resolution: z\n .union([\n z.enum(['360p', '720p', '1080p', '4k']),\n z.custom((val) => typeof val === 'string'),\n ])\n .describe('Resolution of the generated video. Defaults to 720p.')\n .optional(),\n delivery: z\n .union([\n z.enum(['inline', 'uri']),\n z.custom((val) => typeof val === 'string'),\n ])\n .describe(\n 'How the video is returned. By default it is returned inline. ' +\n '\"uri\" stores it in Cloud Storage instead (set gcsUri).'\n )\n .optional(),\n gcsUri: z\n .string()\n .describe(\n 'The Cloud Storage URI to store the video in. Required when ' +\n 'delivery is \"uri\".'\n )\n .optional(),\n })\n .describe('Format of the generated video.')\n .passthrough()\n .optional(),\n videoConfig: z\n .object({\n task: z\n .union([\n z.enum([\n 'text_to_video',\n 'image_to_video',\n 'reference_to_video',\n 'edit',\n 'extend',\n ]),\n z.custom((val) => typeof val === 'string'),\n ])\n .describe(\n 'The kind of generation to perform. If not set, the model infers ' +\n 'it from the prompt and inputs.'\n )\n .optional(),\n })\n .describe('Video generation settings.')\n .passthrough()\n .optional(),\n}).passthrough();\nexport type GeminiVideoConfigSchemaType = typeof GeminiVideoConfigSchema;\nexport type GeminiVideoConfig = z.infer;\n\n// This contains all the Gemini config schema types\ntype ConfigSchemaType =\n | GeminiConfigSchemaType\n | GeminiImageConfigSchemaType\n | GeminiTtsConfigSchemaType\n | GeminiVideoConfigSchemaType;\ntype ConfigSchema = z.infer;\n\n/** Whether each known model uses the Interactions API (see `usesInteractions`). */\nconst modelInteractionsMap = new Map();\n\nfunction commonRef(\n name: string,\n info?: ModelInfo,\n configSchema: ConfigSchemaType = GeminiConfigSchema,\n useInteractions: boolean = true\n): ModelReference {\n modelInteractionsMap.set(name, useInteractions);\n\n return modelRef({\n name: `vertexai/${name}`,\n configSchema,\n info: info ?? {\n supports: {\n multiturn: true,\n media: true,\n tools: true,\n toolChoice: true,\n systemRole: true,\n constrained: 'all',\n },\n },\n });\n}\n\nconst GENERIC_MODEL = commonRef('gemini');\nconst GENERIC_IMAGE_MODEL = commonRef(\n 'gemini-image',\n undefined,\n GeminiImageConfigSchema\n);\nconst GENERIC_TTS_MODEL = commonRef(\n 'gemini-tts',\n {\n supports: {\n multiturn: false,\n media: false,\n tools: false,\n toolChoice: false,\n systemRole: false,\n constrained: 'none',\n output: ['media'],\n },\n },\n GeminiTtsConfigSchema,\n false\n);\n// Per the Omni guide: text, image and video in, video (with audio) out; edits\n// continue a stored interaction. Tools and structured output aren't\n// documented for video generation.\nconst GENERIC_VIDEO_MODEL = commonRef(\n 'gemini-video',\n {\n supports: {\n multiturn: true,\n media: true,\n tools: false,\n toolChoice: false,\n systemRole: true,\n constrained: 'none',\n output: ['media'],\n },\n },\n GeminiVideoConfigSchema\n);\n\n// Models with `false` stay on generateContent: older models (like in the\n// googleAI plugin) and models whose docs page says the Interactions API isn't\n// supported.\nexport const KNOWN_GEMINI_MODELS = {\n 'gemini-4-argon': commonRef('gemini-4-argon'),\n // Allowlist only.\n 'gemini-4-argon-cyber-permissive': commonRef(\n 'gemini-4-argon-cyber-permissive'\n ),\n 'gemini-3.8-flash': commonRef('gemini-3.8-flash'),\n // Allowlist only.\n 'gemini-3.8-flash-cyber': commonRef(\n 'gemini-3.8-flash-cyber',\n undefined,\n GeminiConfigSchema,\n false\n ),\n 'gemini-3.7-flash': commonRef('gemini-3.7-flash'),\n 'gemini-3.5-flash-lite': commonRef('gemini-3.5-flash-lite'),\n 'gemini-3.5-flash': commonRef(\n 'gemini-3.5-flash',\n undefined,\n GeminiConfigSchema,\n false\n ),\n 'gemini-3.1-flash-lite': commonRef(\n 'gemini-3.1-flash-lite',\n undefined,\n GeminiConfigSchema,\n false\n ),\n 'gemini-3.1-pro-preview': commonRef(\n 'gemini-3.1-pro-preview',\n undefined,\n GeminiConfigSchema,\n false\n ),\n // Optimized for custom tools (an extra endpoint of 3.1 Pro).\n 'gemini-3.1-pro-preview-customtools': commonRef(\n 'gemini-3.1-pro-preview-customtools',\n undefined,\n GeminiConfigSchema,\n false\n ),\n 'gemini-3-flash-preview': commonRef(\n 'gemini-3-flash-preview',\n undefined,\n GeminiConfigSchema,\n false\n ),\n} as const;\nexport type KnownGeminiModels = keyof typeof KNOWN_GEMINI_MODELS;\nexport type GeminiModelName = `gemini-${string}`;\nexport function isGeminiModelName(value?: string): value is GeminiModelName {\n return !!(\n value?.startsWith('gemini-') &&\n !value.includes('embedding') &&\n !isImageModelName(value) &&\n !isTTSModelName(value) &&\n !isVideoModelName(value)\n );\n}\n\nexport const KNOWN_IMAGE_MODELS = {\n // seed, topK, logprobs, temperature and topP aren't supported (the server\n // returns an error).\n 'gemini-nano-banana-2.1': commonRef(\n 'gemini-nano-banana-2.1',\n { ...GENERIC_IMAGE_MODEL.info },\n GeminiImageConfigSchema,\n false\n ),\n 'gemini-3.1-flash-lite-image': commonRef(\n 'gemini-3.1-flash-lite-image',\n { ...GENERIC_IMAGE_MODEL.info },\n GeminiImageConfigSchema,\n false\n ),\n 'gemini-3.1-flash-image': commonRef(\n 'gemini-3.1-flash-image',\n { ...GENERIC_IMAGE_MODEL.info },\n GeminiImageConfigSchema,\n false\n ),\n 'gemini-3-pro-image': commonRef(\n 'gemini-3-pro-image',\n { ...GENERIC_IMAGE_MODEL.info },\n GeminiImageConfigSchema,\n false\n ),\n 'gemini-2.5-flash-image': commonRef(\n 'gemini-2.5-flash-image',\n undefined,\n GeminiImageConfigSchema,\n false\n ),\n} as const;\nexport type KnownImageModels = keyof typeof KNOWN_IMAGE_MODELS;\nexport type ImageModelName =\n | `gemini-${string}-image${string}`\n | `gemini-nano-banana-${string}`;\nexport function isImageModelName(value?: string): value is ImageModelName {\n return !!(\n value?.startsWith('gemini-') &&\n (value.includes('-image') || value.startsWith('gemini-nano-banana-'))\n );\n}\n\n// TTS models only work on generateContent (the Interactions API rejects them,\n// checked live Oct 2026).\nexport const KNOWN_TTS_MODELS = {\n 'gemini-3.8-flash-tts': commonRef(\n 'gemini-3.8-flash-tts',\n { ...GENERIC_TTS_MODEL.info },\n GeminiTtsConfigSchema,\n false\n ),\n 'gemini-3.8-flash-lite-tts': commonRef(\n 'gemini-3.8-flash-lite-tts',\n { ...GENERIC_TTS_MODEL.info },\n GeminiTtsConfigSchema,\n false\n ),\n} as const;\nexport type KnownTtsModels = keyof typeof KNOWN_TTS_MODELS;\nexport type TTSModelName = `gemini-${string}-tts${string}`;\nexport function isTTSModelName(value?: string): value is TTSModelName {\n return !!(value?.startsWith('gemini-') && value.includes('-tts'));\n}\n\n// Omni video models use the Interactions API.\nexport const KNOWN_VIDEO_MODELS = {\n 'gemini-omni-1.1-flash-preview': commonRef(\n 'gemini-omni-1.1-flash-preview',\n { ...GENERIC_VIDEO_MODEL.info },\n GeminiVideoConfigSchema\n ),\n 'gemini-omni-flash-preview': commonRef(\n 'gemini-omni-flash-preview',\n { ...GENERIC_VIDEO_MODEL.info },\n GeminiVideoConfigSchema\n ),\n} as const;\nexport type KnownVideoModels = keyof typeof KNOWN_VIDEO_MODELS;\nexport type VideoModelName = `gemini-omni-${string}`;\nexport function isVideoModelName(value?: string): value is VideoModelName {\n return !!value?.startsWith('gemini-omni-');\n}\n\nconst KNOWN_MODELS = {\n ...KNOWN_GEMINI_MODELS,\n ...KNOWN_IMAGE_MODELS,\n ...KNOWN_TTS_MODELS,\n ...KNOWN_VIDEO_MODELS,\n};\nexport type KnownModels = keyof typeof KNOWN_MODELS;\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 Known*Models types, so\n// they don't show up in autocomplete. Set `stage` on each one (keeping the\n// default `supports`):\n// - 'deprecated': deprecated by Google, or < 1 month from shutdown.\n// - 'legacy': not deprecated, but no longer offered to new projects.\n// Remove a model once the server returns 404 for it.\nconst HIDDEN_MODELS = {\n // Retirement date Oct 20 2026; kept until the server stops serving them.\n // They aren't on the Interactions API, so they must stay listed (`false`):\n // unlisted names would be sent to the Interactions API.\n 'gemini-2.5-pro': commonRef(\n 'gemini-2.5-pro',\n { ...GENERIC_MODEL.info, stage: 'deprecated' },\n GeminiConfigSchema,\n false\n ),\n 'gemini-2.5-flash': commonRef(\n 'gemini-2.5-flash',\n { ...GENERIC_MODEL.info, stage: 'deprecated' },\n GeminiConfigSchema,\n false\n ),\n 'gemini-2.5-flash-lite': commonRef(\n 'gemini-2.5-flash-lite',\n { ...GENERIC_MODEL.info, stage: 'deprecated' },\n GeminiConfigSchema,\n false\n ),\n} as const;\n\nconst ALL_MODELS = {\n ...HIDDEN_MODELS,\n ...KNOWN_MODELS,\n};\n\n/**\n * Whether a model is called through the Interactions API. Known older models\n * stay on generateContent (`false` in their `commonRef`); newer and unlisted\n * models use the Interactions API. Tuned model endpoints stay on\n * generateContent.\n */\nfunction usesInteractions(modelVersion: string): boolean {\n if (modelVersion.includes('endpoints/')) {\n return false;\n }\n // The platform's Interactions API doesn't support image or TTS models (it\n // returns 400 \"Model 'gemini-nano-banana-2.1' is not supported by the\n // Interactions API\"; the 3.8 TTS models fail too; Oct 2026), so they stay\n // on generateContent.\n return (\n modelInteractionsMap.get(modelVersion) ??\n !(isImageModelName(modelVersion) || isTTSModelName(modelVersion))\n );\n}\n\nexport function model(\n version: string,\n config: ConfigSchema = {}\n): ModelReference {\n const name = checkModelName(version);\n\n if (isKnownKey(name, ALL_MODELS)) {\n return ALL_MODELS[name].withConfig(config);\n }\n\n if (isImageModelName(name)) {\n return modelRef({\n name: `vertexai/${name}`,\n config,\n configSchema: GeminiImageConfigSchema,\n info: { ...GENERIC_IMAGE_MODEL.info },\n });\n }\n\n if (isVideoModelName(name)) {\n return modelRef({\n name: `vertexai/${name}`,\n config,\n configSchema: GeminiVideoConfigSchema,\n info: { ...GENERIC_VIDEO_MODEL.info },\n });\n }\n\n if (isTTSModelName(name)) {\n return modelRef({\n name: `vertexai/${name}`,\n config,\n configSchema: GeminiTtsConfigSchema,\n info: { ...GENERIC_TTS_MODEL.info },\n });\n }\n\n return modelRef({\n name: `vertexai/${name}`,\n config,\n configSchema: GeminiConfigSchema,\n info: {\n ...GENERIC_MODEL.info,\n },\n });\n}\n\nexport function listActions(models: Model[]): ActionMetadata[] {\n const KNOWN_DECOMISSIONED_MODELS = [\n 'gemini-pro-vision',\n 'gemini-pro',\n 'gemini-ultra',\n 'gemini-ultra-vision',\n ];\n\n return models\n .filter(\n (m) =>\n (isGeminiModelName(modelName(m.name)) ||\n isImageModelName(modelName(m.name)) ||\n isTTSModelName(modelName(m.name)) ||\n isVideoModelName(modelName(m.name))) &&\n !KNOWN_DECOMISSIONED_MODELS.includes(modelName(m.name) || '')\n )\n .map((m) => {\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) =>\n defineModel(name, clientOptions, pluginOptions)\n );\n}\n\n/**\n * Converts the TTS `audioFormat` config (`{ mimeType: 'audio/mulaw' }`) to\n * the generateContent `responseFormat` (`[{ audio: { mimeType:\n * 'AUDIO_MULAW' } }]`). Returns `undefined` if it isn't set, so the server\n * defaults apply (audio/wav, or audio/l16 when streaming).\n */\nfunction toGeminiAudioResponseFormat(\n audioFormat: unknown\n): Record[] | undefined {\n if (!isObject(audioFormat) || typeof audioFormat.mimeType !== 'string') {\n return undefined;\n }\n const { mimeType, ...rest } = audioFormat;\n return [\n {\n audio: {\n ...rest,\n // 'audio/mulaw' -> 'AUDIO_MULAW'; enum values pass through.\n mimeType: mimeType.toUpperCase().replace('/', '_'),\n },\n },\n ];\n}\n\n/**\n * Builds the Agent Search data store path for `vertexRetrieval`, filling in\n * the project and location from the plugin options when not set.\n */\nfunction toVertexAISearchDatastore(\n vertexRetrieval: NonNullable,\n clientOptions: ClientOptions\n): string {\n const projectId =\n vertexRetrieval.datastore.projectId ||\n (clientOptions.kind != 'express' ? clientOptions.projectId : undefined);\n // Data stores are in `global`, `us` or `eu`, so global and multi-regional\n // client locations are valid fallbacks.\n // TODO: Regional locations (e.g. us-central1) aren't data store locations;\n // falling back to one probably fails on the server. Kept as before until\n // checked live.\n const location =\n vertexRetrieval.datastore.location ||\n (clientOptions.kind != 'express' ? clientOptions.location : undefined);\n const dataStoreId = vertexRetrieval.datastore.dataStoreId;\n if (!projectId || !location || !dataStoreId) {\n throw new GenkitError({\n status: 'INVALID_ARGUMENT',\n message:\n 'projectId, location and datastoreId are required for vertexRetrieval and could not be determined from configuration',\n });\n }\n return `projects/${projectId}/locations/${location}/collections/default_collection/dataStores/${dataStoreId}`;\n}\n\n/**\n * Converts the request's tools and tool config fields to Interactions tools.\n */\nfunction toVertexInteractionTools(\n request: GenerateRequest,\n config: ConfigSchema,\n clientOptions: ClientOptions,\n warnDropped: (field: string) => void\n): InteractionTool[] {\n const tools: InteractionTool[] = [];\n if (request.tools?.length) {\n tools.push(...request.tools.map(toInteractionTool));\n }\n if (Array.isArray(config.tools)) {\n tools.push(...config.tools.map(toInteractionConfigTool));\n }\n if (config.googleSearchRetrieval) {\n const { disableAttribution, ...searchConfig } =\n config.googleSearchRetrieval;\n if (disableAttribution !== undefined) {\n warnDropped('googleSearchRetrieval.disableAttribution');\n }\n tools.push(toInteractionGoogleSearch(searchConfig));\n }\n const vertexRetrieval = config.vertexRetrieval;\n if (vertexRetrieval) {\n if (vertexRetrieval.disableAttribution !== undefined) {\n warnDropped('vertexRetrieval.disableAttribution');\n }\n tools.push({\n type: 'retrieval',\n retrieval_types: ['vertex_ai_search'],\n vertex_ai_search_config: {\n datastores: [toVertexAISearchDatastore(vertexRetrieval, clientOptions)],\n },\n });\n }\n if (config.retrievalConfig) {\n // There is no retrieval config on the Interactions path; the user\n // location is set on the google_maps tool instead.\n const { latLng, ...unsupportedRetrievalConfig } = config.retrievalConfig;\n if (latLng && !applyLatLngToGoogleMapsTools(tools, latLng)) {\n logger.warn(\n 'retrievalConfig.latLng is only used with the googleMaps tool for this model and will be ignored.'\n );\n }\n for (const key of Object.keys(unsupportedRetrievalConfig)) {\n warnDropped(`retrievalConfig.${key}`);\n }\n }\n return tools;\n}\n\n/**\n * Generates a response with the Interactions API (see `usesInteractions`).\n */\nasync function generateWithInteractions(params: {\n request: GenerateRequest;\n /** The request messages, without the system message. */\n messages: MessageData[];\n systemMessage?: MessageData;\n modelVersion: string;\n config: ConfigSchema;\n /** The plugin's client options (used for the vertexRetrieval fallback). */\n pluginClientOptions: ClientOptions;\n /** The client options for this request (location, API key, headers). */\n requestClientOptions: ClientOptions;\n pluginOptions?: VertexPluginOptions;\n streamingRequested: boolean;\n sendChunk: (chunk: GenerateResponseChunkData) => void;\n}): Promise {\n const { request, modelVersion, pluginOptions, config } = params;\n const {\n // Applied to the client options and headers by the caller.\n apiKey: _apiKey,\n apiVersion: _apiVersion,\n location: _location,\n payGo: _payGo,\n version: _version,\n // Converted to tools (toVertexInteractionTools).\n tools: _tools,\n googleSearchRetrieval: _googleSearchRetrieval,\n vertexRetrieval: _vertexRetrieval,\n retrievalConfig: _retrievalConfig,\n functionCallingConfig,\n safetySettings,\n labels,\n store: storeFromConfig,\n previousInteractionId: previousInteractionIdFromConfig,\n responseModalities,\n // Sent as `response_format` below.\n videoFormat,\n // TTS models don't use the Interactions API.\n audioFormat,\n ...generationFields\n } = config;\n const warnDropped = (field: string) =>\n warnDroppedConfigField(modelVersion, field);\n if (audioFormat !== undefined) {\n warnDropped('audioFormat');\n }\n\n const systemMessage = params.systemMessage;\n if (systemMessage?.content.some((c) => c.text === undefined)) {\n throw new GenkitError({\n status: 'INVALID_ARGUMENT',\n message:\n 'System message contains non-text content which is not supported for this model.',\n });\n }\n\n if (request.candidates && request.candidates > 1) {\n logger.warn(\n 'Multiple candidates are not supported for this model with the Interactions API; only one candidate will be returned.'\n );\n }\n\n // The vertexRetrieval data store fallback uses the plugin's project and\n // location, not `config.location` (same as on generateContent).\n const tools = toVertexInteractionTools(\n request,\n config,\n params.pluginClientOptions,\n warnDropped\n );\n\n if (functionCallingConfig?.streamFunctionCallArguments !== undefined) {\n warnDropped('functionCallingConfig.streamFunctionCallArguments');\n }\n const generationConfig = toInteractionGenerationConfig(\n generationFields,\n warnDropped\n );\n const toolChoice = toInteractionToolChoice(\n functionCallingConfig,\n request.toolChoice\n );\n if (toolChoice) {\n generationConfig.tool_choice = toolChoice;\n }\n\n const store = storeFromConfig === true || pluginOptions?.store === true;\n const { messages, previousInteractionId } = selectInteractionInput(\n params.messages,\n store,\n previousInteractionIdFromConfig\n );\n\n const req: CreateInteractionRequest = {\n system_instruction: systemMessage?.content.map((c) => c.text).join('\\n'),\n model: modelVersion,\n tools: tools.length ? tools : undefined,\n generation_config: generationConfig,\n stream: params.streamingRequested,\n input: toInteractionSteps(messages),\n store,\n safety_settings: toInteractionSafetySettings(safetySettings),\n labels: toGeminiLabels(labels),\n };\n if (previousInteractionId) {\n req.previous_interaction_id = previousInteractionId;\n }\n\n const jsonMode =\n request.output?.format === 'json' || !!request.output?.schema;\n if (jsonMode) {\n req.response_format = { type: 'text', mime_type: 'application/json' };\n if (request.output?.constrained) {\n req.response_format.schema = pluginOptions?.legacyResponseSchema\n ? cleanSchema(request.output.schema)\n : request.output.schema;\n }\n }\n\n const videoResponseFormat = toInteractionVideoResponseFormat(videoFormat);\n if (videoResponseFormat) {\n req.response_format = videoResponseFormat;\n }\n\n if (Array.isArray(responseModalities) && isStringArray(responseModalities)) {\n req.response_modalities =\n toInteractionResponseModalities(responseModalities);\n } else if (isImageModelName(modelVersion)) {\n req.response_modalities = ['text', 'image'];\n }\n\n if (!params.streamingRequested) {\n return fromInteractionSync(\n await createInteraction(req, params.requestClientOptions)\n );\n }\n const result = await createInteractionStream(\n req,\n params.requestClientOptions\n );\n for await (const event of result.stream) {\n if (event.event_type === 'step.delta') {\n const content = fromInteractionDelta(event.delta);\n if (content.length > 0) {\n params.sendChunk({ index: 0, content });\n }\n }\n }\n return fromInteractionSync(await result.response);\n}\n\n/**\n * Define a Vertex AI Gemini model.\n */\nexport function defineModel(\n name: string,\n clientOptions: ClientOptions,\n pluginOptions?: VertexPluginOptions\n): ModelAction {\n const ref = model(name);\n const middlewares: ModelMiddleware[] = [];\n if (ref.info?.supports?.media) {\n // the gemini api doesn't support downloading media from http(s)\n middlewares.push(\n downloadRequestMedia({\n maxBytes: 1024 * 1024 * 20,\n filter: (part) => {\n try {\n const url = new URL(part.media.url);\n if (\n // Gemini can handle these URLs\n ['www.youtube.com', 'youtube.com', 'youtu.be'].includes(\n url.hostname\n )\n )\n return false;\n } catch {}\n return true;\n },\n })\n );\n }\n\n return pluginModel(\n {\n name: ref.name,\n ...ref.info,\n configSchema: ref.configSchema,\n use: middlewares,\n },\n async (request, { streamingRequested, sendChunk, abortSignal }) => {\n let clientOpt = { ...clientOptions, signal: abortSignal };\n\n // Make a copy of messages to avoid side-effects\n const messages = structuredClone(request.messages);\n if (messages.length === 0) throw new Error('No messages provided.');\n\n // Handle system instructions separately\n let systemInstruction: Content | undefined = undefined;\n const systemMessage = messages.find((m) => m.role === 'system');\n if (systemMessage) {\n messages.splice(messages.indexOf(systemMessage), 1);\n systemInstruction = toGeminiSystemInstruction(systemMessage);\n }\n\n const requestConfig: ConfigSchema = { ...request.config };\n\n const {\n apiKey: apiKeyFromConfig,\n apiVersion: apiVersionFromConfig,\n functionCallingConfig,\n retrievalConfig,\n version: versionFromConfig,\n googleSearchRetrieval,\n tools: toolsFromConfig,\n vertexRetrieval,\n location,\n safetySettings,\n labels: labelsFromConfig,\n payGo,\n store: storeFromConfig,\n previousInteractionId: previousInteractionIdFromConfig,\n // TTS models: sent as `responseFormat` below.\n audioFormat: audioFormatFromConfig,\n // Video (Omni) models use the Interactions API.\n videoFormat: videoFormatFromConfig,\n ...restOfConfig\n } = requestConfig;\n\n clientOpt = calculateRequestOptions(clientOpt, {\n location,\n apiKey: apiKeyFromConfig,\n apiVersion: apiVersionFromConfig,\n });\n\n if (payGo) {\n const payGoHeaders: Record = {};\n if (payGo === 'priority') {\n payGoHeaders['X-Vertex-AI-LLM-Shared-Request-Type'] = 'priority';\n } else if (payGo === 'priority-only') {\n payGoHeaders['X-Vertex-AI-LLM-Request-Type'] = 'shared';\n payGoHeaders['X-Vertex-AI-LLM-Shared-Request-Type'] = 'priority';\n } else if (payGo === 'flex') {\n payGoHeaders['X-Vertex-AI-LLM-Shared-Request-Type'] = 'flex';\n } else if (payGo === 'flex-only') {\n payGoHeaders['X-Vertex-AI-LLM-Request-Type'] = 'shared';\n payGoHeaders['X-Vertex-AI-LLM-Shared-Request-Type'] = 'flex';\n }\n clientOpt.customHeaders = {\n ...clientOpt.customHeaders,\n ...payGoHeaders,\n };\n }\n\n const modelVersion = versionFromConfig || extractVersion(ref);\n if (usesInteractions(modelVersion)) {\n return generateWithInteractions({\n request,\n messages,\n systemMessage,\n modelVersion,\n config: requestConfig,\n pluginClientOptions: clientOptions,\n // A defaulted location (us-central1) becomes global here.\n requestClientOptions: toInteractionsClientOptions(clientOpt),\n pluginOptions,\n streamingRequested,\n sendChunk,\n });\n }\n\n // `store: false` is already how this model behaves, so only warn when\n // something would actually be ignored.\n if (storeFromConfig === true || previousInteractionIdFromConfig) {\n logger.warn(\n 'store and previousInteractionId are not supported for this model and will be ignored.'\n );\n }\n if (videoFormatFromConfig !== undefined) {\n warnDroppedConfigField(modelVersion, 'videoFormat');\n }\n\n const labels = toGeminiLabels(labelsFromConfig);\n\n const tools: Tool[] = [];\n if (request.tools?.length) {\n tools.push({\n functionDeclarations: request.tools.map(toGeminiTool),\n });\n }\n\n let toolConfig: ToolConfig | undefined;\n if (functionCallingConfig) {\n toolConfig = {\n functionCallingConfig: {\n ...functionCallingConfig,\n allowedFunctionNames: functionCallingConfig.allowedFunctionNames,\n mode: toGeminiFunctionModeEnum(functionCallingConfig.mode),\n },\n };\n } else if (request.toolChoice) {\n toolConfig = {\n functionCallingConfig: {\n mode: toGeminiFunctionModeEnum(request.toolChoice),\n },\n };\n }\n\n if (retrievalConfig) {\n if (!toolConfig) {\n toolConfig = {};\n }\n toolConfig.retrievalConfig = structuredClone(retrievalConfig);\n }\n\n const jsonMode =\n request.output?.format === 'json' || !!request.output?.schema;\n\n if (toolsFromConfig) {\n tools.push(...(toolsFromConfig as any[]));\n }\n\n if (googleSearchRetrieval) {\n tools.push({\n googleSearch: googleSearchRetrieval as GoogleSearchRetrieval,\n });\n }\n\n if (vertexRetrieval) {\n tools.push({\n retrieval: {\n vertexAiSearch: {\n datastore: toVertexAISearchDatastore(\n vertexRetrieval,\n clientOptions\n ),\n },\n disableAttribution: vertexRetrieval.disableAttribution,\n },\n });\n }\n\n const generateContentRequest: GenerateContentRequest = {\n systemInstruction,\n generationConfig: {\n ...restOfConfig,\n candidateCount: request.candidates || undefined,\n responseMimeType: jsonMode ? 'application/json' : undefined,\n },\n tools,\n toolConfig,\n safetySettings: toGeminiSafetySettings(safetySettings),\n contents: messages.map((message) => toGeminiMessage(message, ref)),\n labels,\n };\n\n if (isImageModelName(modelVersion)) {\n if (!generateContentRequest.generationConfig!.responseModalities) {\n generateContentRequest.generationConfig!.responseModalities = [\n 'TEXT',\n 'IMAGE',\n ];\n }\n } else if (isTTSModelName(modelVersion)) {\n if (!generateContentRequest.generationConfig!.responseModalities) {\n generateContentRequest.generationConfig!.responseModalities = [\n 'AUDIO',\n ];\n }\n }\n\n const responseFormat = toGeminiAudioResponseFormat(audioFormatFromConfig);\n if (responseFormat) {\n generateContentRequest.generationConfig!.responseFormat =\n responseFormat;\n }\n\n if (jsonMode && request.output?.constrained) {\n if (pluginOptions?.legacyResponseSchema) {\n generateContentRequest.generationConfig!.responseSchema = cleanSchema(\n request.output.schema\n );\n } else {\n generateContentRequest.generationConfig!.responseJsonSchema =\n request.output.schema;\n }\n }\n\n let response: GenerateContentResponse;\n\n // Handle streaming and non-streaming responses\n if (streamingRequested) {\n const result = await generateContentStream(\n modelVersion,\n generateContentRequest,\n clientOpt\n );\n\n const chunks: CandidateData[] = [];\n for await (const item of result.stream) {\n (item as GenerateContentResponse).candidates?.forEach((candidate) => {\n const c = fromGeminiCandidate(candidate, chunks);\n chunks.push(c);\n sendChunk({\n index: c.index,\n content: c.message.content,\n });\n });\n }\n response = await result.response;\n } else {\n response = await generateContent(\n modelVersion,\n generateContentRequest,\n clientOpt\n );\n }\n\n if (!response.candidates?.length) {\n throw new GenkitError({\n status: 'FAILED_PRECONDITION',\n message: 'No valid candidates returned.',\n });\n }\n\n const candidateData = response.candidates.map((c) =>\n fromGeminiCandidate(c)\n );\n if (streamingRequested && isTTSModelName(modelVersion)) {\n // Streamed audio arrives as many headerless chunks (audio/l16 by\n // default); join them into one part, like a non-streamed response.\n for (const candidate of candidateData) {\n candidate.message.content = mergeStreamedAudio(\n candidate.message.content\n );\n }\n }\n\n return {\n candidates: candidateData,\n custom: response,\n usage: {\n ...getBasicUsageStats(request.messages, candidateData),\n inputTokens: response.usageMetadata?.promptTokenCount,\n outputTokens: response.usageMetadata?.candidatesTokenCount,\n thoughtsTokens: response.usageMetadata?.thoughtsTokenCount,\n totalTokens: response.usageMetadata?.totalTokenCount,\n cachedContentTokens: response.usageMetadata?.cachedContentTokenCount,\n },\n };\n }\n );\n}\n\nexport const TEST_ONLY = {\n KNOWN_GEMINI_MODELS,\n KNOWN_IMAGE_MODELS,\n KNOWN_TTS_MODELS,\n KNOWN_VIDEO_MODELS,\n HIDDEN_MODELS,\n ALL_MODELS,\n 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