@genkit-ai/googleai/lib/gemini.mjs.map

Genkit AI framework plugin for Google AI APIs, including Gemini APIs.

{"version":3,"sources":["../src/gemini.ts"],"sourcesContent":["/**\n * Copyright 2024 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  EnhancedGenerateContentResponse,\n  FileDataPart,\n  FunctionCallingMode,\n  FunctionCallPart,\n  FunctionDeclaration,\n  FunctionResponsePart,\n  GenerateContentCandidate as GeminiCandidate,\n  Content as GeminiMessage,\n  Part as GeminiPart,\n  GenerateContentResponse,\n  GenerationConfig,\n  GenerativeModel,\n  GoogleGenerativeAI,\n  InlineDataPart,\n  RequestOptions,\n  Schema,\n  SchemaType,\n  StartChatParams,\n  Tool,\n  ToolConfig,\n} from '@google/generative-ai';\nimport {\n  Genkit,\n  GENKIT_CLIENT_HEADER,\n  GenkitError,\n  JSONSchema,\n  z,\n} from 'genkit';\nimport {\n  CandidateData,\n  GenerationCommonConfigSchema,\n  getBasicUsageStats,\n  MediaPart,\n  MessageData,\n  ModelAction,\n  ModelInfo,\n  ModelMiddleware,\n  modelRef,\n  ModelReference,\n  Part,\n  ToolDefinitionSchema,\n  ToolRequestPart,\n  ToolResponsePart,\n} from 'genkit/model';\nimport {\n  downloadRequestMedia,\n  simulateSystemPrompt,\n} from 'genkit/model/middleware';\nimport { runInNewSpan } from 'genkit/tracing';\nimport { getApiKeyFromEnvVar } from './common';\nimport { handleCacheIfNeeded } from './context-caching';\nimport { extractCacheConfig } from './context-caching/utils';\n\nconst SafetySettingsSchema = z.object({\n  category: z.enum([\n    'HARM_CATEGORY_UNSPECIFIED',\n    'HARM_CATEGORY_HATE_SPEECH',\n    'HARM_CATEGORY_SEXUALLY_EXPLICIT',\n    'HARM_CATEGORY_HARASSMENT',\n    'HARM_CATEGORY_DANGEROUS_CONTENT',\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\nexport const GeminiConfigSchema = GenerationCommonConfigSchema.extend({\n  /** When supplied, override the plugin-configured API key and use this instead. */\n  apiKey: z.string().optional(),\n  safetySettings: z.array(SafetySettingsSchema).optional(),\n  codeExecution: z.union([z.boolean(), z.object({}).strict()]).optional(),\n  contextCache: z.boolean().optional(),\n  functionCallingConfig: z\n    .object({\n      mode: z.enum(['MODE_UNSPECIFIED', 'AUTO', 'ANY', 'NONE']).optional(),\n      allowedFunctionNames: z.array(z.string()).optional(),\n    })\n    .optional(),\n  /**\n   * Specify what modalities should be used in response. Only supported in\n   * 'gemini-2.0-flash-exp' model at present.\n   **/\n  responseModalities: z.array(z.enum(['TEXT', 'IMAGE', 'AUDIO'])).optional(),\n});\nexport type GeminiConfig = z.infer<typeof GeminiConfigSchema>;\n\nexport const gemini10Pro = modelRef({\n  name: 'googleai/gemini-1.0-pro',\n  info: {\n    label: 'Google AI - Gemini Pro',\n    versions: ['gemini-pro', 'gemini-1.0-pro-latest', 'gemini-1.0-pro-001'],\n    supports: {\n      multiturn: true,\n      media: false,\n      tools: true,\n      toolChoice: true,\n      systemRole: true,\n      constrained: 'no-tools',\n    },\n  },\n  configSchema: GeminiConfigSchema,\n});\n\nexport const gemini15Pro = modelRef({\n  name: 'googleai/gemini-1.5-pro',\n  info: {\n    label: 'Google AI - Gemini 1.5 Pro',\n    supports: {\n      multiturn: true,\n      media: true,\n      tools: true,\n      toolChoice: true,\n      systemRole: true,\n      constrained: 'no-tools',\n    },\n    versions: [\n      'gemini-1.5-pro-latest',\n      'gemini-1.5-pro-001',\n      'gemini-1.5-pro-002',\n    ],\n  },\n  configSchema: GeminiConfigSchema,\n});\n\nexport const gemini15Flash = modelRef({\n  name: 'googleai/gemini-1.5-flash',\n  info: {\n    label: 'Google AI - Gemini 1.5 Flash',\n    supports: {\n      multiturn: true,\n      media: true,\n      tools: true,\n      toolChoice: true,\n      systemRole: true,\n      constrained: 'no-tools',\n      // @ts-ignore\n      contextCache: true,\n    },\n    versions: [\n      'gemini-1.5-flash-latest',\n      'gemini-1.5-flash-001',\n      'gemini-1.5-flash-002',\n    ],\n  },\n  configSchema: GeminiConfigSchema,\n});\n\nexport const gemini15Flash8b = modelRef({\n  name: 'googleai/gemini-1.5-flash-8b',\n  info: {\n    label: 'Google AI - Gemini 1.5 Flash',\n    supports: {\n      multiturn: true,\n      media: true,\n      tools: true,\n      toolChoice: true,\n      systemRole: true,\n      constrained: 'no-tools',\n    },\n    versions: ['gemini-1.5-flash-8b-latest', 'gemini-1.5-flash-8b-001'],\n  },\n  configSchema: GeminiConfigSchema,\n});\n\nexport const gemini20Flash = modelRef({\n  name: 'googleai/gemini-2.0-flash',\n  info: {\n    label: 'Google AI - Gemini 2.0 Flash',\n    versions: [],\n    supports: {\n      multiturn: true,\n      media: true,\n      tools: true,\n      toolChoice: true,\n      systemRole: true,\n      constrained: 'no-tools',\n    },\n  },\n  configSchema: GeminiConfigSchema,\n});\n\nexport const gemini20FlashExp = modelRef({\n  name: 'googleai/gemini-2.0-flash-exp',\n  info: {\n    label: 'Google AI - Gemini 2.0 Flash (Experimental)',\n    versions: [],\n    supports: {\n      multiturn: true,\n      media: true,\n      tools: true,\n      toolChoice: true,\n      systemRole: true,\n      constrained: 'no-tools',\n    },\n  },\n  configSchema: GeminiConfigSchema,\n});\n\nexport const gemini20FlashLite = modelRef({\n  name: 'googleai/gemini-2.0-flash-lite',\n  info: {\n    label: 'Google AI - Gemini 2.0 Flash Lite',\n    versions: [],\n    supports: {\n      multiturn: true,\n      media: true,\n      tools: true,\n      toolChoice: true,\n      systemRole: true,\n      constrained: 'no-tools',\n    },\n  },\n  configSchema: GeminiConfigSchema,\n});\n\nexport const gemini20ProExp0205 = modelRef({\n  name: 'googleai/gemini-2.0-pro-exp-02-05',\n  info: {\n    label: 'Google AI - Gemini 2.0 Pro Exp 02-05',\n    versions: [],\n    supports: {\n      multiturn: true,\n      media: true,\n      tools: true,\n      toolChoice: true,\n      systemRole: true,\n      constrained: 'no-tools',\n    },\n  },\n  configSchema: GeminiConfigSchema,\n});\n\nexport const gemini25ProExp0325 = modelRef({\n  name: 'googleai/gemini-2.5-pro-exp-03-25',\n  info: {\n    label: 'Google AI - Gemini 2.5 Pro Exp 03-25',\n    versions: [],\n    supports: {\n      multiturn: true,\n      media: true,\n      tools: true,\n      toolChoice: true,\n      systemRole: true,\n      constrained: 'no-tools',\n    },\n  },\n  configSchema: GeminiConfigSchema,\n});\n\nexport const SUPPORTED_V1_MODELS = {\n  'gemini-1.0-pro': gemini10Pro,\n};\n\nexport const SUPPORTED_V15_MODELS = {\n  'gemini-1.5-pro': gemini15Pro,\n  'gemini-1.5-flash': gemini15Flash,\n  'gemini-1.5-flash-8b': gemini15Flash8b,\n  'gemini-2.0-flash': gemini20Flash,\n  'gemini-2.0-flash-lite': gemini20FlashLite,\n  'gemini-2.0-pro-exp-02-05': gemini20ProExp0205,\n  'gemini-2.0-flash-exp': gemini20FlashExp,\n  'gemini-2.5-pro-exp-03-25': gemini25ProExp0325,\n};\n\nexport const GENERIC_GEMINI_MODEL = modelRef({\n  name: 'googleai/gemini',\n  configSchema: GeminiConfigSchema,\n  info: {\n    label: 'Google Gemini',\n    supports: {\n      multiturn: true,\n      media: true,\n      tools: true,\n      toolChoice: true,\n      systemRole: true,\n      constrained: 'no-tools',\n    },\n  },\n});\n\nexport const SUPPORTED_GEMINI_MODELS = {\n  ...SUPPORTED_V1_MODELS,\n  ...SUPPORTED_V15_MODELS,\n} as const;\n\nfunction longestMatchingPrefix(version: string, potentialMatches: string[]) {\n  return potentialMatches\n    .filter((p) => version.startsWith(p))\n    .reduce(\n      (longest, current) =>\n        current.length > longest.length ? current : longest,\n      ''\n    );\n}\n\n/**\n * Known model names, to allow code completion for convenience. Allows other model names.\n */\nexport type GeminiVersionString =\n  | keyof typeof SUPPORTED_GEMINI_MODELS\n  | (string & {});\n\n/**\n * Returns a reference to a model that can be used in generate calls.\n *\n * ```js\n * await ai.generate({\n *   prompt: 'hi',\n *   model: gemini('gemini-1.5-flash')\n * });\n * ```\n */\nexport function gemini(\n  version: GeminiVersionString,\n  options: GeminiConfig = {}\n): ModelReference<typeof GeminiConfigSchema> {\n  const nearestModel = nearestGeminiModelRef(version);\n  return modelRef({\n    name: `googleai/${version}`,\n    config: options,\n    configSchema: GeminiConfigSchema,\n    info: {\n      ...nearestModel.info,\n      // If exact suffix match for a known model, use its label, otherwise create a new label\n      label: nearestModel.name.endsWith(version)\n        ? nearestModel.info?.label\n        : `Google AI - ${version}`,\n    },\n  });\n}\n\nfunction nearestGeminiModelRef(\n  version: GeminiVersionString,\n  options: GeminiConfig = {}\n): ModelReference<typeof GeminiConfigSchema> {\n  const matchingKey = longestMatchingPrefix(\n    version,\n    Object.keys(SUPPORTED_GEMINI_MODELS)\n  );\n  if (matchingKey) {\n    return SUPPORTED_GEMINI_MODELS[matchingKey].withConfig({\n      ...options,\n      version,\n    });\n  }\n  return GENERIC_GEMINI_MODEL.withConfig({ ...options, version });\n}\n\nfunction toGeminiRole(\n  role: MessageData['role'],\n  model?: ModelReference<z.ZodTypeAny>\n): string {\n  switch (role) {\n    case 'user':\n      return 'user';\n    case 'model':\n      return 'model';\n    case 'system':\n      if (model && SUPPORTED_V15_MODELS[model.name]) {\n        // We should have already pulled out the supported system messages,\n        // anything remaining is unsupported; throw an error.\n        throw new Error(\n          'system role is only supported for a single message in the first position'\n        );\n      } else {\n        throw new Error('system role is not supported');\n      }\n    case 'tool':\n      return 'function';\n    default:\n      return 'user';\n  }\n}\n\nfunction convertSchemaProperty(property) {\n  if (!property || !property.type) {\n    return undefined;\n  }\n  const baseSchema = {} as Schema;\n  if (property.description) {\n    baseSchema.description = property.description;\n  }\n  if (property.enum) {\n    baseSchema.type = SchemaType.STRING;\n    // supported in API but not in SDK\n    (baseSchema as any).enum = property.enum;\n  }\n  if (property.nullable) {\n    baseSchema.nullable = property.nullable;\n  }\n  let propertyType;\n  // nullable schema can ALSO be defined as, for example, type=['string','null']\n  if (Array.isArray(property.type)) {\n    const types = property.type as string[];\n    if (types.includes('null')) {\n      baseSchema.nullable = true;\n    }\n    // grab the type that's not `null`\n    propertyType = types.find((t) => t !== 'null');\n  } else {\n    propertyType = property.type;\n  }\n  if (propertyType === 'object') {\n    const nestedProperties = {};\n    Object.keys(property.properties).forEach((key) => {\n      nestedProperties[key] = convertSchemaProperty(property.properties[key]);\n    });\n    return {\n      ...baseSchema,\n      type: SchemaType.OBJECT,\n      properties: nestedProperties,\n      required: property.required,\n    };\n  } else if (propertyType === 'array') {\n    return {\n      ...baseSchema,\n      type: SchemaType.ARRAY,\n      items: convertSchemaProperty(property.items),\n    };\n  } else {\n    const schemaType = SchemaType[propertyType.toUpperCase()] as SchemaType;\n    if (!schemaType) {\n      throw new GenkitError({\n        status: 'INVALID_ARGUMENT',\n        message: `Unsupported property type ${propertyType.toUpperCase()}`,\n      });\n    }\n    return {\n      ...baseSchema,\n      type: schemaType,\n    };\n  }\n}\n\n/** @hidden */\nexport function toGeminiTool(\n  tool: z.infer<typeof ToolDefinitionSchema>\n): FunctionDeclaration {\n  const declaration: FunctionDeclaration = {\n    name: tool.name.replace(/\\//g, '__'), // Gemini throws on '/' in tool name\n    description: tool.description,\n    parameters: convertSchemaProperty(tool.inputSchema),\n  };\n  return declaration;\n}\n\nfunction toInlineData(part: MediaPart): InlineDataPart {\n  const dataUrl = part.media.url;\n  const b64Data = dataUrl.substring(dataUrl.indexOf(',')! + 1);\n  const contentType =\n    part.media.contentType ||\n    dataUrl.substring(dataUrl.indexOf(':')! + 1, dataUrl.indexOf(';'));\n  return { inlineData: { mimeType: contentType, data: b64Data } };\n}\n\nfunction toFileData(part: MediaPart): FileDataPart {\n  if (!part.media.contentType)\n    throw new Error(\n      'Must supply a `contentType` when sending File URIs to Gemini.'\n    );\n  return {\n    fileData: { mimeType: part.media.contentType, fileUri: part.media.url },\n  };\n}\n\nfunction fromInlineData(inlinePart: InlineDataPart): MediaPart {\n  // Check if the required properties exist\n  if (\n    !inlinePart.inlineData ||\n    !inlinePart.inlineData.hasOwnProperty('mimeType') ||\n    !inlinePart.inlineData.hasOwnProperty('data')\n  ) {\n    throw new Error('Invalid InlineDataPart: missing required properties');\n  }\n  const { mimeType, data } = inlinePart.inlineData;\n  // Combine data and mimeType into a data URL\n  const dataUrl = `data:${mimeType};base64,${data}`;\n  return {\n    media: {\n      url: dataUrl,\n      contentType: mimeType,\n    },\n  };\n}\n\nfunction toFunctionCall(part: ToolRequestPart): FunctionCallPart {\n  if (!part?.toolRequest?.input) {\n    throw Error('Invalid ToolRequestPart: input was missing.');\n  }\n  return {\n    functionCall: {\n      name: part.toolRequest.name,\n      args: part.toolRequest.input,\n    },\n  };\n}\n\nfunction fromFunctionCall(\n  part: FunctionCallPart,\n  ref: string\n): ToolRequestPart {\n  if (!part.functionCall) {\n    throw Error('Invalid FunctionCallPart');\n  }\n  return {\n    toolRequest: {\n      name: part.functionCall.name,\n      input: part.functionCall.args,\n      ref,\n    },\n  };\n}\n\nfunction toFunctionResponse(part: ToolResponsePart): FunctionResponsePart {\n  if (!part?.toolResponse?.output) {\n    throw Error('Invalid ToolResponsePart: output was missing.');\n  }\n  return {\n    functionResponse: {\n      name: part.toolResponse.name,\n      response: {\n        name: part.toolResponse.name,\n        content: part.toolResponse.output,\n      },\n    },\n  };\n}\n\nfunction fromFunctionResponse(part: FunctionResponsePart): ToolResponsePart {\n  if (!part.functionResponse) {\n    throw new Error('Invalid FunctionResponsePart.');\n  }\n  return {\n    toolResponse: {\n      name: part.functionResponse.name.replace(/__/g, '/'), // restore slashes\n      output: part.functionResponse.response,\n    },\n  };\n}\n\nfunction fromExecutableCode(part: GeminiPart): Part {\n  if (!part.executableCode) {\n    throw new Error('Invalid GeminiPart: missing executableCode');\n  }\n  return {\n    custom: {\n      executableCode: {\n        language: part.executableCode.language,\n        code: part.executableCode.code,\n      },\n    },\n  };\n}\n\nfunction fromCodeExecutionResult(part: GeminiPart): Part {\n  if (!part.codeExecutionResult) {\n    throw new Error('Invalid GeminiPart: missing codeExecutionResult');\n  }\n  return {\n    custom: {\n      codeExecutionResult: {\n        outcome: part.codeExecutionResult.outcome,\n        output: part.codeExecutionResult.output,\n      },\n    },\n  };\n}\n\nfunction toCustomPart(part: Part): GeminiPart {\n  if (!part.custom) {\n    throw new Error('Invalid GeminiPart: missing custom');\n  }\n  if (part.custom.codeExecutionResult) {\n    return { codeExecutionResult: part.custom.codeExecutionResult };\n  }\n  if (part.custom.executableCode) {\n    return { executableCode: part.custom.executableCode };\n  }\n  throw new Error('Unsupported Custom Part type');\n}\n\nfunction toGeminiPart(part: Part): GeminiPart {\n  if (part.text !== undefined) return { text: part.text || ' ' };\n  if (part.media) {\n    if (part.media.url.startsWith('data:')) return toInlineData(part);\n    return toFileData(part);\n  }\n  if (part.toolRequest) return toFunctionCall(part);\n  if (part.toolResponse) return toFunctionResponse(part);\n  if (part.custom) return toCustomPart(part);\n  throw new Error('Unsupported Part type' + JSON.stringify(part));\n}\n\nfunction fromGeminiPart(\n  part: GeminiPart,\n  jsonMode: boolean,\n  ref: string\n): Part {\n  if (part.text !== undefined) return { text: part.text };\n  if (part.inlineData) return fromInlineData(part);\n  if (part.functionCall) return fromFunctionCall(part, ref);\n  if (part.functionResponse) return fromFunctionResponse(part);\n  if (part.executableCode) return fromExecutableCode(part);\n  if (part.codeExecutionResult) return fromCodeExecutionResult(part);\n  throw new Error('Unsupported GeminiPart type');\n}\nexport function toGeminiMessage(\n  message: MessageData,\n  model?: ModelReference<z.ZodTypeAny>\n): GeminiMessage {\n  let sortedParts = message.content;\n  if (message.role === 'tool') {\n    sortedParts = [...message.content].sort((a, b) => {\n      const aRef = a.toolResponse?.ref;\n      const bRef = b.toolResponse?.ref;\n      if (!aRef && !bRef) return 0;\n      if (!aRef) return 1;\n      if (!bRef) return -1;\n      return parseInt(aRef, 10) - parseInt(bRef, 10);\n    });\n  }\n  return {\n    role: toGeminiRole(message.role, model),\n    parts: sortedParts.map(toGeminiPart),\n  };\n}\n\nexport function toGeminiSystemInstruction(message: MessageData): GeminiMessage {\n  return {\n    role: 'user',\n    parts: message.content.map(toGeminiPart),\n  };\n}\n\nfunction fromGeminiFinishReason(\n  reason: GeminiCandidate['finishReason']\n): CandidateData['finishReason'] {\n  if (!reason) return 'unknown';\n  switch (reason) {\n    case 'STOP':\n      return 'stop';\n    case 'MAX_TOKENS':\n      return 'length';\n    case 'SAFETY': // blocked for safety\n    case 'RECITATION': // blocked for reciting training data\n      return 'blocked';\n    default:\n      return 'unknown';\n  }\n}\n\nexport function fromGeminiCandidate(\n  candidate: GeminiCandidate,\n  jsonMode: boolean = false\n): CandidateData {\n  const parts = candidate.content?.parts || [];\n  const genkitCandidate: CandidateData = {\n    index: candidate.index || 0,\n    message: {\n      role: 'model',\n      content: parts.map((part, index) =>\n        fromGeminiPart(part, jsonMode, index.toString())\n      ),\n    },\n    finishReason: fromGeminiFinishReason(candidate.finishReason),\n    finishMessage: candidate.finishMessage,\n    custom: {\n      safetyRatings: candidate.safetyRatings,\n      citationMetadata: candidate.citationMetadata,\n    },\n  };\n\n  return genkitCandidate;\n}\nexport function cleanSchema(schema: JSONSchema): JSONSchema {\n  const out = structuredClone(schema);\n  for (const key in out) {\n    if (key === '$schema' || key === 'additionalProperties') {\n      delete out[key];\n      continue;\n    }\n    if (typeof out[key] === 'object') {\n      out[key] = cleanSchema(out[key]);\n    }\n    // Zod nullish() and picoschema optional fields will produce type `[\"string\", \"null\"]`\n    // which is not supported by the model API. Convert them to just `\"string\"`.\n    if (key === 'type' && Array.isArray(out[key])) {\n      // find the first that's not `null`.\n      out[key] = out[key].find((t) => t !== 'null');\n    }\n  }\n  return out;\n}\n\n/**\n * Defines a new GoogleAI model.\n */\nexport function defineGoogleAIModel({\n  ai,\n  name,\n  apiKey: apiKeyOption,\n  apiVersion,\n  baseUrl,\n  info,\n  defaultConfig,\n  debugTraces,\n}: {\n  ai: Genkit;\n  name: string;\n  apiKey?: string | false;\n  apiVersion?: string;\n  baseUrl?: string;\n  info?: ModelInfo;\n  defaultConfig?: GeminiConfig;\n  debugTraces?: boolean;\n}): ModelAction {\n  let apiKey: string | undefined;\n  // DO NOT infer API key from environment variable if plugin was configured with `{apiKey: false}`.\n  if (apiKeyOption !== false) {\n    apiKey = apiKeyOption || getApiKeyFromEnvVar();\n    if (!apiKey) {\n      throw new GenkitError({\n        status: 'FAILED_PRECONDITION',\n        message:\n          'Please pass in the API key or set the GEMINI_API_KEY or GOOGLE_API_KEY environment variable.\\n' +\n          'For more details see https://firebase.google.com/docs/genkit/plugins/google-genai',\n      });\n    }\n  }\n\n  const apiModelName = name.startsWith('googleai/')\n    ? name.substring('googleai/'.length)\n    : name;\n\n  const model: ModelReference<z.ZodTypeAny> =\n    SUPPORTED_GEMINI_MODELS[name] ??\n    modelRef({\n      name: `googleai/${apiModelName}`,\n      info: {\n        label: `Google AI - ${apiModelName}`,\n        supports: {\n          multiturn: true,\n          media: true,\n          tools: true,\n          systemRole: true,\n          output: ['text', 'json'],\n        },\n        ...info,\n      },\n      configSchema: GeminiConfigSchema,\n    });\n\n  const middleware: ModelMiddleware[] = [];\n  if (SUPPORTED_V1_MODELS[name]) {\n    middleware.push(simulateSystemPrompt());\n  }\n  if (model.info?.supports?.media) {\n    // the gemini api doesn't support downloading media from http(s)\n    middleware.push(\n      downloadRequestMedia({\n        maxBytes: 1024 * 1024 * 10,\n        // don't downlaod files that have been uploaded using the Files API\n        filter: (part) => {\n          try {\n            const url = new URL(part.media.url);\n            if (\n              // Gemini can handle these URLs\n              [\n                'generativelanguage.googleapis.com',\n                'www.youtube.com',\n                'youtube.com',\n                'youtu.be',\n              ].includes(url.hostname)\n            )\n              return false;\n          } catch {}\n          return true;\n        },\n      })\n    );\n  }\n\n  return ai.defineModel(\n    {\n      name: model.name,\n      ...model.info,\n      configSchema: model.configSchema,\n      use: middleware,\n    },\n    async (request, sendChunk) => {\n      const options: RequestOptions = { apiClient: GENKIT_CLIENT_HEADER };\n      if (apiVersion) {\n        options.apiVersion = apiVersion;\n      }\n      if (apiVersion) {\n        options.baseUrl = baseUrl;\n      }\n      const requestConfig: z.infer<typeof GeminiConfigSchema> = {\n        ...defaultConfig,\n        ...request.config,\n      };\n\n      // Make a copy so that modifying the request will not produce side-effects\n      const messages = [...request.messages];\n      if (messages.length === 0) throw new Error('No messages provided.');\n\n      // Gemini does not support messages with role system and instead expects\n      // systemInstructions to be provided as a separate input. The first\n      // message detected with role=system will be used for systemInstructions.\n      let systemInstruction: GeminiMessage | undefined = undefined;\n      if (SUPPORTED_V15_MODELS[name]) {\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\n      const tools: Tool[] = [];\n      if (request.tools?.length) {\n        tools.push({\n          functionDeclarations: request.tools.map(toGeminiTool),\n        });\n      }\n\n      if (requestConfig.codeExecution) {\n        tools.push({\n          codeExecution:\n            request.config.codeExecution === true\n              ? {}\n              : request.config.codeExecution,\n        });\n      }\n\n      let toolConfig: ToolConfig | undefined;\n      if (requestConfig.functionCallingConfig) {\n        toolConfig = {\n          functionCallingConfig: {\n            allowedFunctionNames:\n              requestConfig.functionCallingConfig.allowedFunctionNames,\n            mode: toFunctionModeEnum(requestConfig.functionCallingConfig.mode),\n          },\n        };\n      } else if (request.toolChoice) {\n        toolConfig = {\n          functionCallingConfig: {\n            mode: toGeminiFunctionModeEnum(request.toolChoice),\n          },\n        };\n      }\n\n      // Cannot use tools with JSON mode\n      const jsonMode =\n        request.output?.format === 'json' ||\n        (request.output?.contentType === 'application/json' &&\n          tools.length === 0);\n\n      const generationConfig: GenerationConfig = {\n        candidateCount: request.candidates || undefined,\n        temperature: requestConfig.temperature,\n        maxOutputTokens: requestConfig.maxOutputTokens,\n        topK: requestConfig.topK,\n        topP: requestConfig.topP,\n        stopSequences: requestConfig.stopSequences,\n        responseMimeType: jsonMode ? 'application/json' : undefined,\n      };\n      if (requestConfig.responseModalities) {\n        // HACK: cast to any since this isn't officially supported in the old SDK yet\n        (generationConfig as any).responseModalities =\n          requestConfig.responseModalities;\n      }\n\n      if (request.output?.constrained && jsonMode) {\n        generationConfig.responseSchema = cleanSchema(request.output.schema);\n      }\n\n      const msg = toGeminiMessage(messages[messages.length - 1], model);\n\n      const fromJSONModeScopedGeminiCandidate = (\n        candidate: GeminiCandidate\n      ) => {\n        return fromGeminiCandidate(candidate, jsonMode);\n      };\n\n      let chatRequest: StartChatParams = {\n        systemInstruction,\n        generationConfig,\n        tools: tools.length ? tools : undefined,\n        toolConfig,\n        history: messages\n          .slice(0, -1)\n          .map((message) => toGeminiMessage(message, model)),\n        safetySettings: requestConfig.safetySettings,\n      } as StartChatParams;\n      const modelVersion = (request.config?.version ||\n        model.version ||\n        name) as string;\n      const cacheConfigDetails = extractCacheConfig(request);\n\n      const { chatRequest: updatedChatRequest, cache } =\n        await handleCacheIfNeeded(\n          apiKey!,\n          request,\n          chatRequest,\n          modelVersion,\n          cacheConfigDetails\n        );\n\n      if (!requestConfig.apiKey && !apiKey) {\n        throw new GenkitError({\n          status: 'INVALID_ARGUMENT',\n          message:\n            'GoogleAI plugin was initialized with {apiKey: false} but no apiKey configuration was passed at call time.',\n        });\n      }\n      const client = new GoogleGenerativeAI(requestConfig.apiKey || apiKey!);\n      let genModel: GenerativeModel;\n\n      if (cache) {\n        genModel = client.getGenerativeModelFromCachedContent(\n          cache,\n          {\n            model: modelVersion,\n          },\n          options\n        );\n      } else {\n        genModel = client.getGenerativeModel(\n          {\n            model: modelVersion,\n          },\n          options\n        );\n      }\n\n      const callGemini = async () => {\n        let response: EnhancedGenerateContentResponse;\n\n        if (sendChunk) {\n          const result = await genModel\n            .startChat(updatedChatRequest)\n            .sendMessageStream(msg.parts, options);\n\n          for await (const item of result.stream) {\n            (item as GenerateContentResponse).candidates?.forEach(\n              (candidate) => {\n                const c = fromJSONModeScopedGeminiCandidate(candidate);\n                sendChunk({\n                  index: c.index,\n                  content: c.message.content,\n                });\n              }\n            );\n          }\n\n          response = await result.response;\n        } else {\n          const result = await genModel\n            .startChat(updatedChatRequest)\n            .sendMessage(msg.parts, options);\n\n          response = result.response;\n        }\n\n        const candidates = response.candidates || [];\n        if (response.candidates?.['undefined']) {\n          candidates.push(response.candidates['undefined']);\n        }\n        if (!candidates.length) {\n          throw new GenkitError({\n            status: 'FAILED_PRECONDITION',\n            message: 'No valid candidates returned.',\n          });\n        }\n\n        const candidateData =\n          candidates.map(fromJSONModeScopedGeminiCandidate) || [];\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            totalTokens: response.usageMetadata?.totalTokenCount,\n          },\n        };\n      };\n\n      // If debugTraces is enable, we wrap the actual model call with a span, add raw\n      // API params as for input.\n      return debugTraces\n        ? await runInNewSpan(\n            ai.registry,\n            {\n              metadata: {\n                name: sendChunk ? 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