{"version":3,"file":"universal.cjs","names":["className: string","config","module","e: unknown","model: string","modelProvider?: string","params: Record","modelName: string","BaseChatModel","fields: ConfigurableModelFields","config?: RunnableConfig","messages: BaseMessage[]","options?: this[\"ParsedCallOptions\"]","runManager?: CallbackManagerForLLMRun","tools: BindToolsInput[]","params?: Record","modelParams: Record","str: string","prefix: string","mergedConfig: RunnableConfig","remainingConfig: RunnableConfig","RunnableBinding","input: RunInput","options?: CallOptions","AsyncGeneratorWithSetup","IterableReadableStream","inputs: RunInput[]","options?: Partial | Partial[]","batchOptions?: RunnableBatchOptions","generator: AsyncGenerator","options: CallOptions","options?: Partial","streamOptions?: Omit","options: Partial & {\n version: \"v1\" | \"v2\";\n encoding?: \"text/event-stream\" | undefined;\n }","streamOptions?: Omit","model?: string","fields?: Partial> & {\n modelProvider?: string;\n configurableFields?: string[] | \"any\";\n configPrefix?: string;\n profile?: ModelProfile;\n }","paramsCopy: Record","configurableModel: ConfigurableModel"],"sources":["../../src/chat_models/universal.ts"],"sourcesContent":["import {\n BaseLanguageModelInput,\n ToolDefinition,\n} from \"@langchain/core/language_models/base\";\nimport {\n BaseChatModel,\n BaseChatModelParams,\n BindToolsInput,\n type BaseChatModelCallOptions,\n} from \"@langchain/core/language_models/chat_models\";\nimport {\n BaseMessage,\n type AIMessageChunk,\n MessageStructure,\n} from \"@langchain/core/messages\";\nimport {\n type RunnableBatchOptions,\n RunnableBinding,\n type RunnableConfig,\n type RunnableToolLike,\n ensureConfig,\n} from \"@langchain/core/runnables\";\nimport {\n AsyncGeneratorWithSetup,\n IterableReadableStream,\n} from \"@langchain/core/utils/stream\";\nimport {\n type LogStreamCallbackHandlerInput,\n type RunLogPatch,\n type StreamEvent,\n} from \"@langchain/core/tracers/log_stream\";\nimport { type StructuredToolInterface } from \"@langchain/core/tools\";\nimport { CallbackManagerForLLMRun } from \"@langchain/core/callbacks/manager\";\nimport { ChatResult } from \"@langchain/core/outputs\";\nimport { ModelProfile } from \"@langchain/core/language_models/profile\";\n\n// TODO: remove once `EventStreamCallbackHandlerInput` is exposed in core\ninterface EventStreamCallbackHandlerInput\n extends Omit {}\n\nexport interface ConfigurableChatModelCallOptions\n extends BaseChatModelCallOptions {\n tools?: (\n | StructuredToolInterface\n | Record\n | ToolDefinition\n | RunnableToolLike\n )[];\n}\n\n// Configuration map for model providers\nexport const MODEL_PROVIDER_CONFIG = {\n openai: {\n package: \"@langchain/openai\",\n className: \"ChatOpenAI\",\n },\n anthropic: {\n package: \"@langchain/anthropic\",\n className: \"ChatAnthropic\",\n },\n azure_openai: {\n package: \"@langchain/openai\",\n className: \"AzureChatOpenAI\",\n },\n cohere: {\n package: \"@langchain/cohere\",\n className: \"ChatCohere\",\n },\n \"google-vertexai\": {\n package: \"@langchain/google-vertexai\",\n className: \"ChatVertexAI\",\n },\n \"google-vertexai-web\": {\n package: \"@langchain/google-vertexai-web\",\n className: \"ChatVertexAI\",\n },\n \"google-genai\": {\n package: \"@langchain/google-genai\",\n className: \"ChatGoogleGenerativeAI\",\n },\n ollama: {\n package: \"@langchain/ollama\",\n className: \"ChatOllama\",\n },\n mistralai: {\n package: \"@langchain/mistralai\",\n className: \"ChatMistralAI\",\n },\n mistral: {\n package: \"@langchain/mistralai\",\n className: \"ChatMistralAI\",\n },\n groq: {\n package: \"@langchain/groq\",\n className: \"ChatGroq\",\n },\n cerebras: {\n package: \"@langchain/cerebras\",\n className: \"ChatCerebras\",\n },\n bedrock: {\n package: \"@langchain/aws\",\n className: \"ChatBedrockConverse\",\n },\n deepseek: {\n package: \"@langchain/deepseek\",\n className: \"ChatDeepSeek\",\n },\n xai: {\n package: \"@langchain/xai\",\n className: \"ChatXAI\",\n },\n fireworks: {\n package: \"@langchain/community/chat_models/fireworks\",\n className: \"ChatFireworks\",\n hasCircularDependency: true,\n },\n together: {\n package: \"@langchain/community/chat_models/togetherai\",\n className: \"ChatTogetherAI\",\n hasCircularDependency: true,\n },\n perplexity: {\n package: \"@langchain/community/chat_models/perplexity\",\n className: \"ChatPerplexity\",\n hasCircularDependency: true,\n },\n} as const;\n\nconst SUPPORTED_PROVIDERS = Object.keys(\n MODEL_PROVIDER_CONFIG\n) as (keyof typeof MODEL_PROVIDER_CONFIG)[];\nexport type ChatModelProvider = keyof typeof MODEL_PROVIDER_CONFIG;\ntype ModelProviderConfig = {\n package: string;\n className: string;\n hasCircularDependency?: boolean;\n};\n\n/**\n * Helper function to get a chat model class by its class name\n * @param className The class name (e.g., \"ChatOpenAI\", \"ChatAnthropic\")\n * @returns The imported model class or undefined if not found\n */\nexport async function getChatModelByClassName(className: string) {\n // Find the provider config that matches the class name\n const providerEntry = Object.entries(MODEL_PROVIDER_CONFIG).find(\n ([, config]) => config.className === className\n );\n\n if (!providerEntry) {\n return undefined;\n }\n\n const [, config] = providerEntry;\n try {\n const module = await import(config.package);\n return module[config.className];\n } catch (e: unknown) {\n const err = e as Error;\n if (\n \"code\" in err &&\n err.code?.toString().includes(\"ERR_MODULE_NOT_FOUND\") &&\n \"message\" in err &&\n typeof err.message === \"string\"\n ) {\n const msg = err.message.startsWith(\"Error: \")\n ? err.message.slice(\"Error: \".length)\n : err.message;\n const attemptedPackage = msg\n .split(\"Cannot find package '\")[1]\n .split(\"'\")[0];\n throw new Error(\n `Unable to import ${attemptedPackage}. Please install with ` +\n `\\`npm install ${attemptedPackage}\\` or \\`pnpm install ${attemptedPackage}\\``\n );\n }\n throw e;\n }\n}\n\nasync function _initChatModelHelper(\n model: string,\n modelProvider?: string,\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n params: Record = {}\n): Promise {\n const modelProviderCopy = modelProvider || _inferModelProvider(model);\n if (!modelProviderCopy) {\n throw new Error(\n `Unable to infer model provider for { model: ${model} }, please specify modelProvider directly.`\n );\n }\n\n const config = MODEL_PROVIDER_CONFIG[\n modelProviderCopy as keyof typeof MODEL_PROVIDER_CONFIG\n ] as ModelProviderConfig;\n if (!config) {\n const supported = SUPPORTED_PROVIDERS.join(\", \");\n throw new Error(\n `Unsupported { modelProvider: ${modelProviderCopy} }.\\n\\nSupported model providers are: ${supported}`\n );\n }\n\n const { modelProvider: _unused, ...passedParams } = params;\n const ProviderClass = await getChatModelByClassName(config.className);\n return new ProviderClass({ model, ...passedParams });\n}\n\n/**\n * Attempts to infer the model provider based on the given model name.\n *\n * @param {string} modelName - The name of the model to infer the provider for.\n * @returns {string | undefined} The inferred model provider name, or undefined if unable to infer.\n *\n * @example\n * _inferModelProvider(\"gpt-4\"); // returns \"openai\"\n * _inferModelProvider(\"claude-2\"); // returns \"anthropic\"\n * _inferModelProvider(\"unknown-model\"); // returns undefined\n */\nexport function _inferModelProvider(modelName: string): string | undefined {\n if (\n modelName.startsWith(\"gpt-3\") ||\n modelName.startsWith(\"gpt-4\") ||\n modelName.startsWith(\"gpt-5\") ||\n modelName.startsWith(\"o1\") ||\n modelName.startsWith(\"o3\") ||\n modelName.startsWith(\"o4\")\n ) {\n return \"openai\";\n } else if (modelName.startsWith(\"claude\")) {\n return \"anthropic\";\n } else if (modelName.startsWith(\"command\")) {\n return \"cohere\";\n } else if (modelName.startsWith(\"accounts/fireworks\")) {\n return \"fireworks\";\n } else if (modelName.startsWith(\"gemini\")) {\n return \"google-vertexai\";\n } else if (modelName.startsWith(\"amazon.\")) {\n return \"bedrock\";\n } else if (modelName.startsWith(\"mistral\")) {\n return \"mistralai\";\n } else if (modelName.startsWith(\"sonar\") || modelName.startsWith(\"pplx\")) {\n return \"perplexity\";\n } else {\n return undefined;\n }\n}\n\ninterface ConfigurableModelFields extends BaseChatModelParams {\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n defaultConfig?: Record;\n /**\n * @default \"any\"\n */\n configurableFields?: string[] | \"any\";\n /**\n * @default \"\"\n */\n configPrefix?: string;\n /**\n * Methods which should be called after the model is initialized.\n * The key will be the method name, and the value will be the arguments.\n */\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n queuedMethodOperations?: Record;\n /**\n * Overrides the profiling information for the model. If not provided,\n * the profile will be inferred from the inner model instance.\n */\n profile?: ModelProfile;\n}\n\n/**\n * Internal class used to create chat models.\n *\n * @internal\n */\nexport class ConfigurableModel<\n RunInput extends BaseLanguageModelInput = BaseLanguageModelInput,\n CallOptions extends\n ConfigurableChatModelCallOptions = ConfigurableChatModelCallOptions,\n> extends BaseChatModel {\n _llmType(): string {\n return \"chat_model\";\n }\n\n lc_namespace = [\"langchain\", \"chat_models\"];\n\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n _defaultConfig?: Record = {};\n\n /**\n * @default \"any\"\n */\n _configurableFields: string[] | \"any\" = \"any\";\n\n /**\n * @default \"\"\n */\n _configPrefix: string;\n\n /**\n * Methods which should be called after the model is initialized.\n * The key will be the method name, and the value will be the arguments.\n */\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n _queuedMethodOperations: Record = {};\n\n /** @internal */\n private _modelInstanceCache = new Map<\n string,\n BaseChatModel>\n >();\n\n /** @internal */\n private _profile?: ModelProfile;\n\n constructor(fields: ConfigurableModelFields) {\n super(fields);\n this._defaultConfig = fields.defaultConfig ?? {};\n\n if (fields.configurableFields === \"any\") {\n this._configurableFields = \"any\";\n } else {\n this._configurableFields = fields.configurableFields ?? [\n \"model\",\n \"modelProvider\",\n ];\n }\n\n if (fields.configPrefix) {\n this._configPrefix = fields.configPrefix.endsWith(\"_\")\n ? fields.configPrefix\n : `${fields.configPrefix}_`;\n } else {\n this._configPrefix = \"\";\n }\n\n this._queuedMethodOperations =\n fields.queuedMethodOperations ?? this._queuedMethodOperations;\n\n this._profile = fields.profile ?? undefined;\n }\n\n async _getModelInstance(\n config?: RunnableConfig\n ): Promise<\n BaseChatModel>\n > {\n // Check cache first\n const cacheKey = JSON.stringify(config ?? {});\n const cachedModel = this._modelInstanceCache.get(cacheKey);\n if (cachedModel) {\n return cachedModel;\n }\n\n // Initialize model with merged params\n const params = { ...this._defaultConfig, ...this._modelParams(config) };\n let initializedModel = await _initChatModelHelper(\n params.model,\n params.modelProvider,\n params\n );\n\n // Apply queued method operations in sequence\n for (const [method, args] of Object.entries(this._queuedMethodOperations)) {\n if (\n method in initializedModel &&\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n typeof (initializedModel as any)[method] === \"function\"\n ) {\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n initializedModel = await (initializedModel as any)[method](...args);\n }\n }\n\n // Cache and return the initialized model\n this._modelInstanceCache.set(cacheKey, initializedModel);\n return initializedModel;\n }\n\n async _generate(\n messages: BaseMessage[],\n options?: this[\"ParsedCallOptions\"],\n runManager?: CallbackManagerForLLMRun\n ): Promise {\n const model = await this._getModelInstance(options);\n return model._generate(messages, options ?? {}, runManager);\n }\n\n override bindTools(\n tools: BindToolsInput[],\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n params?: Record\n ): ConfigurableModel {\n const newQueuedOperations = { ...this._queuedMethodOperations };\n newQueuedOperations.bindTools = [tools, params];\n return new ConfigurableModel({\n defaultConfig: this._defaultConfig,\n configurableFields: this._configurableFields,\n configPrefix: this._configPrefix,\n queuedMethodOperations: newQueuedOperations,\n });\n }\n\n // Extract the input types from the `BaseModel` class.\n withStructuredOutput: BaseChatModel[\"withStructuredOutput\"] = (\n schema,\n ...args\n ): ReturnType => {\n const newQueuedOperations = { ...this._queuedMethodOperations };\n newQueuedOperations.withStructuredOutput = [schema, ...args];\n return new ConfigurableModel({\n defaultConfig: this._defaultConfig,\n configurableFields: this._configurableFields,\n configPrefix: this._configPrefix,\n queuedMethodOperations: newQueuedOperations,\n }) as unknown as ReturnType;\n };\n\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n _modelParams(config?: RunnableConfig): Record {\n const configurable = config?.configurable ?? {};\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n let modelParams: Record = {};\n\n for (const [key, value] of Object.entries(configurable)) {\n if (key.startsWith(this._configPrefix)) {\n const strippedKey = this._removePrefix(key, this._configPrefix);\n modelParams[strippedKey] = value;\n }\n }\n\n if (this._configurableFields !== \"any\") {\n modelParams = Object.fromEntries(\n Object.entries(modelParams).filter(([key]) =>\n this._configurableFields.includes(key)\n )\n );\n }\n\n return modelParams;\n }\n\n _removePrefix(str: string, prefix: string): string {\n return str.startsWith(prefix) ? str.slice(prefix.length) : str;\n }\n\n /**\n * Bind config to a Runnable, returning a new Runnable.\n * @param {RunnableConfig | undefined} [config] - The config to bind.\n * @returns {RunnableBinding} A new RunnableBinding with the bound config.\n */\n withConfig(\n config?: RunnableConfig\n ): RunnableBinding {\n const mergedConfig: RunnableConfig = { ...(config || {}) };\n const modelParams = this._modelParams(mergedConfig);\n\n const remainingConfig: RunnableConfig = Object.fromEntries(\n Object.entries(mergedConfig).filter(([k]) => k !== \"configurable\")\n );\n\n remainingConfig.configurable = Object.fromEntries(\n Object.entries(mergedConfig.configurable || {}).filter(\n ([k]) =>\n this._configPrefix &&\n !Object.keys(modelParams).includes(\n this._removePrefix(k, this._configPrefix)\n )\n )\n );\n\n const newConfigurableModel = new ConfigurableModel({\n defaultConfig: { ...this._defaultConfig, ...modelParams },\n configurableFields: Array.isArray(this._configurableFields)\n ? [...this._configurableFields]\n : this._configurableFields,\n configPrefix: this._configPrefix,\n queuedMethodOperations: this._queuedMethodOperations,\n });\n\n return new RunnableBinding({\n config: mergedConfig,\n bound: newConfigurableModel,\n });\n }\n\n async invoke(\n input: RunInput,\n options?: CallOptions\n ): Promise {\n const model = await this._getModelInstance(options);\n const config = ensureConfig(options);\n return model.invoke(input, config);\n }\n\n async stream(\n input: RunInput,\n options?: CallOptions\n ): Promise> {\n const model = await this._getModelInstance(options);\n const wrappedGenerator = new AsyncGeneratorWithSetup({\n generator: await model.stream(input, options),\n config: options,\n });\n await wrappedGenerator.setup;\n return IterableReadableStream.fromAsyncGenerator(wrappedGenerator);\n }\n\n async batch(\n inputs: RunInput[],\n options?: Partial | Partial[],\n batchOptions?: RunnableBatchOptions & { returnExceptions?: false }\n ): Promise;\n\n async batch(\n inputs: RunInput[],\n options?: Partial | Partial[],\n batchOptions?: RunnableBatchOptions & { returnExceptions: true }\n ): Promise<(AIMessageChunk | Error)[]>;\n\n async batch(\n inputs: RunInput[],\n options?: Partial | Partial[],\n batchOptions?: RunnableBatchOptions\n ): Promise<(AIMessageChunk | Error)[]>;\n\n async batch(\n inputs: RunInput[],\n options?: Partial | Partial[],\n batchOptions?: RunnableBatchOptions\n ): Promise<(AIMessageChunk | Error)[]> {\n // We can super this since the base runnable implementation of\n // `.batch` will call `.invoke` on each input.\n return super.batch(inputs, options, batchOptions);\n }\n\n async *transform(\n generator: AsyncGenerator,\n options: CallOptions\n ): AsyncGenerator {\n const model = await this._getModelInstance(options);\n const config = ensureConfig(options);\n\n yield* model.transform(generator, config);\n }\n\n async *streamLog(\n input: RunInput,\n options?: Partial,\n streamOptions?: Omit\n ): AsyncGenerator {\n const model = await this._getModelInstance(options);\n const config = ensureConfig(options);\n\n yield* model.streamLog(input, config, {\n ...streamOptions,\n _schemaFormat: \"original\",\n includeNames: streamOptions?.includeNames,\n includeTypes: streamOptions?.includeTypes,\n includeTags: streamOptions?.includeTags,\n excludeNames: streamOptions?.excludeNames,\n excludeTypes: streamOptions?.excludeTypes,\n excludeTags: streamOptions?.excludeTags,\n });\n }\n\n streamEvents(\n input: RunInput,\n options: Partial & { version: \"v1\" | \"v2\" },\n streamOptions?: Omit\n ): IterableReadableStream;\n\n streamEvents(\n input: RunInput,\n options: Partial & {\n version: \"v1\" | \"v2\";\n encoding: \"text/event-stream\";\n },\n streamOptions?: Omit\n ): IterableReadableStream;\n\n streamEvents(\n input: RunInput,\n options: Partial & {\n version: \"v1\" | \"v2\";\n encoding?: \"text/event-stream\" | undefined;\n },\n streamOptions?: Omit\n ): IterableReadableStream {\n const outerThis = this;\n async function* wrappedGenerator() {\n const model = await outerThis._getModelInstance(options);\n const config = ensureConfig(options);\n const eventStream = model.streamEvents(input, config, streamOptions);\n\n for await (const chunk of eventStream) {\n yield chunk;\n }\n }\n return IterableReadableStream.fromAsyncGenerator(wrappedGenerator());\n }\n\n /**\n * Return profiling information for the model.\n *\n * @returns {ModelProfile} An object describing the model's capabilities and constraints\n */\n get profile(): ModelProfile {\n if (this._profile) {\n return this._profile;\n }\n const cacheKey = JSON.stringify({});\n const instance = this._modelInstanceCache.get(cacheKey);\n return instance?.profile ?? {};\n }\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport interface InitChatModelFields extends Partial> {\n modelProvider?: string;\n configurableFields?: string[] | \"any\";\n configPrefix?: string;\n}\n\nexport type ConfigurableFields = \"any\" | string[];\n\nexport async function initChatModel<\n RunInput extends BaseLanguageModelInput = BaseLanguageModelInput,\n CallOptions extends\n ConfigurableChatModelCallOptions = ConfigurableChatModelCallOptions,\n>(\n model: string,\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n fields?: Partial> & {\n modelProvider?: string;\n configurableFields?: never;\n configPrefix?: string;\n }\n): Promise>;\n\nexport async function initChatModel<\n RunInput extends BaseLanguageModelInput = BaseLanguageModelInput,\n CallOptions extends\n ConfigurableChatModelCallOptions = ConfigurableChatModelCallOptions,\n>(\n model: never,\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n options?: Partial> & {\n modelProvider?: string;\n configurableFields?: never;\n configPrefix?: string;\n profile?: ModelProfile;\n }\n): Promise>;\n\nexport async function initChatModel<\n RunInput extends BaseLanguageModelInput = BaseLanguageModelInput,\n CallOptions extends\n ConfigurableChatModelCallOptions = ConfigurableChatModelCallOptions,\n>(\n model?: string,\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n options?: Partial> & {\n modelProvider?: string;\n configurableFields?: ConfigurableFields;\n configPrefix?: string;\n profile?: ModelProfile;\n }\n): Promise>;\n\n// ################################# FOR CONTRIBUTORS #################################\n//\n// If adding support for a new provider, please append the provider\n// name to the supported list in the docstring below.\n//\n// ####################################################################################\n\n/**\n * Initialize a ChatModel from the model name and provider.\n * Must have the integration package corresponding to the model provider installed.\n *\n * @template {extends BaseLanguageModelInput = BaseLanguageModelInput} RunInput - The input type for the model.\n * @template {extends ConfigurableChatModelCallOptions = ConfigurableChatModelCallOptions} CallOptions - Call options for the model.\n *\n * @param {string | ChatModelProvider} [model] - The name of the model, e.g. \"gpt-4\", \"claude-3-opus-20240229\".\n * Can be prefixed with the model provider, e.g. \"openai:gpt-4\", \"anthropic:claude-3-opus-20240229\".\n * @param {Object} [fields] - Additional configuration options.\n * @param {string} [fields.modelProvider] - The model provider. Supported values include:\n * - openai (@langchain/openai)\n * - anthropic (@langchain/anthropic)\n * - azure_openai (@langchain/openai)\n * - google-vertexai (@langchain/google-vertexai)\n * - google-vertexai-web (@langchain/google-vertexai-web)\n * - google-genai (@langchain/google-genai)\n * - bedrock (@langchain/aws)\n * - cohere (@langchain/cohere)\n * - fireworks (@langchain/community/chat_models/fireworks)\n * - together (@langchain/community/chat_models/togetherai)\n * - mistralai (@langchain/mistralai)\n * - groq (@langchain/groq)\n * - ollama (@langchain/ollama)\n * - perplexity (@langchain/community/chat_models/perplexity)\n * - cerebras (@langchain/cerebras)\n * - deepseek (@langchain/deepseek)\n * - xai (@langchain/xai)\n * @param {string[] | \"any\"} [fields.configurableFields] - Which model parameters are configurable:\n * - undefined: No configurable fields.\n * - \"any\": All fields are configurable. (See Security Note in description)\n * - string[]: Specified fields are configurable.\n * @param {string} [fields.configPrefix] - Prefix for configurable fields at runtime.\n * @param {ModelProfile} [fields.profile] - Overrides the profiling information for the model. If not provided,\n * the profile will be inferred from the inner model instance.\n * @param {Record} [fields.params] - Additional keyword args to pass to the ChatModel constructor.\n * @returns {Promise>} A class which extends BaseChatModel.\n * @throws {Error} If modelProvider cannot be inferred or isn't supported.\n * @throws {Error} If the model provider integration package is not installed.\n *\n * @example Initialize non-configurable models\n * ```typescript\n * import { initChatModel } from \"langchain/chat_models/universal\";\n *\n * const gpt4 = await initChatModel(\"openai:gpt-4\", {\n * temperature: 0.25,\n * });\n * const gpt4Result = await gpt4.invoke(\"what's your name\");\n *\n * const claude = await initChatModel(\"anthropic:claude-3-opus-20240229\", {\n * temperature: 0.25,\n * });\n * const claudeResult = await claude.invoke(\"what's your name\");\n *\n * const gemini = await initChatModel(\"gemini-1.5-pro\", {\n * modelProvider: \"google-vertexai\",\n * temperature: 0.25,\n * });\n * const geminiResult = await gemini.invoke(\"what's your name\");\n * ```\n *\n * @example Create a partially configurable model with no default model\n * ```typescript\n * import { initChatModel } from \"langchain/chat_models/universal\";\n *\n * const configurableModel = await initChatModel(undefined, {\n * temperature: 0,\n * configurableFields: [\"model\", \"apiKey\"],\n * });\n *\n * const gpt4Result = await configurableModel.invoke(\"what's your name\", {\n * configurable: {\n * model: \"gpt-4\",\n * },\n * });\n *\n * const claudeResult = await configurableModel.invoke(\"what's your name\", {\n * configurable: {\n * model: \"claude-3-5-sonnet-20240620\",\n * },\n * });\n * ```\n *\n * @example Create a fully configurable model with a default model and a config prefix\n * ```typescript\n * import { initChatModel } from \"langchain/chat_models/universal\";\n *\n * const configurableModelWithDefault = await initChatModel(\"gpt-4\", {\n * modelProvider: \"openai\",\n * configurableFields: \"any\",\n * configPrefix: \"foo\",\n * temperature: 0,\n * });\n *\n * const openaiResult = await configurableModelWithDefault.invoke(\n * \"what's your name\",\n * {\n * configurable: {\n * foo_apiKey: process.env.OPENAI_API_KEY,\n * },\n * }\n * );\n *\n * const claudeResult = await configurableModelWithDefault.invoke(\n * \"what's your name\",\n * {\n * configurable: {\n * foo_model: \"claude-3-5-sonnet-20240620\",\n * foo_modelProvider: \"anthropic\",\n * foo_temperature: 0.6,\n * foo_apiKey: process.env.ANTHROPIC_API_KEY,\n * },\n * }\n * );\n * ```\n *\n * @example Bind tools to a configurable model:\n * ```typescript\n * import { initChatModel } from \"langchain/chat_models/universal\";\n * import { z } from \"zod/v3\";\n * import { tool } from \"@langchain/core/tools\";\n *\n * const getWeatherTool = tool(\n * (input) => {\n * // Do something with the input\n * return JSON.stringify(input);\n * },\n * {\n * schema: z\n * .object({\n * location: z\n * .string()\n * .describe(\"The city and state, e.g. San Francisco, CA\"),\n * })\n * .describe(\"Get the current weather in a given location\"),\n * name: \"GetWeather\",\n * description: \"Get the current weather in a given location\",\n * }\n * );\n *\n * const getPopulationTool = tool(\n * (input) => {\n * // Do something with the input\n * return JSON.stringify(input);\n * },\n * {\n * schema: z\n * .object({\n * location: z\n * .string()\n * .describe(\"The city and state, e.g. San Francisco, CA\"),\n * })\n * .describe(\"Get the current population in a given location\"),\n * name: \"GetPopulation\",\n * description: \"Get the current population in a given location\",\n * }\n * );\n *\n * const configurableModel = await initChatModel(\"gpt-4\", {\n * configurableFields: [\"model\", \"modelProvider\", \"apiKey\"],\n * temperature: 0,\n * });\n *\n * const configurableModelWithTools = configurableModel.bindTools([\n * getWeatherTool,\n * getPopulationTool,\n * ]);\n *\n * const configurableToolResult = await configurableModelWithTools.invoke(\n * \"Which city is hotter today and which is bigger: LA or NY?\",\n * {\n * configurable: {\n * apiKey: process.env.OPENAI_API_KEY,\n * },\n * }\n * );\n *\n * const configurableToolResult2 = await configurableModelWithTools.invoke(\n * \"Which city is hotter today and which is bigger: LA or NY?\",\n * {\n * configurable: {\n * model: \"claude-3-5-sonnet-20240620\",\n * apiKey: process.env.ANTHROPIC_API_KEY,\n * },\n * }\n * );\n * ```\n *\n * @example Initialize a model with a custom profile\n * ```typescript\n * import { initChatModel } from \"langchain/chat_models/universal\";\n *\n * const model = await initChatModel(\"gpt-4o-mini\", {\n * profile: {\n * maxInputTokens: 100000,\n * },\n * });\n *\n * @description\n * This function initializes a ChatModel based on the provided model name and provider.\n * It supports various model providers and allows for runtime configuration of model parameters.\n *\n * Security Note: Setting `configurableFields` to \"any\" means fields like apiKey, baseUrl, etc.\n * can be altered at runtime, potentially redirecting model requests to a different service/user.\n * Make sure that if you're accepting untrusted configurations, you enumerate the\n * `configurableFields` explicitly.\n *\n * The function will attempt to infer the model provider from the model name if not specified.\n * Certain model name prefixes are associated with specific providers:\n * - gpt-3... or gpt-4... -> openai\n * - claude... -> anthropic\n * - amazon.... -> bedrock\n * - gemini... -> google-vertexai\n * - command... -> cohere\n * - accounts/fireworks... -> fireworks\n *\n * @since 0.2.11\n * @version 0.2.11\n */\nexport async function initChatModel<\n RunInput extends BaseLanguageModelInput = BaseLanguageModelInput,\n CallOptions extends\n ConfigurableChatModelCallOptions = ConfigurableChatModelCallOptions,\n>(\n model?: string,\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n fields?: Partial> & {\n modelProvider?: string;\n configurableFields?: string[] | \"any\";\n configPrefix?: string;\n profile?: ModelProfile;\n }\n): Promise> {\n // eslint-disable-next-line prefer-const\n let { configurableFields, configPrefix, modelProvider, profile, ...params } =\n {\n configPrefix: \"\",\n ...(fields ?? {}),\n };\n if (modelProvider === undefined && model?.includes(\":\")) {\n const [provider, ...remainingParts] = model.split(\":\");\n const modelComponents =\n remainingParts.length === 0\n ? [provider]\n : [provider, remainingParts.join(\":\")];\n if (SUPPORTED_PROVIDERS.includes(modelComponents[0] as ChatModelProvider)) {\n // eslint-disable-next-line no-param-reassign\n [modelProvider, model] = modelComponents;\n }\n }\n let configurableFieldsCopy = Array.isArray(configurableFields)\n ? [...configurableFields]\n : configurableFields;\n\n if (!model && configurableFieldsCopy === undefined) {\n configurableFieldsCopy = [\"model\", \"modelProvider\"];\n }\n if (configPrefix && configurableFieldsCopy === undefined) {\n console.warn(\n `{ configPrefix: ${configPrefix} } has been set but no fields are configurable. Set ` +\n `{ configurableFields: [...] } to specify the model params that are ` +\n `configurable.`\n );\n }\n\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n const paramsCopy: Record = { ...params };\n\n let configurableModel: ConfigurableModel;\n\n if (configurableFieldsCopy === undefined) {\n configurableModel = new ConfigurableModel({\n defaultConfig: {\n ...paramsCopy,\n model,\n modelProvider,\n },\n configPrefix,\n profile,\n });\n } else {\n if (model) {\n paramsCopy.model = model;\n }\n if (modelProvider) {\n paramsCopy.modelProvider = modelProvider;\n }\n configurableModel = new ConfigurableModel({\n defaultConfig: paramsCopy,\n configPrefix,\n configurableFields: configurableFieldsCopy,\n profile,\n });\n }\n\n // Initialize the model instance to make sure a profile is available\n await configurableModel._getModelInstance();\n return 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