@langchain/core/dist/utils/testing/chat_models.d.ts.map

Core LangChain.js abstractions and schemas

{"version":3,"file":"chat_models.d.ts","names":["CallbackManagerForLLMRun","BaseChatModel","BaseChatModelCallOptions","BaseChatModelParams","BaseLLMParams","BaseMessage","AIMessage","AIMessageChunk","ChatResult","ChatGenerationChunk","Runnable","StructuredTool","StructuredOutputMethodParams","BaseLanguageModelInput","StructuredOutputMethodOptions","InteropZodType","ToolSpec","Record","FakeStreamingChatModelCallOptions","FakeStreamingChatModelFields","FakeChatModel","Promise","FakeStreamingChatModel","sleep","responses","chunks","toolStyle","thrownErrorString","______messages_message_js0","MessageToolSet","MessageStructure","AsyncGenerator","FakeChatInput","FakeListChatModelCallOptions","FakeListChatModel","RunOutput"],"sources":["../../../src/utils/testing/chat_models.d.ts"],"sourcesContent":["import { CallbackManagerForLLMRun } from \"../../callbacks/manager.js\";\nimport { BaseChatModel, BaseChatModelCallOptions, BaseChatModelParams } from \"../../language_models/chat_models.js\";\nimport { BaseLLMParams } from \"../../language_models/llms.js\";\nimport { BaseMessage, AIMessage, AIMessageChunk } from \"../../messages/index.js\";\nimport { type ChatResult, ChatGenerationChunk } from \"../../outputs.js\";\nimport { Runnable } from \"../../runnables/base.js\";\nimport { StructuredTool } from \"../../tools/index.js\";\nimport { StructuredOutputMethodParams, BaseLanguageModelInput, StructuredOutputMethodOptions } from \"../../language_models/base.js\";\nimport { InteropZodType } from \"../types/zod.js\";\n/** Minimal shape actually needed by `bindTools` */\nexport interface ToolSpec {\n    name: string;\n    description?: string;\n    schema: InteropZodType | Record<string, unknown>;\n}\n/**\n * Interface specific to the Fake Streaming Chat model.\n */\nexport interface FakeStreamingChatModelCallOptions extends BaseChatModelCallOptions {\n}\n/**\n * Interface for the Constructor-field specific to the Fake Streaming Chat model (all optional because we fill in defaults).\n */\nexport interface FakeStreamingChatModelFields extends BaseChatModelParams {\n    /** Milliseconds to pause between fallback char-by-char chunks */\n    sleep?: number;\n    /** Full AI messages to fall back to when no `chunks` supplied */\n    responses?: BaseMessage[];\n    /** Exact chunks to emit (can include tool-call deltas) */\n    chunks?: AIMessageChunk[];\n    /** How tool specs are formatted in `bindTools` */\n    toolStyle?: \"openai\" | \"anthropic\" | \"bedrock\" | \"google\";\n    /** Throw this error instead of streaming (useful in tests) */\n    thrownErrorString?: string;\n}\nexport declare class FakeChatModel extends BaseChatModel {\n    _combineLLMOutput(): never[];\n    _llmType(): string;\n    _generate(messages: BaseMessage[], options?: this[\"ParsedCallOptions\"], runManager?: CallbackManagerForLLMRun): Promise<ChatResult>;\n}\nexport declare class FakeStreamingChatModel extends BaseChatModel<FakeStreamingChatModelCallOptions> {\n    sleep: number;\n    responses: BaseMessage[];\n    chunks: AIMessageChunk[];\n    toolStyle: \"openai\" | \"anthropic\" | \"bedrock\" | \"google\";\n    thrownErrorString?: string;\n    private tools;\n    constructor({ sleep, responses, chunks, toolStyle, thrownErrorString, ...rest }: FakeStreamingChatModelFields & BaseLLMParams);\n    _llmType(): string;\n    bindTools(tools: (StructuredTool | ToolSpec)[]): Runnable<BaseLanguageModelInput, AIMessageChunk<import(\"../../messages/message.js\").MessageStructure<import(\"../../messages/message.js\").MessageToolSet>>, FakeStreamingChatModelCallOptions>;\n    _generate(messages: BaseMessage[], _options: this[\"ParsedCallOptions\"], _runManager?: CallbackManagerForLLMRun): Promise<ChatResult>;\n    _streamResponseChunks(_messages: BaseMessage[], options: this[\"ParsedCallOptions\"], runManager?: CallbackManagerForLLMRun): AsyncGenerator<ChatGenerationChunk>;\n}\n/**\n * Interface for the input parameters specific to the Fake List Chat model.\n */\nexport interface FakeChatInput extends BaseChatModelParams {\n    /** Responses to return */\n    responses: string[];\n    /** Time to sleep in milliseconds between responses */\n    sleep?: number;\n    emitCustomEvent?: boolean;\n    /**\n     * Generation info to include on the last chunk during streaming.\n     * This gets merged into response_metadata by the base chat model.\n     * Useful for testing response_metadata propagation (e.g., finish_reason).\n     */\n    generationInfo?: Record<string, unknown>;\n}\nexport interface FakeListChatModelCallOptions extends BaseChatModelCallOptions {\n    thrownErrorString?: string;\n}\n/**\n * A fake Chat Model that returns a predefined list of responses. It can be used\n * for testing purposes.\n * @example\n * ```typescript\n * const chat = new FakeListChatModel({\n *   responses: [\"I'll callback later.\", \"You 'console' them!\"]\n * });\n *\n * const firstMessage = new HumanMessage(\"You want to hear a JavaScript joke?\");\n * const secondMessage = new HumanMessage(\"How do you cheer up a JavaScript developer?\");\n *\n * // Call the chat model with a message and log the response\n * const firstResponse = await chat.call([firstMessage]);\n * console.log({ firstResponse });\n *\n * const secondResponse = await chat.call([secondMessage]);\n * console.log({ secondResponse });\n * ```\n */\nexport declare class FakeListChatModel extends BaseChatModel<FakeListChatModelCallOptions> {\n    static lc_name(): string;\n    lc_serializable: boolean;\n    responses: string[];\n    i: number;\n    sleep?: number;\n    emitCustomEvent: boolean;\n    generationInfo?: Record<string, unknown>;\n    private tools;\n    toolStyle: \"openai\" | \"anthropic\" | \"bedrock\" | \"google\";\n    constructor(params: FakeChatInput);\n    _combineLLMOutput(): never[];\n    _llmType(): string;\n    _generate(_messages: BaseMessage[], options?: this[\"ParsedCallOptions\"], runManager?: CallbackManagerForLLMRun): Promise<ChatResult>;\n    _formatGeneration(text: string): {\n        message: AIMessage<import(\"../../messages/message.js\").MessageStructure<import(\"../../messages/message.js\").MessageToolSet>>;\n        text: string;\n    };\n    _streamResponseChunks(_messages: BaseMessage[], options: this[\"ParsedCallOptions\"], runManager?: CallbackManagerForLLMRun): AsyncGenerator<ChatGenerationChunk>;\n    _sleepIfRequested(): Promise<void>;\n    _sleep(): Promise<void>;\n    _createResponseChunk(text: string, generationInfo?: Record<string, any>): ChatGenerationChunk;\n    _currentResponse(): string;\n    _incrementResponse(): void;\n    bindTools(tools: (StructuredTool | ToolSpec)[]): Runnable<BaseLanguageModelInput, AIMessageChunk<import(\"../../messages/message.js\").MessageStructure<import(\"../../messages/message.js\").MessageToolSet>>, FakeListChatModelCallOptions>;\n    withStructuredOutput<RunOutput extends Record<string, any> = Record<string, any>>(_params: StructuredOutputMethodParams<RunOutput, false> | InteropZodType<RunOutput> | Record<string, any>, config?: StructuredOutputMethodOptions<false>): Runnable<BaseLanguageModelInput, RunOutput>;\n    withStructuredOutput<RunOutput extends Record<string, any> = Record<string, any>>(_params: StructuredOutputMethodParams<RunOutput, true> | InteropZodType<RunOutput> | Record<string, any>, config?: StructuredOutputMethodOptions<true>): Runnable<BaseLanguageModelInput, {\n        raw: BaseMessage;\n        parsed: RunOutput;\n    }>;\n}\n//# 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