@langchain/core
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Core LangChain.js abstractions and schemas
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{"version":3,"file":"chat_models.cjs","names":["isURLContentBlock","isBase64ContentBlock","convertToOpenAIImageBlock","BaseLanguageModel","iife","getEnvironmentVariable","CallbackManager","castStandardMessageContent","isAIMessageChunk","coerceMessageLikeToMessage","callbackHandlerPrefersStreaming","ModelAbortError","concat","RUN_KEY","isBaseMessage","isAIMessage","getSchemaDescription","isInteropZodSchema","toJsonSchema","RunnableLambda","AIMessageChunk","RunnablePassthrough","RunnableSequence","AIMessage"],"sources":["../../src/language_models/chat_models.ts"],"sourcesContent":["import type { ZodType as ZodTypeV3 } from \"zod/v3\";\nimport type { $ZodType as ZodTypeV4 } from \"zod/v4/core\";\nimport {\n AIMessage,\n type BaseMessage,\n BaseMessageChunk,\n type BaseMessageLike,\n coerceMessageLikeToMessage,\n AIMessageChunk,\n isAIMessageChunk,\n isBaseMessage,\n isAIMessage,\n MessageOutputVersion,\n} from \"../messages/index.js\";\nimport {\n convertToOpenAIImageBlock,\n isURLContentBlock,\n isBase64ContentBlock,\n} from \"../messages/content/data.js\";\nimport type { BasePromptValueInterface } from \"../prompt_values.js\";\nimport {\n LLMResult,\n RUN_KEY,\n type ChatGeneration,\n ChatGenerationChunk,\n type ChatResult,\n type Generation,\n} from \"../outputs.js\";\nimport {\n BaseLanguageModel,\n type StructuredOutputMethodOptions,\n type ToolDefinition,\n type BaseLanguageModelCallOptions,\n type BaseLanguageModelInput,\n type BaseLanguageModelParams,\n} from \"./base.js\";\nimport {\n CallbackManager,\n type CallbackManagerForLLMRun,\n type Callbacks,\n} from \"../callbacks/manager.js\";\nimport type { RunnableConfig } from \"../runnables/config.js\";\nimport type { BaseCache } from \"../caches/index.js\";\nimport {\n StructuredToolInterface,\n StructuredToolParams,\n} from \"../tools/index.js\";\nimport {\n Runnable,\n RunnableLambda,\n RunnableSequence,\n RunnableToolLike,\n} from \"../runnables/base.js\";\nimport { concat } from \"../utils/stream.js\";\nimport { RunnablePassthrough } from \"../runnables/passthrough.js\";\nimport {\n getSchemaDescription,\n InteropZodType,\n isInteropZodSchema,\n} from \"../utils/types/zod.js\";\nimport { ModelAbortError } from \"../errors/index.js\";\nimport { callbackHandlerPrefersStreaming } from \"../callbacks/base.js\";\nimport { toJsonSchema } from \"../utils/json_schema.js\";\nimport { getEnvironmentVariable } from \"../utils/env.js\";\nimport { castStandardMessageContent, iife } from \"./utils.js\";\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport type ToolChoice = string | Record<string, any> | \"auto\" | \"any\";\n\n/**\n * Represents a serialized chat model.\n */\nexport type SerializedChatModel = {\n _model: string;\n _type: string;\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n} & Record<string, any>;\n\n// todo?\n/**\n * Represents a serialized large language model.\n */\nexport type SerializedLLM = {\n _model: string;\n _type: string;\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n} & Record<string, any>;\n\n/**\n * Represents the parameters for a base chat model.\n */\nexport type BaseChatModelParams = BaseLanguageModelParams & {\n /**\n * Whether to disable streaming.\n *\n * If streaming is bypassed, then `stream()` will defer to\n * `invoke()`.\n *\n * - If true, will always bypass streaming case.\n * - If false (default), will always use streaming case if available.\n */\n disableStreaming?: boolean;\n /**\n * Version of `AIMessage` output format to store in message content.\n *\n * `AIMessage.contentBlocks` will lazily parse the contents of `content` into a\n * standard format. This flag can be used to additionally store the standard format\n * as the message content, e.g., for serialization purposes.\n *\n * - \"v0\": provider-specific format in content (can lazily parse with `.contentBlocks`)\n * - \"v1\": standardized format in content (consistent with `.contentBlocks`)\n *\n * You can also set `LC_OUTPUT_VERSION` as an environment variable to \"v1\" to\n * enable this by default.\n *\n * @default \"v0\"\n */\n outputVersion?: MessageOutputVersion;\n};\n\n/**\n * Represents the call options for a base chat model.\n */\nexport type BaseChatModelCallOptions = BaseLanguageModelCallOptions & {\n /**\n * Specifies how the chat model should use tools.\n * @default undefined\n *\n * Possible values:\n * - \"auto\": The model may choose to use any of the provided tools, or none.\n * - \"any\": The model must use one of the provided tools.\n * - \"none\": The model must not use any tools.\n * - A string (not \"auto\", \"any\", or \"none\"): The name of a specific tool the model must use.\n * - An object: A custom schema specifying tool choice parameters. Specific to the provider.\n *\n * Note: Not all providers support tool_choice. An error will be thrown\n * if used with an unsupported model.\n */\n tool_choice?: ToolChoice;\n /**\n * Version of `AIMessage` output format to store in message content.\n *\n * `AIMessage.contentBlocks` will lazily parse the contents of `content` into a\n * standard format. This flag can be used to additionally store the standard format\n * as the message content, e.g., for serialization purposes.\n *\n * - \"v0\": provider-specific format in content (can lazily parse with `.contentBlocks`)\n * - \"v1\": standardized format in content (consistent with `.contentBlocks`)\n *\n * You can also set `LC_OUTPUT_VERSION` as an environment variable to \"v1\" to\n * enable this by default.\n *\n * @default \"v0\"\n */\n outputVersion?: MessageOutputVersion;\n};\n\nfunction _formatForTracing(messages: BaseMessage[]): BaseMessage[] {\n const messagesToTrace: BaseMessage[] = [];\n for (const message of messages) {\n let messageToTrace = message;\n if (Array.isArray(message.content)) {\n for (let idx = 0; idx < message.content.length; idx++) {\n const block = message.content[idx];\n if (isURLContentBlock(block) || isBase64ContentBlock(block)) {\n if (messageToTrace === message) {\n // Also shallow-copy content\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n messageToTrace = new (message.constructor as any)({\n ...messageToTrace,\n content: [\n ...message.content.slice(0, idx),\n convertToOpenAIImageBlock(block),\n ...message.content.slice(idx + 1),\n ],\n });\n }\n }\n }\n }\n messagesToTrace.push(messageToTrace);\n }\n return messagesToTrace;\n}\n\nexport type LangSmithParams = {\n ls_provider?: string;\n ls_model_name?: string;\n ls_model_type: \"chat\";\n ls_temperature?: number;\n ls_max_tokens?: number;\n ls_stop?: Array<string>;\n};\n\nexport type BindToolsInput =\n | StructuredToolInterface\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n | Record<string, any>\n | ToolDefinition\n | RunnableToolLike\n | StructuredToolParams;\n\n/**\n * Base class for chat models. It extends the BaseLanguageModel class and\n * provides methods for generating chat based on input messages.\n */\nexport abstract class BaseChatModel<\n CallOptions extends BaseChatModelCallOptions = BaseChatModelCallOptions,\n // TODO: Fix the parameter order on the next minor version.\n OutputMessageType extends BaseMessageChunk = AIMessageChunk,\n> extends BaseLanguageModel<OutputMessageType, CallOptions> {\n // Backwards compatibility since fields have been moved to RunnableConfig\n declare ParsedCallOptions: Omit<\n CallOptions,\n Exclude<keyof RunnableConfig, \"signal\" | \"timeout\" | \"maxConcurrency\">\n >;\n\n // Only ever instantiated in main LangChain\n lc_namespace = [\"langchain\", \"chat_models\", this._llmType()];\n\n disableStreaming = false;\n\n outputVersion?: MessageOutputVersion;\n\n get callKeys(): string[] {\n return [...super.callKeys, \"outputVersion\"];\n }\n\n constructor(fields: BaseChatModelParams) {\n super(fields);\n this.outputVersion = iife(() => {\n const outputVersion =\n fields.outputVersion ?? getEnvironmentVariable(\"LC_OUTPUT_VERSION\");\n if (outputVersion && [\"v0\", \"v1\"].includes(outputVersion)) {\n return outputVersion as \"v0\" | \"v1\";\n }\n return \"v0\";\n });\n }\n\n _combineLLMOutput?(\n ...llmOutputs: LLMResult[\"llmOutput\"][]\n ): LLMResult[\"llmOutput\"];\n\n protected _separateRunnableConfigFromCallOptionsCompat(\n options?: Partial<CallOptions>\n ): [RunnableConfig, this[\"ParsedCallOptions\"]] {\n // For backwards compat, keep `signal` in both runnableConfig and callOptions\n const [runnableConfig, callOptions] =\n super._separateRunnableConfigFromCallOptions(options);\n (callOptions as this[\"ParsedCallOptions\"]).signal = runnableConfig.signal;\n return [runnableConfig, callOptions as this[\"ParsedCallOptions\"]];\n }\n\n /**\n * Bind tool-like objects to this chat model.\n *\n * @param tools A list of tool definitions to bind to this chat model.\n * Can be a structured tool, an OpenAI formatted tool, or an object\n * matching the provider's specific tool schema.\n * @param kwargs Any additional parameters to bind.\n */\n bindTools?(\n tools: BindToolsInput[],\n kwargs?: Partial<CallOptions>\n ): Runnable<BaseLanguageModelInput, OutputMessageType, CallOptions>;\n\n /**\n * Invokes the chat model with a single input.\n * @param input The input for the language model.\n * @param options The call options.\n * @returns A Promise that resolves to a BaseMessageChunk.\n */\n async invoke(\n input: BaseLanguageModelInput,\n options?: Partial<CallOptions>\n ): Promise<OutputMessageType> {\n const promptValue = BaseChatModel._convertInputToPromptValue(input);\n const result = await this.generatePrompt(\n [promptValue],\n options,\n options?.callbacks\n );\n const chatGeneration = result.generations[0][0] as ChatGeneration;\n // TODO: Remove cast after figuring out inheritance\n return chatGeneration.message as OutputMessageType;\n }\n\n // eslint-disable-next-line require-yield\n async *_streamResponseChunks(\n _messages: BaseMessage[],\n _options: this[\"ParsedCallOptions\"],\n _runManager?: CallbackManagerForLLMRun\n ): AsyncGenerator<ChatGenerationChunk> {\n throw new Error(\"Not implemented.\");\n }\n\n async *_streamIterator(\n input: BaseLanguageModelInput,\n options?: Partial<CallOptions>\n ): AsyncGenerator<OutputMessageType> {\n // Subclass check required to avoid double callbacks with default implementation\n if (\n this._streamResponseChunks ===\n BaseChatModel.prototype._streamResponseChunks ||\n this.disableStreaming\n ) {\n yield this.invoke(input, options);\n } else {\n const prompt = BaseChatModel._convertInputToPromptValue(input);\n const messages = prompt.toChatMessages();\n const [runnableConfig, callOptions] =\n this._separateRunnableConfigFromCallOptionsCompat(options);\n\n const inheritableMetadata = {\n ...runnableConfig.metadata,\n ...this.getLsParams(callOptions),\n };\n const callbackManager_ = await CallbackManager.configure(\n runnableConfig.callbacks,\n this.callbacks,\n runnableConfig.tags,\n this.tags,\n inheritableMetadata,\n this.metadata,\n { verbose: this.verbose }\n );\n const extra = {\n options: callOptions,\n invocation_params: this?.invocationParams(callOptions),\n batch_size: 1,\n };\n const outputVersion = callOptions.outputVersion ?? this.outputVersion;\n const runManagers = await callbackManager_?.handleChatModelStart(\n this.toJSON(),\n [_formatForTracing(messages)],\n runnableConfig.runId,\n undefined,\n extra,\n undefined,\n undefined,\n runnableConfig.runName\n );\n let generationChunk: ChatGenerationChunk | undefined;\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n let llmOutput: Record<string, any> | undefined;\n try {\n for await (const chunk of this._streamResponseChunks(\n messages,\n callOptions,\n runManagers?.[0]\n )) {\n callOptions.signal?.throwIfAborted();\n if (chunk.message.id == null) {\n const runId = runManagers?.at(0)?.runId;\n if (runId != null) chunk.message._updateId(`run-${runId}`);\n }\n chunk.message.response_metadata = {\n ...chunk.generationInfo,\n ...chunk.message.response_metadata,\n };\n if (outputVersion === \"v1\") {\n yield castStandardMessageContent(\n chunk.message\n ) as OutputMessageType;\n } else {\n yield chunk.message as OutputMessageType;\n }\n if (!generationChunk) {\n generationChunk = chunk;\n } else {\n generationChunk = generationChunk.concat(chunk);\n }\n if (\n isAIMessageChunk(chunk.message) &&\n chunk.message.usage_metadata !== undefined\n ) {\n llmOutput = {\n tokenUsage: {\n promptTokens: chunk.message.usage_metadata.input_tokens,\n completionTokens: chunk.message.usage_metadata.output_tokens,\n totalTokens: chunk.message.usage_metadata.total_tokens,\n },\n };\n }\n }\n // Throw error if stream ended due to abort (provider returned early)\n callOptions.signal?.throwIfAborted();\n } catch (err) {\n await Promise.all(\n (runManagers ?? []).map((runManager) =>\n runManager?.handleLLMError(err)\n )\n );\n throw err;\n }\n await Promise.all(\n (runManagers ?? []).map((runManager) =>\n runManager?.handleLLMEnd({\n // TODO: Remove cast after figuring out inheritance\n generations: [[generationChunk as ChatGeneration]],\n llmOutput,\n })\n )\n );\n }\n }\n\n getLsParams(options: this[\"ParsedCallOptions\"]): LangSmithParams {\n const providerName = this.getName().startsWith(\"Chat\")\n ? this.getName().replace(\"Chat\", \"\")\n : this.getName();\n\n return {\n ls_model_type: \"chat\",\n ls_stop: options.stop,\n ls_provider: providerName,\n };\n }\n\n /** @ignore */\n async _generateUncached(\n messages: BaseMessageLike[][],\n parsedOptions: this[\"ParsedCallOptions\"],\n handledOptions: RunnableConfig,\n startedRunManagers?: CallbackManagerForLLMRun[]\n ): Promise<LLMResult> {\n const baseMessages = messages.map((messageList) =>\n messageList.map(coerceMessageLikeToMessage)\n );\n\n let runManagers: CallbackManagerForLLMRun[] | undefined;\n if (\n startedRunManagers !== undefined &&\n startedRunManagers.length === baseMessages.length\n ) {\n runManagers = startedRunManagers;\n } else {\n const inheritableMetadata = {\n ...handledOptions.metadata,\n ...this.getLsParams(parsedOptions),\n };\n // create callback manager and start run\n const callbackManager_ = await CallbackManager.configure(\n handledOptions.callbacks,\n this.callbacks,\n handledOptions.tags,\n this.tags,\n inheritableMetadata,\n this.metadata,\n { verbose: this.verbose }\n );\n const extra = {\n options: parsedOptions,\n invocation_params: this?.invocationParams(parsedOptions),\n batch_size: 1,\n };\n runManagers = await callbackManager_?.handleChatModelStart(\n this.toJSON(),\n baseMessages.map(_formatForTracing),\n handledOptions.runId,\n undefined,\n extra,\n undefined,\n undefined,\n handledOptions.runName\n );\n }\n const outputVersion = parsedOptions.outputVersion ?? this.outputVersion;\n const generations: ChatGeneration[][] = [];\n const llmOutputs: LLMResult[\"llmOutput\"][] = [];\n // Even if stream is not explicitly called, check if model is implicitly\n // called from streamEvents() or streamLog() to get all streamed events.\n // Bail out if _streamResponseChunks not overridden\n const hasStreamingHandler = !!runManagers?.[0].handlers.find(\n callbackHandlerPrefersStreaming\n );\n if (\n hasStreamingHandler &&\n !this.disableStreaming &&\n baseMessages.length === 1 &&\n this._streamResponseChunks !==\n BaseChatModel.prototype._streamResponseChunks\n ) {\n try {\n const stream = await this._streamResponseChunks(\n baseMessages[0],\n parsedOptions,\n runManagers?.[0]\n );\n let aggregated: ChatGenerationChunk | undefined;\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n let llmOutput: Record<string, any> | undefined;\n for await (const chunk of stream) {\n // Check for abort signal - throw ModelAbortError with partial output\n if (parsedOptions.signal?.aborted) {\n const partialMessage = aggregated?.message as\n | AIMessageChunk\n | undefined;\n throw new ModelAbortError(\n \"Model invocation was aborted.\",\n partialMessage\n );\n }\n if (chunk.message.id == null) {\n const runId = runManagers?.at(0)?.runId;\n if (runId != null) chunk.message._updateId(`run-${runId}`);\n }\n if (aggregated === undefined) {\n aggregated = chunk;\n } else {\n aggregated = concat(aggregated, chunk);\n }\n if (\n isAIMessageChunk(chunk.message) &&\n chunk.message.usage_metadata !== undefined\n ) {\n llmOutput = {\n tokenUsage: {\n promptTokens: chunk.message.usage_metadata.input_tokens,\n completionTokens: chunk.message.usage_metadata.output_tokens,\n totalTokens: chunk.message.usage_metadata.total_tokens,\n },\n };\n }\n }\n // Check if stream ended due to abort (provider returned early)\n if (parsedOptions.signal?.aborted) {\n const partialMessage = aggregated?.message as\n | AIMessageChunk\n | undefined;\n throw new ModelAbortError(\n \"Model invocation was aborted.\",\n partialMessage\n );\n }\n if (aggregated === undefined) {\n throw new Error(\"Received empty response from chat model call.\");\n }\n generations.push([aggregated]);\n await runManagers?.[0].handleLLMEnd({\n generations,\n llmOutput,\n });\n } catch (e) {\n await runManagers?.[0].handleLLMError(e);\n throw e;\n }\n } else {\n // generate results\n const results = await Promise.allSettled(\n baseMessages.map(async (messageList, i) => {\n const generateResults = await this._generate(\n messageList,\n { ...parsedOptions, promptIndex: i },\n runManagers?.[i]\n );\n if (outputVersion === \"v1\") {\n for (const generation of generateResults.generations) {\n generation.message = castStandardMessageContent(\n generation.message\n );\n }\n }\n return generateResults;\n })\n );\n // handle results\n await Promise.all(\n results.map(async (pResult, i) => {\n if (pResult.status === \"fulfilled\") {\n const result = pResult.value;\n for (const generation of result.generations) {\n if (generation.message.id == null) {\n const runId = runManagers?.at(0)?.runId;\n if (runId != null) generation.message._updateId(`run-${runId}`);\n }\n generation.message.response_metadata = {\n ...generation.generationInfo,\n ...generation.message.response_metadata,\n };\n }\n if (result.generations.length === 1) {\n result.generations[0].message.response_metadata = {\n ...result.llmOutput,\n ...result.generations[0].message.response_metadata,\n };\n }\n generations[i] = result.generations;\n llmOutputs[i] = result.llmOutput;\n return runManagers?.[i]?.handleLLMEnd({\n generations: [result.generations],\n llmOutput: result.llmOutput,\n });\n } else {\n // status === \"rejected\"\n await runManagers?.[i]?.handleLLMError(pResult.reason);\n return Promise.reject(pResult.reason);\n }\n })\n );\n }\n // create combined output\n const output: LLMResult = {\n generations,\n llmOutput: llmOutputs.length\n ? this._combineLLMOutput?.(...llmOutputs)\n : undefined,\n };\n Object.defineProperty(output, RUN_KEY, {\n value: runManagers\n ? { runIds: runManagers?.map((manager) => manager.runId) }\n : undefined,\n configurable: true,\n });\n return output;\n }\n\n async _generateCached({\n messages,\n cache,\n llmStringKey,\n parsedOptions,\n handledOptions,\n }: {\n messages: BaseMessageLike[][];\n cache: BaseCache<Generation[]>;\n llmStringKey: string;\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n parsedOptions: any;\n handledOptions: RunnableConfig;\n }): Promise<\n LLMResult & {\n missingPromptIndices: number[];\n startedRunManagers?: CallbackManagerForLLMRun[];\n }\n > {\n const baseMessages = messages.map((messageList) =>\n messageList.map(coerceMessageLikeToMessage)\n );\n\n const inheritableMetadata = {\n ...handledOptions.metadata,\n ...this.getLsParams(parsedOptions),\n };\n // create callback manager and start run\n const callbackManager_ = await CallbackManager.configure(\n handledOptions.callbacks,\n this.callbacks,\n handledOptions.tags,\n this.tags,\n inheritableMetadata,\n this.metadata,\n { verbose: this.verbose }\n );\n const extra = {\n options: parsedOptions,\n invocation_params: this?.invocationParams(parsedOptions),\n batch_size: 1,\n };\n const runManagers = await callbackManager_?.handleChatModelStart(\n this.toJSON(),\n baseMessages.map(_formatForTracing),\n handledOptions.runId,\n undefined,\n extra,\n undefined,\n undefined,\n handledOptions.runName\n );\n\n // generate results\n const missingPromptIndices: number[] = [];\n const results = await Promise.allSettled(\n baseMessages.map(async (baseMessage, index) => {\n // Join all content into one string for the prompt index\n const prompt =\n BaseChatModel._convertInputToPromptValue(baseMessage).toString();\n const result = await cache.lookup(prompt, llmStringKey);\n\n if (result == null) {\n missingPromptIndices.push(index);\n }\n\n return result;\n })\n );\n\n // Map run managers to the results before filtering out null results\n // Null results are just absent from the cache.\n const cachedResults = results\n .map((result, index) => ({ result, runManager: runManagers?.[index] }))\n .filter(\n ({ result }) =>\n (result.status === \"fulfilled\" && result.value != null) ||\n result.status === \"rejected\"\n );\n\n // Handle results and call run managers\n const outputVersion = parsedOptions.outputVersion ?? this.outputVersion;\n const generations: Generation[][] = [];\n await Promise.all(\n cachedResults.map(async ({ result: promiseResult, runManager }, i) => {\n if (promiseResult.status === \"fulfilled\") {\n const result = promiseResult.value as Generation[];\n generations[i] = result.map((result) => {\n if (\n \"message\" in result &&\n isBaseMessage(result.message) &&\n isAIMessage(result.message)\n ) {\n result.message.usage_metadata = {\n input_tokens: 0,\n output_tokens: 0,\n total_tokens: 0,\n };\n if (outputVersion === \"v1\") {\n result.message = castStandardMessageContent(result.message);\n }\n }\n result.generationInfo = {\n ...result.generationInfo,\n tokenUsage: {},\n };\n return result;\n });\n if (result.length) {\n await runManager?.handleLLMNewToken(result[0].text);\n }\n return runManager?.handleLLMEnd(\n {\n generations: [result],\n },\n undefined,\n undefined,\n undefined,\n {\n cached: true,\n }\n );\n } else {\n // status === \"rejected\"\n await runManager?.handleLLMError(\n promiseResult.reason,\n undefined,\n undefined,\n undefined,\n {\n cached: true,\n }\n );\n return Promise.reject(promiseResult.reason);\n }\n })\n );\n\n const output = {\n generations,\n missingPromptIndices,\n startedRunManagers: runManagers,\n };\n\n // This defines RUN_KEY as a non-enumerable property on the output object\n // so that it is not serialized when the output is stringified, and so that\n // it isnt included when listing the keys of the output object.\n Object.defineProperty(output, RUN_KEY, {\n value: runManagers\n ? { runIds: runManagers?.map((manager) => manager.runId) }\n : undefined,\n configurable: true,\n });\n\n return output;\n }\n\n /**\n * Generates chat based on the input messages.\n * @param messages An array of arrays of BaseMessage instances.\n * @param options The call options or an array of stop sequences.\n * @param callbacks The callbacks for the language model.\n * @returns A Promise that resolves to an LLMResult.\n */\n async generate(\n messages: BaseMessageLike[][],\n options?: string[] | Partial<CallOptions>,\n callbacks?: Callbacks\n ): Promise<LLMResult> {\n // parse call options\n let parsedOptions: Partial<CallOptions> | undefined;\n if (Array.isArray(options)) {\n parsedOptions = { stop: options } as Partial<CallOptions>;\n } else {\n parsedOptions = options;\n }\n\n const baseMessages = messages.map((messageList) =>\n messageList.map(coerceMessageLikeToMessage)\n );\n\n const [runnableConfig, callOptions] =\n this._separateRunnableConfigFromCallOptionsCompat(parsedOptions);\n runnableConfig.callbacks = runnableConfig.callbacks ?? callbacks;\n\n if (!this.cache) {\n return this._generateUncached(baseMessages, callOptions, runnableConfig);\n }\n\n const { cache } = this;\n const llmStringKey = this._getSerializedCacheKeyParametersForCall(\n callOptions as CallOptions\n );\n\n const { generations, missingPromptIndices, startedRunManagers } =\n await this._generateCached({\n messages: baseMessages,\n cache,\n llmStringKey,\n parsedOptions: callOptions,\n handledOptions: runnableConfig,\n });\n\n let llmOutput = {};\n if (missingPromptIndices.length > 0) {\n const results = await this._generateUncached(\n missingPromptIndices.map((i) => baseMessages[i]),\n callOptions,\n runnableConfig,\n startedRunManagers !== undefined\n ? missingPromptIndices.map((i) => startedRunManagers?.[i])\n : undefined\n );\n await Promise.all(\n results.generations.map(async (generation, index) => {\n const promptIndex = missingPromptIndices[index];\n generations[promptIndex] = generation;\n // Join all content into one string for the prompt index\n const prompt = BaseChatModel._convertInputToPromptValue(\n baseMessages[promptIndex]\n ).toString();\n return cache.update(prompt, llmStringKey, generation);\n })\n );\n llmOutput = results.llmOutput ?? {};\n }\n\n return { generations, llmOutput } as LLMResult;\n }\n\n /**\n * Get the parameters used to invoke the model\n */\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n invocationParams(_options?: this[\"ParsedCallOptions\"]): any {\n return {};\n }\n\n _modelType(): string {\n return \"base_chat_model\" as const;\n }\n\n abstract _llmType(): string;\n\n /**\n * Generates a prompt based on the input prompt values.\n * @param promptValues An array of BasePromptValue instances.\n * @param options The call options or an array of stop sequences.\n * @param callbacks The callbacks for the language model.\n * @returns A Promise that resolves to an LLMResult.\n */\n async generatePrompt(\n promptValues: BasePromptValueInterface[],\n options?: string[] | Partial<CallOptions>,\n callbacks?: Callbacks\n ): Promise<LLMResult> {\n const promptMessages: BaseMessage[][] = promptValues.map((promptValue) =>\n promptValue.toChatMessages()\n );\n return this.generate(promptMessages, options, callbacks);\n }\n\n abstract _generate(\n messages: BaseMessage[],\n options: this[\"ParsedCallOptions\"],\n runManager?: CallbackManagerForLLMRun\n ): Promise<ChatResult>;\n\n withStructuredOutput<\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n RunOutput extends Record<string, any> = Record<string, any>,\n >(\n outputSchema:\n | ZodTypeV4<RunOutput>\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n | Record<string, any>,\n config?: StructuredOutputMethodOptions<false>\n ): Runnable<BaseLanguageModelInput, RunOutput>;\n\n withStructuredOutput<\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n RunOutput extends Record<string, any> = Record<string, any>,\n >(\n outputSchema:\n | ZodTypeV4<RunOutput>\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n | Record<string, any>,\n config?: StructuredOutputMethodOptions<true>\n ): Runnable<BaseLanguageModelInput, { raw: BaseMessage; parsed: RunOutput }>;\n\n withStructuredOutput<\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n RunOutput extends Record<string, any> = Record<string, any>,\n >(\n outputSchema:\n | ZodTypeV3<RunOutput>\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n | Record<string, any>,\n config?: StructuredOutputMethodOptions<false>\n ): Runnable<BaseLanguageModelInput, RunOutput>;\n\n withStructuredOutput<\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n RunOutput extends Record<string, any> = Record<string, any>,\n >(\n outputSchema:\n | ZodTypeV3<RunOutput>\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n | Record<string, any>,\n config?: StructuredOutputMethodOptions<true>\n ): Runnable<BaseLanguageModelInput, { raw: BaseMessage; parsed: RunOutput }>;\n\n withStructuredOutput<\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n RunOutput extends Record<string, any> = Record<string, any>,\n >(\n outputSchema:\n | InteropZodType<RunOutput>\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n | Record<string, any>,\n config?: StructuredOutputMethodOptions<boolean>\n ):\n | Runnable<BaseLanguageModelInput, RunOutput>\n | Runnable<\n BaseLanguageModelInput,\n {\n raw: BaseMessage;\n parsed: RunOutput;\n }\n > {\n if (typeof this.bindTools !== \"function\") {\n throw new Error(\n `Chat model must implement \".bindTools()\" to use withStructuredOutput.`\n );\n }\n if (config?.strict) {\n throw new Error(\n `\"strict\" mode is not supported for this model by default.`\n );\n }\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n const schema: Record<string, any> | InteropZodType<RunOutput> =\n outputSchema;\n const name = config?.name;\n const description =\n getSchemaDescription(schema) ?? \"A function available to call.\";\n const method = config?.method;\n const includeRaw = config?.includeRaw;\n if (method === \"jsonMode\") {\n throw new Error(\n `Base withStructuredOutput implementation only supports \"functionCalling\" as a method.`\n );\n }\n\n let functionName = name ?? \"extract\";\n let tools: ToolDefinition[];\n if (isInteropZodSchema(schema)) {\n tools = [\n {\n type: \"function\",\n function: {\n name: functionName,\n description,\n parameters: toJsonSchema(schema),\n },\n },\n ];\n } else {\n if (\"name\" in schema) {\n functionName = schema.name;\n }\n tools = [\n {\n type: \"function\",\n function: {\n name: functionName,\n description,\n parameters: schema,\n },\n },\n ];\n }\n\n const llm = this.bindTools(tools);\n const outputParser = RunnableLambda.from<OutputMessageType, RunOutput>(\n (input: BaseMessageChunk): RunOutput => {\n if (!AIMessageChunk.isInstance(input)) {\n throw new Error(\"Input is not an AIMessageChunk.\");\n }\n if (!input.tool_calls || input.tool_calls.length === 0) {\n throw new Error(\"No tool calls found in the response.\");\n }\n const toolCall = input.tool_calls.find(\n (tc) => tc.name === functionName\n );\n if (!toolCall) {\n throw new Error(`No tool call found with name ${functionName}.`);\n }\n return toolCall.args as RunOutput;\n }\n );\n\n if (!includeRaw) {\n return llm.pipe(outputParser).withConfig({\n runName: \"StructuredOutput\",\n }) as Runnable<BaseLanguageModelInput, RunOutput>;\n }\n\n const parserAssign = RunnablePassthrough.assign({\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n parsed: (input: any, config) => outputParser.invoke(input.raw, config),\n });\n const parserNone = RunnablePassthrough.assign({\n parsed: () => null,\n });\n const parsedWithFallback = parserAssign.withFallbacks({\n fallbacks: [parserNone],\n });\n return RunnableSequence.from<\n BaseLanguageModelInput,\n { raw: BaseMessage; parsed: RunOutput }\n >([\n {\n raw: llm,\n },\n parsedWithFallback,\n ]).withConfig({\n runName: \"StructuredOutputRunnable\",\n });\n }\n}\n\n/**\n * An abstract class that extends BaseChatModel and provides a simple\n * implementation of _generate.\n */\nexport abstract class SimpleChatModel<\n CallOptions extends BaseChatModelCallOptions = BaseChatModelCallOptions,\n> extends BaseChatModel<CallOptions> {\n abstract _call(\n messages: BaseMessage[],\n options: this[\"ParsedCallOptions\"],\n runManager?: CallbackManagerForLLMRun\n ): Promise<string>;\n\n async _generate(\n messages: BaseMessage[],\n options: this[\"ParsedCallOptions\"],\n runManager?: CallbackManagerForLLMRun\n ): Promise<ChatResult> {\n const text = await this._call(messages, options, runManager);\n const message = new AIMessage(text);\n if (typeof message.content !== \"string\") {\n throw new Error(\n \"Cannot generate with a simple chat model when output is not a string.\"\n );\n }\n return {\n generations: [\n {\n text: message.content,\n message,\n },\n ],\n };\n 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