{"version":3,"file":"chat_models.cjs","names":["AIMessage","_convertToolCallIdToMistralCompatible","convertLangChainToolCallToOpenAI","_mistralContentChunkToMessageContentComplex","HumanMessage","AIMessageChunk","HumanMessageChunk","ToolMessageChunk","FunctionMessageChunk","ChatMessageChunk","BaseChatModel","RunnableBinding","AsyncCaller","MistralClient","ChatGenerationChunk","MistralAIHTTPClient"],"sources":["../src/chat_models.ts"],"sourcesContent":["import { v4 as uuidv4 } from \"@langchain/core/utils/uuid\";\nimport { Mistral as MistralClient } from \"@mistralai/mistralai\";\nimport {\n ChatCompletionRequest as MistralAIChatCompletionRequest,\n ChatCompletionRequestToolChoice as MistralAIToolChoice,\n Messages as MistralAIMessage,\n} from \"@mistralai/mistralai/models/components/chatcompletionrequest.js\";\nimport { ContentChunk as MistralAIContentChunk } from \"@mistralai/mistralai/models/components/contentchunk.js\";\nimport { Tool as MistralAITool } from \"@mistralai/mistralai/models/components/tool.js\";\nimport { ToolCall as MistralAIToolCall } from \"@mistralai/mistralai/models/components/toolcall.js\";\nimport { ChatCompletionStreamRequest as MistralAIChatCompletionStreamRequest } from \"@mistralai/mistralai/models/components/chatcompletionstreamrequest.js\";\nimport { UsageInfo as MistralAITokenUsage } from \"@mistralai/mistralai/models/components/usageinfo.js\";\nimport { CompletionEvent as MistralAIChatCompletionEvent } from \"@mistralai/mistralai/models/components/completionevent.js\";\nimport { ChatCompletionResponse as MistralAIChatCompletionResponse } from \"@mistralai/mistralai/models/components/chatcompletionresponse.js\";\nimport {\n type BeforeRequestHook,\n type RequestErrorHook,\n type ResponseHook,\n HTTPClient as MistralAIHTTPClient,\n} from \"@mistralai/mistralai/lib/http.js\";\nimport {\n BaseMessage,\n MessageType,\n MessageContent,\n MessageContentComplex,\n AIMessage,\n HumanMessage,\n HumanMessageChunk,\n AIMessageChunk,\n ToolMessageChunk,\n ChatMessageChunk,\n FunctionMessageChunk,\n} from \"@langchain/core/messages\";\nimport type {\n BaseLanguageModelInput,\n BaseLanguageModelCallOptions,\n StructuredOutputMethodOptions,\n FunctionDefinition,\n} from \"@langchain/core/language_models/base\";\nimport { CallbackManagerForLLMRun } from \"@langchain/core/callbacks/manager\";\nimport {\n type BaseChatModelParams,\n BaseChatModel,\n BindToolsInput,\n LangSmithParams,\n} from \"@langchain/core/language_models/chat_models\";\n\nimport {\n ChatGeneration,\n ChatGenerationChunk,\n ChatResult,\n} from \"@langchain/core/outputs\";\nimport { AsyncCaller } from \"@langchain/core/utils/async_caller\";\nimport { getEnvironmentVariable } from \"@langchain/core/utils/env\";\nimport { NewTokenIndices } from \"@langchain/core/callbacks/base\";\nimport { type BaseLLMOutputParser } from \"@langchain/core/output_parsers\";\nimport {\n convertLangChainToolCallToOpenAI,\n makeInvalidToolCall,\n parseToolCall,\n} from \"@langchain/core/output_parsers/openai_tools\";\nimport { Runnable, RunnableBinding } from \"@langchain/core/runnables\";\nimport { toJsonSchema } from \"@langchain/core/utils/json_schema\";\nimport { ToolCallChunk } from \"@langchain/core/messages/tool\";\nimport { isLangChainTool } from \"@langchain/core/utils/function_calling\";\nimport {\n InteropZodType,\n isInteropZodSchema,\n} from \"@langchain/core/utils/types\";\nimport {\n _convertToolCallIdToMistralCompatible,\n _mistralContentChunkToMessageContentComplex,\n} from \"./utils.js\";\nimport {\n isSerializableSchema,\n SerializableSchema,\n} from \"@langchain/core/utils/standard_schema\";\nimport {\n assembleStructuredOutputPipeline,\n createContentParser,\n createFunctionCallingParser,\n} from \"@langchain/core/language_models/structured_output\";\n\ninterface TokenUsage {\n completionTokens?: number;\n promptTokens?: number;\n totalTokens?: number;\n}\n\ntype ChatMistralAIToolType = MistralAIToolCall | MistralAITool | BindToolsInput;\n\nexport interface ChatMistralAICallOptions extends Omit<\n BaseLanguageModelCallOptions,\n \"stop\"\n> {\n response_format?: {\n type: \"text\" | \"json_object\";\n };\n tools?: ChatMistralAIToolType[];\n tool_choice?: MistralAIToolChoice;\n /**\n * Whether or not to include token usage in the stream.\n * @default {true}\n */\n streamUsage?: boolean;\n}\n\n/**\n * Input to chat model class.\n */\nexport interface ChatMistralAIInput\n extends BaseChatModelParams, Pick {\n /**\n * The API key to use.\n * @default {process.env.MISTRAL_API_KEY}\n */\n apiKey?: string;\n /**\n * The name of the model to use.\n * Alias for `model`\n * @deprecated Use `model` instead.\n * @default {\"mistral-small-latest\"}\n */\n modelName?: MistralAIChatCompletionRequest[\"model\"];\n /**\n * The name of the model to use.\n * @default {\"mistral-small-latest\"}\n */\n model?: MistralAIChatCompletionRequest[\"model\"];\n /**\n * Override the default server URL used by the Mistral SDK.\n * @deprecated use serverURL instead\n */\n endpoint?: string;\n /**\n * Override the default server URL used by the Mistral SDK.\n */\n serverURL?: string;\n /**\n * What sampling temperature to use, between 0.0 and 2.0.\n * Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.\n * @default {0.7}\n */\n temperature?: number;\n /**\n * Nucleus sampling, where the model considers the results of the tokens with `top_p` probability mass.\n * So 0.1 means only the tokens comprising the top 10% probability mass are considered.\n * Should be between 0 and 1.\n * @default {1}\n */\n topP?: number;\n /**\n * The maximum number of tokens to generate in the completion.\n * The token count of your prompt plus max_tokens cannot exceed the model's context length.\n */\n maxTokens?: number;\n /**\n * Whether or not to stream the response.\n * @default {false}\n */\n streaming?: boolean;\n /**\n * Whether to inject a safety prompt before all conversations.\n * @default {false}\n * @deprecated use safePrompt instead\n */\n safeMode?: boolean;\n /**\n * Whether to inject a safety prompt before all conversations.\n * @default {false}\n */\n safePrompt?: boolean;\n /**\n * The seed to use for random sampling. If set, different calls will generate deterministic results.\n * Alias for `seed`\n */\n randomSeed?: number;\n /**\n * The seed to use for random sampling. If set, different calls will generate deterministic results.\n */\n seed?: number;\n /**\n * A list of custom hooks that must follow (req: Request) => Awaitable\n * They are automatically added when a ChatMistralAI instance is created.\n */\n beforeRequestHooks?: BeforeRequestHook[];\n /**\n * A list of custom hooks that must follow (err: unknown, req: Request) => Awaitable.\n * They are automatically added when a ChatMistralAI instance is created.\n */\n requestErrorHooks?: RequestErrorHook[];\n /**\n * A list of custom hooks that must follow (res: Response, req: Request) => Awaitable.\n * They are automatically added when a ChatMistralAI instance is created.\n */\n responseHooks?: ResponseHook[];\n /**\n * Custom HTTP client to manage API requests.\n * Allows users to add custom fetch implementations, hooks, as well as error and response processing.\n */\n httpClient?: MistralAIHTTPClient;\n /**\n * Determines how much the model penalizes the repetition of words or phrases. A higher presence\n * penalty encourages the model to use a wider variety of words and phrases, making the output\n * more diverse and creative.\n */\n presencePenalty?: number;\n /**\n * Penalizes the repetition of words based on their frequency in the generated text. A higher\n * frequency penalty discourages the model from repeating words that have already appeared frequently\n * in the output, promoting diversity and reducing repetition.\n */\n frequencyPenalty?: number;\n /**\n * Number of completions to return for each request, input tokens are only billed once.\n */\n numCompletions?: number;\n}\n\nexport function convertMessagesToMistralMessages(\n messages: Array\n): Array {\n const getRole = (role: MessageType) => {\n switch (role) {\n case \"human\":\n return \"user\";\n case \"ai\":\n return \"assistant\";\n case \"system\":\n return \"system\";\n case \"tool\":\n return \"tool\";\n case \"function\":\n return \"assistant\";\n default:\n throw new Error(`Unknown message type: ${role}`);\n }\n };\n\n const getContent = (\n content: MessageContent,\n type: MessageType\n ): string | MistralAIContentChunk[] => {\n const _generateContentChunk = (\n complex: MessageContentComplex,\n role: string\n ): MistralAIContentChunk => {\n if (\n complex.type === \"image_url\" &&\n (role === \"user\" || role === \"assistant\")\n ) {\n return {\n type: complex.type,\n imageUrl: complex?.image_url,\n };\n }\n\n if (complex.type === \"text\") {\n return {\n type: complex.type,\n text: complex?.text,\n };\n }\n\n throw new Error(\n `ChatMistralAI only supports messages of \"image_url\" for roles \"user\" and \"assistant\", and \"text\" for all others.\\n\\nReceived: ${JSON.stringify(\n content,\n null,\n 2\n )}`\n );\n };\n\n if (typeof content === \"string\") {\n return content;\n }\n\n if (Array.isArray(content)) {\n const mistralRole = getRole(type);\n // Mistral \"assistant\" and \"user\" roles can support Mistral ContentChunks\n // Mistral \"system\" role can support Mistral TextChunks\n const newContent: MistralAIContentChunk[] = [];\n content.forEach((messageContentComplex) => {\n // Mistral content chunks only support type \"text\" and \"image_url\"\n if (\n messageContentComplex.type === \"text\" ||\n messageContentComplex.type === \"image_url\"\n ) {\n newContent.push(\n _generateContentChunk(messageContentComplex, mistralRole)\n );\n } else {\n throw new Error(\n `Mistral only supports types \"text\" or \"image_url\" for complex message types.`\n );\n }\n });\n return newContent;\n }\n\n throw new Error(\n `Message content must be a string or an array.\\n\\nReceived: ${JSON.stringify(\n content,\n null,\n 2\n )}`\n );\n };\n\n const getTools = (message: BaseMessage): MistralAIToolCall[] | undefined => {\n if (AIMessage.isInstance(message) && !!message.tool_calls?.length) {\n return message.tool_calls\n .map((toolCall) => ({\n ...toolCall,\n id: _convertToolCallIdToMistralCompatible(toolCall.id ?? \"\"),\n }))\n .map(convertLangChainToolCallToOpenAI) as MistralAIToolCall[];\n }\n return undefined;\n };\n\n // Build a set of toolCallIds that have corresponding tool responses present\n // to ensure 1:1 assistant toolCalls <-> tool responses.\n const toolResponseIds = new Set();\n for (const m of messages) {\n if (\"tool_call_id\" in m && typeof m.tool_call_id === \"string\") {\n toolResponseIds.add(\n _convertToolCallIdToMistralCompatible(m.tool_call_id)\n );\n }\n }\n\n return messages.flatMap((message) => {\n const toolCalls = getTools(message);\n const content = getContent(message.content, message.getType());\n if (\"tool_call_id\" in message && typeof message.tool_call_id === \"string\") {\n return [\n {\n role: getRole(message.getType()),\n content,\n name: message.name,\n toolCallId: _convertToolCallIdToMistralCompatible(\n message.tool_call_id\n ),\n } as MistralAIMessage,\n ];\n // Mistral \"assistant\" role can only support either content or tool calls but not both\n } else if (AIMessage.isInstance(message)) {\n if (toolCalls === undefined) {\n return [\n {\n role: getRole(message.getType()),\n content,\n } as MistralAIMessage,\n ];\n } else {\n // Filter out toolCalls that do not have a matching tool response later in the list\n const filteredToolCalls = toolCalls.filter((tc) =>\n toolResponseIds.has(\n _convertToolCallIdToMistralCompatible(tc.id ?? \"\")\n )\n );\n\n if (filteredToolCalls.length === 0) {\n // If there are no matching tool responses, and there's no content, drop this message\n const isEmptyContent =\n (typeof content === \"string\" && content.trim() === \"\") ||\n (Array.isArray(content) && content.length === 0);\n if (isEmptyContent) {\n return [];\n }\n // Otherwise, send content only\n return [\n {\n role: getRole(message.getType()),\n content,\n } as MistralAIMessage,\n ];\n }\n\n return [\n {\n role: getRole(message.getType()),\n toolCalls: filteredToolCalls as MistralAIToolCall[],\n } as MistralAIMessage,\n ];\n }\n }\n\n return [\n {\n role: getRole(message.getType()),\n content,\n } as MistralAIMessage,\n ];\n }) as MistralAIMessage[];\n}\n\nfunction mistralAIResponseToChatMessage(\n choice: NonNullable[0],\n usage?: MistralAITokenUsage\n): BaseMessage {\n const { message } = choice;\n if (message === undefined) {\n throw new Error(\"No message found in response\");\n }\n // MistralAI SDK does not include toolCalls in the non\n // streaming return type, so we need to extract it like this\n // to satisfy typescript.\n let rawToolCalls: MistralAIToolCall[] = [];\n if (\"toolCalls\" in message && Array.isArray(message.toolCalls)) {\n rawToolCalls = message.toolCalls;\n }\n const content = _mistralContentChunkToMessageContentComplex(message.content);\n switch (message.role) {\n case \"assistant\": {\n const toolCalls = [];\n const invalidToolCalls = [];\n for (const rawToolCall of rawToolCalls) {\n try {\n const parsed = parseToolCall(rawToolCall, { returnId: true });\n toolCalls.push({\n ...parsed,\n id: parsed.id ?? uuidv4().replace(/-/g, \"\"),\n });\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n } catch (e: any) {\n invalidToolCalls.push(makeInvalidToolCall(rawToolCall, e.message));\n }\n }\n return new AIMessage({\n content,\n tool_calls: toolCalls,\n invalid_tool_calls: invalidToolCalls,\n additional_kwargs: {},\n usage_metadata: usage\n ? {\n input_tokens: usage.promptTokens ?? 0,\n output_tokens: usage.completionTokens ?? 0,\n total_tokens: usage.totalTokens ?? 0,\n }\n : undefined,\n });\n }\n default:\n return new HumanMessage({ content });\n }\n}\n\nfunction _convertDeltaToMessageChunk(\n delta: {\n role?: string | null | undefined;\n content?: string | MistralAIContentChunk[] | null | undefined;\n toolCalls?: MistralAIToolCall[] | null | undefined;\n },\n usage?: MistralAITokenUsage | null\n) {\n if (!delta.content && !delta.toolCalls) {\n if (usage) {\n return new AIMessageChunk({\n content: \"\",\n usage_metadata: usage\n ? {\n input_tokens: usage.promptTokens ?? 0,\n output_tokens: usage.completionTokens ?? 0,\n total_tokens: usage.totalTokens ?? 0,\n }\n : undefined,\n });\n }\n return null;\n }\n // Our merge additional kwargs util function will throw unless there\n // is an index key in each tool object (as seen in OpenAI's) so we\n // need to insert it here.\n const rawToolCallChunksWithIndex = delta.toolCalls?.length\n ? delta.toolCalls?.map(\n (toolCall, index): MistralAIToolCall & { index: number } => ({\n ...toolCall,\n index,\n id: toolCall.id ?? uuidv4().replace(/-/g, \"\"),\n type: \"function\",\n })\n )\n : undefined;\n\n let role = \"assistant\";\n if (delta.role) {\n role = delta.role;\n }\n const content = _mistralContentChunkToMessageContentComplex(delta.content);\n\n let additional_kwargs;\n const toolCallChunks: ToolCallChunk[] = [];\n if (rawToolCallChunksWithIndex !== undefined) {\n for (const rawToolCallChunk of rawToolCallChunksWithIndex) {\n const rawArgs = rawToolCallChunk.function?.arguments;\n const args =\n rawArgs === undefined || typeof rawArgs === \"string\"\n ? rawArgs\n : JSON.stringify(rawArgs);\n toolCallChunks.push({\n name: rawToolCallChunk.function?.name,\n args,\n id: rawToolCallChunk.id,\n index: rawToolCallChunk.index,\n type: \"tool_call_chunk\",\n });\n }\n } else {\n additional_kwargs = {};\n }\n\n if (role === \"user\") {\n return new HumanMessageChunk({ content });\n } else if (role === \"assistant\") {\n return new AIMessageChunk({\n content,\n tool_call_chunks: toolCallChunks,\n additional_kwargs,\n usage_metadata: usage\n ? {\n input_tokens: usage.promptTokens ?? 0,\n output_tokens: usage.completionTokens ?? 0,\n total_tokens: usage.totalTokens ?? 0,\n }\n : undefined,\n });\n } else if (role === \"tool\") {\n return new ToolMessageChunk({\n content,\n additional_kwargs,\n tool_call_id: rawToolCallChunksWithIndex?.[0].id ?? \"\",\n });\n } else if (role === \"function\") {\n return new FunctionMessageChunk({\n content,\n additional_kwargs,\n });\n } else {\n return new ChatMessageChunk({ content, role });\n }\n}\n\nfunction _convertToolToMistralTool(\n tools: ChatMistralAIToolType[]\n): MistralAITool[] | undefined {\n if (!tools || !tools.length) {\n return undefined;\n }\n return tools.map((tool) => {\n // If already a MistralAITool with a 'function' property, return as is\n if (\"function\" in tool) {\n return {\n type: tool.type ?? \"function\",\n function: tool.function,\n };\n }\n\n // If it's a LangChain tool, convert to MistralAITool\n if (isLangChainTool(tool)) {\n const description = tool.description ?? `Tool: ${tool.name}`;\n return {\n type: \"function\",\n function: {\n name: tool.name,\n description,\n parameters: isInteropZodSchema(tool.schema)\n ? toJsonSchema(tool.schema)\n : tool.schema,\n },\n };\n }\n\n throw new Error(\n `Unknown tool type passed to ChatMistral: ${JSON.stringify(\n tool,\n null,\n 2\n )}`\n );\n });\n}\n\n/**\n * Mistral AI chat model integration.\n *\n * Setup:\n * Install `@langchain/mistralai` and set an environment variable named `MISTRAL_API_KEY`.\n *\n * ```bash\n * npm install @langchain/mistralai\n * export MISTRAL_API_KEY=\"your-api-key\"\n * ```\n *\n * ## [Constructor args](https://api.js.langchain.com/classes/_langchain_mistralai.ChatMistralAI.html#constructor)\n *\n * ## [Runtime args](https://api.js.langchain.com/interfaces/_langchain_mistralai.ChatMistralAICallOptions.html)\n *\n * Runtime args can be passed as the second argument to any of the base runnable methods `.invoke`. `.stream`, `.batch`, etc.\n * They can also be passed via `.withConfig`, or the second arg in `.bindTools`, like shown in the examples below:\n *\n * ```typescript\n * // When calling `.withConfig`, call options should be passed via the first argument\n * const llmWithArgsBound = llm.bindTools([...]) // tools array\n * .withConfig({\n * stop: [\"\\n\"], // other call options\n * });\n *\n * // You can also bind tools and call options like this\n * const llmWithTools = llm.bindTools([...], {\n * tool_choice: \"auto\",\n * });\n * ```\n *\n * ## Examples\n *\n *
\n * Instantiate\n *\n * ```typescript\n * import { ChatMistralAI } from '@langchain/mistralai';\n *\n * const llm = new ChatMistralAI({\n * model: \"mistral-large-2402\",\n * temperature: 0,\n * // other params...\n * });\n * ```\n *
\n *\n *
\n *\n *
\n * Invoking\n *\n * ```typescript\n * const input = `Translate \"I love programming\" into French.`;\n *\n * // Models also accept a list of chat messages or a formatted prompt\n * const result = await llm.invoke(input);\n * console.log(result);\n * ```\n *\n * ```txt\n * AIMessage {\n * \"content\": \"The translation of \\\"I love programming\\\" into French is \\\"J'aime la programmation\\\". Here's the breakdown:\\n\\n- \\\"I\\\" translates to \\\"Je\\\"\\n- \\\"love\\\" translates to \\\"aime\\\"\\n- \\\"programming\\\" translates to \\\"la programmation\\\"\\n\\nSo, \\\"J'aime la programmation\\\" means \\\"I love programming\\\" in French.\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"tokenUsage\": {\n * \"completionTokens\": 89,\n * \"promptTokens\": 13,\n * \"totalTokens\": 102\n * },\n * \"finish_reason\": \"stop\"\n * },\n * \"tool_calls\": [],\n * \"invalid_tool_calls\": [],\n * \"usage_metadata\": {\n * \"input_tokens\": 13,\n * \"output_tokens\": 89,\n * \"total_tokens\": 102\n * }\n * }\n * ```\n *
\n *\n *
\n *\n *
\n * Streaming Chunks\n *\n * ```typescript\n * for await (const chunk of await llm.stream(input)) {\n * console.log(chunk);\n * }\n * ```\n *\n * ```txt\n * AIMessageChunk {\n * \"content\": \"The\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"prompt\": 0,\n * \"completion\": 0\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \" translation\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"prompt\": 0,\n * \"completion\": 0\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \" of\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"prompt\": 0,\n * \"completion\": 0\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \" \\\"\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"prompt\": 0,\n * \"completion\": 0\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \"I\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"prompt\": 0,\n * \"completion\": 0\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n * }\n * AIMessageChunk {\n * \"content\": \".\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"prompt\": 0,\n * \"completion\": 0\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": []\n *}\n *AIMessageChunk {\n * \"content\": \"\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"prompt\": 0,\n * \"completion\": 0\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": [],\n * \"usage_metadata\": {\n * \"input_tokens\": 13,\n * \"output_tokens\": 89,\n * \"total_tokens\": 102\n * }\n *}\n * ```\n *
\n *\n *
\n *\n *
\n * Aggregate Streamed Chunks\n *\n * ```typescript\n * import { AIMessageChunk } from '@langchain/core/messages';\n * import { concat } from '@langchain/core/utils/stream';\n *\n * const stream = await llm.stream(input);\n * let full: AIMessageChunk | undefined;\n * for await (const chunk of stream) {\n * full = !full ? chunk : concat(full, chunk);\n * }\n * console.log(full);\n * ```\n *\n * ```txt\n * AIMessageChunk {\n * \"content\": \"The translation of \\\"I love programming\\\" into French is \\\"J'aime la programmation\\\". Here's the breakdown:\\n\\n- \\\"I\\\" translates to \\\"Je\\\"\\n- \\\"love\\\" translates to \\\"aime\\\"\\n- \\\"programming\\\" translates to \\\"la programmation\\\"\\n\\nSo, \\\"J'aime la programmation\\\" means \\\"I love programming\\\" in French.\",\n * \"additional_kwargs\": {},\n * \"response_metadata\": {\n * \"prompt\": 0,\n * \"completion\": 0\n * },\n * \"tool_calls\": [],\n * \"tool_call_chunks\": [],\n * \"invalid_tool_calls\": [],\n * \"usage_metadata\": {\n * \"input_tokens\": 13,\n * \"output_tokens\": 89,\n * \"total_tokens\": 102\n * }\n * }\n * ```\n *
\n *\n *
\n *\n *
\n * Bind tools\n *\n * ```typescript\n * import { z } from 'zod';\n *\n * const GetWeather = {\n * name: \"GetWeather\",\n * description: \"Get the current weather in a given location\",\n * schema: z.object({\n * location: z.string().describe(\"The city and state, e.g. San Francisco, CA\")\n * }),\n * }\n *\n * const GetPopulation = {\n * name: \"GetPopulation\",\n * description: \"Get the current population in a given location\",\n * schema: z.object({\n * location: z.string().describe(\"The city and state, e.g. San Francisco, CA\")\n * }),\n * }\n *\n * const llmWithTools = llm.bindTools([GetWeather, GetPopulation]);\n * const aiMsg = await llmWithTools.invoke(\n * \"Which city is hotter today and which is bigger: LA or NY?\"\n * );\n * console.log(aiMsg.tool_calls);\n * ```\n *\n * ```txt\n * [\n * {\n * name: 'GetWeather',\n * args: { location: 'Los Angeles, CA' },\n * type: 'tool_call',\n * id: '47i216yko'\n * },\n * {\n * name: 'GetWeather',\n * args: { location: 'New York, NY' },\n * type: 'tool_call',\n * id: 'nb3v8Fpcn'\n * },\n * {\n * name: 'GetPopulation',\n * args: { location: 'Los Angeles, CA' },\n * type: 'tool_call',\n * id: 'EedWzByIB'\n * },\n * {\n * name: 'GetPopulation',\n * args: { location: 'New York, NY' },\n * type: 'tool_call',\n * id: 'jLdLia7zC'\n * }\n * ]\n * ```\n *
\n *\n *
\n *\n *
\n * Structured Output\n *\n * ```typescript\n * import { z } from 'zod';\n *\n * const Joke = z.object({\n * setup: z.string().describe(\"The setup of the joke\"),\n * punchline: z.string().describe(\"The punchline to the joke\"),\n * rating: z.number().optional().describe(\"How funny the joke is, from 1 to 10\")\n * }).describe('Joke to tell user.');\n *\n * const structuredLlm = llm.withStructuredOutput(Joke, { name: \"Joke\" });\n * const jokeResult = await structuredLlm.invoke(\"Tell me a joke about cats\");\n * console.log(jokeResult);\n * ```\n *\n * ```txt\n * {\n * setup: \"Why don't cats play poker in the jungle?\",\n * punchline: 'Too many cheetahs!',\n * rating: 7\n * }\n * ```\n *
\n *\n *
\n *\n *
\n * Usage Metadata\n *\n * ```typescript\n * const aiMsgForMetadata = await llm.invoke(input);\n * console.log(aiMsgForMetadata.usage_metadata);\n * ```\n *\n * ```txt\n * { input_tokens: 13, output_tokens: 89, total_tokens: 102 }\n * ```\n *
\n *\n *
\n */\nexport class ChatMistralAI<\n CallOptions extends ChatMistralAICallOptions = ChatMistralAICallOptions,\n>\n extends BaseChatModel\n implements ChatMistralAIInput\n{\n // Used for tracing, replace with the same name as your class\n static lc_name() {\n return \"ChatMistralAI\";\n }\n\n lc_namespace = [\"langchain\", \"chat_models\", \"mistralai\"];\n\n model = \"mistral-small-latest\";\n\n apiKey: string;\n\n /**\n * @deprecated use serverURL instead\n */\n endpoint: string;\n\n serverURL?: string;\n\n temperature = 0.7;\n\n streaming = false;\n\n topP = 1;\n\n maxTokens: number;\n\n /**\n * @deprecated use safePrompt instead\n */\n safeMode = false;\n\n safePrompt = false;\n\n randomSeed?: number;\n\n seed?: number;\n\n maxRetries?: number;\n\n lc_serializable = true;\n\n streamUsage = true;\n\n beforeRequestHooks?: Array;\n\n requestErrorHooks?: Array;\n\n responseHooks?: Array;\n\n httpClient?: MistralAIHTTPClient;\n\n presencePenalty?: number;\n\n frequencyPenalty?: number;\n\n numCompletions?: number;\n\n constructor(\n model: MistralAIChatCompletionRequest[\"model\"],\n fields?: Omit\n );\n constructor(fields?: ChatMistralAIInput);\n constructor(\n modelOrFields?: string | ChatMistralAIInput,\n fieldsArg?: Omit\n ) {\n const fields =\n typeof modelOrFields === \"string\"\n ? { ...(fieldsArg ?? {}), model: modelOrFields }\n : (modelOrFields ?? {});\n super(fields);\n this._addVersion(\"@langchain/mistralai\", __PKG_VERSION__);\n const apiKey = fields.apiKey ?? getEnvironmentVariable(\"MISTRAL_API_KEY\");\n if (!apiKey) {\n throw new Error(\n \"API key MISTRAL_API_KEY is missing for MistralAI, but it is required.\"\n );\n }\n this.apiKey = apiKey;\n this.streaming = fields.streaming ?? this.streaming;\n this.serverURL = fields.serverURL ?? this.serverURL;\n this.temperature = fields.temperature ?? this.temperature;\n this.topP = fields.topP ?? this.topP;\n this.maxTokens = fields.maxTokens ?? this.maxTokens;\n this.safePrompt = fields.safePrompt ?? this.safePrompt;\n this.randomSeed = fields.seed ?? fields.randomSeed ?? this.seed;\n this.seed = this.randomSeed;\n this.maxRetries = fields.maxRetries;\n this.httpClient = fields.httpClient;\n this.model = fields.model ?? fields.modelName ?? this.model;\n this.streamUsage = fields.streamUsage ?? this.streamUsage;\n this.beforeRequestHooks =\n fields.beforeRequestHooks ?? this.beforeRequestHooks;\n this.requestErrorHooks = fields.requestErrorHooks ?? this.requestErrorHooks;\n this.responseHooks = fields.responseHooks ?? this.responseHooks;\n this.presencePenalty = fields.presencePenalty ?? this.presencePenalty;\n this.frequencyPenalty = fields.frequencyPenalty ?? this.frequencyPenalty;\n this.numCompletions = fields.numCompletions ?? this.numCompletions;\n this.addAllHooksToHttpClient();\n }\n\n get lc_secrets(): { [key: string]: string } | undefined {\n return {\n apiKey: \"MISTRAL_API_KEY\",\n };\n }\n\n get lc_aliases(): { [key: string]: string } | undefined {\n return {\n apiKey: \"mistral_api_key\",\n };\n }\n\n getLsParams(options: this[\"ParsedCallOptions\"]): LangSmithParams {\n const params = this.invocationParams(options);\n return {\n ls_provider: \"mistral\",\n ls_model_name: this.model,\n ls_model_type: \"chat\",\n ls_temperature: params.temperature ?? undefined,\n ls_max_tokens: params.maxTokens ?? undefined,\n };\n }\n\n _llmType() {\n return \"mistral_ai\";\n }\n\n /**\n * Get the parameters used to invoke the model\n */\n invocationParams(\n options?: this[\"ParsedCallOptions\"]\n ): Omit<\n MistralAIChatCompletionRequest | MistralAIChatCompletionStreamRequest,\n \"messages\"\n > {\n const { response_format, tools, tool_choice } = options ?? {};\n const mistralAITools: Array | undefined = tools?.length\n ? _convertToolToMistralTool(tools)\n : undefined;\n const params: Omit = {\n model: this.model,\n tools: mistralAITools,\n temperature: this.temperature,\n maxTokens: this.maxTokens,\n topP: this.topP,\n randomSeed: this.seed,\n safePrompt: this.safePrompt,\n toolChoice: tool_choice,\n responseFormat: response_format,\n presencePenalty: this.presencePenalty,\n frequencyPenalty: this.frequencyPenalty,\n n: this.numCompletions,\n };\n return params;\n }\n\n override bindTools(\n tools: ChatMistralAIToolType[],\n kwargs?: Partial\n ): Runnable {\n const mistralTools = _convertToolToMistralTool(tools);\n return new RunnableBinding({\n bound: this,\n kwargs: {\n ...(kwargs ?? {}),\n tools: mistralTools,\n } as Partial,\n config: {},\n });\n }\n\n /**\n * Calls the MistralAI API with retry logic in case of failures.\n * @param {ChatRequest} input The input to send to the MistralAI API.\n * @returns {Promise>} The response from the MistralAI API.\n */\n async completionWithRetry(\n input: MistralAIChatCompletionStreamRequest,\n streaming: true\n ): Promise>;\n\n async completionWithRetry(\n input: MistralAIChatCompletionRequest,\n streaming: false\n ): Promise;\n\n async completionWithRetry(\n input:\n | MistralAIChatCompletionRequest\n | MistralAIChatCompletionStreamRequest,\n streaming: boolean\n ): Promise<\n | MistralAIChatCompletionResponse\n | AsyncIterable\n > {\n const caller = new AsyncCaller({\n maxRetries: this.maxRetries,\n });\n const client = new MistralClient({\n apiKey: this.apiKey,\n serverURL: this.serverURL,\n // If httpClient exists, pass it into constructor\n ...(this.httpClient ? { httpClient: this.httpClient } : {}),\n });\n\n return caller.call(async () => {\n try {\n let res:\n | MistralAIChatCompletionResponse\n | AsyncIterable;\n if (streaming) {\n // The Mistral SDK requires `stream: true` to be explicitly set\n // in the request body for streaming to work properly\n res = await client.chat.stream({ ...input, stream: true });\n } else {\n res = await client.chat.complete(input);\n }\n return res;\n // oxlint-disable-next-line @typescript-eslint/no-explicit-any\n } catch (e: any) {\n if (\n e.message?.includes(\"status: 400\") ||\n e.message?.toLowerCase().includes(\"status 400\") ||\n e.message?.includes(\"validation failed\")\n ) {\n e.status = 400;\n }\n throw e;\n }\n });\n }\n\n /** @ignore */\n async _generate(\n messages: BaseMessage[],\n options: this[\"ParsedCallOptions\"],\n runManager?: CallbackManagerForLLMRun\n ): Promise {\n options.signal?.throwIfAborted();\n const tokenUsage: TokenUsage = {};\n const params = this.invocationParams(options);\n const mistralMessages = convertMessagesToMistralMessages(messages);\n const input = {\n ...params,\n messages: mistralMessages,\n };\n\n // Enable streaming for signal controller or timeout due\n // to SDK limitations on canceling requests.\n const shouldStream = options.signal ?? !!options.timeout;\n\n // Handle streaming\n if (this.streaming || shouldStream) {\n const stream = this._streamResponseChunks(messages, options, runManager);\n const finalChunks: Record = {};\n for await (const chunk of stream) {\n const index =\n (chunk.generationInfo as NewTokenIndices)?.completion ?? 0;\n if (finalChunks[index] === undefined) {\n finalChunks[index] = chunk;\n } else {\n finalChunks[index] = finalChunks[index].concat(chunk);\n }\n }\n const generations = Object.entries(finalChunks)\n .sort(([aKey], [bKey]) => parseInt(aKey, 10) - parseInt(bKey, 10))\n .map(([_, value]) => value);\n\n return { generations, llmOutput: { estimatedTokenUsage: tokenUsage } };\n }\n\n // Not streaming, so we can just call the API once.\n const response = await this.completionWithRetry(input, false);\n\n const { completionTokens, promptTokens, totalTokens } =\n response?.usage ?? {};\n\n if (completionTokens) {\n tokenUsage.completionTokens =\n (tokenUsage.completionTokens ?? 0) + completionTokens;\n }\n\n if (promptTokens) {\n tokenUsage.promptTokens = (tokenUsage.promptTokens ?? 0) + promptTokens;\n }\n\n if (totalTokens) {\n tokenUsage.totalTokens = (tokenUsage.totalTokens ?? 0) + totalTokens;\n }\n\n const generations: ChatGeneration[] = [];\n for (const part of response?.choices ?? []) {\n if (\"delta\" in part) {\n throw new Error(\"Delta not supported in non-streaming mode.\");\n }\n if (!(\"message\" in part)) {\n throw new Error(\"No message found in the choice.\");\n }\n let text = part.message?.content ?? \"\";\n if (Array.isArray(text)) {\n text = text[0].type === \"text\" ? text[0].text : \"\";\n }\n const generation: ChatGeneration = {\n text,\n message: mistralAIResponseToChatMessage(part, response?.usage),\n };\n if (part.finishReason) {\n generation.generationInfo = { finishReason: part.finishReason };\n }\n generations.push(generation);\n }\n return {\n generations,\n llmOutput: { tokenUsage },\n };\n }\n\n async *_streamResponseChunks(\n messages: BaseMessage[],\n options: this[\"ParsedCallOptions\"],\n runManager?: CallbackManagerForLLMRun\n ): AsyncGenerator {\n const mistralMessages = convertMessagesToMistralMessages(messages);\n const params = this.invocationParams(options);\n const input = {\n ...params,\n messages: mistralMessages,\n };\n\n 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