@ai-sdk/mistral/dist/index.mjs.map

The **[Mistral provider](https://ai-sdk.dev/providers/ai-sdk-providers/mistral)** for the [AI SDK](https://ai-sdk.dev/docs) contains language model support for the Mistral chat API.

{"version":3,"sources":["../src/mistral-provider.ts","../src/mistral-chat-language-model.ts","../src/convert-to-mistral-chat-messages.ts","../src/get-response-metadata.ts","../src/map-mistral-finish-reason.ts","../src/mistral-chat-options.ts","../src/mistral-error.ts","../src/mistral-prepare-tools.ts","../src/mistral-embedding-model.ts"],"sourcesContent":["import {\n  EmbeddingModelV2,\n  LanguageModelV2,\n  NoSuchModelError,\n  ProviderV2,\n} from '@ai-sdk/provider';\nimport {\n  FetchFunction,\n  loadApiKey,\n  withoutTrailingSlash,\n} from '@ai-sdk/provider-utils';\nimport { MistralChatLanguageModel } from './mistral-chat-language-model';\nimport { MistralChatModelId } from './mistral-chat-options';\nimport { MistralEmbeddingModel } from './mistral-embedding-model';\nimport { MistralEmbeddingModelId } from './mistral-embedding-options';\n\nexport interface MistralProvider extends ProviderV2 {\n  (modelId: MistralChatModelId): LanguageModelV2;\n\n  /**\nCreates a model for text generation.\n*/\n  languageModel(modelId: MistralChatModelId): LanguageModelV2;\n\n  /**\nCreates a model for text generation.\n*/\n  chat(modelId: MistralChatModelId): LanguageModelV2;\n\n  /**\n@deprecated Use `textEmbedding()` instead.\n   */\n  embedding(modelId: MistralEmbeddingModelId): EmbeddingModelV2<string>;\n\n  textEmbedding(modelId: MistralEmbeddingModelId): EmbeddingModelV2<string>;\n\n  textEmbeddingModel: (\n    modelId: MistralEmbeddingModelId,\n  ) => EmbeddingModelV2<string>;\n}\n\nexport interface MistralProviderSettings {\n  /**\nUse a different URL prefix for API calls, e.g. to use proxy servers.\nThe default prefix is `https://api.mistral.ai/v1`.\n   */\n  baseURL?: string;\n\n  /**\nAPI key that is being send using the `Authorization` header.\nIt defaults to the `MISTRAL_API_KEY` environment variable.\n   */\n  apiKey?: string;\n\n  /**\nCustom headers to include in the requests.\n     */\n  headers?: Record<string, string>;\n\n  /**\nCustom fetch implementation. You can use it as a middleware to intercept requests,\nor to provide a custom fetch implementation for e.g. testing.\n    */\n  fetch?: FetchFunction;\n\n  generateId?: () => string;\n}\n\n/**\nCreate a Mistral AI provider instance.\n */\nexport function createMistral(\n  options: MistralProviderSettings = {},\n): MistralProvider {\n  const baseURL =\n    withoutTrailingSlash(options.baseURL) ?? 'https://api.mistral.ai/v1';\n\n  const getHeaders = () => ({\n    Authorization: `Bearer ${loadApiKey({\n      apiKey: options.apiKey,\n      environmentVariableName: 'MISTRAL_API_KEY',\n      description: 'Mistral',\n    })}`,\n    ...options.headers,\n  });\n\n  const createChatModel = (modelId: MistralChatModelId) =>\n    new MistralChatLanguageModel(modelId, {\n      provider: 'mistral.chat',\n      baseURL,\n      headers: getHeaders,\n      fetch: options.fetch,\n      generateId: options.generateId,\n    });\n\n  const createEmbeddingModel = (modelId: MistralEmbeddingModelId) =>\n    new MistralEmbeddingModel(modelId, {\n      provider: 'mistral.embedding',\n      baseURL,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const provider = function (modelId: MistralChatModelId) {\n    if (new.target) {\n      throw new Error(\n        'The Mistral model function cannot be called with the new keyword.',\n      );\n    }\n\n    return createChatModel(modelId);\n  };\n\n  provider.languageModel = createChatModel;\n  provider.chat = createChatModel;\n  provider.embedding = createEmbeddingModel;\n  provider.textEmbedding = createEmbeddingModel;\n  provider.textEmbeddingModel = createEmbeddingModel;\n\n  provider.imageModel = (modelId: string) => {\n    throw new NoSuchModelError({ modelId, modelType: 'imageModel' });\n  };\n\n  return provider;\n}\n\n/**\nDefault Mistral provider instance.\n */\nexport const mistral = createMistral();\n","import {\n  LanguageModelV2,\n  LanguageModelV2CallWarning,\n  LanguageModelV2Content,\n  LanguageModelV2FinishReason,\n  LanguageModelV2StreamPart,\n  LanguageModelV2Usage,\n} from '@ai-sdk/provider';\nimport {\n  combineHeaders,\n  createEventSourceResponseHandler,\n  createJsonResponseHandler,\n  FetchFunction,\n  generateId,\n  injectJsonInstructionIntoMessages,\n  parseProviderOptions,\n  ParseResult,\n  postJsonToApi,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { convertToMistralChatMessages } from './convert-to-mistral-chat-messages';\nimport { getResponseMetadata } from './get-response-metadata';\nimport { mapMistralFinishReason } from './map-mistral-finish-reason';\nimport {\n  MistralChatModelId,\n  mistralLanguageModelOptions,\n} from './mistral-chat-options';\nimport { mistralFailedResponseHandler } from './mistral-error';\nimport { prepareTools } from './mistral-prepare-tools';\n\ntype MistralChatConfig = {\n  provider: string;\n  baseURL: string;\n  headers: () => Record<string, string | undefined>;\n  fetch?: FetchFunction;\n  generateId?: () => string;\n};\n\nexport class MistralChatLanguageModel implements LanguageModelV2 {\n  readonly specificationVersion = 'v2';\n\n  readonly modelId: MistralChatModelId;\n\n  private readonly config: MistralChatConfig;\n  private readonly generateId: () => string;\n\n  constructor(modelId: MistralChatModelId, config: MistralChatConfig) {\n    this.modelId = modelId;\n    this.config = config;\n    this.generateId = config.generateId ?? generateId;\n  }\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  readonly supportedUrls: Record<string, RegExp[]> = {\n    'application/pdf': [/^https:\\/\\/.*$/],\n  };\n\n  private async getArgs({\n    prompt,\n    maxOutputTokens,\n    temperature,\n    topP,\n    topK,\n    frequencyPenalty,\n    presencePenalty,\n    stopSequences,\n    responseFormat,\n    seed,\n    providerOptions,\n    tools,\n    toolChoice,\n  }: Parameters<LanguageModelV2['doGenerate']>[0]) {\n    const warnings: LanguageModelV2CallWarning[] = [];\n\n    const options =\n      (await parseProviderOptions({\n        provider: 'mistral',\n        providerOptions,\n        schema: mistralLanguageModelOptions,\n      })) ?? {};\n\n    if (topK != null) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'topK',\n      });\n    }\n\n    if (frequencyPenalty != null) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'frequencyPenalty',\n      });\n    }\n\n    if (presencePenalty != null) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'presencePenalty',\n      });\n    }\n\n    if (stopSequences != null) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'stopSequences',\n      });\n    }\n\n    const structuredOutputs = options.structuredOutputs ?? true;\n    const strictJsonSchema = options.strictJsonSchema ?? false;\n\n    // For Mistral we need to need to instruct the model to return a JSON object.\n    // https://docs.mistral.ai/capabilities/structured-output/structured_output_overview/\n    if (responseFormat?.type === 'json' && !responseFormat?.schema) {\n      prompt = injectJsonInstructionIntoMessages({\n        messages: prompt,\n        schema: responseFormat.schema,\n      });\n    }\n\n    const baseArgs = {\n      // model id:\n      model: this.modelId,\n\n      // model specific settings:\n      safe_prompt: options.safePrompt,\n\n      // standardized settings:\n      max_tokens: maxOutputTokens,\n      temperature,\n      top_p: topP,\n      random_seed: seed,\n\n      // response format:\n      response_format:\n        responseFormat?.type === 'json'\n          ? structuredOutputs && responseFormat?.schema != null\n            ? {\n                type: 'json_schema',\n                json_schema: {\n                  schema: responseFormat.schema,\n                  strict: strictJsonSchema,\n                  name: responseFormat.name ?? 'response',\n                  description: responseFormat.description,\n                },\n              }\n            : { type: 'json_object' }\n          : undefined,\n\n      // mistral-specific provider options:\n      document_image_limit: options.documentImageLimit,\n      document_page_limit: options.documentPageLimit,\n\n      // messages:\n      messages: convertToMistralChatMessages(prompt),\n    };\n\n    const {\n      tools: mistralTools,\n      toolChoice: mistralToolChoice,\n      toolWarnings,\n    } = prepareTools({\n      tools,\n      toolChoice,\n    });\n\n    return {\n      args: {\n        ...baseArgs,\n        tools: mistralTools,\n        tool_choice: mistralToolChoice,\n      },\n      warnings: [...warnings, ...toolWarnings],\n    };\n  }\n\n  async doGenerate(\n    options: Parameters<LanguageModelV2['doGenerate']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV2['doGenerate']>>> {\n    const { args: body, warnings } = await this.getArgs(options);\n\n    const {\n      responseHeaders,\n      value: response,\n      rawValue: rawResponse,\n    } = await postJsonToApi({\n      url: `${this.config.baseURL}/chat/completions`,\n      headers: combineHeaders(this.config.headers(), options.headers),\n      body,\n      failedResponseHandler: mistralFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        mistralChatResponseSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const choice = response.choices[0];\n    const content: Array<LanguageModelV2Content> = [];\n\n    // process content parts in order to preserve sequence\n    if (\n      choice.message.content != null &&\n      Array.isArray(choice.message.content)\n    ) {\n      for (const part of choice.message.content) {\n        if (part.type === 'thinking') {\n          const reasoningText = extractReasoningContent(part.thinking);\n          if (reasoningText.length > 0) {\n            content.push({ type: 'reasoning', text: reasoningText });\n          }\n        } else if (part.type === 'text') {\n          if (part.text.length > 0) {\n            content.push({ type: 'text', text: part.text });\n          }\n        }\n      }\n    } else {\n      // handle legacy string content\n      const text = extractTextContent(choice.message.content);\n      if (text != null && text.length > 0) {\n        content.push({ type: 'text', text });\n      }\n    }\n\n    // when there is a trailing assistant message, mistral will send the\n    // content of that message again. we skip this repeated content to\n    // avoid duplication, e.g. in continuation mode.\n\n    // tool calls:\n    if (choice.message.tool_calls != null) {\n      for (const toolCall of choice.message.tool_calls) {\n        content.push({\n          type: 'tool-call',\n          toolCallId: toolCall.id,\n          toolName: toolCall.function.name,\n          input: toolCall.function.arguments!,\n        });\n      }\n    }\n\n    return {\n      content,\n      finishReason: mapMistralFinishReason(choice.finish_reason),\n      usage: {\n        inputTokens: response.usage.prompt_tokens,\n        outputTokens: response.usage.completion_tokens,\n        totalTokens: response.usage.total_tokens,\n      },\n      request: { body },\n      response: {\n        ...getResponseMetadata(response),\n        headers: responseHeaders,\n        body: rawResponse,\n      },\n      warnings,\n    };\n  }\n\n  async doStream(\n    options: Parameters<LanguageModelV2['doStream']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV2['doStream']>>> {\n    const { args, warnings } = await this.getArgs(options);\n    const body = { ...args, stream: true };\n\n    const { responseHeaders, value: response } = await postJsonToApi({\n      url: `${this.config.baseURL}/chat/completions`,\n      headers: combineHeaders(this.config.headers(), options.headers),\n      body,\n      failedResponseHandler: mistralFailedResponseHandler,\n      successfulResponseHandler: createEventSourceResponseHandler(\n        mistralChatChunkSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    let finishReason: LanguageModelV2FinishReason = 'unknown';\n    const usage: LanguageModelV2Usage = {\n      inputTokens: undefined,\n      outputTokens: undefined,\n      totalTokens: undefined,\n    };\n\n    let isFirstChunk = true;\n    let activeText = false;\n    let activeReasoningId: string | null = null;\n\n    const generateId = this.generateId;\n\n    return {\n      stream: response.pipeThrough(\n        new TransformStream<\n          ParseResult<z.infer<typeof mistralChatChunkSchema>>,\n          LanguageModelV2StreamPart\n        >({\n          start(controller) {\n            controller.enqueue({ type: 'stream-start', warnings });\n          },\n\n          transform(chunk, controller) {\n            // Emit raw chunk if requested (before anything else)\n            if (options.includeRawChunks) {\n              controller.enqueue({ type: 'raw', rawValue: chunk.rawValue });\n            }\n\n            if (!chunk.success) {\n              controller.enqueue({ type: 'error', error: chunk.error });\n              return;\n            }\n\n            const value = chunk.value;\n\n            if (isFirstChunk) {\n              isFirstChunk = false;\n\n              controller.enqueue({\n                type: 'response-metadata',\n                ...getResponseMetadata(value),\n              });\n            }\n\n            if (value.usage != null) {\n              usage.inputTokens = value.usage.prompt_tokens;\n              usage.outputTokens = value.usage.completion_tokens;\n              usage.totalTokens = value.usage.total_tokens;\n            }\n\n            const choice = value.choices[0];\n            const delta = choice.delta;\n\n            const textContent = extractTextContent(delta.content);\n\n            if (delta.content != null && Array.isArray(delta.content)) {\n              for (const part of delta.content) {\n                if (part.type === 'thinking') {\n                  const reasoningDelta = extractReasoningContent(part.thinking);\n                  if (reasoningDelta.length > 0) {\n                    if (activeReasoningId == null) {\n                      // end any active text before starting reasoning\n                      if (activeText) {\n                        controller.enqueue({ type: 'text-end', id: '0' });\n                        activeText = false;\n                      }\n\n                      activeReasoningId = generateId();\n                      controller.enqueue({\n                        type: 'reasoning-start',\n                        id: activeReasoningId,\n                      });\n                    }\n                    controller.enqueue({\n                      type: 'reasoning-delta',\n                      id: activeReasoningId,\n                      delta: reasoningDelta,\n                    });\n                  }\n                }\n              }\n            }\n\n            if (textContent != null && textContent.length > 0) {\n              if (!activeText) {\n                // if we were in reasoning mode, end it before starting text\n                if (activeReasoningId != null) {\n                  controller.enqueue({\n                    type: 'reasoning-end',\n                    id: activeReasoningId,\n                  });\n                  activeReasoningId = null;\n                }\n                controller.enqueue({ type: 'text-start', id: '0' });\n                activeText = true;\n              }\n\n              controller.enqueue({\n                type: 'text-delta',\n                id: '0',\n                delta: textContent,\n              });\n            }\n\n            if (delta?.tool_calls != null) {\n              for (const toolCall of delta.tool_calls) {\n                const toolCallId = toolCall.id;\n                const toolName = toolCall.function.name;\n                const input = toolCall.function.arguments;\n\n                controller.enqueue({\n                  type: 'tool-input-start',\n                  id: toolCallId,\n                  toolName,\n                });\n\n                controller.enqueue({\n                  type: 'tool-input-delta',\n                  id: toolCallId,\n                  delta: input,\n                });\n\n                controller.enqueue({\n                  type: 'tool-input-end',\n                  id: toolCallId,\n                });\n\n                controller.enqueue({\n                  type: 'tool-call',\n                  toolCallId,\n                  toolName,\n                  input,\n                });\n              }\n            }\n\n            if (choice.finish_reason != null) {\n              finishReason = mapMistralFinishReason(choice.finish_reason);\n            }\n          },\n\n          flush(controller) {\n            if (activeReasoningId != null) {\n              controller.enqueue({\n                type: 'reasoning-end',\n                id: activeReasoningId,\n              });\n            }\n            if (activeText) {\n              controller.enqueue({ type: 'text-end', id: '0' });\n            }\n\n            controller.enqueue({\n              type: 'finish',\n              finishReason,\n              usage,\n            });\n          },\n        }),\n      ),\n      request: { body },\n      response: { headers: responseHeaders },\n    };\n  }\n}\n\nfunction extractReasoningContent(\n  thinking: Array<{ type: string; text: string }>,\n) {\n  return thinking\n    .filter(chunk => chunk.type === 'text')\n    .map(chunk => chunk.text)\n    .join('');\n}\n\nfunction extractTextContent(content: z.infer<typeof mistralContentSchema>) {\n  if (typeof content === 'string') {\n    return content;\n  }\n\n  if (content == null) {\n    return undefined;\n  }\n\n  const textContent: string[] = [];\n\n  for (const chunk of content) {\n    const { type } = chunk;\n\n    switch (type) {\n      case 'text':\n        textContent.push(chunk.text);\n        break;\n      case 'thinking':\n      case 'image_url':\n      case 'reference':\n        // thinking, image content, and reference content are currently ignored\n        break;\n      default: {\n        const _exhaustiveCheck: never = type;\n        throw new Error(`Unsupported type: ${_exhaustiveCheck}`);\n      }\n    }\n  }\n\n  return textContent.length ? textContent.join('') : undefined;\n}\n\nconst mistralContentSchema = z\n  .union([\n    z.string(),\n    z.array(\n      z.discriminatedUnion('type', [\n        z.object({\n          type: z.literal('text'),\n          text: z.string(),\n        }),\n        z.object({\n          type: z.literal('image_url'),\n          image_url: z.union([\n            z.string(),\n            z.object({\n              url: z.string(),\n              detail: z.string().nullable(),\n            }),\n          ]),\n        }),\n        z.object({\n          type: z.literal('reference'),\n          reference_ids: z.array(z.number()),\n        }),\n        z.object({\n          type: z.literal('thinking'),\n          thinking: z.array(\n            z.object({\n              type: z.literal('text'),\n              text: z.string(),\n            }),\n          ),\n        }),\n      ]),\n    ),\n  ])\n  .nullish();\n\nconst mistralUsageSchema = z.object({\n  prompt_tokens: z.number(),\n  completion_tokens: z.number(),\n  total_tokens: z.number(),\n});\n\n// limited version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst mistralChatResponseSchema = z.object({\n  id: z.string().nullish(),\n  created: z.number().nullish(),\n  model: z.string().nullish(),\n  choices: z.array(\n    z.object({\n      message: z.object({\n        role: z.literal('assistant'),\n        content: mistralContentSchema,\n        tool_calls: z\n          .array(\n            z.object({\n              id: z.string(),\n              function: z.object({ name: z.string(), arguments: z.string() }),\n            }),\n          )\n          .nullish(),\n      }),\n      index: z.number(),\n      finish_reason: z.string().nullish(),\n    }),\n  ),\n  object: z.literal('chat.completion'),\n  usage: mistralUsageSchema,\n});\n\n// limited version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst mistralChatChunkSchema = z.object({\n  id: z.string().nullish(),\n  created: z.number().nullish(),\n  model: z.string().nullish(),\n  choices: z.array(\n    z.object({\n      delta: z.object({\n        role: z.enum(['assistant']).optional(),\n        content: mistralContentSchema,\n        tool_calls: z\n          .array(\n            z.object({\n              id: z.string(),\n              function: z.object({ name: z.string(), arguments: z.string() }),\n            }),\n          )\n          .nullish(),\n      }),\n      finish_reason: z.string().nullish(),\n      index: z.number(),\n    }),\n  ),\n  usage: mistralUsageSchema.nullish(),\n});\n","import {\n  LanguageModelV2Prompt,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\nimport { MistralPrompt } from './mistral-chat-prompt';\nimport { convertToBase64 } from '@ai-sdk/provider-utils';\n\nexport function convertToMistralChatMessages(\n  prompt: LanguageModelV2Prompt,\n): MistralPrompt {\n  const messages: MistralPrompt = [];\n\n  for (let i = 0; i < prompt.length; i++) {\n    const { role, content } = prompt[i];\n    const isLastMessage = i === prompt.length - 1;\n\n    switch (role) {\n      case 'system': {\n        messages.push({ role: 'system', content });\n        break;\n      }\n\n      case 'user': {\n        messages.push({\n          role: 'user',\n          content: content.map(part => {\n            switch (part.type) {\n              case 'text': {\n                return { type: 'text', text: part.text };\n              }\n\n              case 'file': {\n                if (part.mediaType.startsWith('image/')) {\n                  const mediaType =\n                    part.mediaType === 'image/*'\n                      ? 'image/jpeg'\n                      : part.mediaType;\n\n                  return {\n                    type: 'image_url',\n                    image_url:\n                      part.data instanceof URL\n                        ? part.data.toString()\n                        : `data:${mediaType};base64,${convertToBase64(part.data)}`,\n                  };\n                } else if (part.mediaType === 'application/pdf') {\n                  return {\n                    type: 'document_url',\n                    document_url: part.data.toString(),\n                  };\n                } else {\n                  throw new UnsupportedFunctionalityError({\n                    functionality:\n                      'Only images and PDF file parts are supported',\n                  });\n                }\n              }\n            }\n          }),\n        });\n        break;\n      }\n\n      case 'assistant': {\n        let text = '';\n        const toolCalls: Array<{\n          id: string;\n          type: 'function';\n          function: { name: string; arguments: string };\n        }> = [];\n\n        for (const part of content) {\n          switch (part.type) {\n            case 'text': {\n              text += part.text;\n              break;\n            }\n            case 'tool-call': {\n              toolCalls.push({\n                id: part.toolCallId,\n                type: 'function',\n                function: {\n                  name: part.toolName,\n                  arguments: JSON.stringify(part.input),\n                },\n              });\n              break;\n            }\n            case 'reasoning': {\n              text += part.text;\n              break;\n            }\n            default: {\n              throw new Error(\n                `Unsupported content type in assistant message: ${part.type}`,\n              );\n            }\n          }\n        }\n\n        messages.push({\n          role: 'assistant',\n          content: text,\n          prefix: isLastMessage ? true : undefined,\n          tool_calls: toolCalls.length > 0 ? toolCalls : undefined,\n        });\n\n        break;\n      }\n      case 'tool': {\n        for (const toolResponse of content) {\n          const output = toolResponse.output;\n\n          let contentValue: string;\n          switch (output.type) {\n            case 'text':\n            case 'error-text':\n              contentValue = output.value;\n              break;\n            case 'content':\n            case 'json':\n            case 'error-json':\n              contentValue = JSON.stringify(output.value);\n              break;\n          }\n\n          messages.push({\n            role: 'tool',\n            name: toolResponse.toolName,\n            tool_call_id: toolResponse.toolCallId,\n            content: contentValue,\n          });\n        }\n        break;\n      }\n      default: {\n        const _exhaustiveCheck: never = role;\n        throw new Error(`Unsupported role: ${_exhaustiveCheck}`);\n      }\n    }\n  }\n\n  return messages;\n}\n","export function getResponseMetadata({\n  id,\n  model,\n  created,\n}: {\n  id?: string | undefined | null;\n  created?: number | undefined | null;\n  model?: string | undefined | null;\n}) {\n  return {\n    id: id ?? undefined,\n    modelId: model ?? undefined,\n    timestamp: created != null ? new Date(created * 1000) : undefined,\n  };\n}\n","import { LanguageModelV2FinishReason } from '@ai-sdk/provider';\n\nexport function mapMistralFinishReason(\n  finishReason: string | null | undefined,\n): LanguageModelV2FinishReason {\n  switch (finishReason) {\n    case 'stop':\n      return 'stop';\n    case 'length':\n    case 'model_length':\n      return 'length';\n    case 'tool_calls':\n      return 'tool-calls';\n    default:\n      return 'unknown';\n  }\n}\n","import { z } from 'zod/v4';\n\n// https://docs.mistral.ai/getting-started/models/models_overview/\nexport type MistralChatModelId =\n  // premier\n  | 'ministral-3b-latest'\n  | 'ministral-8b-latest'\n  | 'mistral-large-latest'\n  | 'mistral-medium-latest'\n  | 'mistral-medium-2508'\n  | 'mistral-medium-2505'\n  | 'mistral-small-latest'\n  | 'pixtral-large-latest'\n  // reasoning models\n  | 'magistral-small-2507'\n  | 'magistral-medium-2507'\n  | 'magistral-small-2506'\n  | 'magistral-medium-2506'\n  // free\n  | 'pixtral-12b-2409'\n  // legacy\n  | 'open-mistral-7b'\n  | 'open-mixtral-8x7b'\n  | 'open-mixtral-8x22b'\n  | (string & {});\n\nexport const mistralLanguageModelOptions = z.object({\n  /**\nWhether to inject a safety prompt before all conversations.\n\nDefaults to `false`.\n   */\n  safePrompt: z.boolean().optional(),\n\n  documentImageLimit: z.number().optional(),\n  documentPageLimit: z.number().optional(),\n\n  /**\n   * Whether to use structured outputs.\n   *\n   * @default true\n   */\n  structuredOutputs: z.boolean().optional(),\n\n  /**\n   * Whether to use strict JSON schema validation.\n   *\n   * @default false\n   */\n  strictJsonSchema: z.boolean().optional(),\n});\n\nexport type MistralLanguageModelOptions = z.infer<\n  typeof mistralLanguageModelOptions\n>;\n","import { createJsonErrorResponseHandler } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nconst mistralErrorDataSchema = z.object({\n  object: z.literal('error'),\n  message: z.string(),\n  type: z.string(),\n  param: z.string().nullable(),\n  code: z.string().nullable(),\n});\n\nexport type MistralErrorData = z.infer<typeof mistralErrorDataSchema>;\n\nexport const mistralFailedResponseHandler = createJsonErrorResponseHandler({\n  errorSchema: mistralErrorDataSchema,\n  errorToMessage: data => data.message,\n});\n","import {\n  LanguageModelV2CallOptions,\n  LanguageModelV2CallWarning,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\nimport { MistralToolChoice } from './mistral-chat-prompt';\n\nexport function prepareTools({\n  tools,\n  toolChoice,\n}: {\n  tools: LanguageModelV2CallOptions['tools'];\n  toolChoice?: LanguageModelV2CallOptions['toolChoice'];\n}): {\n  tools:\n    | Array<{\n        type: 'function';\n        function: {\n          name: string;\n          description: string | undefined;\n          parameters: unknown;\n        };\n      }>\n    | undefined;\n  toolChoice: MistralToolChoice | undefined;\n  toolWarnings: LanguageModelV2CallWarning[];\n} {\n  // when the tools array is empty, change it to undefined to prevent errors:\n  tools = tools?.length ? tools : undefined;\n\n  const toolWarnings: LanguageModelV2CallWarning[] = [];\n\n  if (tools == null) {\n    return { tools: undefined, toolChoice: undefined, toolWarnings };\n  }\n\n  const mistralTools: Array<{\n    type: 'function';\n    function: {\n      name: string;\n      description: string | undefined;\n      parameters: unknown;\n    };\n  }> = [];\n\n  for (const tool of tools) {\n    if (tool.type === 'provider-defined') {\n      toolWarnings.push({ type: 'unsupported-tool', tool });\n    } else {\n      mistralTools.push({\n        type: 'function',\n        function: {\n          name: tool.name,\n          description: tool.description,\n          parameters: tool.inputSchema,\n        },\n      });\n    }\n  }\n\n  if (toolChoice == null) {\n    return { tools: mistralTools, toolChoice: undefined, toolWarnings };\n  }\n\n  const type = toolChoice.type;\n\n  switch (type) {\n    case 'auto':\n    case 'none':\n      return { tools: mistralTools, toolChoice: type, toolWarnings };\n    case 'required':\n      return { tools: mistralTools, toolChoice: 'any', toolWarnings };\n\n    // mistral does not support tool mode directly,\n    // so we filter the tools and force the tool choice through 'any'\n    case 'tool':\n      return {\n        tools: mistralTools.filter(\n          tool => tool.function.name === toolChoice.toolName,\n        ),\n        toolChoice: 'any',\n        toolWarnings,\n      };\n    default: {\n      const _exhaustiveCheck: never = type;\n      throw new UnsupportedFunctionalityError({\n        functionality: `tool choice type: ${_exhaustiveCheck}`,\n      });\n    }\n  }\n}\n","import {\n  EmbeddingModelV2,\n  TooManyEmbeddingValuesForCallError,\n} from '@ai-sdk/provider';\nimport {\n  combineHeaders,\n  createJsonResponseHandler,\n  FetchFunction,\n  postJsonToApi,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { MistralEmbeddingModelId } from './mistral-embedding-options';\nimport { mistralFailedResponseHandler } from './mistral-error';\n\ntype MistralEmbeddingConfig = {\n  provider: string;\n  baseURL: string;\n  headers: () => Record<string, string | undefined>;\n  fetch?: FetchFunction;\n};\n\nexport class MistralEmbeddingModel implements EmbeddingModelV2<string> {\n  readonly specificationVersion = 'v2';\n  readonly modelId: MistralEmbeddingModelId;\n  readonly maxEmbeddingsPerCall = 32;\n  readonly supportsParallelCalls = false;\n\n  private readonly config: MistralEmbeddingConfig;\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  constructor(\n    modelId: MistralEmbeddingModelId,\n    config: MistralEmbeddingConfig,\n  ) {\n    this.modelId = modelId;\n    this.config = config;\n  }\n\n  async doEmbed({\n    values,\n    abortSignal,\n    headers,\n  }: Parameters<EmbeddingModelV2<string>['doEmbed']>[0]): Promise<\n    Awaited<ReturnType<EmbeddingModelV2<string>['doEmbed']>>\n  > {\n    if (values.length > this.maxEmbeddingsPerCall) {\n      throw new TooManyEmbeddingValuesForCallError({\n        provider: this.provider,\n        modelId: this.modelId,\n        maxEmbeddingsPerCall: this.maxEmbeddingsPerCall,\n        values,\n      });\n    }\n\n    const {\n      responseHeaders,\n      value: response,\n      rawValue,\n    } = await postJsonToApi({\n      url: `${this.config.baseURL}/embeddings`,\n      headers: combineHeaders(this.config.headers(), headers),\n      body: {\n        model: this.modelId,\n        input: values,\n        encoding_format: 'float',\n      },\n      failedResponseHandler: mistralFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        MistralTextEmbeddingResponseSchema,\n      ),\n      abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    return {\n      embeddings: response.data.map(item => item.embedding),\n      usage: response.usage\n        ? { tokens: response.usage.prompt_tokens }\n        : undefined,\n      response: { headers: responseHeaders, body: rawValue },\n    };\n  }\n}\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst MistralTextEmbeddingResponseSchema = z.object({\n  data: z.array(z.object({ embedding: z.array(z.number()) })),\n  usage: z.object({ prompt_tokens: z.number() 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