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

The **[Mistral provider](https://sdk.khulnasoft.com/providers/ai-toolkit-providers/mistral)** for the [AI TOOLKIT](https://sdk.khulnasoft.com/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/map-mistral-finish-reason.ts","../src/mistral-error.ts","../src/get-response-metadata.ts","../src/mistral-prepare-tools.ts","../src/mistral-embedding-model.ts"],"sourcesContent":["import {\n  EmbeddingModelV1,\n  LanguageModelV1,\n  ProviderV1,\n} from '@ai-toolkit/provider';\nimport {\n  FetchFunction,\n  loadApiKey,\n  withoutTrailingSlash,\n} from '@ai-toolkit/provider-utils';\nimport { MistralChatLanguageModel } from './mistral-chat-language-model';\nimport {\n  MistralChatModelId,\n  MistralChatSettings,\n} from './mistral-chat-settings';\nimport { MistralEmbeddingModel } from './mistral-embedding-model';\nimport {\n  MistralEmbeddingModelId,\n  MistralEmbeddingSettings,\n} from './mistral-embedding-settings';\n\nexport interface MistralProvider extends ProviderV1 {\n  (\n    modelId: MistralChatModelId,\n    settings?: MistralChatSettings,\n  ): LanguageModelV1;\n\n  /**\nCreates a model for text generation.\n*/\n  languageModel(\n    modelId: MistralChatModelId,\n    settings?: MistralChatSettings,\n  ): LanguageModelV1;\n\n  /**\nCreates a model for text generation.\n*/\n  chat(\n    modelId: MistralChatModelId,\n    settings?: MistralChatSettings,\n  ): LanguageModelV1;\n\n  /**\n@deprecated Use `textEmbeddingModel()` instead.\n   */\n  embedding(\n    modelId: MistralEmbeddingModelId,\n    settings?: MistralEmbeddingSettings,\n  ): EmbeddingModelV1<string>;\n\n  /**\n@deprecated Use `textEmbeddingModel()` instead.\n   */\n  textEmbedding(\n    modelId: MistralEmbeddingModelId,\n    settings?: MistralEmbeddingSettings,\n  ): EmbeddingModelV1<string>;\n\n  textEmbeddingModel: (\n    modelId: MistralEmbeddingModelId,\n    settings?: MistralEmbeddingSettings,\n  ) => EmbeddingModelV1<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\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 = (\n    modelId: MistralChatModelId,\n    settings: MistralChatSettings = {},\n  ) =>\n    new MistralChatLanguageModel(modelId, settings, {\n      provider: 'mistral.chat',\n      baseURL,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const createEmbeddingModel = (\n    modelId: MistralEmbeddingModelId,\n    settings: MistralEmbeddingSettings = {},\n  ) =>\n    new MistralEmbeddingModel(modelId, settings, {\n      provider: 'mistral.embedding',\n      baseURL,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const provider = function (\n    modelId: MistralChatModelId,\n    settings?: MistralChatSettings,\n  ) {\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, settings);\n  };\n\n  provider.languageModel = createChatModel;\n  provider.chat = createChatModel;\n  provider.embedding = createEmbeddingModel;\n  provider.textEmbedding = createEmbeddingModel;\n  provider.textEmbeddingModel = createEmbeddingModel;\n\n  return provider;\n}\n\n/**\nDefault Mistral provider instance.\n */\nexport const mistral = createMistral();\n","import {\n  LanguageModelV1,\n  LanguageModelV1CallWarning,\n  LanguageModelV1FinishReason,\n  LanguageModelV1StreamPart,\n} from '@ai-toolkit/provider';\nimport {\n  FetchFunction,\n  ParseResult,\n  combineHeaders,\n  createEventSourceResponseHandler,\n  createJsonResponseHandler,\n  postJsonToApi,\n} from '@ai-toolkit/provider-utils';\nimport { z } from 'zod';\nimport { convertToMistralChatMessages } from './convert-to-mistral-chat-messages';\nimport { mapMistralFinishReason } from './map-mistral-finish-reason';\nimport {\n  MistralChatModelId,\n  MistralChatSettings,\n} from './mistral-chat-settings';\nimport { mistralFailedResponseHandler } from './mistral-error';\nimport { getResponseMetadata } from './get-response-metadata';\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};\n\nexport class MistralChatLanguageModel implements LanguageModelV1 {\n  readonly specificationVersion = 'v1';\n  readonly defaultObjectGenerationMode = 'json';\n  readonly supportsImageUrls = false;\n\n  readonly modelId: MistralChatModelId;\n  readonly settings: MistralChatSettings;\n\n  private readonly config: MistralChatConfig;\n\n  constructor(\n    modelId: MistralChatModelId,\n    settings: MistralChatSettings,\n    config: MistralChatConfig,\n  ) {\n    this.modelId = modelId;\n    this.settings = settings;\n    this.config = config;\n  }\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  supportsUrl(url: URL): boolean {\n    return url.protocol === 'https:';\n  }\n\n  private getArgs({\n    mode,\n    prompt,\n    maxTokens,\n    temperature,\n    topP,\n    topK,\n    frequencyPenalty,\n    presencePenalty,\n    stopSequences,\n    responseFormat,\n    seed,\n    providerMetadata,\n  }: Parameters<LanguageModelV1['doGenerate']>[0]) {\n    const type = mode.type;\n\n    const warnings: LanguageModelV1CallWarning[] = [];\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    if (\n      responseFormat != null &&\n      responseFormat.type === 'json' &&\n      responseFormat.schema != null\n    ) {\n      warnings.push({\n        type: 'unsupported-setting',\n        setting: 'responseFormat',\n        details: 'JSON response format schema is not supported',\n      });\n    }\n\n    const baseArgs = {\n      // model id:\n      model: this.modelId,\n\n      // model specific settings:\n      safe_prompt: this.settings.safePrompt,\n\n      // standardized settings:\n      max_tokens: maxTokens,\n      temperature,\n      top_p: topP,\n      random_seed: seed,\n\n      // response format:\n      response_format:\n        responseFormat?.type === 'json' ? { type: 'json_object' } : undefined,\n\n      // mistral-specific provider options:\n      document_image_limit: providerMetadata?.mistral?.documentImageLimit,\n      document_page_limit: providerMetadata?.mistral?.documentPageLimit,\n\n      // messages:\n      messages: convertToMistralChatMessages(prompt),\n    };\n\n    switch (type) {\n      case 'regular': {\n        const { tools, tool_choice, toolWarnings } = prepareTools(mode);\n\n        return {\n          args: { ...baseArgs, tools, tool_choice },\n          warnings: [...warnings, ...toolWarnings],\n        };\n      }\n\n      case 'object-json': {\n        return {\n          args: {\n            ...baseArgs,\n            response_format: { type: 'json_object' },\n          },\n          warnings,\n        };\n      }\n\n      case 'object-tool': {\n        return {\n          args: {\n            ...baseArgs,\n            tool_choice: 'any',\n            tools: [{ type: 'function', function: mode.tool }],\n          },\n          warnings,\n        };\n      }\n\n      default: {\n        const _exhaustiveCheck: never = type;\n        throw new Error(`Unsupported type: ${_exhaustiveCheck}`);\n      }\n    }\n  }\n\n  async doGenerate(\n    options: Parameters<LanguageModelV1['doGenerate']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV1['doGenerate']>>> {\n    const { args, warnings } = this.getArgs(options);\n\n    const {\n      responseHeaders,\n      value: response,\n      rawValue: rawResponse,\n    } = await postJsonToApi({\n      url: `${this.config.baseURL}/chat/completions`,\n      headers: combineHeaders(this.config.headers(), options.headers),\n      body: args,\n      failedResponseHandler: mistralFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        mistralChatResponseSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const { messages: rawPrompt, ...rawSettings } = args;\n    const choice = response.choices[0];\n\n    // extract text content.\n    // image content or reference content is currently ignored.\n    let text = extractTextContent(choice.message.content);\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    const lastMessage = rawPrompt[rawPrompt.length - 1];\n    if (\n      lastMessage.role === 'assistant' &&\n      text?.startsWith(lastMessage.content)\n    ) {\n      text = text.slice(lastMessage.content.length);\n    }\n\n    return {\n      text,\n      toolCalls: choice.message.tool_calls?.map(toolCall => ({\n        toolCallType: 'function',\n        toolCallId: toolCall.id,\n        toolName: toolCall.function.name,\n        args: toolCall.function.arguments!,\n      })),\n      finishReason: mapMistralFinishReason(choice.finish_reason),\n      usage: {\n        promptTokens: response.usage.prompt_tokens,\n        completionTokens: response.usage.completion_tokens,\n      },\n      rawCall: { rawPrompt, rawSettings },\n      rawResponse: {\n        headers: responseHeaders,\n        body: rawResponse,\n      },\n      request: { body: JSON.stringify(args) },\n      response: getResponseMetadata(response),\n      warnings,\n    };\n  }\n\n  async doStream(\n    options: Parameters<LanguageModelV1['doStream']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV1['doStream']>>> {\n    const { args, warnings } = this.getArgs(options);\n\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    const { messages: rawPrompt, ...rawSettings } = args;\n\n    let finishReason: LanguageModelV1FinishReason = 'unknown';\n    let usage: { promptTokens: number; completionTokens: number } = {\n      promptTokens: Number.NaN,\n      completionTokens: Number.NaN,\n    };\n    let chunkNumber = 0;\n    let trimLeadingSpace = false;\n\n    return {\n      stream: response.pipeThrough(\n        new TransformStream<\n          ParseResult<z.infer<typeof mistralChatChunkSchema>>,\n          LanguageModelV1StreamPart\n        >({\n          transform(chunk, controller) {\n            if (!chunk.success) {\n              controller.enqueue({ type: 'error', error: chunk.error });\n              return;\n            }\n\n            chunkNumber++;\n\n            const value = chunk.value;\n\n            if (chunkNumber === 1) {\n              controller.enqueue({\n                type: 'response-metadata',\n                ...getResponseMetadata(value),\n              });\n            }\n\n            if (value.usage != null) {\n              usage = {\n                promptTokens: value.usage.prompt_tokens,\n                completionTokens: value.usage.completion_tokens,\n              };\n            }\n\n            const choice = value.choices[0];\n\n            if (choice?.finish_reason != null) {\n              finishReason = mapMistralFinishReason(choice.finish_reason);\n            }\n\n            if (choice?.delta == null) {\n              return;\n            }\n\n            const delta = choice.delta;\n\n            // extract text content.\n            // image content or reference content is currently ignored.\n            const textContent = extractTextContent(delta.content);\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            if (chunkNumber <= 2) {\n              const lastMessage = rawPrompt[rawPrompt.length - 1];\n\n              if (\n                lastMessage.role === 'assistant' &&\n                textContent === lastMessage.content.trimEnd()\n              ) {\n                // Mistral moves the trailing space from the prefix to the next chunk.\n                // We trim the leading space to avoid duplication.\n                if (textContent.length < lastMessage.content.length) {\n                  trimLeadingSpace = true;\n                }\n\n                // skip the repeated content:\n                return;\n              }\n            }\n\n            if (textContent != null) {\n              controller.enqueue({\n                type: 'text-delta',\n                textDelta: trimLeadingSpace\n                  ? textContent.trimStart()\n                  : textContent,\n              });\n\n              trimLeadingSpace = false;\n            }\n\n            if (delta.tool_calls != null) {\n              for (const toolCall of delta.tool_calls) {\n                // mistral tool calls come in one piece:\n                controller.enqueue({\n                  type: 'tool-call-delta',\n                  toolCallType: 'function',\n                  toolCallId: toolCall.id,\n                  toolName: toolCall.function.name,\n                  argsTextDelta: toolCall.function.arguments,\n                });\n                controller.enqueue({\n                  type: 'tool-call',\n                  toolCallType: 'function',\n                  toolCallId: toolCall.id,\n                  toolName: toolCall.function.name,\n                  args: toolCall.function.arguments,\n                });\n              }\n            }\n          },\n\n          flush(controller) {\n            controller.enqueue({ type: 'finish', finishReason, usage });\n          },\n        }),\n      ),\n      rawCall: { rawPrompt, rawSettings },\n      rawResponse: { headers: responseHeaders },\n      request: { body: JSON.stringify(body) },\n      warnings,\n    };\n  }\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 'image_url':\n      case 'reference':\n        // image content or reference content is 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      ]),\n    ),\n  ])\n  .nullish();\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: z.object({\n    prompt_tokens: z.number(),\n    completion_tokens: z.number(),\n  }),\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: z\n    .object({\n      prompt_tokens: z.number(),\n      completion_tokens: z.number(),\n    })\n    .nullish(),\n});\n","import {\n  LanguageModelV1Prompt,\n  UnsupportedFunctionalityError,\n} from '@ai-toolkit/provider';\nimport { convertUint8ArrayToBase64 } from '@ai-toolkit/provider-utils';\nimport { MistralPrompt } from './mistral-chat-prompt';\n\nexport function convertToMistralChatMessages(\n  prompt: LanguageModelV1Prompt,\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              case 'image': {\n                return {\n                  type: 'image_url',\n                  image_url:\n                    part.image instanceof URL\n                      ? part.image.toString()\n                      : `data:${\n                          part.mimeType ?? 'image/jpeg'\n                        };base64,${convertUint8ArrayToBase64(part.image)}`,\n                };\n              }\n              case 'file': {\n                if (!(part.data instanceof URL)) {\n                  throw new UnsupportedFunctionalityError({\n                    functionality: 'File content parts in user messages',\n                  });\n                }\n\n                switch (part.mimeType) {\n                  case 'application/pdf': {\n                    return {\n                      type: 'document_url',\n                      document_url: part.data.toString(),\n                    };\n                  }\n                  default: {\n                    throw new UnsupportedFunctionalityError({\n                      functionality:\n                        'Only PDF files are supported in user messages',\n                    });\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 'redacted-reasoning':\n            case 'reasoning': {\n              break; // ignored\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.args),\n                },\n              });\n              break;\n            }\n            default: {\n              const _exhaustiveCheck: never = part;\n              throw new Error(`Unsupported part: ${_exhaustiveCheck}`);\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          messages.push({\n            role: 'tool',\n            name: toolResponse.toolName,\n            content: JSON.stringify(toolResponse.result),\n            tool_call_id: toolResponse.toolCallId,\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","import { LanguageModelV1FinishReason } from '@ai-toolkit/provider';\n\nexport function mapMistralFinishReason(\n  finishReason: string | null | undefined,\n): LanguageModelV1FinishReason {\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 { createJsonErrorResponseHandler } from '@ai-toolkit/provider-utils';\nimport { z } from 'zod';\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","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 {\n  LanguageModelV1,\n  LanguageModelV1CallWarning,\n  UnsupportedFunctionalityError,\n} from '@ai-toolkit/provider';\n\nexport function prepareTools(\n  mode: Parameters<LanguageModelV1['doGenerate']>[0]['mode'] & {\n    type: 'regular';\n  },\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  tool_choice:\n    | { type: 'function'; function: { name: string } }\n    | 'auto'\n    | 'none'\n    | 'any'\n    | undefined;\n  toolWarnings: LanguageModelV1CallWarning[];\n} {\n  // when the tools array is empty, change it to undefined to prevent errors:\n  const tools = mode.tools?.length ? mode.tools : undefined;\n  const toolWarnings: LanguageModelV1CallWarning[] = [];\n\n  if (tools == null) {\n    return { tools: undefined, tool_choice: 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.parameters,\n        },\n      });\n    }\n  }\n\n  const toolChoice = mode.toolChoice;\n\n  if (toolChoice == null) {\n    return { tools: mistralTools, tool_choice: undefined, toolWarnings };\n  }\n\n  const type = toolChoice.type;\n\n  switch (type) {\n    case 'auto':\n    case 'none':\n      return { tools: mistralTools, tool_choice: type, toolWarnings };\n    case 'required':\n      return { tools: mistralTools, tool_choice: '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        tool_choice: 'any',\n        toolWarnings,\n      };\n    default: {\n      const _exhaustiveCheck: never = type;\n      throw new UnsupportedFunctionalityError({\n        functionality: `Unsupported tool choice type: ${_exhaustiveCheck}`,\n      });\n    }\n  }\n}\n","import {\n  EmbeddingModelV1,\n  TooManyEmbeddingValuesForCallError,\n} from '@ai-toolkit/provider';\nimport {\n  combineHeaders,\n  createJsonResponseHandler,\n  FetchFunction,\n  postJsonToApi,\n} from '@ai-toolkit/provider-utils';\nimport { z } from 'zod';\nimport {\n  MistralEmbeddingModelId,\n  MistralEmbeddingSettings,\n} from './mistral-embedding-settings';\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 EmbeddingModelV1<string> {\n  readonly specificationVersion = 'v1';\n  readonly modelId: MistralEmbeddingModelId;\n\n  private readonly config: MistralEmbeddingConfig;\n  private readonly settings: MistralEmbeddingSettings;\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  get maxEmbeddingsPerCall(): number {\n    return this.settings.maxEmbeddingsPerCall ?? 32;\n  }\n\n  get supportsParallelCalls(): boolean {\n    // Parallel calls are technically possible,\n    // but I have been hitting rate limits and disable them for now.\n    return this.settings.supportsParallelCalls ?? false;\n  }\n\n  constructor(\n    modelId: MistralEmbeddingModelId,\n    settings: MistralEmbeddingSettings,\n    config: MistralEmbeddingConfig,\n  ) {\n    this.modelId = modelId;\n    this.settings = settings;\n    this.config = config;\n  }\n\n  async doEmbed({\n    values,\n    abortSignal,\n    headers,\n  }: Parameters<EmbeddingModelV1<string>['doEmbed']>[0]): Promise<\n    Awaited<ReturnType<EmbeddingModelV1<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 { responseHeaders, value: response } = 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      rawResponse: { headers: responseHeaders },\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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