{"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-sdk/provider';\nimport {\n FetchFunction,\n loadApiKey,\n withoutTrailingSlash,\n} from '@ai-sdk/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;\n\n /**\n@deprecated Use `textEmbeddingModel()` instead.\n */\n textEmbedding(\n modelId: MistralEmbeddingModelId,\n settings?: MistralEmbeddingSettings,\n ): EmbeddingModelV1;\n\n textEmbeddingModel: (\n modelId: MistralEmbeddingModelId,\n settings?: MistralEmbeddingSettings,\n ) => EmbeddingModelV1;\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;\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-sdk/provider';\nimport {\n FetchFunction,\n ParseResult,\n combineHeaders,\n createEventSourceResponseHandler,\n createJsonResponseHandler,\n postJsonToApi,\n} from '@ai-sdk/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;\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 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 }: Parameters[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 // 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: ${_exhaustiveCheck}`);\n }\n }\n }\n\n async doGenerate(\n options: Parameters[0],\n ): Promise>> {\n const { args, warnings } = this.getArgs(options);\n\n const { responseHeaders, value: response } = await postJsonToApi({\n url: `${this.config.baseURL}/chat/completions`,\n headers: combineHeaders(this.config.headers(), options.headers),\n body: 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: { headers: responseHeaders },\n request: { body: JSON.stringify(args) },\n response: getResponseMetadata(response),\n warnings,\n };\n }\n\n async doStream(\n options: Parameters[0],\n ): Promise>> {\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>,\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) {\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-sdk/provider';\nimport { convertUint8ArrayToBase64 } from '@ai-sdk/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 throw new UnsupportedFunctionalityError({\n functionality: 'File content parts in user messages',\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-sdk/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-sdk/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;\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-sdk/provider';\n\nexport function prepareTools(\n mode: Parameters[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: ${_exhaustiveCheck}`,\n });\n }\n }\n}\n","import {\n EmbeddingModelV1,\n TooManyEmbeddingValuesForCallError,\n} from '@ai-sdk/provider';\nimport {\n combineHeaders,\n createJsonResponseHandler,\n FetchFunction,\n postJsonToApi,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod';\nimport {\n MistralEmbeddingModelId,\n MistralEmbeddingSettings,\n} from './mistral-embedding-settings';\nimport { mistralFailedResponseHandler } from './mistral-error';\n\ntype MistralEmbeddingConfig = {\n provider: string;\n baseURL: string;\n headers: () => Record;\n fetch?: FetchFunction;\n};\n\nexport class MistralEmbeddingModel implements EmbeddingModelV1 {\n readonly specificationVersion = 'v1';\n readonly modelId: MistralEmbeddingModelId;\n\n private readonly config: MistralEmbeddingConfig;\n private readonly settings: MistralEmbeddingSettings;\n\n get provider(): string {\n return this.config.provider;\n }\n\n get maxEmbeddingsPerCall(): number {\n return this.settings.maxEmbeddingsPerCall ?? 32;\n }\n\n get supportsParallelCalls(): boolean {\n // Parallel calls are technically possible,\n // but I have been hitting rate limits and disable them for now.\n return this.settings.supportsParallelCalls ?? false;\n }\n\n constructor(\n modelId: MistralEmbeddingModelId,\n settings: MistralEmbeddingSettings,\n config: MistralEmbeddingConfig,\n ) {\n this.modelId = modelId;\n this.settings = settings;\n this.config = config;\n }\n\n async doEmbed({\n values,\n abortSignal,\n headers,\n }: Parameters['doEmbed']>[0]): Promise<\n Awaited['doEmbed']>>\n > {\n if (values.length > this.maxEmbeddingsPerCall) {\n throw new TooManyEmbeddingValuesForCallError({\n provider: this.provider,\n modelId: this.modelId,\n maxEmbeddingsPerCall: this.maxEmbeddingsPerCall,\n values,\n });\n }\n\n const { responseHeaders, value: response } = await postJsonToApi({\n url: `${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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