{"version":3,"sources":["../src/friendli-provider.ts","../src/friendli-settings.ts","../src/friendli-chat-language-model.ts","../src/friendli-error.ts","../src/friendli-prepare-tools.ts"],"sourcesContent":["import { LanguageModelV1, NoSuchModelError } from '@ai-sdk/provider'\nimport {\n FetchFunction,\n loadApiKey,\n withoutTrailingSlash,\n} from '@ai-sdk/provider-utils'\nimport { OpenAICompatibleCompletionLanguageModel } from '@ai-sdk/openai-compatible'\n\nimport {\n FriendliAILanguageModelId,\n FriendliAIBetaChatModelId,\n FriendliAIChatSettings,\n FriendliAICompletionSettings,\n FriendliAIServerlessModelIds,\n FriendliAIServerlessModelId,\n} from './friendli-settings'\nimport { FriendliAIChatLanguageModel } from './friendli-chat-language-model'\nimport { friendliaiErrorStructure } from './friendli-error'\n\nexport interface FriendliAIProviderSettings {\n /**\n * FriendliAI API key. (FRIENDLI__TOKEN)\n */\n apiKey?: string\n /**\n * Base URL for the API calls.\n */\n baseURL?: string\n /**\n * Custom headers to include in the requests.\n */\n headers?: Record\n /**\n * FriendliAI Team ID.\n */\n teamId?: string\n /**\n * Custom fetch implementation. You can use it as a middleware to intercept requests,\n * or to provide a custom fetch implementation for e.g. testing.\n */\n fetch?: FetchFunction\n}\n\nexport interface FriendliAIProvider {\n /**\n * Creates a model for text generation.\n */\n (\n modelId: FriendliAILanguageModelId,\n settings?: FriendliAIChatSettings,\n ): LanguageModelV1\n /**\n * A model that has not yet been officially released\n */\n beta(\n modelId: FriendliAIBetaChatModelId,\n settings?: FriendliAIChatSettings,\n ): LanguageModelV1\n /**\n * Creates a chat model for text generation.\n */\n chat(\n modelId: FriendliAILanguageModelId,\n settings?: FriendliAIChatSettings,\n ): LanguageModelV1\n chatModel(\n modelId: FriendliAILanguageModelId,\n settings?: FriendliAIChatSettings,\n ): LanguageModelV1\n /**\n * Creates a completion model for text generation.\n */\n completion(\n modelId: FriendliAILanguageModelId,\n settings?: FriendliAIChatSettings,\n ): LanguageModelV1\n completionModel(\n modelId: FriendliAILanguageModelId,\n settings?: FriendliAIChatSettings,\n ): LanguageModelV1\n /**\n * Creates a text embedding model for text generation.\n */\n embedding(\n modelId: string & {},\n settings?: FriendliAIChatSettings,\n ): LanguageModelV1\n textEmbeddingModel(\n modelId: string & {},\n settings?: FriendliAIChatSettings,\n ): LanguageModelV1\n}\n\n/**\nCreate an FriendliAI provider instance.\n */\nexport function createFriendli(\n options: FriendliAIProviderSettings = {},\n): FriendliAIProvider {\n const getHeaders = () => ({\n Authorization: `Bearer ${loadApiKey({\n apiKey: options.apiKey,\n environmentVariableName: 'FRIENDLI_TOKEN',\n description: 'FRIENDLI_TOKEN',\n })}`,\n 'X-Friendli-Team': options.teamId,\n ...options.headers,\n })\n\n const baseURLAutoSelect = (\n modelId: string,\n endpoint: 'auto' | 'dedicated' | 'serverless' | 'beta',\n baseURL: string | undefined,\n tools?: Array,\n ): {\n baseURL: string\n type: 'dedicated' | 'serverless' | 'tools' | 'custom' | 'beta'\n } => {\n // Ignore options if baseURL is specified\n const customBaseURL = withoutTrailingSlash(baseURL)\n if (typeof customBaseURL === 'string') {\n return { baseURL: customBaseURL, type: 'custom' }\n }\n\n const FriendliBaseURL = {\n beta: 'https://api.friendli.ai/serverless/beta',\n serverless: 'https://api.friendli.ai/serverless/v1',\n tools: 'https://api.friendli.ai/serverless/tools/v1',\n dedicated: 'https://api.friendli.ai/dedicated/v1',\n }\n\n if (endpoint === 'beta') {\n return {\n baseURL: FriendliBaseURL.beta,\n type: 'beta',\n }\n }\n\n if (\n // If the endpoint setting is serverless or auto and the model is floating on serverless,\n endpoint === 'serverless' ||\n (endpoint === 'auto' &&\n Object.values(FriendliAIServerlessModelIds).includes(\n modelId as FriendliAIServerlessModelId,\n ))\n ) {\n if (tools && tools.length > 0) {\n return {\n baseURL: FriendliBaseURL.tools,\n type: 'tools',\n }\n }\n\n return {\n baseURL: FriendliBaseURL.serverless,\n type: 'serverless',\n }\n } else {\n return {\n baseURL: FriendliBaseURL.dedicated,\n type: 'dedicated',\n }\n }\n }\n\n const createChatModel = (\n modelId: FriendliAILanguageModelId,\n settings: FriendliAIChatSettings = {},\n ) => {\n const { baseURL, type } = baseURLAutoSelect(\n modelId,\n settings.endpoint || 'auto',\n options.baseURL,\n settings.tools,\n )\n\n return new FriendliAIChatLanguageModel(modelId, settings, {\n provider: `friendliai.${type}.chat`,\n url: ({ path }: { path: string }) => `${baseURL}${path}`,\n headers: getHeaders,\n fetch: options.fetch,\n defaultObjectGenerationMode: 'json',\n })\n }\n\n const createCompletionModel = (\n modelId: FriendliAILanguageModelId,\n settings: FriendliAICompletionSettings = {},\n ) => {\n const { baseURL, type } = baseURLAutoSelect(\n modelId,\n settings.endpoint || 'auto',\n options.baseURL,\n )\n\n return new OpenAICompatibleCompletionLanguageModel(modelId, settings, {\n provider: `friendliai.${type}.completion`,\n url: ({ path }) => `${baseURL}${path}`,\n headers: getHeaders,\n fetch: options.fetch,\n errorStructure: friendliaiErrorStructure,\n })\n }\n\n const createBetaModel = (\n modelId: FriendliAIBetaChatModelId,\n settings: FriendliAIChatSettings = {},\n ) => {\n const { baseURL, type } = baseURLAutoSelect(\n modelId,\n 'beta',\n options.baseURL,\n )\n\n return new FriendliAIChatLanguageModel(modelId, settings, {\n provider: `friendliai.${type}.chat`,\n url: ({ path }: { path: string }) => `${baseURL}${path}`,\n headers: getHeaders,\n fetch: options.fetch,\n defaultObjectGenerationMode: 'json',\n })\n }\n\n const createTextEmbeddingModel = (modelId: string) => {\n throw new NoSuchModelError({ modelId, modelType: 'textEmbeddingModel' })\n }\n\n const provider = function (\n modelId: FriendliAILanguageModelId,\n settings?: FriendliAIChatSettings,\n ) {\n return createChatModel(modelId, settings)\n }\n\n provider.beta = createBetaModel\n\n provider.chat = createChatModel\n provider.chatModel = createChatModel\n\n provider.completion = createCompletionModel\n provider.completionModel = createCompletionModel\n\n provider.embedding = createTextEmbeddingModel\n provider.textEmbeddingModel = createTextEmbeddingModel\n\n return provider as FriendliAIProvider\n}\n\nexport const friendli = createFriendli({})\n","import {\n OpenAICompatibleChatSettings,\n OpenAICompatibleCompletionSettings,\n} from '@ai-sdk/openai-compatible'\n\n// https://friendli.ai/products/serverless-endpoints\n// Below is just a subset of the available models.\nexport const FriendliAIServerlessModelIds = [\n 'meta-llama-3.1-8b-instruct',\n 'meta-llama-3.1-70b-instruct',\n 'meta-llama-3.3-70b-instruct',\n 'deepseek-r1',\n] as const\n\nexport type FriendliAIServerlessModelId =\n (typeof FriendliAIServerlessModelIds)[number]\n\nexport type FriendliAILanguageModelId =\n | FriendliAIServerlessModelId\n | (string & {})\n\nexport type FriendliAIBetaChatModelId =\n | 'llama-3.2-11b-vision-instruct'\n | (string & {})\n\ninterface FriendliAISharedSettings {\n /**\n * Sets the endpoint to which the request will be sent.\n * auto: automatically selected based on model_id\n * dedicated: Fixed to \"/dedicated/v1\"\n * serverless: automatically selected as one of \"/serverless/beta\", \"/serverless/v1\", or \"/serverless/tools/v1\"\n * Ignored if baseURL is specified.\n */\n endpoint?: 'auto' | 'dedicated' | 'serverless'\n}\n\nexport interface FriendliAIChatSettings\n extends FriendliAISharedSettings,\n OpenAICompatibleChatSettings {\n /**\n * BETA FEATURE: Include the model's training loss in the response.\n */\n tools?: Array<{\n type:\n | 'web:url'\n | 'web:search'\n | 'math:calendar'\n | 'math:statistics'\n | 'math:calculator'\n | 'code:python-interpreter'\n }>\n\n /**\n * Whether to enable parallel function calling during tool use. Default to true.\n */\n parallelToolCalls?: boolean\n\n /**\n * BETA FEATURE: You can write a regular expression to force output that satisfies that regular expression.\n */\n regex?: RegExp\n}\n\nexport interface FriendliAICompletionSettings\n extends FriendliAISharedSettings,\n OpenAICompatibleCompletionSettings {}\n","import {\n APICallError,\n InvalidResponseDataError,\n LanguageModelV1,\n LanguageModelV1CallWarning,\n LanguageModelV1FinishReason,\n LanguageModelV1ProviderMetadata,\n LanguageModelV1StreamPart,\n UnsupportedFunctionalityError,\n} from '@ai-sdk/provider'\nimport {\n ParseResult,\n ResponseHandler,\n combineHeaders,\n createEventSourceResponseHandler,\n createJsonErrorResponseHandler,\n createJsonResponseHandler,\n generateId,\n isParsableJson,\n postJsonToApi,\n} from '@ai-sdk/provider-utils'\nimport {\n convertToOpenAICompatibleChatMessages,\n getResponseMetadata,\n mapOpenAICompatibleFinishReason,\n OpenAICompatibleChatConfig,\n} from '@ai-sdk/openai-compatible/internal'\n\nimport { z } from 'zod'\n\nimport {\n FriendliAILanguageModelId,\n FriendliAIChatSettings,\n} from './friendli-settings'\nimport {\n friendliaiErrorSchema,\n friendliaiErrorStructure,\n friendliaiFailedResponseHandler,\n} from './friendli-error'\nimport { prepareTools } from './friendli-prepare-tools'\n\nexport class FriendliAIChatLanguageModel implements LanguageModelV1 {\n readonly specificationVersion = 'v1'\n\n readonly supportsStructuredOutputs: boolean\n\n readonly modelId: FriendliAILanguageModelId\n readonly settings: FriendliAIChatSettings\n\n private readonly config: OpenAICompatibleChatConfig\n private readonly failedResponseHandler: ResponseHandler\n\n constructor(\n modelId: FriendliAILanguageModelId,\n settings: FriendliAIChatSettings,\n config: OpenAICompatibleChatConfig,\n ) {\n this.modelId = modelId\n this.settings = settings\n this.config = config\n\n this.failedResponseHandler = createJsonErrorResponseHandler(\n friendliaiErrorStructure,\n )\n\n this.supportsStructuredOutputs = config.supportsStructuredOutputs ?? true\n }\n\n get defaultObjectGenerationMode(): 'json' | 'tool' {\n return this.config.defaultObjectGenerationMode ?? 'json'\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 (\n responseFormat?.type === 'json' &&\n responseFormat.schema != null &&\n !this.supportsStructuredOutputs\n ) {\n warnings.push({\n type: 'unsupported-setting',\n setting: 'responseFormat',\n details:\n 'JSON response format schema is only supported with structuredOutputs',\n })\n }\n\n const baseArgs = {\n // model id:\n model: this.modelId,\n\n // model specific settings:\n user: this.settings.user,\n parallel_tool_calls: this.settings.parallelToolCalls,\n\n // standardized settings:\n max_tokens: maxTokens,\n temperature,\n top_p: topP,\n top_k: topK,\n frequency_penalty: frequencyPenalty,\n presence_penalty: presencePenalty,\n response_format:\n responseFormat?.type === 'json'\n ? this.supportsStructuredOutputs === true &&\n responseFormat.schema != null\n ? {\n type: 'json_schema',\n json_schema: {\n schema: responseFormat.schema,\n description: responseFormat.description,\n },\n }\n : { type: 'json_object' }\n : undefined,\n\n stop: stopSequences,\n seed,\n\n // messages:\n messages: convertToOpenAICompatibleChatMessages(prompt),\n }\n\n if (this.settings.regex != null && type !== 'regular') {\n throw new UnsupportedFunctionalityError({\n functionality:\n 'egular expression is only supported with regular mode (generateText, streamText)',\n })\n }\n\n switch (type) {\n case 'regular': {\n if (this.settings.regex != null) {\n if (this.settings.tools != null || mode.tools != null) {\n throw new UnsupportedFunctionalityError({\n functionality:\n 'Regular expression and tools cannot be used together. Use either regular expression or tools.',\n })\n }\n\n return {\n args: {\n ...baseArgs,\n response_format: {\n type: 'regex',\n schema: this.settings.regex.source,\n },\n },\n warnings,\n }\n }\n\n const { tools, tool_choice, toolWarnings } = prepareTools({\n mode,\n tools: this.settings.tools,\n })\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:\n this.supportsStructuredOutputs === true && mode.schema != null\n ? {\n type: 'json_schema',\n json_schema: {\n schema: mode.schema,\n description: mode.description,\n },\n }\n : { type: 'json_object' },\n },\n warnings,\n }\n }\n\n case 'object-tool': {\n return {\n args: {\n ...baseArgs,\n tool_choice: {\n type: 'function',\n function: { name: mode.tool.name },\n },\n tools: [\n {\n type: 'function',\n function: {\n name: mode.tool.name,\n description: mode.tool.description,\n parameters: mode.tool.parameters,\n },\n },\n ],\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 body = JSON.stringify({ ...args, stream: false })\n\n const { responseHeaders, value: response } = await postJsonToApi({\n url: this.config.url({\n path: '/chat/completions',\n modelId: this.modelId,\n }),\n headers: combineHeaders(this.config.headers(), options.headers),\n body: {\n ...args,\n stream: false,\n },\n failedResponseHandler: this.failedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n friendliAIChatResponseSchema,\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 return {\n text: choice.message.content ?? undefined,\n toolCalls: choice.message.tool_calls?.map((toolCall) => ({\n toolCallType: 'function',\n toolCallId: toolCall.id ?? generateId(),\n toolName: toolCall.function.name,\n args:\n typeof toolCall.function.arguments! === 'string'\n ? toolCall.function.arguments!\n : JSON.stringify(toolCall.function.arguments),\n })),\n finishReason: mapOpenAICompatibleFinishReason(choice.finish_reason),\n usage: {\n promptTokens: response.usage?.prompt_tokens ?? NaN,\n completionTokens: response.usage?.completion_tokens ?? NaN,\n },\n rawCall: { rawPrompt, rawSettings },\n rawResponse: { headers: responseHeaders },\n response: getResponseMetadata(response),\n warnings,\n request: { body },\n }\n }\n\n async doStream(\n options: Parameters[0],\n ): Promise>> {\n const { args, warnings } = this.getArgs({ ...options })\n\n const body = JSON.stringify({ ...args, stream: true })\n\n const { responseHeaders, value: response } = await postJsonToApi({\n url: this.config.url({\n path: '/chat/completions',\n modelId: this.modelId,\n }),\n headers: combineHeaders(this.config.headers(), options.headers),\n body: {\n ...args,\n stream: true,\n stream_options: { include_usage: true },\n },\n failedResponseHandler: friendliaiFailedResponseHandler,\n successfulResponseHandler: createEventSourceResponseHandler(\n friendliaiChatChunkSchema,\n ),\n abortSignal: options.abortSignal,\n fetch: this.config.fetch,\n })\n\n const { messages: rawPrompt, ...rawSettings } = args\n\n const toolCalls: Array<{\n id: string\n type: 'function'\n function: {\n name: string\n arguments: string\n }\n }> = []\n\n let finishReason: LanguageModelV1FinishReason = 'unknown'\n let usage: {\n promptTokens: number | undefined\n completionTokens: number | undefined\n } = {\n promptTokens: undefined,\n completionTokens: undefined,\n }\n let isFirstChunk = true\n\n let providerMetadata: LanguageModelV1ProviderMetadata | undefined\n return {\n stream: response.pipeThrough(\n new TransformStream<\n ParseResult>,\n LanguageModelV1StreamPart\n >({\n transform(chunk, controller) {\n // handle failed chunk parsing / validation:\n if (!chunk.success) {\n finishReason = 'error'\n controller.enqueue({ type: 'error', error: chunk.error })\n return\n }\n\n const value = chunk.value\n\n // hosted tool execution case\n if ('status' in value) {\n switch (value.status) {\n case 'STARTED':\n break\n\n case 'UPDATING':\n break\n\n case 'ENDED':\n break\n\n case 'ERRORED':\n finishReason = 'error'\n break\n\n default:\n finishReason = 'error'\n controller.enqueue({\n type: 'error',\n error: new Error(\n `Unsupported tool call status: ${value.status}`,\n ),\n })\n }\n return\n }\n\n // handle error chunks:\n if ('message' in value) {\n console.error('Error chunk:', value)\n finishReason = 'error'\n controller.enqueue({ type: 'error', error: value.message })\n return\n }\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 = {\n promptTokens: value.usage.prompt_tokens ?? undefined,\n completionTokens: value.usage.completion_tokens ?? undefined,\n }\n }\n\n const choice = value.choices[0]\n\n if (choice?.finish_reason != null) {\n finishReason = mapOpenAICompatibleFinishReason(\n choice.finish_reason,\n )\n }\n\n if (choice?.delta == null) {\n return\n }\n\n const delta = choice.delta\n\n if (delta.content != null) {\n controller.enqueue({\n type: 'text-delta',\n textDelta: delta.content,\n })\n }\n\n if (delta.tool_calls != null) {\n for (const toolCallDelta of delta.tool_calls) {\n const index = toolCallDelta.index\n\n // Tool call start. FriendliAI returns all information except the arguments in the first chunk.\n if (toolCalls[index] == null) {\n if (toolCallDelta.type !== 'function') {\n throw new InvalidResponseDataError({\n data: toolCallDelta,\n message: `Expected 'function' type.`,\n })\n }\n\n if (toolCallDelta.id == null) {\n throw new InvalidResponseDataError({\n data: toolCallDelta,\n message: `Expected 'id' to be a string.`,\n })\n }\n\n if (toolCallDelta.function?.name == null) {\n throw new InvalidResponseDataError({\n data: toolCallDelta,\n message: `Expected 'function.name' to be a string.`,\n })\n }\n\n toolCalls[index] = {\n id: toolCallDelta.id,\n type: 'function',\n function: {\n name: toolCallDelta.function.name,\n arguments: toolCallDelta.function.arguments ?? '',\n },\n }\n\n const toolCall = toolCalls[index]\n\n if (\n toolCall.function?.name != null &&\n toolCall.function?.arguments != null\n ) {\n // send delta if the argument text has already started:\n if (toolCall.function.arguments.length > 0) {\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 }\n\n // check if tool call is complete\n // (some providers send the full tool call in one chunk):\n if (isParsableJson(toolCall.function.arguments)) {\n controller.enqueue({\n type: 'tool-call',\n toolCallType: 'function',\n toolCallId: toolCall.id ?? generateId(),\n toolName: toolCall.function.name,\n args: toolCall.function.arguments,\n })\n }\n }\n\n continue\n }\n\n // existing tool call, merge\n const toolCall = toolCalls[index]\n\n if (toolCallDelta.function?.arguments != null) {\n toolCall.function!.arguments +=\n toolCallDelta.function?.arguments ?? ''\n }\n\n // send delta\n controller.enqueue({\n type: 'tool-call-delta',\n toolCallType: 'function',\n toolCallId: toolCall.id,\n toolName: toolCall.function.name,\n argsTextDelta: toolCallDelta.function.arguments ?? '',\n })\n\n // check if tool call is complete\n if (\n toolCall.function?.name != null &&\n toolCall.function?.arguments != null &&\n isParsableJson(toolCall.function.arguments)\n ) {\n controller.enqueue({\n type: 'tool-call',\n toolCallType: 'function',\n toolCallId: toolCall.id ?? generateId(),\n toolName: toolCall.function.name,\n args: toolCall.function.arguments,\n })\n }\n }\n }\n },\n\n flush(controller) {\n controller.enqueue({\n type: 'finish',\n finishReason,\n usage: {\n promptTokens: usage.promptTokens ?? NaN,\n completionTokens: usage.completionTokens ?? NaN,\n },\n ...(providerMetadata != null ? { providerMetadata } : {}),\n })\n },\n }),\n ),\n rawCall: { rawPrompt, rawSettings },\n rawResponse: { headers: responseHeaders },\n warnings,\n request: { body },\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 friendliAIChatResponseSchema = 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').nullish(),\n content: z.string().nullish(),\n tool_calls: z\n .array(\n z.object({\n id: z.string().nullish(),\n type: z.literal('function'),\n function: z.object({\n name: z.string(),\n arguments: z.union([z.string(), z.any()]).nullish(),\n }),\n }),\n )\n .nullish(),\n }),\n finish_reason: z.string().nullish(),\n }),\n ),\n usage: z\n .object({\n prompt_tokens: z.number().nullish(),\n completion_tokens: z.number().nullish(),\n })\n .nullish(),\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 friendliaiChatChunkSchema = z.union([\n 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\n .object({\n role: z.enum(['assistant']).nullish(),\n content: z.string().nullish(),\n tool_calls: z\n .array(\n z.object({\n index: z.number(),\n id: z.string().nullish(),\n type: z.literal('function').optional(),\n function: z.object({\n name: z.string().nullish(),\n arguments: z.string().nullish(),\n }),\n }),\n )\n .nullish(),\n })\n .nullish(),\n finish_reason: z.string().nullish(),\n }),\n ),\n usage: z\n .object({\n prompt_tokens: z.number().nullish(),\n completion_tokens: z.number().nullish(),\n })\n .nullish(),\n }),\n z.object({\n name: z.string(),\n status: z.enum(['ENDED', 'STARTED', 'ERRORED', 'UPDATING']),\n message: z.null(),\n parameters: z.array(\n z.object({\n name: z.string(),\n value: z.string(),\n }),\n ),\n result: z.string().nullable(),\n error: z\n .object({\n type: z.enum(['INVALID_PARAMETER', 'UNKNOWN']),\n msg: z.string(),\n })\n .nullable(),\n timestamp: z.number(),\n usage: z.null(),\n tool_call_id: z.string().nullable(), // temporary fix for \"file:text\" tool calls\n }),\n friendliaiErrorSchema,\n])\n","import { z } from 'zod'\nimport { ProviderErrorStructure } from '@ai-sdk/openai-compatible'\nimport { createJsonErrorResponseHandler } from '@ai-sdk/provider-utils'\n\nexport const friendliaiErrorSchema = z.object({\n message: z.string(),\n})\n\nexport type FriendliAIErrorData = z.infer\n\nexport const friendliaiErrorStructure: ProviderErrorStructure =\n {\n errorSchema: friendliaiErrorSchema,\n errorToMessage: (data) => data.message,\n }\n\nexport const friendliaiFailedResponseHandler = createJsonErrorResponseHandler(\n friendliaiErrorStructure,\n)\n","import {\n LanguageModelV1,\n LanguageModelV1CallWarning,\n UnsupportedFunctionalityError,\n} from '@ai-sdk/provider'\n\nimport { FriendliAIChatSettings } from './friendli-settings'\n\nexport function prepareTools({\n mode,\n tools: hostedTools,\n}: {\n mode: Parameters[0]['mode'] & {\n type: 'regular'\n }\n\n tools?: FriendliAIChatSettings['tools']\n}): {\n tools:\n | undefined\n | Array<{\n type: string\n files?: string[]\n }>\n | Array<{\n type: 'function'\n function: {\n name: string\n description: string | undefined\n parameters: unknown\n }\n }>\n tool_choice:\n | { type: 'function'; function: { name: string } }\n | 'auto'\n | 'none'\n | 'required'\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 && hostedTools == null) {\n return { tools: undefined, tool_choice: undefined, toolWarnings }\n }\n\n const toolChoice = mode.toolChoice\n\n const mappedTools: Array<{\n type: 'function'\n function: {\n name: string\n description: string | undefined\n parameters: unknown\n }\n }> = []\n\n if (tools) {\n for (const tool of tools) {\n if (tool.type === 'provider-defined') {\n toolWarnings.push({ type: 'unsupported-tool', tool })\n } else {\n mappedTools.push({\n type: 'function',\n function: {\n name: tool.name,\n description: tool.description,\n parameters: tool.parameters,\n },\n })\n }\n }\n }\n\n const mappedHostedTools = hostedTools?.map((tool) => {\n return {\n type: tool.type,\n }\n })\n\n if (toolChoice == null) {\n return {\n tools: [...(mappedTools ?? []), ...(mappedHostedTools ?? [])],\n tool_choice: undefined,\n toolWarnings,\n }\n }\n\n const type = toolChoice.type\n\n switch (type) {\n case 'auto':\n case 'none':\n case 'required':\n return {\n tools: [...(mappedTools ?? []), ...(mappedHostedTools ?? [])],\n tool_choice: type,\n toolWarnings,\n }\n case 'tool':\n return {\n tools: [...(mappedTools ?? []), ...(mappedHostedTools ?? [])],\n tool_choice: {\n type: 'function',\n function: {\n name: toolChoice.toolName,\n },\n },\n toolWarnings,\n }\n default: {\n const _exhaustiveCheck: never = type\n throw new UnsupportedFunctionalityError({\n functionality: `Unsupported tool choice type: ${_exhaustiveCheck}`,\n })\n }\n 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