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type {\n LanguageModelV3CallOptions,\n LanguageModelV3Prompt,\n SharedV3Warning,\n} from '@ai-sdk/provider';\nimport type { DeepSeekChatPrompt } from './deepseek-chat-api-types';\n\nexport function convertToDeepSeekChatMessages({\n prompt,\n responseFormat,\n modelId,\n}: {\n prompt: LanguageModelV3Prompt;\n responseFormat: LanguageModelV3CallOptions['responseFormat'];\n modelId: string;\n}): {\n messages: DeepSeekChatPrompt;\n warnings: Array;\n} {\n const isDeepSeekV4 = modelId.includes('deepseek-v4');\n const messages: DeepSeekChatPrompt = [];\n const warnings: Array = [];\n\n // Inject system message if response format is JSON\n if (responseFormat?.type === 'json') {\n if (responseFormat.schema == null) {\n messages.push({\n role: 'system',\n content: 'Return JSON.',\n });\n } else {\n messages.push({\n role: 'system',\n content:\n 'Return JSON that conforms to the following schema: ' +\n JSON.stringify(responseFormat.schema),\n });\n warnings.push({\n type: 'compatibility',\n feature: 'responseFormat JSON schema',\n details: 'JSON response schema is injected into the system message.',\n });\n }\n }\n\n // TODO use findLastIndex once we use ES2023\n let lastUserMessageIndex = -1;\n for (let i = prompt.length - 1; i >= 0; i--) {\n if (prompt[i].role === 'user') {\n lastUserMessageIndex = i;\n break;\n }\n }\n\n let index = -1;\n for (const { role, content } of prompt) {\n index++;\n\n switch (role) {\n case 'system': {\n messages.push({ role: 'system', content });\n break;\n }\n\n case 'user': {\n let userContent = '';\n for (const part of content) {\n if (part.type === 'text') {\n userContent += part.text;\n } else {\n warnings.push({\n type: 'unsupported',\n feature: `user message part type: ${part.type}`,\n });\n }\n }\n\n messages.push({\n role: 'user',\n content: userContent,\n });\n\n break;\n }\n case 'assistant': {\n let text = '';\n let reasoning: string | undefined;\n\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 'reasoning': {\n // R1 must not receive prior reasoning; V4 requires it.\n if (index <= lastUserMessageIndex && !isDeepSeekV4) {\n break;\n }\n\n if (reasoning == null) {\n reasoning = part.text;\n } else {\n reasoning += part.text;\n }\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 }\n }\n\n // V4 demands the field on every assistant turn — back-fill an empty\n // string when the source message had no reasoning part at all.\n messages.push({\n role: 'assistant',\n content: text,\n reasoning_content: reasoning ?? (isDeepSeekV4 ? '' : undefined),\n tool_calls: toolCalls.length > 0 ? toolCalls : undefined,\n });\n\n break;\n }\n\n case 'tool': {\n for (const toolResponse of content) {\n if (toolResponse.type === 'tool-approval-response') {\n continue;\n }\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 'execution-denied':\n contentValue = output.reason ?? 'Tool execution denied.';\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 tool_call_id: toolResponse.toolCallId,\n content: contentValue,\n });\n }\n break;\n }\n\n default: {\n warnings.push({\n type: 'unsupported',\n feature: `message role: ${role}`,\n });\n break;\n }\n }\n }\n\n return { messages, warnings };\n}\n","import type { LanguageModelV3Usage } from '@ai-sdk/provider';\n\nexport function convertDeepSeekUsage(\n usage:\n | {\n prompt_tokens?: number | null | undefined;\n completion_tokens?: number | null | undefined;\n prompt_cache_hit_tokens?: number | null | undefined;\n completion_tokens_details?:\n | {\n reasoning_tokens?: number | null | undefined;\n }\n | null\n | undefined;\n }\n | undefined\n | null,\n): LanguageModelV3Usage {\n if (usage == null) {\n return {\n inputTokens: {\n total: undefined,\n noCache: undefined,\n cacheRead: undefined,\n cacheWrite: undefined,\n },\n outputTokens: {\n total: undefined,\n text: undefined,\n reasoning: undefined,\n },\n raw: undefined,\n };\n }\n\n const promptTokens = usage.prompt_tokens ?? 0;\n const completionTokens = usage.completion_tokens ?? 0;\n const cacheReadTokens = usage.prompt_cache_hit_tokens ?? 0;\n const reasoningTokens =\n usage.completion_tokens_details?.reasoning_tokens ?? 0;\n\n return {\n inputTokens: {\n total: promptTokens,\n noCache: promptTokens - cacheReadTokens,\n cacheRead: cacheReadTokens,\n cacheWrite: undefined,\n },\n outputTokens: {\n total: completionTokens,\n text: completionTokens - reasoningTokens,\n reasoning: reasoningTokens,\n },\n raw: usage,\n };\n}\n","import { lazySchema, zodSchema } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nexport type DeepSeekChatPrompt = Array;\n\nexport type DeepSeekMessage =\n | DeepSeekSystemMessage\n | DeepSeekUserMessage\n | DeepSeekAssistantMessage\n | DeepSeekToolMessage;\n\nexport interface DeepSeekSystemMessage {\n role: 'system';\n content: string;\n}\n\nexport interface DeepSeekUserMessage {\n role: 'user';\n content: string;\n}\n\nexport interface DeepSeekAssistantMessage {\n role: 'assistant';\n content?: string | null;\n reasoning_content?: string;\n tool_calls?: Array;\n}\n\nexport interface DeepSeekMessageToolCall {\n type: 'function';\n id: string;\n function: {\n arguments: string;\n name: string;\n };\n}\n\nexport interface DeepSeekToolMessage {\n role: 'tool';\n content: string;\n tool_call_id: string;\n}\n\nexport interface DeepSeekFunctionTool {\n type: 'function';\n function: {\n name: string;\n description: string | undefined;\n parameters: unknown;\n strict?: boolean;\n };\n}\n\nexport type DeepSeekToolChoice =\n | { type: 'function'; function: { name: string } }\n | 'auto'\n | 'none'\n | 'required'\n | undefined;\n\nconst tokenUsageSchema = z\n .object({\n prompt_tokens: z.number().nullish(),\n completion_tokens: z.number().nullish(),\n prompt_cache_hit_tokens: z.number().nullish(),\n prompt_cache_miss_tokens: z.number().nullish(),\n total_tokens: z.number().nullish(),\n completion_tokens_details: z\n .object({\n reasoning_tokens: z.number().nullish(),\n })\n .nullish(),\n })\n .nullish();\n\nexport type DeepSeekChatTokenUsage = z.infer;\n\nexport const deepSeekErrorSchema = z.object({\n error: z.object({\n message: z.string(),\n type: z.string().nullish(),\n param: z.any().nullish(),\n code: z.union([z.string(), z.number()]).nullish(),\n }),\n});\n\nexport type DeepSeekErrorData = z.infer;\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\nexport const deepseekChatResponseSchema = 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 reasoning_content: z.string().nullish(),\n tool_calls: z\n .array(\n z.object({\n id: z.string().nullish(),\n function: z.object({\n name: z.string(),\n arguments: z.string(),\n }),\n }),\n )\n .nullish(),\n }),\n finish_reason: z.string().nullish(),\n }),\n ),\n usage: tokenUsageSchema,\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\nexport const deepseekChatChunkSchema = lazySchema(() =>\n zodSchema(\n 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 reasoning_content: z.string().nullish(),\n tool_calls: z\n .array(\n z.object({\n index: z.number(),\n id: z.string().nullish(),\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: tokenUsageSchema,\n }),\n deepSeekErrorSchema,\n ]),\n ),\n);\n","import { z } from 'zod/v4';\n\n// https://api-docs.deepseek.com/quick_start/pricing\nexport type DeepSeekChatModelId =\n | 'deepseek-chat'\n | 'deepseek-reasoner'\n | (string & {});\n\nexport const deepseekLanguageModelOptions = z.object({\n /**\n * Type of thinking to use. Defaults to `enabled`.\n */\n thinking: z\n .object({\n type: z.enum(['enabled', 'disabled']).optional(),\n })\n .optional(),\n\n /**\n * Controls the thinking strength for DeepSeek V4 reasoning models.\n */\n reasoningEffort: z.enum(['high', 'max']).optional(),\n});\n\nexport type DeepSeekLanguageModelOptions = z.infer<\n typeof deepseekLanguageModelOptions\n>;\n","import type {\n LanguageModelV3CallOptions,\n SharedV3Warning,\n} from '@ai-sdk/provider';\nimport type {\n DeepSeekFunctionTool,\n DeepSeekToolChoice,\n} from './deepseek-chat-api-types';\n\nexport function prepareTools({\n tools,\n toolChoice,\n}: {\n tools: LanguageModelV3CallOptions['tools'];\n toolChoice?: LanguageModelV3CallOptions['toolChoice'];\n}): {\n tools: undefined | Array;\n toolChoice: DeepSeekToolChoice;\n toolWarnings: SharedV3Warning[];\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: SharedV3Warning[] = [];\n\n if (tools == null) {\n return { tools: undefined, toolChoice: undefined, toolWarnings };\n }\n\n const deepseekTools: Array = [];\n\n for (const tool of tools) {\n if (tool.type === 'provider') {\n toolWarnings.push({\n type: 'unsupported',\n feature: `provider-defined tool ${tool.id}`,\n });\n } else {\n deepseekTools.push({\n type: 'function',\n function: {\n name: tool.name,\n description: tool.description,\n parameters: tool.inputSchema,\n ...(tool.strict != null ? { strict: tool.strict } : {}),\n },\n });\n }\n }\n\n if (toolChoice == null) {\n return { tools: deepseekTools, toolChoice: undefined, toolWarnings };\n }\n\n const type = toolChoice?.type;\n\n switch (type) {\n case 'auto':\n case 'none':\n case 'required':\n return { tools: deepseekTools, toolChoice: type, toolWarnings };\n case 'tool':\n return {\n tools: deepseekTools,\n toolChoice: {\n type: 'function',\n function: { name: toolChoice.toolName },\n },\n toolWarnings,\n };\n default: {\n return {\n tools: deepseekTools,\n toolChoice: undefined,\n toolWarnings: [\n ...toolWarnings,\n {\n type: 'unsupported',\n feature: `tool choice type: ${type}`,\n },\n ],\n };\n }\n }\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 type { LanguageModelV3FinishReason } from '@ai-sdk/provider';\n\nexport function mapDeepSeekFinishReason(\n finishReason: string | null | undefined,\n): LanguageModelV3FinishReason['unified'] {\n switch (finishReason) {\n case 'stop':\n return 'stop';\n case 'length':\n return 'length';\n case 'content_filter':\n return 'content-filter';\n case 'tool_calls':\n return 'tool-calls';\n case 'insufficient_system_resource':\n return 'error';\n default:\n return 'other';\n }\n}\n","import {\n InvalidResponseDataError,\n type APICallError,\n type LanguageModelV3,\n type LanguageModelV3CallOptions,\n type LanguageModelV3Content,\n type LanguageModelV3FinishReason,\n type LanguageModelV3GenerateResult,\n type LanguageModelV3StreamPart,\n type LanguageModelV3StreamResult,\n} from '@ai-sdk/provider';\nimport {\n combineHeaders,\n createEventSourceResponseHandler,\n createJsonErrorResponseHandler,\n createJsonResponseHandler,\n generateId,\n isParsableJson,\n parseProviderOptions,\n postJsonToApi,\n type FetchFunction,\n type InferSchema,\n type ParseResult,\n type ResponseHandler,\n} from '@ai-sdk/provider-utils';\nimport { convertToDeepSeekChatMessages } from './convert-to-deepseek-chat-messages';\nimport { convertDeepSeekUsage } from './convert-to-deepseek-usage';\nimport {\n deepseekChatChunkSchema,\n deepseekChatResponseSchema,\n deepSeekErrorSchema,\n type DeepSeekChatTokenUsage,\n} from './deepseek-chat-api-types';\nimport {\n deepseekLanguageModelOptions,\n type DeepSeekChatModelId,\n} from './deepseek-chat-options';\nimport { prepareTools } from './deepseek-prepare-tools';\nimport { getResponseMetadata } from './get-response-metadata';\nimport { mapDeepSeekFinishReason } from './map-deepseek-finish-reason';\n\nexport type DeepSeekChatConfig = {\n provider: string;\n headers: () => Record;\n url: (options: { modelId: string; path: string }) => string;\n fetch?: FetchFunction;\n};\n\nexport class DeepSeekChatLanguageModel implements LanguageModelV3 {\n readonly specificationVersion = 'v3';\n\n readonly modelId: DeepSeekChatModelId;\n readonly supportedUrls = {};\n\n private readonly config: DeepSeekChatConfig;\n private readonly failedResponseHandler: ResponseHandler;\n\n constructor(modelId: DeepSeekChatModelId, config: DeepSeekChatConfig) {\n this.modelId = modelId;\n this.config = config;\n\n this.failedResponseHandler = createJsonErrorResponseHandler({\n errorSchema: deepSeekErrorSchema,\n errorToMessage: (error: InferSchema) =>\n error.error.message,\n });\n }\n\n get provider(): string {\n return this.config.provider;\n }\n\n private get providerOptionsName(): string {\n return this.config.provider.split('.')[0].trim();\n }\n\n private async getArgs({\n prompt,\n maxOutputTokens,\n temperature,\n topP,\n topK,\n frequencyPenalty,\n presencePenalty,\n providerOptions,\n stopSequences,\n responseFormat,\n seed,\n toolChoice,\n tools,\n }: LanguageModelV3CallOptions) {\n const deepseekOptions =\n (await parseProviderOptions({\n provider: this.providerOptionsName,\n providerOptions,\n schema: deepseekLanguageModelOptions,\n })) ?? {};\n\n const { messages, warnings } = convertToDeepSeekChatMessages({\n prompt,\n responseFormat,\n modelId: this.modelId,\n });\n\n if (topK != null) {\n warnings.push({ type: 'unsupported', feature: 'topK' });\n }\n\n if (seed != null) {\n warnings.push({ type: 'unsupported', feature: 'seed' });\n }\n\n const {\n tools: deepseekTools,\n toolChoice: deepseekToolChoices,\n toolWarnings,\n } = prepareTools({\n tools,\n toolChoice,\n });\n\n const thinking =\n deepseekOptions.thinking?.type != null\n ? { type: deepseekOptions.thinking.type }\n : undefined;\n\n return {\n args: {\n model: this.modelId,\n max_tokens: maxOutputTokens,\n temperature,\n top_p: topP,\n frequency_penalty: frequencyPenalty,\n presence_penalty: presencePenalty,\n response_format:\n responseFormat?.type === 'json' ? { type: 'json_object' } : undefined,\n stop: stopSequences,\n messages,\n tools: deepseekTools,\n tool_choice: deepseekToolChoices,\n thinking,\n ...(thinking?.type !== 'disabled' &&\n deepseekOptions.reasoningEffort != null && {\n reasoning_effort: deepseekOptions.reasoningEffort,\n }),\n },\n warnings: [...warnings, ...toolWarnings],\n };\n }\n\n async doGenerate(\n options: LanguageModelV3CallOptions,\n ): Promise {\n const { args, warnings } = await this.getArgs({ ...options });\n\n const {\n responseHeaders,\n value: responseBody,\n rawValue: rawResponse,\n } = 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: args,\n failedResponseHandler: this.failedResponseHandler,\n successfulResponseHandler: createJsonResponseHandler(\n deepseekChatResponseSchema,\n ),\n abortSignal: options.abortSignal,\n fetch: this.config.fetch,\n });\n\n const choice = responseBody.choices[0];\n const content: Array = [];\n\n // reasoning content (before text):\n const reasoning = choice.message.reasoning_content;\n if (reasoning != null && reasoning.length > 0) {\n content.push({\n type: 'reasoning',\n text: reasoning,\n });\n }\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 ?? generateId(),\n toolName: toolCall.function.name,\n input: toolCall.function.arguments!,\n });\n }\n }\n\n // text content:\n const text = choice.message.content;\n if (text != null && text.length > 0) {\n content.push({ type: 'text', text });\n }\n\n return {\n content,\n finishReason: {\n unified: mapDeepSeekFinishReason(choice.finish_reason),\n raw: choice.finish_reason ?? undefined,\n },\n usage: convertDeepSeekUsage(responseBody.usage),\n providerMetadata: {\n [this.providerOptionsName]: {\n promptCacheHitTokens: responseBody.usage?.prompt_cache_hit_tokens,\n promptCacheMissTokens: responseBody.usage?.prompt_cache_miss_tokens,\n },\n },\n request: { body: args },\n response: {\n ...getResponseMetadata(responseBody),\n headers: responseHeaders,\n body: rawResponse,\n },\n warnings,\n };\n }\n\n async doStream(\n options: LanguageModelV3CallOptions,\n ): Promise {\n const { args, warnings } = await this.getArgs({ ...options });\n\n const body = {\n ...args,\n stream: true,\n stream_options: { include_usage: true },\n };\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 failedResponseHandler: this.failedResponseHandler,\n successfulResponseHandler: createEventSourceResponseHandler(\n deepseekChatChunkSchema,\n ),\n abortSignal: options.abortSignal,\n fetch: this.config.fetch,\n });\n\n const toolCalls: Array<{\n id: string;\n type: 'function';\n function: {\n name: string;\n arguments: string;\n };\n hasFinished: boolean;\n }> = [];\n\n let finishReason: LanguageModelV3FinishReason = {\n unified: 'other',\n raw: undefined,\n };\n let usage: DeepSeekChatTokenUsage | undefined = undefined;\n let isFirstChunk = true;\n const providerOptionsName = this.providerOptionsName;\n let isActiveReasoning = false;\n let isActiveText = false;\n\n return {\n stream: response.pipeThrough(\n new TransformStream<\n ParseResult>,\n LanguageModelV3StreamPart\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 // handle failed chunk parsing / validation:\n if (!chunk.success) {\n finishReason = { unified: 'error', raw: undefined };\n controller.enqueue({ type: 'error', error: chunk.error });\n return;\n }\n const value = chunk.value;\n\n // handle error chunks:\n if ('error' in value) {\n finishReason = { unified: 'error', raw: undefined };\n controller.enqueue({ type: 'error', error: value.error.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 = value.usage;\n }\n\n const choice = value.choices[0];\n\n if (choice?.finish_reason != null) {\n finishReason = {\n unified: mapDeepSeekFinishReason(choice.finish_reason),\n raw: choice.finish_reason,\n };\n }\n\n if (choice?.delta == null) {\n return;\n }\n\n const delta = choice.delta;\n\n // enqueue reasoning before text deltas:\n const reasoningContent = delta.reasoning_content;\n if (reasoningContent) {\n if (!isActiveReasoning) {\n controller.enqueue({\n type: 'reasoning-start',\n id: 'reasoning-0',\n });\n isActiveReasoning = true;\n }\n\n controller.enqueue({\n type: 'reasoning-delta',\n id: 'reasoning-0',\n delta: reasoningContent,\n });\n }\n\n if (delta.content) {\n if (!isActiveText) {\n controller.enqueue({ type: 'text-start', id: 'txt-0' });\n isActiveText = true;\n }\n\n // end reasoning when text starts:\n if (isActiveReasoning) {\n controller.enqueue({\n type: 'reasoning-end',\n id: 'reasoning-0',\n });\n isActiveReasoning = false;\n }\n\n controller.enqueue({\n type: 'text-delta',\n id: 'txt-0',\n delta: delta.content,\n });\n }\n\n if (delta.tool_calls != null) {\n // end reasoning when tool calls start:\n if (isActiveReasoning) {\n controller.enqueue({\n type: 'reasoning-end',\n id: 'reasoning-0',\n });\n isActiveReasoning = false;\n }\n\n for (const toolCallDelta of delta.tool_calls) {\n const index = toolCallDelta.index;\n\n if (toolCalls[index] == null) {\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 controller.enqueue({\n type: 'tool-input-start',\n id: toolCallDelta.id,\n toolName: toolCallDelta.function.name,\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 hasFinished: false,\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-input-delta',\n id: toolCall.id,\n delta: 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-input-end',\n id: toolCall.id,\n });\n\n controller.enqueue({\n type: 'tool-call',\n toolCallId: toolCall.id ?? generateId(),\n toolName: toolCall.function.name,\n input: toolCall.function.arguments,\n });\n toolCall.hasFinished = true;\n }\n }\n\n continue;\n }\n\n // existing tool call, merge if not finished\n const toolCall = toolCalls[index];\n\n if (toolCall.hasFinished) {\n continue;\n }\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-input-delta',\n id: toolCall.id,\n delta: 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-input-end',\n id: toolCall.id,\n });\n\n controller.enqueue({\n type: 'tool-call',\n toolCallId: toolCall.id ?? generateId(),\n toolName: toolCall.function.name,\n input: toolCall.function.arguments,\n });\n toolCall.hasFinished = true;\n }\n }\n }\n },\n\n flush(controller) {\n if (isActiveReasoning) {\n controller.enqueue({ type: 'reasoning-end', id: 'reasoning-0' });\n }\n\n if (isActiveText) {\n controller.enqueue({ type: 'text-end', id: 'txt-0' });\n }\n\n // go through all tool calls and send the ones that are not finished\n for (const toolCall of toolCalls.filter(\n toolCall => !toolCall.hasFinished,\n )) {\n controller.enqueue({\n type: 'tool-input-end',\n id: toolCall.id,\n });\n\n controller.enqueue({\n type: 'tool-call',\n toolCallId: toolCall.id ?? generateId(),\n toolName: toolCall.function.name,\n input: toolCall.function.arguments,\n });\n }\n\n controller.enqueue({\n type: 'finish',\n finishReason,\n usage: convertDeepSeekUsage(usage),\n providerMetadata: {\n [providerOptionsName]: {\n promptCacheHitTokens:\n usage?.prompt_cache_hit_tokens ?? undefined,\n promptCacheMissTokens:\n usage?.prompt_cache_miss_tokens ?? undefined,\n },\n },\n });\n },\n }),\n ),\n request: { body },\n response: { headers: responseHeaders },\n };\n }\n}\n","// Version string of this package injected at build time.\ndeclare const __PACKAGE_VERSION__: string | undefined;\nexport const VERSION: string =\n typeof __PACKAGE_VERSION__ !== 'undefined'\n ? __PACKAGE_VERSION__\n : '0.0.0-test';\n","import {\n NoSuchModelError,\n type LanguageModelV3,\n type ProviderV3,\n} from '@ai-sdk/provider';\nimport {\n loadApiKey,\n withoutTrailingSlash,\n withUserAgentSuffix,\n type FetchFunction,\n} from '@ai-sdk/provider-utils';\nimport type { DeepSeekChatModelId } from './chat/deepseek-chat-options';\nimport { DeepSeekChatLanguageModel } from './chat/deepseek-chat-language-model';\nimport { VERSION } from './version';\n\nexport interface DeepSeekProviderSettings {\n /**\n * DeepSeek API key.\n */\n apiKey?: string;\n\n /**\n * Base URL for the API calls.\n */\n baseURL?: string;\n\n /**\n * Custom headers to include in the requests.\n */\n headers?: Record;\n\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 DeepSeekProvider extends ProviderV3 {\n /**\n * Creates a DeepSeek model for text generation.\n */\n (modelId: DeepSeekChatModelId): LanguageModelV3;\n\n /**\n * Creates a DeepSeek model for text generation.\n */\n languageModel(modelId: DeepSeekChatModelId): LanguageModelV3;\n\n /**\n * Creates a DeepSeek chat model for text generation.\n */\n chat(modelId: DeepSeekChatModelId): LanguageModelV3;\n\n /**\n * @deprecated Use `embeddingModel` instead.\n */\n textEmbeddingModel(modelId: string): never;\n}\n\nexport function createDeepSeek(\n options: DeepSeekProviderSettings = {},\n): DeepSeekProvider {\n const baseURL = withoutTrailingSlash(\n options.baseURL ?? 'https://api.deepseek.com',\n );\n\n const getHeaders = () =>\n withUserAgentSuffix(\n {\n Authorization: `Bearer ${loadApiKey({\n apiKey: options.apiKey,\n environmentVariableName: 'DEEPSEEK_API_KEY',\n description: 'DeepSeek API key',\n })}`,\n ...options.headers,\n },\n `ai-sdk/deepseek/${VERSION}`,\n );\n\n const createLanguageModel = (modelId: DeepSeekChatModelId) => {\n return new DeepSeekChatLanguageModel(modelId, {\n provider: `deepseek.chat`,\n url: ({ path }) => `${baseURL}${path}`,\n headers: getHeaders,\n fetch: options.fetch,\n });\n };\n\n const provider = (modelId: DeepSeekChatModelId) =>\n createLanguageModel(modelId);\n\n provider.specificationVersion = 'v3' as const;\n provider.languageModel = createLanguageModel;\n provider.chat = createLanguageModel;\n\n provider.embeddingModel = (modelId: string) => {\n throw new NoSuchModelError({ modelId, modelType: 'embeddingModel' });\n };\n provider.textEmbeddingModel = provider.embeddingModel;\n provider.imageModel = (modelId: string) => {\n throw new NoSuchModelError({ modelId, modelType: 'imageModel' });\n };\n\n return provider;\n}\n\nexport const deepseek = createDeepSeek();\n"]}