mirror of
https://github.com/hellodigua/ChatLab.git
synced 2026-05-04 04:01:16 +08:00
chore: remove @ai-sdk dependencies and legacy LLM service implementations
- Remove @ai-sdk/google, @ai-sdk/openai, ai packages from dependencies - Delete SDK-based service files: gemini.ts, openai-compatible.ts, sdkUtils.ts - Clean up unused types from llm/types.ts (ChatMessage, ChatOptions, LLMConfig, ILLMService, etc.)
This commit is contained in:
digua
@@ -1,266 +0,0 @@
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/**
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* Google Gemini LLM Provider
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* 使用 AI SDK 的 Google Generative AI Provider，支持 Function Calling
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*/
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import { createGoogleGenerativeAI } from '@ai-sdk/google'
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import { generateText, streamText } from 'ai'
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import type { ContentPart, ModelMessage, ToolSet, TypedToolCall } from 'ai'
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import type {
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ILLMService,
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LLMProvider,
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ChatMessage,
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ChatOptions,
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ChatResponse,
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ChatStreamChunk,
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ToolCall,
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ProviderInfo,
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} from './types'
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import { aiLogger } from '../logger'
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import { buildModelMessages, buildToolSet, mapFinishReason, mapUsage } from './sdkUtils'
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const DEFAULT_BASE_URL = 'https://generativelanguage.googleapis.com'
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const DEFAULT_API_VERSION = '/v1beta'
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const GEMINI_MAX_RETRIES = 1
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const MODELS = [
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{ id: 'gemini-3-flash-preview', name: 'Gemini 3 Flash Preview', description: '高速预览版' },
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{ id: 'gemini-3-pro-preview', name: 'Gemini 3 Pro Preview', description: '专业预览版' },
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]
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export const GEMINI_INFO: ProviderInfo = {
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id: 'gemini',
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name: 'Gemini',
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description: 'Google Gemini 大语言模型',
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defaultBaseUrl: DEFAULT_BASE_URL,
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models: MODELS,
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}
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/**
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* 统一处理 Gemini 的 baseUrl，确保包含 /v1beta
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*/
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function normalizeBaseUrl(baseUrl?: string): string {
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const normalized = (baseUrl || DEFAULT_BASE_URL).replace(/\/+$/, '')
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if (normalized.endsWith(DEFAULT_API_VERSION)) {
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return normalized
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}
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return `${normalized}${DEFAULT_API_VERSION}`
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}
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const GEMINI_PROVIDER_KEYS = ['google', 'vertex', 'gemini']
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/**
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* 从 AI SDK 的 providerMetadata 中提取 thoughtSignature
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*/
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function getThoughtSignature(metadata?: unknown): string | undefined {
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if (!metadata || typeof metadata !== 'object') {
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return undefined
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}
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const record = metadata as Record<string, unknown>
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for (const key of GEMINI_PROVIDER_KEYS) {
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const providerMeta = record[key]
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if (providerMeta && typeof providerMeta === 'object') {
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const signature = (providerMeta as Record<string, unknown>).thoughtSignature
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if (typeof signature === 'string' && signature) {
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return signature
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}
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}
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}
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const directSignature = record.thoughtSignature
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if (typeof directSignature === 'string' && directSignature) {
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return directSignature
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}
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return undefined
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}
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/**
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* Gemini 工具调用需要 thoughtSignature
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*/
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function mapGeminiToolCalls(toolCalls: TypedToolCall<ToolSet>[]): ToolCall[] {
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return toolCalls.map((tc) => ({
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id: tc.toolCallId,
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type: 'function' as const,
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function: {
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name: tc.toolName,
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arguments: JSON.stringify(tc.input ?? {}),
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},
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thoughtSignature: getThoughtSignature(tc.providerMetadata),
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}))
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}
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/**
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* 构建 Gemini 的模型消息，补充 thoughtSignature
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*/
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function buildGeminiModelMessages(messages: ChatMessage[]): ModelMessage[] {
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const modelMessages = buildModelMessages(messages)
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const signatureMap = new Map<string, string>()
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for (const message of messages) {
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if (message.role !== 'assistant' || !message.tool_calls) continue
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for (const toolCall of message.tool_calls) {
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if (toolCall.thoughtSignature) {
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signatureMap.set(toolCall.id, toolCall.thoughtSignature)
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}
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}
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}
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if (signatureMap.size === 0) {
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return modelMessages
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}
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return modelMessages.map((message) => {
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if (message.role !== 'assistant' || !Array.isArray(message.content)) {
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return message
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}
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const contentParts = message.content.map((part) => {
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if (part.type !== 'tool-call') {
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return part
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}
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const signature = signatureMap.get(part.toolCallId)
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if (!signature) {
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return part
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}
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const nextPart: ContentPart<ToolSet> & {
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providerOptions?: Record<string, { thoughtSignature?: string }>
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} = {
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...part,
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providerOptions: {
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google: { thoughtSignature: signature },
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},
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}
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return nextPart
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})
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return {
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...message,
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content: contentParts,
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}
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})
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}
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export class GeminiService implements ILLMService {
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private apiKey: string
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private baseUrl: string
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private model: string
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private provider = createGoogleGenerativeAI()
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constructor(apiKey: string, model?: string, baseUrl?: string) {
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this.apiKey = apiKey
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this.baseUrl = normalizeBaseUrl(baseUrl)
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this.model = model || 'gemini-3-flash-preview'
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this.provider = createGoogleGenerativeAI({
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apiKey: this.apiKey,
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baseURL: this.baseUrl,
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name: 'gemini',
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})
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}
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getProvider(): LLMProvider {
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return 'gemini'
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}
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getModels(): string[] {
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return MODELS.map((m) => m.id)
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}
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getDefaultModel(): string {
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return 'gemini-3-flash-preview'
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}
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async chat(messages: ChatMessage[], options?: ChatOptions): Promise<ChatResponse> {
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const model = this.provider.chat(this.model)
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const toolSet = buildToolSet(options?.tools)
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const result = await generateText({
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model,
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messages: buildGeminiModelMessages(messages),
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tools: toolSet,
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temperature: options?.temperature ?? 0.7,
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maxTokens: options?.maxTokens ?? 2048,
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// 降低 Gemini 自动重试次数，避免触发免费配额限制
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maxRetries: GEMINI_MAX_RETRIES,
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abortSignal: options?.abortSignal,
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})
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const toolCalls = result.toolCalls.length > 0 ? mapGeminiToolCalls(result.toolCalls) : undefined
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return {
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content: result.text,
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finishReason: mapFinishReason(result.finishReason),
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tool_calls: toolCalls,
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usage: mapUsage(result.usage),
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}
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}
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async *chatStream(messages: ChatMessage[], options?: ChatOptions): AsyncGenerator<ChatStreamChunk> {
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const model = this.provider.chat(this.model)
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const toolSet = buildToolSet(options?.tools)
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const result = streamText({
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model,
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messages: buildGeminiModelMessages(messages),
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tools: toolSet,
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temperature: options?.temperature ?? 0.7,
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maxTokens: options?.maxTokens ?? 2048,
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// 降低 Gemini 自动重试次数，避免触发免费配额限制
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maxRetries: GEMINI_MAX_RETRIES,
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abortSignal: options?.abortSignal,
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})
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try {
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for await (const chunk of result.textStream) {
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if (options?.abortSignal?.aborted) {
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yield { content: '', isFinished: true, finishReason: 'stop' }
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return
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}
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if (chunk) {
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yield { content: chunk, isFinished: false }
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}
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}
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const finishReason = mapFinishReason(await result.finishReason)
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const toolCalls = await result.toolCalls
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const usage = mapUsage(await result.totalUsage)
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yield {
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content: '',
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isFinished: true,
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finishReason,
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tool_calls: toolCalls.length > 0 ? mapGeminiToolCalls(toolCalls) : undefined,
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usage,
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}
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} catch (error) {
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if (error instanceof Error && error.name === 'AbortError') {
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yield { content: '', isFinished: true, finishReason: 'stop' }
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return
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}
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aiLogger.error('Gemini', 'Stream request failed', { error: String(error) })
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throw error
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}
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}
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async validateApiKey(): Promise<{ success: boolean; error?: string }> {
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try {
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const model = this.provider.chat(this.model)
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await generateText({
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model,
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prompt: 'Hi',
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maxTokens: 1,
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})
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return { success: true }
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} catch (error) {
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const errorMessage = error instanceof Error ? error.message : String(error)
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return { success: false, error: errorMessage }
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}
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}
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}
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@@ -1,543 +0,0 @@
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/**
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* OpenAI Compatible LLM Provider
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* 支持任何兼容 OpenAI API 格式的服务（如 Ollama、LocalAI、vLLM 等）
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*/
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import { createOpenAI } from '@ai-sdk/openai'
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import { generateText, streamText, wrapLanguageModel, extractReasoningMiddleware } from 'ai'
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import type { ModelMessage, ToolSet, TypedToolCall, LanguageModel } from 'ai'
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import type {
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ILLMService,
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LLMProvider,
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ChatMessage,
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ChatOptions,
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ChatResponse,
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ChatStreamChunk,
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ToolCall,
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ProviderInfo,
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} from './types'
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import { aiLogger } from '../logger'
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import { buildModelMessages, buildToolSet, mapFinishReason, mapToolCalls, mapUsage } from './sdkUtils'
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/**
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* 从 AI SDK 错误中提取详细信息
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* 特别是从 responseBody 中获取模型/服务端返回的错误消息
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*/
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interface DetailedError {
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message: string
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statusCode?: number
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responseBody?: string
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originalMessage?: string
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}
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function extractDetailedError(error: unknown): DetailedError {
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const result: DetailedError = {
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message: '未知错误',
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}
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if (!error) {
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return result
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}
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// 处理 Error 对象
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if (error instanceof Error) {
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result.originalMessage = error.message
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result.message = error.message
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// 尝试从错误对象中提取更多信息
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const anyError = error as Record<string, unknown>
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// 检查状态码
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if (typeof anyError.statusCode === 'number') {
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result.statusCode = anyError.statusCode
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}
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// 检查是否有 responseBody（API 错误通常包含）- 直接使用原始内容
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if (typeof anyError.responseBody === 'string') {
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result.responseBody = anyError.responseBody
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result.message = anyError.responseBody
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}
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// 检查是否是 RetryError（包含多个错误）
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if (Array.isArray(anyError.errors)) {
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const errors = anyError.errors as Array<Record<string, unknown>>
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for (const innerError of errors) {
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// 提取状态码
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if (typeof innerError.statusCode === 'number' && !result.statusCode) {
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result.statusCode = innerError.statusCode
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}
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// 提取 responseBody - 直接使用原始内容
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if (typeof innerError.responseBody === 'string') {
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result.responseBody = innerError.responseBody
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result.message = innerError.responseBody
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break
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}
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}
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}
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} else if (typeof error === 'string') {
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result.message = error
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} else {
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result.message = String(error)
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}
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return result
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}
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const DEFAULT_BASE_URL = 'http://localhost:11434/v1'
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const DEFAULT_THOUGHT_SIGNATURE = 'context_engineering_is_the_way_to_go'
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export const OPENAI_COMPATIBLE_INFO: ProviderInfo = {
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id: 'openai-compatible',
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name: 'OpenAI 兼容',
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description: '支持任何兼容 OpenAI API 的服务（如 Ollama、LocalAI、vLLM 等）',
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defaultBaseUrl: DEFAULT_BASE_URL,
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models: [
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{ id: 'llama3.2', name: 'Llama 3.2', description: 'Meta Llama 3.2 模型' },
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{ id: 'qwen2.5', name: 'Qwen 2.5', description: '通义千问 2.5 模型' },
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{ id: 'deepseek-r1', name: 'DeepSeek R1', description: 'DeepSeek R1 推理模型' },
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],
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}
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/**
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* 统一处理 baseUrl：去掉尾部斜杠和多余路径
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*/
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function normalizeBaseUrl(baseUrl?: string): string {
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let processed = baseUrl || DEFAULT_BASE_URL
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processed = processed.replace(/\/+$/, '')
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if (processed.endsWith('/chat/completions')) {
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processed = processed.slice(0, -'/chat/completions'.length)
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}
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return processed
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}
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/**
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* MiniMax 流式返回可能是累计文本，这里按前缀增量去重
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*/
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function dedupeCumulativeStreamChunk(chunk: string, previousText: string): { delta: string; nextText: string } {
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if (!previousText) {
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return { delta: chunk, nextText: chunk }
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}
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if (chunk.startsWith(previousText)) {
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return { delta: chunk.slice(previousText.length), nextText: chunk }
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}
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if (previousText.startsWith(chunk)) {
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// 偶发回退或重复帧，保持已输出内容
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return { delta: '', nextText: previousText }
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}
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// 无法判定为累计时，退化为增量追加
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return { delta: chunk, nextText: previousText + chunk }
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}
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/**
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* 包装 fetch：注入思考开关和 thought_signature
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*/
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function createCompatFetch(disableThinking: boolean): typeof fetch {
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return async (input, init) => {
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if (!init?.body || typeof init.body !== 'string') {
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return fetch(input, init)
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}
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let parsedBody: Record<string, unknown> | null = null
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try {
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parsedBody = JSON.parse(init.body) as Record<string, unknown>
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} catch {
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return fetch(input, init)
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}
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|
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if (!parsedBody) {
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return fetch(input, init)
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}
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let changed = false
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if (Array.isArray(parsedBody.messages)) {
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const messages = parsedBody.messages as Array<Record<string, unknown>>
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// 为 Gemini 兼容后端补充 thought_signature
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for (const message of messages) {
|
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if (
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message &&
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typeof message === 'object' &&
|
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(message as { role?: string }).role === 'assistant' &&
|
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Array.isArray((message as { tool_calls?: unknown[] }).tool_calls)
|
||||
) {
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const toolCalls = (message as { tool_calls: Array<Record<string, unknown>> }).tool_calls
|
||||
for (const toolCall of toolCalls) {
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const typedCall = toolCall as Record<string, unknown> & {
|
||||
thought_signature?: string
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||||
thoughtSignature?: string
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}
|
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if (!typedCall.thought_signature && !typedCall.thoughtSignature) {
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typedCall.thought_signature = DEFAULT_THOUGHT_SIGNATURE
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changed = true
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}
|
||||
}
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}
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}
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// 禁用思考模式（用于本地模型）
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if (disableThinking) {
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const chatTemplate = parsedBody.chat_template_kwargs
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if (!chatTemplate || typeof chatTemplate !== 'object') {
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parsedBody.chat_template_kwargs = { enable_thinking: false }
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changed = true
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} else if (!(chatTemplate as { enable_thinking?: boolean }).enable_thinking) {
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parsedBody.chat_template_kwargs = {
|
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...(chatTemplate as Record<string, unknown>),
|
||||
enable_thinking: false,
|
||||
}
|
||||
changed = true
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (!changed) {
|
||||
return fetch(input, init)
|
||||
}
|
||||
|
||||
const nextInit: RequestInit = {
|
||||
...init,
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||||
body: JSON.stringify(parsedBody),
|
||||
}
|
||||
|
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return fetch(input, nextInit)
|
||||
}
|
||||
}
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||||
|
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export class OpenAICompatibleService implements ILLMService {
|
||||
private apiKey: string
|
||||
private baseUrl: string
|
||||
private model: string
|
||||
private providerId: LLMProvider
|
||||
private models: ProviderInfo['models']
|
||||
private defaultModel: string
|
||||
private provider = createOpenAI()
|
||||
private isReasoning: boolean = false
|
||||
|
||||
constructor(
|
||||
apiKey: string,
|
||||
model?: string,
|
||||
baseUrl?: string,
|
||||
disableThinking?: boolean,
|
||||
providerId?: LLMProvider,
|
||||
models?: ProviderInfo['models'],
|
||||
isReasoningModelConfig?: boolean
|
||||
) {
|
||||
const normalizedBaseUrl = normalizeBaseUrl(baseUrl)
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const resolvedApiKey = apiKey || 'sk-no-key-required'
|
||||
const resolvedDisableThinking = disableThinking ?? true
|
||||
const resolvedProviderId = providerId ?? 'openai-compatible'
|
||||
const resolvedModels = models && models.length > 0 ? models : OPENAI_COMPATIBLE_INFO.models
|
||||
const defaultModel = resolvedModels[0]?.id || 'llama3.2'
|
||||
const resolvedModel = model || defaultModel
|
||||
|
||||
this.apiKey = resolvedApiKey
|
||||
this.baseUrl = normalizedBaseUrl
|
||||
this.providerId = resolvedProviderId
|
||||
this.models = resolvedModels
|
||||
this.defaultModel = defaultModel
|
||||
this.model = resolvedModel
|
||||
|
||||
// 推理模型判断：完全依赖用户配置
|
||||
this.isReasoning = isReasoningModelConfig ?? false
|
||||
if (this.isReasoning) {
|
||||
aiLogger.info(this.providerId, `User configured reasoning model: ${resolvedModel}`)
|
||||
}
|
||||
|
||||
this.provider = createOpenAI({
|
||||
apiKey: resolvedApiKey,
|
||||
baseURL: normalizedBaseUrl,
|
||||
name: 'openai-compatible',
|
||||
fetch: createCompatFetch(resolvedDisableThinking),
|
||||
})
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取语言模型实例
|
||||
* 对于推理模型，会应用 extractReasoningMiddleware 中间件
|
||||
*/
|
||||
private getModel(): LanguageModel {
|
||||
const baseModel = this.provider.chat(this.model)
|
||||
|
||||
if (this.isReasoning) {
|
||||
// 对推理模型应用中间件，提取 <think> 标签中的思考内容
|
||||
// startWithReasoning: true - 因为 Ollama 上的 DeepSeek-R1 响应总是以思考开始
|
||||
return wrapLanguageModel({
|
||||
model: baseModel,
|
||||
middleware: extractReasoningMiddleware({
|
||||
tagName: 'think',
|
||||
startWithReasoning: true,
|
||||
}),
|
||||
})
|
||||
}
|
||||
|
||||
return baseModel
|
||||
}
|
||||
|
||||
getProvider(): LLMProvider {
|
||||
return this.providerId
|
||||
}
|
||||
|
||||
getModels(): string[] {
|
||||
return this.models.map((m) => m.id)
|
||||
}
|
||||
|
||||
getDefaultModel(): string {
|
||||
return this.defaultModel
|
||||
}
|
||||
|
||||
// 统一处理消息映射，保持与旧实现一致
|
||||
private buildMessages(messages: ChatMessage[]): ModelMessage[] {
|
||||
return buildModelMessages(messages)
|
||||
}
|
||||
|
||||
// 统一映射工具调用结果
|
||||
private mapToolCalls(toolCalls: TypedToolCall<ToolSet>[]): ToolCall[] {
|
||||
return mapToolCalls(toolCalls)
|
||||
}
|
||||
|
||||
// 仅 MiniMax 需要累计去重，避免其他模型缓存全文
|
||||
private shouldTrackStreamText(): boolean {
|
||||
return this.providerId === 'minimax'
|
||||
}
|
||||
|
||||
// MiniMax 流式返回可能是累计文本，按前缀增量去重
|
||||
private getStreamChunkDelta(chunk: string, previousText: string): { delta: string; nextText: string } {
|
||||
if (this.providerId !== 'minimax') {
|
||||
return { delta: chunk, nextText: previousText + chunk }
|
||||
}
|
||||
return dedupeCumulativeStreamChunk(chunk, previousText)
|
||||
}
|
||||
|
||||
async chat(messages: ChatMessage[], options?: ChatOptions): Promise<ChatResponse> {
|
||||
const model = this.getModel()
|
||||
// 推理模型（如 DeepSeek-R1）不支持 tool-calling，需要禁用 tools
|
||||
const toolSet = this.isReasoning ? undefined : buildToolSet(options?.tools)
|
||||
|
||||
aiLogger.info(this.providerId, 'Starting non-streaming request', {
|
||||
model: this.model,
|
||||
isReasoning: this.isReasoning,
|
||||
hasTools: !!toolSet,
|
||||
messagesCount: messages.length,
|
||||
})
|
||||
|
||||
const result = await generateText({
|
||||
model,
|
||||
messages: this.buildMessages(messages),
|
||||
tools: toolSet,
|
||||
temperature: options?.temperature ?? 0.7,
|
||||
maxTokens: options?.maxTokens ?? 2048,
|
||||
abortSignal: options?.abortSignal,
|
||||
})
|
||||
|
||||
const toolCalls = result.toolCalls.length > 0 ? this.mapToolCalls(result.toolCalls) : undefined
|
||||
|
||||
// 对于推理模型，reasoningText 包含思考内容
|
||||
if (this.isReasoning && result.reasoningText) {
|
||||
aiLogger.info(this.providerId, 'Reasoning model returned thinking content', {
|
||||
reasoningLength: result.reasoningText.length,
|
||||
textLength: result.text.length,
|
||||
})
|
||||
}
|
||||
|
||||
return {
|
||||
content: result.text,
|
||||
finishReason: mapFinishReason(result.finishReason),
|
||||
tool_calls: toolCalls,
|
||||
usage: mapUsage(result.usage),
|
||||
}
|
||||
}
|
||||
|
||||
async *chatStream(messages: ChatMessage[], options?: ChatOptions): AsyncGenerator<ChatStreamChunk> {
|
||||
const model = this.getModel()
|
||||
const shouldTrack = this.shouldTrackStreamText()
|
||||
let streamedText = ''
|
||||
|
||||
// 推理模型（如 DeepSeek-R1）不支持 tool-calling，需要禁用 tools
|
||||
// 参考: https://sdk.vercel.ai/docs/guides/r1#limitations
|
||||
const toolSet = this.isReasoning ? undefined : buildToolSet(options?.tools)
|
||||
|
||||
aiLogger.info(this.providerId, 'Starting streaming request', {
|
||||
model: this.model,
|
||||
isReasoning: this.isReasoning,
|
||||
hasTools: !!toolSet,
|
||||
messagesCount: messages.length,
|
||||
})
|
||||
|
||||
const result = streamText({
|
||||
model,
|
||||
messages: this.buildMessages(messages),
|
||||
tools: toolSet,
|
||||
temperature: options?.temperature ?? 0.7,
|
||||
maxTokens: options?.maxTokens ?? 2048,
|
||||
abortSignal: options?.abortSignal,
|
||||
})
|
||||
|
||||
let partCount = 0
|
||||
let textChunkCount = 0
|
||||
let reasoningChunkCount = 0
|
||||
|
||||
try {
|
||||
// 使用 fullStream 替代 textStream，以支持 reasoning 模型（如 DeepSeek-R1）
|
||||
// fullStream 包含 text/reasoning/tool-call 等多种类型的 chunk
|
||||
for await (const part of result.fullStream) {
|
||||
partCount++
|
||||
|
||||
if (options?.abortSignal?.aborted) {
|
||||
yield { content: '', isFinished: true, finishReason: 'stop' }
|
||||
return
|
||||
}
|
||||
|
||||
// 记录每个 part 的类型（用于调试）
|
||||
if (partCount <= 5 || partCount % 50 === 0) {
|
||||
aiLogger.info(this.providerId, `Stream part #${partCount}`, { type: part.type })
|
||||
}
|
||||
|
||||
// 处理 error part - 获取详细错误信息并抛出更有意义的错误
|
||||
if (part.type === 'error') {
|
||||
const errorPart = part as { type: 'error'; error: unknown }
|
||||
const detailedError = extractDetailedError(errorPart.error)
|
||||
|
||||
aiLogger.error(this.providerId, 'Stream received error part', {
|
||||
detailedError,
|
||||
originalError: String(errorPart.error),
|
||||
})
|
||||
|
||||
// 抛出包含详细信息的错误，让前端能够显示
|
||||
throw new Error(detailedError.message)
|
||||
}
|
||||
|
||||
// 处理不同类型的 stream part
|
||||
// 注意：fullStream 返回的文本增量类型可能是 'text' 或 'text-delta'
|
||||
const textPart = part as { type: string; text?: string; textDelta?: string }
|
||||
if (part.type === 'text' || part.type === 'text-delta') {
|
||||
// 普通文本内容
|
||||
textChunkCount++
|
||||
const chunk = textPart.text || textPart.textDelta
|
||||
if (chunk) {
|
||||
const { delta, nextText } = this.getStreamChunkDelta(chunk, streamedText)
|
||||
if (shouldTrack) {
|
||||
streamedText = nextText
|
||||
}
|
||||
if (delta) {
|
||||
yield { content: delta, isFinished: false }
|
||||
}
|
||||
}
|
||||
} else if (part.type === 'reasoning') {
|
||||
// 推理内容（DeepSeek-R1 等推理模型）
|
||||
// 包装为 <think> 标签，让 Agent 的 createStreamParser 能够正确解析
|
||||
reasoningChunkCount++
|
||||
const reasoningText = part.text
|
||||
if (reasoningText) {
|
||||
yield { content: `<think>${reasoningText}</think>`, isFinished: false }
|
||||
}
|
||||
} else if (part.type === 'finish') {
|
||||
// 流结束
|
||||
const finishReason = mapFinishReason(part.finishReason)
|
||||
const toolCalls = await result.toolCalls
|
||||
const usage = mapUsage(part.totalUsage)
|
||||
|
||||
// 详细记录流式请求完成信息，包括工具调用
|
||||
aiLogger.info(this.providerId, 'Streaming request completed', {
|
||||
partCount,
|
||||
textChunkCount,
|
||||
reasoningChunkCount,
|
||||
finishReason: part.finishReason,
|
||||
mappedFinishReason: finishReason,
|
||||
toolCallsCount: toolCalls.length,
|
||||
toolCallNames: toolCalls.map((tc) => tc.toolName),
|
||||
})
|
||||
|
||||
yield {
|
||||
content: '',
|
||||
isFinished: true,
|
||||
finishReason,
|
||||
tool_calls: toolCalls.length > 0 ? this.mapToolCalls(toolCalls) : undefined,
|
||||
usage,
|
||||
}
|
||||
return
|
||||
}
|
||||
// 其他类型（tool-call, tool-result, start, start-step, finish-step 等）暂时忽略
|
||||
}
|
||||
|
||||
// 兜底：如果没有收到 finish 事件，手动获取结果
|
||||
aiLogger.warn(this.providerId, 'Stream ended without receiving finish event', {
|
||||
partCount,
|
||||
textChunkCount,
|
||||
reasoningChunkCount,
|
||||
})
|
||||
|
||||
const finishReason = mapFinishReason(await result.finishReason)
|
||||
const toolCalls = await result.toolCalls
|
||||
const usage = mapUsage(await result.totalUsage)
|
||||
|
||||
yield {
|
||||
content: '',
|
||||
isFinished: true,
|
||||
finishReason,
|
||||
tool_calls: toolCalls.length > 0 ? this.mapToolCalls(toolCalls) : undefined,
|
||||
usage,
|
||||
}
|
||||
} catch (error) {
|
||||
aiLogger.error(this.providerId, 'Streaming request error', {
|
||||
partCount,
|
||||
textChunkCount,
|
||||
reasoningChunkCount,
|
||||
error: String(error),
|
||||
})
|
||||
|
||||
if (error instanceof Error && error.name === 'AbortError') {
|
||||
yield { content: '', isFinished: true, finishReason: 'stop' }
|
||||
return
|
||||
}
|
||||
throw error
|
||||
}
|
||||
}
|
||||
|
||||
async validateApiKey(): Promise<{ success: boolean; error?: string }> {
|
||||
try {
|
||||
const headers: Record<string, string> = {
|
||||
'Content-Type': 'application/json',
|
||||
}
|
||||
if (this.apiKey && this.apiKey !== 'sk-no-key-required') {
|
||||
headers['Authorization'] = `Bearer ${this.apiKey}`
|
||||
}
|
||||
|
||||
const response = await fetch(`${this.baseUrl}/chat/completions`, {
|
||||
method: 'POST',
|
||||
headers,
|
||||
body: JSON.stringify({
|
||||
model: this.model,
|
||||
messages: [{ role: 'user', content: 'Hi' }],
|
||||
max_tokens: 1,
|
||||
}),
|
||||
})
|
||||
|
||||
if (response.ok) {
|
||||
return { success: true }
|
||||
}
|
||||
|
||||
const errorText = await response.text()
|
||||
let errorMessage = `HTTP ${response.status}`
|
||||
try {
|
||||
const errorJson = JSON.parse(errorText) as { error?: { message?: string }; message?: string }
|
||||
errorMessage = errorJson.error?.message || errorJson.message || errorMessage
|
||||
} catch {
|
||||
if (errorText) {
|
||||
errorMessage = errorText.slice(0, 200)
|
||||
}
|
||||
}
|
||||
|
||||
if (response.status === 401 || response.status === 403) {
|
||||
return { success: false, error: errorMessage }
|
||||
}
|
||||
|
||||
return { success: true }
|
||||
} catch (error) {
|
||||
const errorMessage = error instanceof Error ? error.message : String(error)
|
||||
return { success: false, error: errorMessage }
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,150 +0,0 @@
|
||||
/**
|
||||
* AI SDK 适配通用工具
|
||||
*/
|
||||
|
||||
import { jsonSchema } from 'ai'
|
||||
import type { ContentPart, FinishReason, LanguageModelUsage, ModelMessage, ToolSet, TypedToolCall, JSONValue } from 'ai'
|
||||
import type { ChatMessage, ChatResponse, ToolCall, ToolDefinition } from './types'
|
||||
|
||||
const UNKNOWN_TOOL_NAME = 'unknown_tool'
|
||||
|
||||
/**
|
||||
* 将 OpenAI 风格的工具定义转换为 AI SDK ToolSet
|
||||
*/
|
||||
export function buildToolSet(tools?: ToolDefinition[]): ToolSet | undefined {
|
||||
if (!tools || tools.length === 0) return undefined
|
||||
|
||||
const toolSet: ToolSet = {}
|
||||
for (const tool of tools) {
|
||||
toolSet[tool.function.name] = {
|
||||
description: tool.function.description,
|
||||
inputSchema: jsonSchema(tool.function.parameters),
|
||||
}
|
||||
}
|
||||
|
||||
return toolSet
|
||||
}
|
||||
|
||||
/**
|
||||
* 解析 JSON 字符串，失败返回 null
|
||||
*/
|
||||
export function safeParseJson(value: string): JSONValue | null {
|
||||
try {
|
||||
return JSON.parse(value) as JSONValue
|
||||
} catch {
|
||||
return null
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 将 AI SDK finishReason 映射为现有的 LLM finishReason
|
||||
*/
|
||||
export function mapFinishReason(reason: FinishReason): ChatResponse['finishReason'] {
|
||||
if (reason === 'stop') return 'stop'
|
||||
if (reason === 'length') return 'length'
|
||||
if (reason === 'tool-calls') return 'tool_calls'
|
||||
return 'error'
|
||||
}
|
||||
|
||||
/**
|
||||
* 将 AI SDK usage 映射为现有的 usage 结构
|
||||
*/
|
||||
export function mapUsage(usage?: LanguageModelUsage): ChatResponse['usage'] | undefined {
|
||||
if (!usage) return undefined
|
||||
|
||||
const promptTokens = usage.inputTokens
|
||||
const completionTokens = usage.outputTokens
|
||||
const totalTokens = usage.totalTokens
|
||||
|
||||
if (promptTokens == null && completionTokens == null && totalTokens == null) {
|
||||
return undefined
|
||||
}
|
||||
|
||||
return {
|
||||
promptTokens: promptTokens ?? 0,
|
||||
completionTokens: completionTokens ?? 0,
|
||||
totalTokens: totalTokens ?? 0,
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 将 AI SDK toolCalls 映射为现有 ToolCall 结构
|
||||
*/
|
||||
export function mapToolCalls(toolCalls: TypedToolCall<ToolSet>[]): ToolCall[] {
|
||||
return toolCalls.map((tc) => ({
|
||||
id: tc.toolCallId,
|
||||
type: 'function' as const,
|
||||
function: {
|
||||
name: tc.toolName,
|
||||
arguments: JSON.stringify(tc.input ?? {}),
|
||||
},
|
||||
}))
|
||||
}
|
||||
|
||||
/**
|
||||
* 将现有 ChatMessage 转为 AI SDK 的 ModelMessage
|
||||
*/
|
||||
export function buildModelMessages(messages: ChatMessage[]): ModelMessage[] {
|
||||
const toolCallNameMap = new Map<string, string>()
|
||||
|
||||
// 先建立 tool_call_id 到 toolName 的映射，供 tool 消息使用
|
||||
for (const message of messages) {
|
||||
if (message.role !== 'assistant' || !message.tool_calls) continue
|
||||
for (const toolCall of message.tool_calls) {
|
||||
toolCallNameMap.set(toolCall.id, toolCall.function.name)
|
||||
}
|
||||
}
|
||||
|
||||
return messages.map((message) => {
|
||||
if (message.role === 'assistant') {
|
||||
if (message.tool_calls && message.tool_calls.length > 0) {
|
||||
const contentParts: Array<ContentPart<ToolSet>> = []
|
||||
|
||||
if (message.content) {
|
||||
contentParts.push({ type: 'text', text: message.content })
|
||||
}
|
||||
|
||||
for (const toolCall of message.tool_calls) {
|
||||
const input = safeParseJson(toolCall.function.arguments || '{}') ?? {}
|
||||
contentParts.push({
|
||||
type: 'tool-call',
|
||||
toolCallId: toolCall.id,
|
||||
toolName: toolCall.function.name,
|
||||
input,
|
||||
})
|
||||
}
|
||||
|
||||
return {
|
||||
role: 'assistant',
|
||||
content: contentParts,
|
||||
}
|
||||
}
|
||||
|
||||
return { role: 'assistant', content: message.content }
|
||||
}
|
||||
|
||||
if (message.role === 'tool') {
|
||||
const toolCallId = message.tool_call_id || ''
|
||||
const toolName = toolCallNameMap.get(toolCallId) || UNKNOWN_TOOL_NAME
|
||||
const parsed = safeParseJson(message.content)
|
||||
|
||||
return {
|
||||
role: 'tool',
|
||||
content: [
|
||||
{
|
||||
type: 'tool-result',
|
||||
toolCallId,
|
||||
toolName,
|
||||
// 工具结果只允许 JSON/文本，保持与旧实现一致
|
||||
output: parsed ? { type: 'json', value: parsed } : { type: 'text', value: message.content },
|
||||
},
|
||||
],
|
||||
}
|
||||
}
|
||||
|
||||
return {
|
||||
role: message.role,
|
||||
content: message.content,
|
||||
}
|
||||
})
|
||||
}
|
||||
@@ -7,162 +7,6 @@
|
||||
*/
|
||||
export type LLMProvider = 'deepseek' | 'qwen' | 'minimax' | 'glm' | 'kimi' | 'gemini' | 'doubao' | 'openai-compatible'
|
||||
|
||||
/**
|
||||
* LLM 配置
|
||||
*/
|
||||
export interface LLMConfig {
|
||||
provider: LLMProvider
|
||||
apiKey: string
|
||||
model?: string
|
||||
baseUrl?: string
|
||||
maxTokens?: number
|
||||
}
|
||||
|
||||
/**
|
||||
* 聊天消息
|
||||
*/
|
||||
export interface ChatMessage {
|
||||
role: 'system' | 'user' | 'assistant' | 'tool'
|
||||
content: string
|
||||
/** tool 角色时的 tool_call_id */
|
||||
tool_call_id?: string
|
||||
/** assistant 角色时的 tool_calls */
|
||||
tool_calls?: ToolCall[]
|
||||
}
|
||||
|
||||
/**
|
||||
* 聊天请求选项
|
||||
*/
|
||||
export interface ChatOptions {
|
||||
temperature?: number
|
||||
maxTokens?: number
|
||||
stream?: boolean
|
||||
/** 可用的工具列表 */
|
||||
tools?: ToolDefinition[]
|
||||
/** 中止信号，用于取消请求 */
|
||||
abortSignal?: AbortSignal
|
||||
}
|
||||
|
||||
/**
|
||||
* 非流式响应
|
||||
*/
|
||||
export interface ChatResponse {
|
||||
content: string
|
||||
finishReason: 'stop' | 'length' | 'error' | 'tool_calls'
|
||||
/** 如果 LLM 决定调用工具，返回 tool_calls */
|
||||
tool_calls?: ToolCall[]
|
||||
usage?: {
|
||||
promptTokens: number
|
||||
completionTokens: number
|
||||
totalTokens: number
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 流式响应 chunk
|
||||
*/
|
||||
export interface ChatStreamChunk {
|
||||
content: string
|
||||
isFinished: boolean
|
||||
finishReason?: 'stop' | 'length' | 'error' | 'tool_calls'
|
||||
/** 流式过程中的 tool_calls（增量） */
|
||||
tool_calls?: ToolCall[]
|
||||
/** Token 使用量（通常在最后一个 chunk 中返回） */
|
||||
usage?: {
|
||||
promptTokens: number
|
||||
completionTokens: number
|
||||
totalTokens: number
|
||||
}
|
||||
}
|
||||
|
||||
// ==================== Function Calling 相关类型 ====================
|
||||
|
||||
/**
|
||||
* 工具定义（OpenAI 兼容格式）
|
||||
*/
|
||||
export interface ToolDefinition {
|
||||
type: 'function'
|
||||
function: {
|
||||
name: string
|
||||
description: string
|
||||
parameters: {
|
||||
type: 'object'
|
||||
properties: Record<
|
||||
string,
|
||||
{
|
||||
type: string
|
||||
description: string
|
||||
enum?: string[]
|
||||
items?: { type: string }
|
||||
}
|
||||
>
|
||||
required?: string[]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 工具调用
|
||||
*/
|
||||
export interface ToolCall {
|
||||
id: string
|
||||
type: 'function'
|
||||
function: {
|
||||
name: string
|
||||
arguments: string // JSON 字符串
|
||||
}
|
||||
/** Gemini 3+ 模型需要的思考签名（用于工具调用验证） */
|
||||
thoughtSignature?: string
|
||||
}
|
||||
|
||||
/**
|
||||
* 工具调用结果
|
||||
*/
|
||||
export interface ToolCallResult {
|
||||
tool_call_id: string
|
||||
result: unknown
|
||||
}
|
||||
|
||||
/**
|
||||
* LLM 服务接口
|
||||
*/
|
||||
export interface ILLMService {
|
||||
/**
|
||||
* 获取提供商名称
|
||||
*/
|
||||
getProvider(): LLMProvider
|
||||
|
||||
/**
|
||||
* 获取可用模型列表
|
||||
*/
|
||||
getModels(): string[]
|
||||
|
||||
/**
|
||||
* 获取默认模型
|
||||
*/
|
||||
getDefaultModel(): string
|
||||
|
||||
/**
|
||||
* 发送聊天请求（非流式）
|
||||
* @param messages 消息列表
|
||||
* @param options 选项，可包含 tools 参数启用 Function Calling
|
||||
*/
|
||||
chat(messages: ChatMessage[], options?: ChatOptions): Promise<ChatResponse>
|
||||
|
||||
/**
|
||||
* 发送聊天请求（流式）
|
||||
* @param messages 消息列表
|
||||
* @param options 选项，可包含 tools 参数启用 Function Calling
|
||||
*/
|
||||
chatStream(messages: ChatMessage[], options?: ChatOptions): AsyncGenerator<ChatStreamChunk>
|
||||
|
||||
/**
|
||||
* 验证 API Key 是否有效
|
||||
* @returns 验证结果和可能的错误信息
|
||||
*/
|
||||
validateApiKey(): Promise<{ success: boolean; error?: string }>
|
||||
}
|
||||
|
||||
/**
|
||||
* 提供商信息
|
||||
*/
|
||||
|
||||
Reference in New Issue
Block a user