update at 2025-10-15 15:07:45
This commit is contained in:
douboer
@@ -301,7 +301,8 @@ class ChatService {
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async sendMessageStream(
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options: SendMessageOptions,
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onChunk: (event: StreamEvent) => void,
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mcpServerId?: string // 新增：可选的 MCP 服务器 ID
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mcpServerId?: string, // 新增：可选的 MCP 服务器 ID
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signal?: AbortSignal // 新增：取消信号
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): Promise<void> {
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const { topicId, content, role = 'user', model } = options
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@@ -378,7 +379,8 @@ class ChatService {
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this.saveConversations()
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onChunk({ type: 'delta', content: chunk, messageId: assistantMessage.id })
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},
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mcpServerId // 传递 MCP 服务器 ID
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mcpServerId, // 传递 MCP 服务器 ID
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signal // 传递取消信号
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)
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assistantMessage.status = 'success'
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@@ -396,14 +398,41 @@ class ChatService {
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this.saveTopics()
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}
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} catch (error) {
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assistantMessage.status = 'error'
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assistantMessage.error = error instanceof Error ? error.message : '发送失败'
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// 检查是否是用户主动取消（参考 cherry-studio 的 PAUSED 状态）
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const isAborted = error instanceof Error && error.name === 'AbortError'
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if (isAborted) {
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// 用户主动停止，保留已生成的内容，状态标记为 paused
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console.log('⏸️ [sendMessageStream] 用户主动停止生成，保留已生成内容')
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assistantMessage.status = 'paused'
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assistantMessage.error = undefined // 清除错误信息
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} else {
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// 其他错误
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assistantMessage.status = 'error'
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assistantMessage.error = error instanceof Error ? error.message : '发送失败'
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}
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conversation.updatedAt = new Date()
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this.conversations.set(conversation.id, conversation)
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this.saveConversations()
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onChunk({
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type: 'error',
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error: assistantMessage.error,
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messageId: assistantMessage.id
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})
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if (isAborted) {
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onChunk({ type: 'paused', messageId: assistantMessage.id })
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// 更新话题（暂停）
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if (topic) {
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topic.messageCount = conversation.messages.length
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topic.lastMessage = this.getMessagePreview(assistantMessage.content)
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topic.updatedAt = new Date()
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this.topics.set(topicId, topic)
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this.saveTopics()
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}
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} else {
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onChunk({
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type: 'error',
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error: assistantMessage.error,
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messageId: assistantMessage.id
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})
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}
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}
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}
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@@ -583,31 +612,57 @@ class ChatService {
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conversation: Conversation,
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model: string | undefined,
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onChunk: (chunk: string) => void,
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mcpServerId?: string // 可选的 MCP 服务器 ID
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mcpServerId?: string, // 可选的 MCP 服务器 ID
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signal?: AbortSignal // 取消信号
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): Promise<void> {
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const streamStartTime = performance.now()
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console.log('⏱️ [callModelStream] 开始真流式处理')
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// 获取 MCP 工具列表(如果选择了 MCP 服务器)
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let tools: any[] = []
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let mcpServerName = ''
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if (mcpServerId) {
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console.log('🔧 [callModelStream] 获取 MCP 服务器工具:', mcpServerId)
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const mcpTools = this.mcpClient.getTools(mcpServerId)
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const serverInfo = this.mcpClient.getServerInfo(mcpServerId)
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mcpServerName = serverInfo?.name || 'mcp'
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console.log('🔧 [callModelStream] MCP 服务器名称:', mcpServerName)
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console.log('🔧 [callModelStream] MCP 原始工具列表:', mcpTools)
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tools = this.convertToolsToOpenAIFormat(mcpTools)
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tools = this.convertToolsToOpenAIFormat(mcpTools, mcpServerName)
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console.log('🔧 [callModelStream] 转换后的工具:', tools.length, '个', tools)
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} else {
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console.log('⚠️ [callModelStream] 未选择 MCP 服务器，不注入工具')
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}
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// 准备消息历史
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const messages = conversation.messages
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let messages = conversation.messages
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.filter(m => m.status === 'success')
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.map(m => ({
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role: m.role,
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content: m.content
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}))
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// 如果有工具，添加系统提示词指导 AI 使用工具
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if (tools.length > 0 && messages.length > 0 && messages[0].role !== 'system') {
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const systemPrompt = this.createSystemPromptWithTools(tools, mcpServerName)
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messages = [
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{ role: 'system', content: systemPrompt },
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...messages
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]
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}
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console.log('━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━')
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console.log('🎯 [callModelStream] === 完整的消息列表 ===')
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console.log(' 消息总数:', messages.length)
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messages.forEach((msg, idx) => {
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console.log(` 消息 [${idx}]:`, {
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role: msg.role,
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content: msg.content?.substring(0, 100) + (msg.content?.length > 100 ? '...' : ''),
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contentLength: msg.content?.length || 0
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})
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})
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console.log('━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━')
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// 获取已连接的服务
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const allServices = modelServiceManager.getAllServices()
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const services = allServices.filter(s => s.status === 'connected')
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@@ -673,7 +728,8 @@ class ChatService {
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onChunk(output)
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}
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},
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tools.length > 0 ? tools : undefined
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tools.length > 0 ? tools : undefined,
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signal // 传递取消信号
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)
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// 输出剩余的缓冲区内容
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@@ -690,9 +746,21 @@ class ChatService {
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}
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// 处理工具调用
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console.log('🔍 [callModelStream] 检查工具调用:', {
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hasData: !!result.data,
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hasToolCalls: !!result.data?.toolCalls,
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toolCallsCount: result.data?.toolCalls?.length || 0,
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hasMcpServerId: !!mcpServerId,
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mcpServerId,
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toolCalls: result.data?.toolCalls
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})
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if (result.data?.toolCalls && result.data.toolCalls.length > 0 && mcpServerId) {
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console.log('🔧 [callModelStream] 开始执行工具调用')
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await this.executeToolCalls(conversation, result.data.toolCalls, mcpServerId, model, onChunk)
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console.log('🔧 [callModelStream] 开始执行工具调用，共', result.data.toolCalls.length, '个')
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// 传递 tools 参数，让 AI 可以继续调用其他工具
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await this.executeToolCalls(conversation, result.data.toolCalls, mcpServerId, model, onChunk, tools)
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} else {
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console.log('⚠️ [callModelStream] 没有工具调用需要执行')
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}
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const endTime = performance.now()
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@@ -821,13 +889,73 @@ class ChatService {
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}
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/**
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* 将 MCP 工具转换为 OpenAI 函数调用格式
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* 创建包含工具信息的系统提示词
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* @param tools OpenAI 格式的工具列表
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* @param serverName MCP 服务器名称
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*/
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private convertToolsToOpenAIFormat(mcpTools: any[]): any[] {
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private createSystemPromptWithTools(tools: any[], serverName: string): string {
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console.log('━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━')
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console.log('📝 [createSystemPromptWithTools] 开始生成 System Prompt')
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console.log(' - 服务器名称:', serverName)
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console.log(' - 工具数量:', tools.length)
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console.log('━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━')
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const toolDescriptions = tools.map(tool => {
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const func = tool.function
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const params = func.parameters?.properties || {}
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const required = func.parameters?.required || []
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console.log(` 工具: ${func.name}`)
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console.log(` 描述: ${func.description}`)
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// 生成参数描述
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const paramDesc = Object.entries(params).map(([name, schema]: [string, any]) => {
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const isRequired = required.includes(name)
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const requiredMark = isRequired ? '[必填]' : '[可选]'
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return ` - ${name} ${requiredMark}: ${schema.description || schema.type}`
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}).join('\n')
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return `• ${func.name}\n 描述: ${func.description}\n 参数:\n${paramDesc || ' 无参数'}`
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}).join('\n\n')
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const systemPrompt = `你是一个智能助手，可以使用以下工具完成任务：
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${toolDescriptions}
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使用指南：
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1. 当用户需要完成某个任务时，请分析哪个工具最合适
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2. 如果需要发布内容（如文章、笔记等），请根据用户意图创作完整的内容
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3. 为内容生成合适的标题、正文、标签等所有必需参数
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4. 自动调用相应工具，将生成的内容作为参数传递
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5. 根据工具执行结果，给用户友好的反馈
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注意事项：
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- **标题必须控制在20字以内**（重要！超过会导致发布失败）
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- 保持内容质量和平台特色
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- 标签要相关且有吸引力
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- 分类要准确
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- 如果工具执行失败，给出明确的错误说明和建议
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当前连接的 MCP 服务器: ${serverName}`
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console.log('━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━')
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console.log('📝 [createSystemPromptWithTools] === System Prompt 内容 ===')
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console.log(systemPrompt)
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console.log('━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━')
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return systemPrompt
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}
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/**
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* 将 MCP 工具转换为 OpenAI 函数调用格式
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* @param mcpTools MCP 工具列表
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* @param serverName 服务器名称，用于工具名称前缀
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*/
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private convertToolsToOpenAIFormat(mcpTools: any[], serverName: string): any[] {
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return mcpTools.map(tool => ({
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type: 'function',
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function: {
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name: tool.name,
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name: `${serverName}__${tool.name}`, // 添加服务器前缀避免冲突
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description: tool.description || '',
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parameters: tool.inputSchema || {
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type: 'object',
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@@ -846,7 +974,8 @@ class ChatService {
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toolCalls: any[],
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mcpServerId: string,
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model: string | undefined,
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onChunk: (chunk: string) => void
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onChunk: (chunk: string) => void,
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tools?: any[] // 添加 tools 参数
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): Promise<void> {
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console.log('🔧 [executeToolCalls] 执行', toolCalls.length, '个工具调用')
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@@ -861,21 +990,32 @@ class ChatService {
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const toolResults = []
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for (const toolCall of toolCalls) {
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try {
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const functionName = toolCall.function.name
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const fullFunctionName = toolCall.function.name
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// 解析工具名称：serverName__toolName
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const toolName = fullFunctionName.includes('__')
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? fullFunctionName.split('__')[1]
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: fullFunctionName
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const functionArgs = JSON.parse(toolCall.function.arguments)
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console.log(`🔧 [executeToolCalls] 调用工具: ${functionName}`, functionArgs)
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onChunk(`\n\n🔧 正在调用工具: ${functionName}...\n`)
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console.log('━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━')
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console.log(`🔧 [executeToolCalls] 工具调用详情:`)
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console.log(` - 完整工具名: ${fullFunctionName}`)
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console.log(` - 提取工具名: ${toolName}`)
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console.log(` - MCP服务器ID: ${mcpServerId}`)
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console.log(` - 参数:`, JSON.stringify(functionArgs, null, 2))
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console.log('━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━')
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onChunk(`\n\n🔧 正在调用工具: ${toolName}...\n`)
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const result = await this.mcpClient.callTool(mcpServerId, functionName, functionArgs)
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const result = await this.mcpClient.callTool(mcpServerId, toolName, functionArgs)
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console.log(`✅ [executeToolCalls] 工具调用成功: ${functionName}`, result)
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console.log(`✅ [executeToolCalls] 工具调用成功: ${toolName}`, result)
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onChunk(`✅ 工具执行完成\n`)
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toolResults.push({
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tool_call_id: toolCall.id,
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role: 'tool',
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name: functionName,
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name: fullFunctionName, // 保持与 AI 调用时的名称一致
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content: JSON.stringify(result)
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})
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} catch (error) {
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@@ -925,14 +1065,24 @@ class ChatService {
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// 向 AI 发送工具结果,获取最终回复
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console.log('🤖 [executeToolCalls] 将工具结果发送给 AI')
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console.log('🔧 [executeToolCalls] 继续传递工具列表:', tools?.length || 0, '个')
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onChunk('\n\n🤖 正在生成回复...\n')
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await modelServiceManager.sendChatRequestStream(
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const result = await modelServiceManager.sendChatRequestStream(
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service.id,
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messages,
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selectedModel,
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onChunk
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onChunk,
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tools // ← 传递工具列表，让 AI 可以继续调用工具
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)
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// 递归处理：如果 AI 再次调用工具，继续执行
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if (result.data?.toolCalls && result.data.toolCalls.length > 0) {
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console.log('🔁 [executeToolCalls] AI 再次调用工具，递归执行:', result.data.toolCalls.length, '个')
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await this.executeToolCalls(conversation, result.data.toolCalls, mcpServerId, model, onChunk, tools)
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} else {
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console.log('✅ [executeToolCalls] 工具调用链完成')
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}
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}
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/**
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Reference in New Issue
Block a user