mirror of
https://github.com/hellodigua/ChatLab.git
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digua
835 lines
31 KiB
TypeScript
835 lines
31 KiB
TypeScript
/**
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* AI Agent 执行器
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* 处理 Function Calling 循环，支持多轮工具调用
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*/
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import type { ChatMessage, ChatOptions, ChatStreamChunk, ToolCall } from './llm/types'
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import { chatStream, chat } from './llm'
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import { getAllToolDefinitions, executeToolCalls } from './tools'
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import type { ToolContext, OwnerInfo } from './tools/types'
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import { aiLogger } from './logger'
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import { randomUUID } from 'crypto'
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// ==================== Fallback 解析器 ====================
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/**
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* 从文本内容中提取 <think> 标签内容
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*/
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function extractThinkingContent(content: string): { thinking: string; cleanContent: string } {
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const thinkRegex = /<think>([\s\S]*?)<\/think>/gi
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let thinking = ''
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let cleanContent = content
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const matches = content.matchAll(thinkRegex)
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for (const match of matches) {
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thinking += match[1].trim() + '\n'
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cleanContent = cleanContent.replace(match[0], '')
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}
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return { thinking: thinking.trim(), cleanContent: cleanContent.trim() }
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}
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/**
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* 从文本内容中解析 <tool_call> 标签并转换为标准 ToolCall 格式
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*/
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function parseToolCallTags(content: string): ToolCall[] | null {
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const toolCallRegex = /<tool_call>\s*([\s\S]*?)\s*<\/tool_call>/gi
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const toolCalls: ToolCall[] = []
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const matches = content.matchAll(toolCallRegex)
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for (const match of matches) {
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try {
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const jsonStr = match[1].trim()
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const parsed = JSON.parse(jsonStr)
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if (parsed.name && parsed.arguments) {
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toolCalls.push({
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id: `fallback-${randomUUID()}`,
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type: 'function',
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function: {
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name: parsed.name,
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arguments: typeof parsed.arguments === 'string' ? parsed.arguments : JSON.stringify(parsed.arguments),
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},
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})
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}
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} catch (e) {
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aiLogger.warn('Agent', 'Failed to parse tool_call tag', { content: match[1], error: String(e) })
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}
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}
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return toolCalls.length > 0 ? toolCalls : null
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}
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/**
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* 检测内容是否包含工具调用标签（用于判断是否需要 fallback 解析）
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*/
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function hasToolCallTags(content: string): boolean {
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return /<tool_call>/i.test(content)
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}
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/**
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* Agent 配置
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*/
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export interface AgentConfig {
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/** 最大工具调用轮数（防止无限循环） */
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maxToolRounds?: number
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/** LLM 选项 */
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llmOptions?: ChatOptions
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/** 中止信号，用于取消执行 */
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abortSignal?: AbortSignal
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}
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/**
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* Token 使用量
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*/
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export interface TokenUsage {
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promptTokens: number
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completionTokens: number
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totalTokens: number
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}
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/**
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* Agent 流式响应 chunk
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*/
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export interface AgentStreamChunk {
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/** chunk 类型 */
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type: 'content' | 'tool_start' | 'tool_result' | 'done' | 'error'
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/** 文本内容（type=content 时） */
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content?: string
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/** 工具名称（type=tool_start/tool_result 时） */
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toolName?: string
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/** 工具调用参数（type=tool_start 时） */
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toolParams?: Record<string, unknown>
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/** 工具执行结果（type=tool_result 时） */
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toolResult?: unknown
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/** 错误信息（type=error 时） */
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error?: string
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/** 是否完成 */
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isFinished?: boolean
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/** Token 使用量（type=done 时返回累计值） */
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usage?: TokenUsage
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}
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/**
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* Agent 执行结果
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*/
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export interface AgentResult {
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/** 最终文本响应 */
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content: string
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/** 使用的工具列表 */
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toolsUsed: string[]
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/** 工具调用轮数 */
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toolRounds: number
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/** 总 Token 使用量（累计所有 LLM 调用） */
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totalUsage?: TokenUsage
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}
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// ==================== 提示词配置类型 ====================
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/**
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* 用户自定义提示词配置
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*/
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export interface PromptConfig {
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/** 角色定义（可编辑区） */
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roleDefinition: string
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/** 回答要求（可编辑区） */
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responseRules: string
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}
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// 国际化内容
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const i18nContent = {
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'zh-CN': {
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currentDateIs: '当前日期是',
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chatTypeDesc: { private: '私聊记录', group: '群聊记录' },
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chatContext: { private: '对话', group: '群聊' },
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ownerNote: (displayName: string, platformId: string, chatContext: string) => `当前用户身份：
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- 用户在${chatContext}中的身份是「${displayName}」（platformId: ${platformId}）
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- 当用户提到"我"、"我的"时，指的就是「${displayName}」
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- 查询"我"的发言时，使用 sender_id 参数筛选该成员
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`,
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memberNotePrivate: `成员查询策略：
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- 私聊只有两个人，可以直接获取成员列表
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- 当用户提到"对方"、"他/她"时，通过 get_group_members 获取另一方信息
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`,
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memberNoteGroup: `成员查询策略：
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- 当用户提到特定群成员（如"张三说过什么"、"小明的发言"等）时，应先调用 get_group_members 获取成员列表
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- 群成员有三种名称：accountName（原始昵称）、groupNickname（群昵称）、aliases（用户自定义别名）
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- 通过 get_group_members 的 search 参数可以模糊搜索这三种名称
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- 找到成员后，使用其 id 字段作为 search_messages 的 sender_id 参数来获取该成员的发言
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`,
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toolsIntro: (chatTypeDesc: string) => `你可以使用以下工具来获取${chatTypeDesc}数据：`,
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toolDescriptions: [
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'search_messages - 根据关键词搜索聊天记录，支持时间筛选和发送者筛选',
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'get_recent_messages - 获取指定时间段的聊天消息',
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'get_member_stats - 获取成员活跃度统计',
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'get_time_stats - 获取时间分布统计',
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'get_group_members - 获取成员列表，包括 id、QQ号、账号名称、昵称、别名和消息统计',
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'get_member_name_history - 获取成员的昵称变更历史，需要先通过 get_group_members 获取成员 ID',
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'get_conversation_between - 获取两个成员之间的对话记录，需要先通过 get_group_members 获取两人的成员 ID',
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'get_message_context - 根据消息 ID 获取前后的上下文消息，支持批量查询，消息 ID 可从其他搜索工具的返回结果中获取',
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],
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timeParamsIntro: '时间参数：按用户提到的精度组合 year/month/day/hour',
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timeParamExample1: (year: number) => `"10月" → year: ${year}, month: 10`,
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timeParamExample2: (year: number) => `"10月1号" → year: ${year}, month: 10, day: 1`,
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timeParamExample3: (year: number) => `"10月1号下午3点" → year: ${year}, month: 10, day: 1, hour: 15`,
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defaultYearNote: (year: number, prevYear: number) => `未指定年份默认${year}年，若该月份未到则用${prevYear}年`,
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responseInstruction: '根据用户的问题，选择合适的工具获取数据，然后基于数据给出回答。',
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responseRulesTitle: '回答要求：',
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fallbackRoleDefinition: (chatType: string) => `你是一个专业的${chatType}记录分析助手。
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你的任务是帮助用户理解和分析他们的${chatType}记录数据。`,
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fallbackResponseRules: `1. 基于工具返回的数据回答，不要编造信息
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2. 如果数据不足以回答问题，请说明
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3. 回答要简洁明了，使用 Markdown 格式`,
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},
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'en-US': {
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currentDateIs: 'Current date is',
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chatTypeDesc: { private: 'private chat records', group: 'group chat records' },
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chatContext: { private: 'conversation', group: 'group chat' },
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ownerNote: (displayName: string, platformId: string, chatContext: string) => `Current user identity:
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- The user's identity in this ${chatContext} is "${displayName}" (platformId: ${platformId})
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- When the user refers to "I" or "my", it refers to "${displayName}"
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- When querying "my" messages, use the sender_id parameter to filter for this member
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`,
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memberNotePrivate: `Member query strategy:
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- Private chats only have two participants, so the member list can be directly obtained
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- When the user refers to "the other party" or "he/she", get the other participant's information via get_group_members
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`,
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memberNoteGroup: `Member query strategy:
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- When the user refers to specific group members (e.g., "what did John say", "Mary's messages"), first call get_group_members to get the member list
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- Group members have three names: accountName (original nickname), groupNickname (group nickname), aliases (user-defined aliases)
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- The search parameter of get_group_members can be used for fuzzy searching these three names
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- Once a member is found, use their id field as the sender_id parameter for search_messages to retrieve their messages
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`,
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toolsIntro: (chatTypeDesc: string) => `You can use the following tools to get ${chatTypeDesc} data:`,
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toolDescriptions: [
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'search_messages - Search chat records by keywords, supports time and sender filtering',
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'get_recent_messages - Get chat messages for a specified time period',
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'get_member_stats - Get member activity statistics',
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'get_time_stats - Get time distribution statistics',
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'get_group_members - Get member list, including ID, QQ number, account name, nickname, aliases, and message statistics',
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'get_member_name_history - Get member nickname change history, requires member ID from get_group_members first',
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'get_conversation_between - Get conversation records between two members, requires member IDs from get_group_members first',
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'get_message_context - Get context messages before and after a message ID, supports batch queries, message ID can be obtained from other search tool results',
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],
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timeParamsIntro: 'Time parameters: combine year/month/day/hour based on user mention',
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timeParamExample1: (year: number) => `"October" → year: ${year}, month: 10`,
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timeParamExample2: (year: number) => `"October 1st" → year: ${year}, month: 10, day: 1`,
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timeParamExample3: (year: number) => `"October 1st 3 PM" → year: ${year}, month: 10, day: 1, hour: 15`,
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defaultYearNote: (year: number, prevYear: number) =>
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`If year is not specified, defaults to ${year}. If the month has not yet occurred, ${prevYear} is used.`,
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responseInstruction:
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"Based on the user's question, select appropriate tools to retrieve data, then provide an answer based on the data.",
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responseRulesTitle: 'Response requirements:',
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fallbackRoleDefinition: (chatType: string) => `You are a professional ${chatType} analysis assistant.
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Your task is to help users understand and analyze their ${chatType} data.`,
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fallbackResponseRules: `1. Answer based on data returned by tools, do not fabricate information
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2. If data is insufficient to answer, please state so
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3. Keep answers concise and clear, use Markdown format`,
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},
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}
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/**
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* 获取系统锁定部分的提示词（工具说明、时间处理等）
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* @param chatType 聊天类型 ('group' | 'private')
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* @param ownerInfo Owner 信息（当前用户在对话中的身份）
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* @param locale 语言设置
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*/
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function getLockedPromptSection(
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chatType: 'group' | 'private',
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ownerInfo?: OwnerInfo,
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locale: string = 'zh-CN'
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): string {
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const content = i18nContent[locale as keyof typeof i18nContent] || i18nContent['zh-CN']
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const now = new Date()
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const dateLocale = locale === 'zh-CN' ? 'zh-CN' : 'en-US'
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const currentDate = now.toLocaleDateString(dateLocale, {
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year: 'numeric',
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month: 'long',
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day: 'numeric',
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weekday: 'long',
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})
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const isPrivate = chatType === 'private'
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const chatTypeDesc = content.chatTypeDesc[chatType]
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const chatContext = content.chatContext[chatType]
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// Owner 说明（当用户设置了"我是谁"时）
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const ownerNote = ownerInfo ? content.ownerNote(ownerInfo.displayName, ownerInfo.platformId, chatContext) : ''
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// 成员说明（私聊只有2人）
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const memberNote = isPrivate ? content.memberNotePrivate : content.memberNoteGroup
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const toolsList = content.toolDescriptions.map((desc, i) => `${i + 1}. ${desc}`).join('\n')
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const year = now.getFullYear()
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const prevYear = year - 1
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return `${content.currentDateIs} ${currentDate}。
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${ownerNote}
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${content.toolsIntro(chatTypeDesc)}
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${toolsList}
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${memberNote}
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${content.timeParamsIntro}
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- ${content.timeParamExample1(year)}
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- ${content.timeParamExample2(year)}
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- ${content.timeParamExample3(year)}
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${content.defaultYearNote(year, prevYear)}
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${content.responseInstruction}`
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}
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/**
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* 获取 Fallback 角色定义（主要配置来自前端 src/config/prompts.ts）
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* 仅在前端未传递 promptConfig 时使用
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*/
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function getFallbackRoleDefinition(chatType: 'group' | 'private', locale: string = 'zh-CN'): string {
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const content = i18nContent[locale as keyof typeof i18nContent] || i18nContent['zh-CN']
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const chatTypeDesc = chatType === 'private' ? (locale === 'zh-CN' ? '私聊' : 'private chat') : (locale === 'zh-CN' ? '群聊' : 'group chat')
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return content.fallbackRoleDefinition(chatTypeDesc)
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}
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/**
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* 获取 Fallback 回答要求（主要配置来自前端 src/config/prompts.ts）
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* 仅在前端未传递 promptConfig 时使用
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*/
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function getFallbackResponseRules(locale: string = 'zh-CN'): string {
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const content = i18nContent[locale as keyof typeof i18nContent] || i18nContent['zh-CN']
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return content.fallbackResponseRules
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}
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/**
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* 构建完整的系统提示词
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*
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* 提示词配置主要来自前端 src/config/prompts.ts，通过 promptConfig 参数传递。
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* Fallback 仅在前端未传递配置时使用（一般不会发生）。
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*
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* @param chatType 聊天类型 ('group' | 'private')
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* @param promptConfig 用户自定义提示词配置（来自前端激活的预设）
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* @param ownerInfo Owner 信息（当前用户在对话中的身份）
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* @param locale 语言设置
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*/
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function buildSystemPrompt(
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chatType: 'group' | 'private' = 'group',
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promptConfig?: PromptConfig,
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ownerInfo?: OwnerInfo,
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locale: string = 'zh-CN'
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): string {
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const content = i18nContent[locale as keyof typeof i18nContent] || i18nContent['zh-CN']
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// 使用用户配置或 fallback
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const roleDefinition = promptConfig?.roleDefinition || getFallbackRoleDefinition(chatType, locale)
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const responseRules = promptConfig?.responseRules || getFallbackResponseRules(locale)
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// 获取锁定的系统部分（包含动态日期、工具说明和 Owner 信息）
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const lockedSection = getLockedPromptSection(chatType, ownerInfo, locale)
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// 组合完整提示词
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return `${roleDefinition}
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${lockedSection}
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${content.responseRulesTitle}
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${responseRules}`
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}
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/**
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* Agent 执行器类
|
||
* 处理带 Function Calling 的对话流程
|
||
*/
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export class Agent {
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private context: ToolContext
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private config: AgentConfig
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private messages: ChatMessage[] = []
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private toolsUsed: string[] = []
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private toolRounds: number = 0
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private abortSignal?: AbortSignal
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private historyMessages: ChatMessage[] = []
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private chatType: 'group' | 'private' = 'group'
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private promptConfig?: PromptConfig
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private locale: string = 'zh-CN'
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/** 累计 Token 使用量 */
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private totalUsage: TokenUsage = { promptTokens: 0, completionTokens: 0, totalTokens: 0 }
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constructor(
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context: ToolContext,
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config: AgentConfig = {},
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historyMessages: ChatMessage[] = [],
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chatType: 'group' | 'private' = 'group',
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promptConfig?: PromptConfig,
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locale: string = 'zh-CN'
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) {
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this.context = context
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this.abortSignal = config.abortSignal
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this.historyMessages = historyMessages
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this.chatType = chatType
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this.promptConfig = promptConfig
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this.locale = locale
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this.config = {
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maxToolRounds: config.maxToolRounds ?? 5,
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llmOptions: config.llmOptions ?? { temperature: 0.7, maxTokens: 2048 },
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}
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}
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||
|
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/**
|
||
* 检查是否已中止
|
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*/
|
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private isAborted(): boolean {
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return this.abortSignal?.aborted ?? false
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}
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/**
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* 累加 Token 使用量
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*/
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||
private addUsage(usage?: { promptTokens: number; completionTokens: number; totalTokens: number }): void {
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if (usage) {
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this.totalUsage.promptTokens += usage.promptTokens
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this.totalUsage.completionTokens += usage.completionTokens
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this.totalUsage.totalTokens += usage.totalTokens
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}
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}
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/**
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* 执行对话（非流式）
|
||
* @param userMessage 用户消息
|
||
*/
|
||
async execute(userMessage: string): Promise<AgentResult> {
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||
aiLogger.info('Agent', '用户问题', userMessage)
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||
|
||
// 检查是否已中止
|
||
if (this.isAborted()) {
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||
return { content: '', toolsUsed: [], toolRounds: 0, totalUsage: this.totalUsage }
|
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}
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// 初始化消息（包含历史记录）
|
||
const systemPrompt = buildSystemPrompt(this.chatType, this.promptConfig, this.context.ownerInfo, this.locale)
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this.messages = [
|
||
{ role: 'system', content: systemPrompt },
|
||
...this.historyMessages, // 插入历史对话
|
||
{ role: 'user', content: userMessage },
|
||
]
|
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this.toolsUsed = []
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||
this.toolRounds = 0
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||
|
||
// 获取所有工具定义
|
||
const tools = await getAllToolDefinitions()
|
||
|
||
// 执行循环
|
||
while (this.toolRounds < this.config.maxToolRounds!) {
|
||
// 每轮开始时检查是否中止
|
||
if (this.isAborted()) {
|
||
return {
|
||
content: '',
|
||
toolsUsed: this.toolsUsed,
|
||
toolRounds: this.toolRounds,
|
||
totalUsage: this.totalUsage,
|
||
}
|
||
}
|
||
|
||
const response = await chat(this.messages, {
|
||
...this.config.llmOptions,
|
||
tools,
|
||
abortSignal: this.abortSignal,
|
||
})
|
||
|
||
// 累加 Token 使用量
|
||
this.addUsage(response.usage)
|
||
|
||
let toolCallsToProcess = response.tool_calls
|
||
|
||
// 如果没有标准 tool_calls，尝试 fallback 解析
|
||
if (response.finishReason !== 'tool_calls' || !response.tool_calls) {
|
||
// Fallback: 检查内容中是否有 <tool_call> 标签
|
||
if (hasToolCallTags(response.content)) {
|
||
// 提取 thinking 内容
|
||
const { cleanContent } = extractThinkingContent(response.content)
|
||
|
||
// 解析 tool_call 标签
|
||
const fallbackToolCalls = parseToolCallTags(response.content)
|
||
if (fallbackToolCalls && fallbackToolCalls.length > 0) {
|
||
toolCallsToProcess = fallbackToolCalls
|
||
} else {
|
||
// 解析失败，返回清理后的内容
|
||
aiLogger.info('Agent', 'AI 回复', cleanContent)
|
||
return {
|
||
content: cleanContent,
|
||
toolsUsed: this.toolsUsed,
|
||
toolRounds: this.toolRounds,
|
||
totalUsage: this.totalUsage,
|
||
}
|
||
}
|
||
} else {
|
||
// 没有 tool_call 标签，正常完成
|
||
aiLogger.info('Agent', 'AI 回复', response.content)
|
||
return {
|
||
content: response.content,
|
||
toolsUsed: this.toolsUsed,
|
||
toolRounds: this.toolRounds,
|
||
totalUsage: this.totalUsage,
|
||
}
|
||
}
|
||
}
|
||
|
||
// 处理工具调用
|
||
await this.handleToolCalls(toolCallsToProcess!)
|
||
this.toolRounds++
|
||
}
|
||
|
||
// 超过最大轮数，强制让 LLM 总结
|
||
aiLogger.warn('Agent', '达到最大工具调用轮数', { maxRounds: this.config.maxToolRounds })
|
||
this.messages.push({
|
||
role: 'user',
|
||
content: '请根据已获取的信息给出回答，不要再调用工具。',
|
||
})
|
||
|
||
const finalResponse = await chat(this.messages, this.config.llmOptions)
|
||
this.addUsage(finalResponse.usage)
|
||
return {
|
||
content: finalResponse.content,
|
||
toolsUsed: this.toolsUsed,
|
||
toolRounds: this.toolRounds,
|
||
totalUsage: this.totalUsage,
|
||
}
|
||
}
|
||
|
||
/**
|
||
* 执行对话（流式）
|
||
* @param userMessage 用户消息
|
||
* @param onChunk 流式回调
|
||
*/
|
||
async executeStream(userMessage: string, onChunk: (chunk: AgentStreamChunk) => void): Promise<AgentResult> {
|
||
aiLogger.info('Agent', '用户问题', userMessage)
|
||
|
||
// 检查是否已中止
|
||
if (this.isAborted()) {
|
||
onChunk({ type: 'done', isFinished: true, usage: this.totalUsage })
|
||
return { content: '', toolsUsed: [], toolRounds: 0, totalUsage: this.totalUsage }
|
||
}
|
||
|
||
// 初始化消息（包含历史记录）
|
||
const systemPrompt = buildSystemPrompt(this.chatType, this.promptConfig, this.context.ownerInfo, this.locale)
|
||
this.messages = [
|
||
{ role: 'system', content: systemPrompt },
|
||
...this.historyMessages, // 插入历史对话
|
||
{ role: 'user', content: userMessage },
|
||
]
|
||
this.toolsUsed = []
|
||
this.toolRounds = 0
|
||
|
||
const tools = await getAllToolDefinitions()
|
||
let finalContent = ''
|
||
|
||
// 执行循环
|
||
while (this.toolRounds < this.config.maxToolRounds!) {
|
||
// 每轮开始时检查是否中止
|
||
if (this.isAborted()) {
|
||
onChunk({ type: 'done', isFinished: true, usage: this.totalUsage })
|
||
return {
|
||
content: finalContent,
|
||
toolsUsed: this.toolsUsed,
|
||
toolRounds: this.toolRounds,
|
||
totalUsage: this.totalUsage,
|
||
}
|
||
}
|
||
|
||
let accumulatedContent = ''
|
||
let displayedContent = '' // 已发送给前端的内容
|
||
let toolCalls: ToolCall[] | undefined
|
||
let isBufferingToolCall = false // 是否正在缓冲 tool_call 内容
|
||
let isBufferingThink = false // 是否正在缓冲 <think> 内容
|
||
|
||
// 流式调用 LLM（传入 abortSignal）
|
||
for await (const chunk of chatStream(this.messages, {
|
||
...this.config.llmOptions,
|
||
tools,
|
||
abortSignal: this.abortSignal,
|
||
})) {
|
||
// 每个 chunk 时检查是否中止
|
||
if (this.isAborted()) {
|
||
onChunk({ type: 'done', isFinished: true, usage: this.totalUsage })
|
||
return {
|
||
content: finalContent + accumulatedContent,
|
||
toolsUsed: this.toolsUsed,
|
||
toolRounds: this.toolRounds,
|
||
totalUsage: this.totalUsage,
|
||
}
|
||
}
|
||
if (chunk.content) {
|
||
accumulatedContent += chunk.content
|
||
|
||
// 检测是否开始出现 <think> 标签（过滤思考内容）
|
||
if (!isBufferingThink && /<think>/i.test(accumulatedContent)) {
|
||
isBufferingThink = true
|
||
// 发送 <think> 标签之前的内容
|
||
const thinkStart = accumulatedContent.toLowerCase().indexOf('<think>')
|
||
if (thinkStart > displayedContent.length) {
|
||
const newContent = accumulatedContent.slice(displayedContent.length, thinkStart)
|
||
if (newContent) {
|
||
onChunk({ type: 'content', content: newContent })
|
||
displayedContent = accumulatedContent.slice(0, thinkStart)
|
||
}
|
||
}
|
||
}
|
||
|
||
// 检测 </think> 结束标签，退出思考缓冲模式
|
||
if (isBufferingThink && /<\/think>/i.test(accumulatedContent)) {
|
||
isBufferingThink = false
|
||
// 跳过 <think>...</think> 内容，更新 displayedContent
|
||
const thinkEnd = accumulatedContent.toLowerCase().indexOf('</think>') + '</think>'.length
|
||
displayedContent = accumulatedContent.slice(0, thinkEnd)
|
||
}
|
||
|
||
// 如果正在缓冲思考内容，不发送
|
||
if (isBufferingThink) {
|
||
continue
|
||
}
|
||
|
||
// 检测是否开始出现 <tool_call> 标签（用于 fallback 解析）
|
||
if (!isBufferingToolCall) {
|
||
if (/<tool_call>/i.test(accumulatedContent)) {
|
||
isBufferingToolCall = true
|
||
// 发送标签之前的内容（如果有）
|
||
const tagStart = accumulatedContent.indexOf('<tool_call>')
|
||
if (tagStart > displayedContent.length) {
|
||
const newContent = accumulatedContent.slice(displayedContent.length, tagStart)
|
||
if (newContent) {
|
||
onChunk({ type: 'content', content: newContent })
|
||
displayedContent = accumulatedContent.slice(0, tagStart)
|
||
}
|
||
}
|
||
} else {
|
||
// 正常发送内容（但要排除已发送的部分）
|
||
const newContent = accumulatedContent.slice(displayedContent.length)
|
||
if (newContent) {
|
||
onChunk({ type: 'content', content: newContent })
|
||
displayedContent = accumulatedContent
|
||
}
|
||
}
|
||
}
|
||
// 如果已经在缓冲模式，不发送内容
|
||
}
|
||
|
||
if (chunk.tool_calls) {
|
||
toolCalls = chunk.tool_calls
|
||
}
|
||
|
||
// 累加 Token 使用量（流式响应在最后一个 chunk 返回 usage）
|
||
if (chunk.usage) {
|
||
this.addUsage(chunk.usage)
|
||
}
|
||
|
||
if (chunk.isFinished) {
|
||
// 如果没有标准 tool_calls，尝试 fallback 解析
|
||
if (chunk.finishReason !== 'tool_calls' || !toolCalls) {
|
||
// Fallback: 检查内容中是否有 <tool_call> 标签
|
||
if (hasToolCallTags(accumulatedContent)) {
|
||
// 提取 thinking 内容
|
||
const { cleanContent } = extractThinkingContent(accumulatedContent)
|
||
|
||
// 解析 tool_call 标签
|
||
const fallbackToolCalls = parseToolCallTags(accumulatedContent)
|
||
if (fallbackToolCalls && fallbackToolCalls.length > 0) {
|
||
toolCalls = fallbackToolCalls
|
||
// 更新累积内容为清理后的内容（移除 think 和 tool_call 标签）
|
||
accumulatedContent = cleanContent.replace(/<tool_call>[\s\S]*?<\/tool_call>/gi, '').trim()
|
||
// 不返回，继续执行工具调用
|
||
} else {
|
||
// 解析失败，作为普通响应处理
|
||
const remainingContent = cleanContent.slice(displayedContent.length)
|
||
if (remainingContent) {
|
||
onChunk({ type: 'content', content: remainingContent })
|
||
}
|
||
finalContent = cleanContent
|
||
aiLogger.info('Agent', 'AI 回复', finalContent)
|
||
onChunk({ type: 'done', isFinished: true, usage: this.totalUsage })
|
||
return {
|
||
content: finalContent,
|
||
toolsUsed: this.toolsUsed,
|
||
toolRounds: this.toolRounds,
|
||
totalUsage: this.totalUsage,
|
||
}
|
||
}
|
||
} else {
|
||
// 没有 tool_call 标签，正常完成
|
||
finalContent = extractThinkingContent(accumulatedContent).cleanContent
|
||
aiLogger.info('Agent', 'AI 回复', finalContent)
|
||
onChunk({ type: 'done', isFinished: true, usage: this.totalUsage })
|
||
return {
|
||
content: finalContent,
|
||
toolsUsed: this.toolsUsed,
|
||
toolRounds: this.toolRounds,
|
||
totalUsage: this.totalUsage,
|
||
}
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
// 处理工具调用
|
||
if (toolCalls && toolCalls.length > 0) {
|
||
// 通知前端开始执行工具（包含参数和时间范围）
|
||
for (const tc of toolCalls) {
|
||
let toolParams: Record<string, unknown> | undefined
|
||
try {
|
||
toolParams = JSON.parse(tc.function.arguments || '{}')
|
||
|
||
// 对于消息获取类工具，用用户配置的 limit 覆盖 LLM 传递的值（保持显示一致）
|
||
const toolsWithLimit = ['search_messages', 'get_recent_messages', 'get_conversation_between']
|
||
if (this.context.maxMessagesLimit && toolsWithLimit.includes(tc.function.name)) {
|
||
toolParams = {
|
||
...toolParams,
|
||
limit: this.context.maxMessagesLimit, // 用户配置优先
|
||
}
|
||
}
|
||
|
||
// 对于搜索类工具，添加时间范围信息
|
||
if (
|
||
this.context.timeFilter &&
|
||
(tc.function.name === 'search_messages' || tc.function.name === 'get_recent_messages')
|
||
) {
|
||
toolParams = {
|
||
...toolParams,
|
||
_timeFilter: this.context.timeFilter,
|
||
}
|
||
}
|
||
} catch {
|
||
toolParams = undefined
|
||
}
|
||
onChunk({ type: 'tool_start', toolName: tc.function.name, toolParams })
|
||
}
|
||
|
||
await this.handleToolCalls(toolCalls, onChunk)
|
||
this.toolRounds++
|
||
}
|
||
}
|
||
|
||
// 超过最大轮数
|
||
aiLogger.warn('Agent', '达到最大工具调用轮数', { maxRounds: this.config.maxToolRounds })
|
||
|
||
// 检查是否已中止
|
||
if (this.isAborted()) {
|
||
onChunk({ type: 'done', isFinished: true, usage: this.totalUsage })
|
||
return {
|
||
content: finalContent,
|
||
toolsUsed: this.toolsUsed,
|
||
toolRounds: this.toolRounds,
|
||
totalUsage: this.totalUsage,
|
||
}
|
||
}
|
||
|
||
this.messages.push({
|
||
role: 'user',
|
||
content: '请根据已获取的信息给出回答，不要再调用工具。',
|
||
})
|
||
|
||
// 最后一轮不带 tools（传入 abortSignal）
|
||
for await (const chunk of chatStream(this.messages, {
|
||
...this.config.llmOptions,
|
||
abortSignal: this.abortSignal,
|
||
})) {
|
||
if (this.isAborted()) {
|
||
onChunk({ type: 'done', isFinished: true, usage: this.totalUsage })
|
||
break
|
||
}
|
||
if (chunk.content) {
|
||
finalContent += chunk.content
|
||
onChunk({ type: 'content', content: chunk.content })
|
||
}
|
||
// 累加 Token 使用量
|
||
if (chunk.usage) {
|
||
this.addUsage(chunk.usage)
|
||
}
|
||
if (chunk.isFinished) {
|
||
onChunk({ type: 'done', isFinished: true, usage: this.totalUsage })
|
||
}
|
||
}
|
||
|
||
return {
|
||
content: finalContent,
|
||
toolsUsed: this.toolsUsed,
|
||
toolRounds: this.toolRounds,
|
||
totalUsage: this.totalUsage,
|
||
}
|
||
}
|
||
|
||
/**
|
||
* 处理工具调用
|
||
*/
|
||
private async handleToolCalls(toolCalls: ToolCall[], onChunk?: (chunk: AgentStreamChunk) => void): Promise<void> {
|
||
// 记录调用的工具及参数
|
||
for (const tc of toolCalls) {
|
||
aiLogger.info('Agent', `工具调用: ${tc.function.name}`, tc.function.arguments)
|
||
}
|
||
|
||
// 添加 assistant 消息（包含 tool_calls）
|
||
this.messages.push({
|
||
role: 'assistant',
|
||
content: '',
|
||
tool_calls: toolCalls,
|
||
})
|
||
|
||
// 执行工具（传递 locale 用于工具返回结果的国际化）
|
||
const results = await executeToolCalls(toolCalls, { ...this.context, locale: this.locale })
|
||
|
||
// 添加 tool 消息
|
||
for (let i = 0; i < toolCalls.length; i++) {
|
||
const tc = toolCalls[i]
|
||
const result = results[i]
|
||
|
||
this.toolsUsed.push(tc.function.name)
|
||
|
||
// 通知前端工具执行结果
|
||
if (onChunk) {
|
||
onChunk({
|
||
type: 'tool_result',
|
||
toolName: tc.function.name,
|
||
toolResult: result.success ? result.result : result.error,
|
||
})
|
||
}
|
||
|
||
// 记录工具执行结果
|
||
if (result.success) {
|
||
aiLogger.info('Agent', `工具结果: ${tc.function.name}`, result.result)
|
||
} else {
|
||
aiLogger.warn('Agent', `工具失败: ${tc.function.name}`, result.error)
|
||
}
|
||
|
||
// 添加工具结果消息
|
||
this.messages.push({
|
||
role: 'tool',
|
||
content: result.success ? JSON.stringify(result.result) : `错误: ${result.error}`,
|
||
tool_call_id: tc.id,
|
||
})
|
||
}
|
||
}
|
||
}
|
||
|
||
/**
|
||
* 创建 Agent 并执行对话（便捷函数）
|
||
*/
|
||
export async function runAgent(
|
||
userMessage: string,
|
||
context: ToolContext,
|
||
config?: AgentConfig,
|
||
historyMessages?: ChatMessage[],
|
||
chatType?: 'group' | 'private'
|
||
): Promise<AgentResult> {
|
||
const agent = new Agent(context, config, historyMessages, chatType)
|
||
return agent.execute(userMessage)
|
||
}
|
||
|
||
/**
|
||
* 创建 Agent 并流式执行对话（便捷函数）
|
||
*/
|
||
export async function runAgentStream(
|
||
userMessage: string,
|
||
context: ToolContext,
|
||
onChunk: (chunk: AgentStreamChunk) => void,
|
||
config?: AgentConfig,
|
||
historyMessages?: ChatMessage[],
|
||
chatType?: 'group' | 'private'
|
||
): Promise<AgentResult> {
|
||
const agent = new Agent(context, config, historyMessages, chatType)
|
||
return agent.executeStream(userMessage, onChunk)
|
||
}
|