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293 lines
9.6 KiB
TypeScript
293 lines
9.6 KiB
TypeScript
/**
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* AI Tools 模块入口
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* 工具创建、预处理管道与管理
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*
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* 架构：工具返回结构化数据（rawMessages） → 处理层执行预处理 + 格式化 → 生成 LLM 内容
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*/
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import type { AgentTool } from '@mariozechner/pi-agent-core'
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import type { ToolContext } from './types'
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import {
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createSearchMessages,
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createGetRecentMessages,
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createGetMemberStats,
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createGetTimeStats,
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createGetMembers,
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createGetMemberNameHistory,
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createGetConversationBetween,
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createGetMessageContext,
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createSearchSessions,
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createGetSessionMessages,
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createGetSessionSummaries,
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createSemanticSearchMessages,
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sqlToolFactories,
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} from './definitions'
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import { isEmbeddingEnabled } from '../rag'
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import { t as i18nT } from '../../i18n'
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import { preprocessMessages, type PreprocessableMessage } from '../preprocessor'
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import { formatMessageCompact } from './utils/format'
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import { getSkillConfig } from '../skills'
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import type { SkillDef } from '../skills/types'
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// 导出类型
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export * from './types'
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type ToolFactory = (context: ToolContext) => AgentTool<any>
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const coreFactories: ToolFactory[] = [
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createSearchMessages,
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createGetRecentMessages,
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createGetMemberStats,
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createGetTimeStats,
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createGetMembers,
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createGetMemberNameHistory,
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createGetConversationBetween,
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createGetMessageContext,
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createSearchSessions,
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createGetSessionMessages,
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createGetSessionSummaries,
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...sqlToolFactories,
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]
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/**
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* 将工具返回的结构化数据格式化为 LLM 友好的纯文本
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*
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* 从 JSON.stringify 改为纯文本，节省 token 且更易于 LLM 理解。
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* 元数据作为头部，消息逐行排列。
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*/
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function formatToolResultAsText(details: Record<string, unknown>): string {
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const lines: string[] = []
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const messages = details.messages as string[] | undefined
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for (const [key, value] of Object.entries(details)) {
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if (key === 'messages') continue
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if (value === undefined || value === null) continue
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if (typeof value === 'object') {
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if ('start' in (value as Record<string, unknown>) && 'end' in (value as Record<string, unknown>)) {
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const range = value as { start: string; end: string }
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lines.push(`${key}: ${range.start} ~ ${range.end}`)
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} else if (Array.isArray(value)) {
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lines.push(`${key}: ${value.join(', ')}`)
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} else {
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lines.push(`${key}: ${JSON.stringify(value)}`)
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}
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} else {
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lines.push(`${key}: ${value}`)
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}
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}
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if (messages && messages.length > 0) {
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lines.push('')
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let lastDate = ''
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for (const msg of messages) {
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const spaceIdx = msg.indexOf(' ')
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const secondSpaceIdx = msg.indexOf(' ', spaceIdx + 1)
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if (spaceIdx > 0 && secondSpaceIdx > 0) {
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const date = msg.slice(0, spaceIdx)
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const rest = msg.slice(spaceIdx + 1)
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if (date !== lastDate) {
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lines.push(`--- ${date} ---`)
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lastDate = date
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}
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lines.push(rest)
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} else {
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lines.push(msg)
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}
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}
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}
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return lines.join('\n')
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}
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/**
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* 翻译 AgentTool 的描述（工具级 + 参数级）
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*
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* i18n 键命名规则：
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* - 工具描述：ai.tools.{toolName}.desc
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* - 参数描述：ai.tools.{toolName}.params.{paramName}
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*/
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function translateTool(tool: AgentTool<any>): AgentTool<any> {
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const name = tool.name
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const descKey = `ai.tools.${name}.desc`
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const translatedDesc = i18nT(descKey)
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const params = tool.parameters as Record<string, unknown>
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if (params?.properties && typeof params.properties === 'object') {
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for (const [paramName, param] of Object.entries(params.properties as Record<string, Record<string, unknown>>)) {
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const paramKey = `ai.tools.${name}.params.${paramName}`
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const translated = i18nT(paramKey)
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if (translated !== paramKey) {
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param.description = translated
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}
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}
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}
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return {
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...tool,
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description: translatedDesc !== descKey ? translatedDesc : tool.description,
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}
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}
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/**
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* 预处理包装层
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* 拦截工具的 execute 结果：如果 details 中包含 rawMessages，
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* 则执行预处理管道 + 格式化，替换为最终的 LLM 内容
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*
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* 工具约定：返回消息的工具在 details 中放置 rawMessages 字段（结构化消息数组），
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* 处理层负责 preprocess + formatMessageCompact，工具无需感知预处理逻辑。
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*/
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function wrapWithPreprocessing(tool: AgentTool<any>, context: ToolContext): AgentTool<any> {
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const originalExecute = tool.execute
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return {
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...tool,
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execute: async (toolCallId: string, params: any) => {
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const result = await originalExecute(toolCallId, params)
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const details = result.details as Record<string, unknown> | undefined
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if (!details?.rawMessages || !Array.isArray(details.rawMessages)) {
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return result
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}
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const raw = details.rawMessages as PreprocessableMessage[]
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const processed = preprocessMessages(raw, context.preprocessConfig)
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let nameMapLine = ''
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if (context.preprocessConfig?.anonymizeNames) {
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nameMapLine = anonymizeMessageNames(processed, context.ownerInfo?.platformId)
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}
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const formatted = processed.map((m) => formatMessageCompact(m, context.locale))
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const finalDetails = { ...details, messages: formatted, returned: processed.length }
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delete finalDetails.rawMessages
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let textContent = formatToolResultAsText(finalDetails)
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if (nameMapLine) {
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textContent = nameMapLine + '\n' + textContent
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}
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return {
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content: [{ type: 'text' as const, text: textContent }],
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details: finalDetails,
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}
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},
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}
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}
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/**
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* 昵称匿名化：用 U{senderId} 替代真实昵称
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* 就地修改 messages 的 senderName，返回映射表文本行
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*/
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function anonymizeMessageNames(messages: PreprocessableMessage[], ownerPlatformId?: string): string {
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const nameMap = new Map<number, { name: string; platformId?: string }>()
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for (const msg of messages) {
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if (msg.senderId != null && !nameMap.has(msg.senderId)) {
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nameMap.set(msg.senderId, { name: msg.senderName, platformId: msg.senderPlatformId })
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}
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}
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if (nameMap.size === 0) return ''
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for (const msg of messages) {
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if (msg.senderId != null) {
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msg.senderName = `U${msg.senderId}`
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}
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}
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const entries: string[] = []
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for (const [id, { name, platformId }] of nameMap) {
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const isOwner = ownerPlatformId && platformId === ownerPlatformId
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entries.push(`U${id}=${name}${isOwner ? '(owner)' : ''}`)
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}
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return `[Name Map] ${entries.join(' | ')}`
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}
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/**
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* 获取所有可用的 AgentTool
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*
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* 根据配置动态过滤工具（如：语义搜索工具仅在启用 Embedding 时可用）
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* 根据当前 locale 动态翻译工具描述
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* 统一包装预处理层
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*
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* @param context 工具上下文
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* @param allowedTools 工具名称白名单（为空或 undefined 时返回全部工具）
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*/
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export function getAllTools(context: ToolContext, allowedTools?: string[]): AgentTool<any>[] {
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let tools: AgentTool<any>[] = coreFactories.map((f) => f(context))
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if (isEmbeddingEnabled()) {
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tools.push(createSemanticSearchMessages(context))
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}
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if (allowedTools && allowedTools.length > 0) {
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tools = tools.filter((t) => allowedTools.includes(t.name))
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}
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return tools.map(translateTool).map((t) => wrapWithPreprocessing(t, context))
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}
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/**
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* 创建 activate_skill 元工具（AI 自选模式专用）
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* LLM 判断用户问题适合某个技能时调用此工具，获取技能的完整执行指导
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*/
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export function createActivateSkillTool(
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chatType: 'group' | 'private',
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allowedTools?: string[],
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locale: string = 'zh-CN'
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): AgentTool<any> {
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const isZh = locale.startsWith('zh')
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return {
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name: 'activate_skill',
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description: isZh
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? '激活一个分析技能，获取该技能的详细执行指导'
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: 'Activate an analysis skill and get its detailed execution instructions',
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parameters: {
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type: 'object',
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properties: {
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skill_id: {
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type: 'string',
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description: isZh ? '技能 ID' : 'Skill ID',
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},
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},
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required: ['skill_id'],
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},
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execute: async (_toolCallId: string, params: { skill_id: string }) => {
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const skill: SkillDef | null = getSkillConfig(params.skill_id)
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if (!skill) {
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return {
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content: [{ type: 'text' as const, text: isZh ? '技能不存在' : 'Skill not found' }],
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}
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}
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if (skill.chatScope !== 'all' && skill.chatScope !== chatType) {
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const scopeMsg = isZh
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? `该技能仅适用于${skill.chatScope === 'group' ? '群聊' : '私聊'}场景`
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: `This skill is only applicable to ${skill.chatScope === 'group' ? 'group chat' : 'private chat'} scenarios`
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return { content: [{ type: 'text' as const, text: scopeMsg }] }
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}
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if (skill.tools.length > 0 && allowedTools && allowedTools.length > 0) {
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const missing = skill.tools.filter((t) => !allowedTools.includes(t))
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if (missing.length > 0) {
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const msg = isZh
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? `当前助手缺少该技能所需的工具：${missing.join(', ')}`
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: `Current assistant lacks tools required by this skill: ${missing.join(', ')}`
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return { content: [{ type: 'text' as const, text: msg }] }
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}
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}
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const actionPrompt = isZh
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? '\n\n[System]: 你已成功加载该技能手册。现在，请立即、自动地开始执行步骤1，调用相关的基础数据工具，不要等待用户的进一步确认！'
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: '\n\n[System]: You have successfully loaded this skill manual. Now, immediately start executing step 1 by calling the relevant data tools. Do not wait for further user confirmation!'
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return {
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content: [{ type: 'text' as const, text: `${skill.prompt}${actionPrompt}` }],
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}
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},
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}
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}
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