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646efa132e
| Author | SHA1 | Date | |
|---|---|---|---|
| 646efa132e | |||
| eecbe4172e |
@@ -99,9 +99,162 @@ export async function uploadToKnowledgeBase(baseUrl, datasetId, file, secretLeve
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return resp.json()
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}
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/**
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* 按模型编码（code）获取平台模型配置
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* 接口：GET ${baseUrl}/api/system/model/getByCode/{code}
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* 返回结构：data.providerModels.{ modelName, encryptedConfig:"{apiKey,endpointUrl}" }
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* @param {string} baseUrl - 平台地址
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* @param {string} code - 模型编码，默认 generic（通用模型）
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* @returns {Promise<{modelName: string, apiKey: string, endpointUrl: string}>}
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*/
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export async function getWanchuanModelConfig(baseUrl, code = 'generic') {
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const resp = await fetch(`${baseUrl}/api/system/model/getByCode/${encodeURIComponent(code)}`, {
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method: 'GET',
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headers: authHeaders(baseUrl),
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})
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if (!resp.ok) throw new Error(`获取模型[${code}]失败 HTTP ${resp.status}`)
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const result = await resp.json()
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const pm = result?.data?.providerModels
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if (!pm) throw new Error(`平台未返回模型[${code}]配置${result?.msg ? `：${result.msg}` : ''}`)
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// encryptedConfig 是 JSON 字符串：{ apiKey, endpointUrl, ... }
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let cfg = {}
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if (pm.encryptedConfig) {
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try { cfg = JSON.parse(pm.encryptedConfig) } catch { cfg = {} }
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}
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return {
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modelName: pm.modelName || '',
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apiKey: cfg.apiKey || '',
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endpointUrl: cfg.endpointUrl || '',
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}
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}
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/**
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* 从万川平台拉取模型配置并写入后端 AI 设置（/api/settings）。
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*
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* 平台按用途分多个模型编码（code）：
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* - generic：通用聊天模型 → 话题分析(ai_model) + 报告生成(summary_model)，
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* 并提供共享的接口地址(ai_base_url) 与密钥(ai_api_key)
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* - vision ：视觉模型 → vision_model
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* - voice ：语音模型 → voice_model
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*
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* 仅写入拉取到的非空字段；vision/voice 拉取失败时跳过对应字段（不影响聊天模型同步），
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* 避免某个 code 在平台未配置时把整次同步打断或清空已有配置。
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*
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* 后端 voice/vision 各有独立的 base_url / api_key / model；为空时后端回退到
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* generic 的 ai_base_url / ai_api_key。这里按各自 code 分别拉取并写入对应字段。
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*
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* @param {string} [baseUrl] - 平台地址，缺省时从后端万川配置读取
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* @param {{chat?: string, vision?: string, voice?: string}} [codes] - 各用途模型编码
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* @returns {Promise<object>} 实际写入 /api/settings 的字段
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*/
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export async function syncWanchuanModelToSettings(baseUrl, codes = {}) {
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const { chat = 'generic', vision = 'vision', voice = 'voice' } = codes
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let url = baseUrl
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if (!url) {
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const cfg = await getWanchuanConfig()
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url = cfg.platformUrl
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}
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if (!url) throw new Error('未配置平台地址')
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// 通用聊天模型：必拉，提供共享地址/密钥与两个聊天模型。
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// 话题分析用 ai_*；报告生成单独存 summary_*（同 generic 网关，便于各组独立回显与覆盖）。
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const generic = await getWanchuanModelConfig(url, chat)
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/** @type {Record<string, string>} */
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const payload = {}
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if (generic.endpointUrl) {
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payload.ai_base_url = generic.endpointUrl
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payload.summary_base_url = generic.endpointUrl
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}
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if (generic.apiKey) {
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payload.ai_api_key = generic.apiKey
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payload.summary_api_key = generic.apiKey
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}
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if (generic.modelName) {
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payload.ai_model = generic.modelName
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payload.summary_model = generic.modelName
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}
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// 视觉 / 语音模型：可选，各自按 code 拉取独立地址/密钥/模型名；失败则跳过
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await Promise.all([
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getWanchuanModelConfig(url, vision)
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.then(v => {
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if (v.endpointUrl) payload.vision_base_url = v.endpointUrl
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if (v.apiKey) payload.vision_api_key = v.apiKey
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if (v.modelName) payload.vision_model = v.modelName
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})
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.catch(() => {}),
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getWanchuanModelConfig(url, voice)
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.then(v => {
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if (v.endpointUrl) payload.voice_base_url = v.endpointUrl
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if (v.apiKey) payload.voice_api_key = v.apiKey
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if (v.modelName) payload.voice_model = v.modelName
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})
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.catch(() => {}),
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])
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if (Object.keys(payload).length === 0) throw new Error('平台模型配置为空')
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const resp = await fetch('/api/settings', {
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method: 'PUT',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify(payload),
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})
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if (!resp.ok) throw new Error('保存 AI 设置失败')
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return payload
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}
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/**
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* 清除缓存的 token
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*/
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export function clearWanchuanToken() {
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try { localStorage.removeItem(TOKEN_KEY) } catch {}
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}
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// ─────────────────────────────────────────────
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// 万川平台配置：后端为唯一数据源（/api/settings/wanchuan，同源相对路径）
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//
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// 配置（平台地址、账号、密码、已选知识库）存在后端 SQLite，与前端 origin 无关，
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// 桌面应用（exe）端口/origin 变化也能正常读取。用时直接请求后端，不再依赖 localStorage。
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// ─────────────────────────────────────────────
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/** 读取万川配置；无数据或失败返回 {} */
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export async function getWanchuanConfig() {
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try {
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const resp = await fetch('/api/settings/wanchuan')
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if (!resp.ok) return {}
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const data = await resp.json()
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return (data && typeof data === 'object') ? data : {}
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} catch {
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return {}
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}
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}
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/** 保存万川配置到后端 */
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export async function saveWanchuanConfig(config) {
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try {
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await fetch('/api/settings/wanchuan', {
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method: 'PUT',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify(config || {}),
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})
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} catch {}
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}
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/**
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* 获取当前绑定的知识库信息（供报告上传等处调用）。
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* 直接从后端读取配置，返回归一化结构；未绑定返回 null。
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*/
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export async function getBoundKnowledgeBase() {
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const cfg = await getWanchuanConfig()
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if (!cfg.selectedKbId) return null
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const info = cfg.selectedKbInfo || {}
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return {
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id: info.kbDatasetId || cfg.selectedKbId,
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platformUrl: cfg.platformUrl,
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kbName: info.kbName || cfg.selectedKbId,
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kbDatasetId: info.kbDatasetId || cfg.selectedKbId,
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kbType: info.kbType,
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}
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}
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@@ -2,8 +2,7 @@ import { useState, useEffect, useCallback } from 'react'
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import { Search, RefreshCw, Edit3, Check, X, Download, UploadCloud } from 'lucide-react'
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import dayjs from 'dayjs'
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import { getKnowledge, getKnowledgeDoc, patchKnowledge } from '../api'
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import { uploadToKnowledgeBase } from '../api/wanchuan'
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import { getBoundKnowledgeBase } from './SettingsPage'
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import { uploadToKnowledgeBase, getBoundKnowledgeBase } from '../api/wanchuan'
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import ReportDocumentView from '../components/ReportDocumentView'
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import { exportWordDoc } from '../utils/wordExport'
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@@ -87,7 +86,7 @@ export default function KnowledgePage({ onToast }) {
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onToast?.('暂无可上传的售后报告', 'error')
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return
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}
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const bound = getBoundKnowledgeBase()
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const bound = await getBoundKnowledgeBase()
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// 上传接口必须使用 datasetId
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const datasetId = bound?.kbDatasetId || bound?.id
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if (!bound?.platformUrl || !datasetId) {
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@@ -138,7 +137,7 @@ export default function KnowledgePage({ onToast }) {
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onToast?.('请先勾选要上传的报告', 'error')
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return
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}
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const bound = getBoundKnowledgeBase()
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const bound = await getBoundKnowledgeBase()
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const datasetId = bound?.kbDatasetId || bound?.id
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if (!bound?.platformUrl || !datasetId) {
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onToast?.('未绑定知识库，请先到「设置」选择万川知识库', 'error')
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@@ -1,6 +1,6 @@
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import { useState, useEffect } from 'react'
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import { useState, useEffect, useRef } from 'react'
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import { Copy, Check, Save, Link2, Loader, AlertCircle, ChevronDown, ChevronRight, RefreshCw, BookOpen, ExternalLink, FileText } from 'lucide-react'
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import { wanchuanLogin, getWanchuanKnowledgeBases, uploadToKnowledgeBase, clearWanchuanToken } from '../api/wanchuan'
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import { wanchuanLogin, getWanchuanKnowledgeBases, uploadToKnowledgeBase, clearWanchuanToken, getWanchuanConfig, saveWanchuanConfig, syncWanchuanModelToSettings } from '../api/wanchuan'
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const CONFIG_ITEMS = [
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{
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@@ -27,13 +27,41 @@ const CONFIG_ITEMS = [
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},
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]
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const AI_FIELDS = [
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{ key: 'ai_base_url', label: 'AI 接口地址', placeholder: 'https://dashscope.aliyuncs.com/compatible-mode/v1', desc: '兼容 OpenAI 格式的 API 地址' },
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{ key: 'ai_api_key', label: 'AI API Key', placeholder: 'sk-...', desc: '留空则 AI 功能不可用', type: 'password' },
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{ key: 'ai_model', label: '话题分析模型', placeholder: 'qwen-plus', desc: '用于消息分类的模型' },
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{ key: 'summary_model', label: '报告生成模型', placeholder: 'qwen-max', desc: '用于生成售后报告的模型' },
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{ key: 'vision_model', label: '视觉模型', placeholder: 'qwen-vl-plus', desc: '用于图片/视频描述' },
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{ key: 'voice_model', label: '语音模型', placeholder: 'paraformer-v2', desc: '用于语音转文字' },
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// 按模块分组：每个模块独立展示自己的接口地址 + 密钥 + 模型。
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// 话题分析用 ai_*（也是视觉/语音/报告的回退网关）；报告生成用 summary_*。
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const AI_GROUPS = [
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{
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group: '话题分析模型',
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fields: [
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{ key: 'ai_base_url', label: '接口地址', placeholder: 'https://dashscope.aliyuncs.com/compatible-mode/v1', desc: '兼容 OpenAI 格式的 API 地址' },
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{ key: 'ai_api_key', label: '密钥', placeholder: 'sk-...', desc: '留空则 AI 功能不可用', type: 'password' },
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{ key: 'ai_model', label: '模型', placeholder: 'qwen-plus', desc: '用于消息分类的模型' },
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],
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},
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{
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group: '报告生成模型',
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fields: [
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{ key: 'summary_base_url', label: '接口地址', placeholder: '留空则用话题分析地址', desc: '独立网关；留空回退话题分析接口地址' },
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{ key: 'summary_api_key', label: '密钥', placeholder: '留空则用话题分析密钥', desc: '留空回退话题分析密钥', type: 'password' },
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{ key: 'summary_model', label: '模型', placeholder: 'qwen-max', desc: '用于生成售后报告的模型' },
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],
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},
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{
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group: '视觉模型（图片 / 视频描述）',
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fields: [
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{ key: 'vision_base_url', label: '接口地址', placeholder: '留空则用话题分析地址', desc: '独立网关；留空回退话题分析接口地址' },
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{ key: 'vision_api_key', label: '密钥', placeholder: '留空则用话题分析密钥', desc: '留空回退话题分析密钥', type: 'password' },
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{ key: 'vision_model', label: '模型', placeholder: 'qwen-vl-plus', desc: '用于图片/视频描述' },
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],
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},
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{
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group: '语音模型（语音转文字）',
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fields: [
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{ key: 'voice_base_url', label: '接口地址', placeholder: '留空则用话题分析地址', desc: '异步 ASR 网关，填到 .../api/v1；留空回退话题分析地址' },
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{ key: 'voice_api_key', label: '密钥', placeholder: '留空则用话题分析密钥', desc: '留空回退话题分析密钥', type: 'password' },
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{ key: 'voice_model', label: '模型', placeholder: 'paraformer-v2', desc: '用于语音转文字' },
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],
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},
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]
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const TOPIC_PROMPT_PLACEHOLDER = '例如：本群主要是某类设备售后群，请优先按设备部件、故障现象、处理进度来拆分话题；不要按客户名或日期拆分。'
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@@ -57,17 +85,37 @@ function CopyButton({ text }) {
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)
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}
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function AISettingsForm() {
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function AISettingsForm({ refreshKey = 0 }) {
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const [form, setForm] = useState({})
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const [saving, setSaving] = useState(false)
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const [syncing, setSyncing] = useState(false)
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const [msg, setMsg] = useState('')
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useEffect(() => {
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const loadSettings = () =>
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fetch('/api/settings')
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.then(r => r.json())
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.then(data => setForm(data))
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.catch(() => {})
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}, [])
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// refreshKey 由父级在万川平台登录/同步后递增，触发重新读取已写入的 AI 配置
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useEffect(() => {
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loadSettings()
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}, [refreshKey])
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// 手动从万川平台拉取模型配置（base url / api key / 各模型），写入后端并回显
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const handleSyncFromPlatform = async () => {
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setSyncing(true)
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setMsg('')
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try {
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await syncWanchuanModelToSettings()
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await loadSettings()
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setMsg('已从平台获取')
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setTimeout(() => setMsg(''), 2000)
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} catch (e) {
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setMsg(e.message || '获取失败')
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}
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setSyncing(false)
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}
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const handleChange = (key, value) => {
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setForm(prev => ({ ...prev, [key]: value }))
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@@ -102,7 +150,7 @@ function AISettingsForm() {
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AI 模型配置
|
||||
</div>
|
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<div style={{ fontSize: 11, color: 'var(--text-muted)', marginBottom: 12 }}>
|
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首次使用请填入你的 API Key 和接口地址，保存后立即生效，无需重启服务。
|
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已对接万川平台时，登录后会自动从平台获取模型配置；也可点「从平台获取」手动同步。字段支持手动修改并保存。
|
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</div>
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|
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{/* 未配置 API Key 时显示橙色警告横条 */}
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@@ -117,15 +165,18 @@ function AISettingsForm() {
|
||||
</div>
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)}
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{AI_GROUPS.map((g) => (
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<div key={g.group} style={{ marginBottom: 14 }}>
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<div style={{ fontSize: 12, fontWeight: 600, color: 'var(--text)', marginBottom: 6 }}>{g.group}</div>
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<div style={{ border: '1px solid var(--border)', borderRadius: 10, overflow: 'hidden' }}>
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{AI_FIELDS.map((field, i) => (
|
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{g.fields.map((field, i) => (
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<div
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key={field.key}
|
||||
style={{
|
||||
display: 'flex',
|
||||
alignItems: 'center',
|
||||
padding: '12px 16px',
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borderBottom: i < AI_FIELDS.length - 1 ? '1px solid var(--border)' : 'none',
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||||
borderBottom: i < g.fields.length - 1 ? '1px solid var(--border)' : 'none',
|
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gap: 12,
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||||
}}
|
||||
>
|
||||
@@ -161,6 +212,8 @@ function AISettingsForm() {
|
||||
</div>
|
||||
))}
|
||||
</div>
|
||||
</div>
|
||||
))}
|
||||
<div style={{ marginTop: 14 }}>
|
||||
<div style={{ fontSize: 13, fontWeight: 500, marginBottom: 4 }}>AI 话题分析提示词</div>
|
||||
<div style={{ fontSize: 11, color: 'var(--text-muted)', marginBottom: 8 }}>
|
||||
@@ -207,7 +260,27 @@ function AISettingsForm() {
|
||||
<Save size={14} />
|
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{saving ? '保存中...' : '保存配置'}
|
||||
</button>
|
||||
{msg && <span style={{ fontSize: 12, color: msg === '已保存' ? 'var(--success, #10b981)' : '#ef4444' }}>{msg}</span>}
|
||||
<button
|
||||
onClick={handleSyncFromPlatform}
|
||||
disabled={syncing}
|
||||
title="从已对接的万川平台拉取模型配置并填入"
|
||||
style={{
|
||||
display: 'flex', alignItems: 'center', gap: 6,
|
||||
padding: '8px 16px',
|
||||
background: 'transparent',
|
||||
color: 'var(--accent, #6366f1)',
|
||||
border: '1px solid var(--accent, #6366f1)',
|
||||
borderRadius: 8,
|
||||
fontSize: 13,
|
||||
fontWeight: 500,
|
||||
cursor: syncing ? 'not-allowed' : 'pointer',
|
||||
opacity: syncing ? 0.6 : 1,
|
||||
}}
|
||||
>
|
||||
{syncing ? <Loader size={14} style={{ animation: 'spin 1s linear infinite' }} /> : <RefreshCw size={14} />}
|
||||
{syncing ? '获取中...' : '从平台获取'}
|
||||
</button>
|
||||
{msg && <span style={{ fontSize: 12, color: (msg === '已保存' || msg === '已从平台获取') ? 'var(--success, #10b981)' : '#ef4444' }}>{msg}</span>}
|
||||
</div>
|
||||
</div>
|
||||
)
|
||||
@@ -217,8 +290,7 @@ function AISettingsForm() {
|
||||
* 万川 AI 平台对接配置
|
||||
* 支持输入平台地址 + 账号密码，测试连接后可展示平台模块内容（预留）
|
||||
*/
|
||||
function WanchuanPlatformForm() {
|
||||
const STORAGE_KEY = 'chatlab_wanchuan_config'
|
||||
function WanchuanPlatformForm({ onModelSynced }) {
|
||||
const [form, setForm] = useState({ platformUrl: '', username: '', password: '' })
|
||||
const [connecting, setConnecting] = useState(false)
|
||||
const [connected, setConnected] = useState(false)
|
||||
@@ -233,60 +305,115 @@ function WanchuanPlatformForm() {
|
||||
const [kbLoading, setKbLoading] = useState(false)
|
||||
const [kbError, setKbError] = useState('')
|
||||
const [kbExpanded, setKbExpanded] = useState(false)
|
||||
// 模型同步状态：成功提示已回填，失败展示原因（便于排查路径/token/平台未配模型）
|
||||
const [modelSyncMsg, setModelSyncMsg] = useState('')
|
||||
const [modelSyncError, setModelSyncError] = useState(false)
|
||||
|
||||
// 从 localStorage 加载已保存配置
|
||||
// 后端为唯一数据源：打开设置页时拉取已保存配置并填入表单，
|
||||
// 然后按已保存凭证自动恢复会话（拉取知识库列表）。
|
||||
useEffect(() => {
|
||||
try {
|
||||
const raw = localStorage.getItem(STORAGE_KEY)
|
||||
if (raw) setForm(JSON.parse(raw))
|
||||
} catch {}
|
||||
}, [])
|
||||
|
||||
// 从 localStorage 加载已选知识库；若有缓存 token + 平台地址则自动拉取列表回显
|
||||
useEffect(() => {
|
||||
try {
|
||||
const raw = localStorage.getItem(STORAGE_KEY)
|
||||
if (!raw) return
|
||||
const saved = JSON.parse(raw)
|
||||
let cancelled = false
|
||||
getWanchuanConfig().then(saved => {
|
||||
if (cancelled || !saved || Object.keys(saved).length === 0) return
|
||||
const loadedForm = {
|
||||
platformUrl: saved.platformUrl || '',
|
||||
username: saved.username || '',
|
||||
password: saved.password || '',
|
||||
}
|
||||
setForm(loadedForm)
|
||||
// 同步更新 ref，确保紧接着的 fetchKnowledgeBases → handleSave 用到的是
|
||||
// 刚加载的凭证，而不是首次渲染闭包里的空表单（否则会把后端配置覆盖成空）。
|
||||
formRef.current = loadedForm
|
||||
if (saved.selectedKbId) setSelectedKbId(saved.selectedKbId)
|
||||
if (saved.selectedKbInfo) setSavedKbInfo(saved.selectedKbInfo)
|
||||
// 已登录过（有缓存 token）则自动拉取知识库列表，无需手动点连接
|
||||
if (!saved.platformUrl) return
|
||||
const token = localStorage.getItem('chatlab_wanchuan_token')
|
||||
if (token && saved.platformUrl) {
|
||||
fetchKnowledgeBases(saved.platformUrl)
|
||||
if (token) {
|
||||
fetchKnowledgeBases(saved.platformUrl, { allowRelogin: true, savedKbId: saved.selectedKbId, credsForRelogin: saved })
|
||||
} else if (saved.username && saved.password) {
|
||||
autoLogin(saved).then(ok => {
|
||||
if (ok) {
|
||||
fetchKnowledgeBases(saved.platformUrl, { savedKbId: saved.selectedKbId })
|
||||
}
|
||||
} catch {}
|
||||
})
|
||||
}
|
||||
// 注意：挂载时不自动 syncModel。自动同步会用平台配置覆盖用户在「AI 模型配置」里
|
||||
// 手动改过并保存的值（曾导致"改了保存后重进页面又被重置"）。模型同步只在用户
|
||||
// 显式操作时进行：点「测试连接」或「从平台获取」。
|
||||
})
|
||||
return () => { cancelled = true }
|
||||
}, [])
|
||||
|
||||
// 防抖保存：把最新待存配置放进 ref，停止输入 500ms 后写一次后端。
|
||||
// 既避免每次按键都发 PUT（并发乱序可能让后端存到中间态），
|
||||
// 也保证切到别的页（组件卸载）前把挂起的修改 flush 掉，不丢数据。
|
||||
const pendingConfigRef = useRef(null)
|
||||
const saveTimerRef = useRef(null)
|
||||
|
||||
// 始终持有最新表单值。persistConfig 不再依赖渲染闭包里的 form：
|
||||
// 挂载时自动恢复会跑首次渲染闭包里的 fetchKnowledgeBases → handleSave，
|
||||
// 那时闭包 form 还是空的，若直接 ...form 会把后端凭证覆盖成空。读 ref 可避免。
|
||||
const formRef = useRef(form)
|
||||
formRef.current = form
|
||||
|
||||
const flushSave = () => {
|
||||
if (saveTimerRef.current) {
|
||||
clearTimeout(saveTimerRef.current)
|
||||
saveTimerRef.current = null
|
||||
}
|
||||
if (pendingConfigRef.current) {
|
||||
saveWanchuanConfig(pendingConfigRef.current)
|
||||
pendingConfigRef.current = null
|
||||
}
|
||||
}
|
||||
|
||||
useEffect(() => {
|
||||
// 卸载（切换标签页）时把挂起的保存立即写出
|
||||
return () => flushSave()
|
||||
}, [])
|
||||
|
||||
// 把当前完整配置写入后端（平台地址/账号/密码 + 已选知识库）。
|
||||
// kbIdOverride / listOverride 用于在 React 状态尚未刷新时（setState 异步）传入即时值。
|
||||
// immediate=true 时跳过防抖立即保存（如选择知识库、测试连接成功后）。
|
||||
const persistConfig = (nextForm, kbIdOverride, listOverride, immediate = false) => {
|
||||
const effectiveKbId = kbIdOverride !== undefined ? kbIdOverride : selectedKbId
|
||||
const kbs = listOverride || knowledgeBases
|
||||
const selectedKb = kbs.find(k => kbKey(k) === effectiveKbId) || null
|
||||
const data = {
|
||||
...nextForm,
|
||||
selectedKbId: effectiveKbId || '',
|
||||
selectedKbInfo: selectedKb ? {
|
||||
selectedKbId: kbKey(selectedKb),
|
||||
kbName: selectedKb.relationName || selectedKb.id,
|
||||
kbDatasetId: selectedKb.datasetId || selectedKb.id,
|
||||
kbType: selectedKb.kbType,
|
||||
} : (savedKbInfo || null),
|
||||
}
|
||||
pendingConfigRef.current = data
|
||||
if (immediate) {
|
||||
flushSave()
|
||||
return
|
||||
}
|
||||
if (saveTimerRef.current) clearTimeout(saveTimerRef.current)
|
||||
saveTimerRef.current = setTimeout(flushSave, 500)
|
||||
}
|
||||
|
||||
const handleChange = (key, value) => {
|
||||
setForm(prev => ({ ...prev, [key]: value }))
|
||||
const next = { ...form, [key]: value }
|
||||
setForm(next)
|
||||
if (connected) setConnected(false)
|
||||
// 改字段防抖存回后端，避免未点「测试连接」就离开导致凭证丢失。
|
||||
persistConfig(next)
|
||||
}
|
||||
|
||||
// 知识库的唯一标识：上传接口需要的是 datasetId，没有则回退到 id。
|
||||
// 全程以此作为 selectedKbId，保证选择、回显、持久化、上传四处一致。
|
||||
const kbKey = (kb) => String(kb?.datasetId ?? kb?.id ?? '')
|
||||
|
||||
// 持久化整个知识库配置。
|
||||
// kbIdOverride / listOverride 用于在 React 状态尚未刷新时（setState 异步）
|
||||
// 传入即时值，避免读到旧的 selectedKbId 或旧的知识库列表。
|
||||
// 知识库选择 / 测试连接后的保存走立即写，确保关键操作不被防抖延迟。
|
||||
// 用 formRef.current 而非闭包 form：挂载恢复时跑的是首次渲染闭包，form 还是空的。
|
||||
const handleSave = (kbIdOverride, listOverride) => {
|
||||
try {
|
||||
const effectiveKbId = kbIdOverride !== undefined ? kbIdOverride : selectedKbId
|
||||
const kbs = listOverride || knowledgeBases
|
||||
const selectedKb = kbs.find(k => kbKey(k) === effectiveKbId) || null
|
||||
const data = {
|
||||
...form,
|
||||
selectedKbId: effectiveKbId,
|
||||
selectedKbInfo: selectedKb ? {
|
||||
selectedKbId: kbKey(selectedKb),
|
||||
kbName: selectedKb.relationName || selectedKb.id,
|
||||
kbDatasetId: selectedKb.datasetId || selectedKb.id,
|
||||
kbType: selectedKb.kbType,
|
||||
} : null,
|
||||
}
|
||||
localStorage.setItem(STORAGE_KEY, JSON.stringify(data))
|
||||
} catch {}
|
||||
persistConfig(formRef.current, kbIdOverride, listOverride, true)
|
||||
}
|
||||
|
||||
const handleSelectKb = (key) => {
|
||||
@@ -318,9 +445,21 @@ function WanchuanPlatformForm() {
|
||||
return uploadToKnowledgeBase(form.platformUrl, targetDatasetId, file)
|
||||
}
|
||||
|
||||
// 获取当前绑定的知识库信息（供外部调用）
|
||||
const getBoundKnowledgeBase = () => {
|
||||
return selectedKb || null
|
||||
// 登录成功后从平台拉取模型配置并写入后端 AI 设置；成功后通知父级刷新 AI 表单。
|
||||
// 同步结果用 modelSyncMsg/Error 展示，便于排查（路径错=404、token 失效=401、平台未配模型等）；
|
||||
// 不阻断主流程（知识库等仍可用）。
|
||||
const syncModel = async (baseUrl) => {
|
||||
setModelSyncMsg('')
|
||||
setModelSyncError(false)
|
||||
try {
|
||||
const written = await syncWanchuanModelToSettings(baseUrl)
|
||||
onModelSynced?.()
|
||||
const n = Object.keys(written || {}).length
|
||||
setModelSyncMsg(`已从平台同步模型配置（${n} 项），已回填到 AI 模型配置`)
|
||||
} catch (e) {
|
||||
setModelSyncError(true)
|
||||
setModelSyncMsg(`模型配置同步失败：${e.message || '未知错误'}`)
|
||||
}
|
||||
}
|
||||
|
||||
const handleTestConnection = async () => {
|
||||
@@ -348,8 +487,9 @@ function WanchuanPlatformForm() {
|
||||
setConnected(true)
|
||||
handleSave()
|
||||
|
||||
// 登录成功后自动拉取岗位知识库列表
|
||||
// 登录成功后自动拉取岗位知识库列表 + 同步平台模型配置
|
||||
fetchKnowledgeBases(form.platformUrl)
|
||||
syncModel(form.platformUrl)
|
||||
} catch (e) {
|
||||
setConnectError(e.message || '登录失败')
|
||||
} finally {
|
||||
@@ -357,20 +497,53 @@ function WanchuanPlatformForm() {
|
||||
}
|
||||
}
|
||||
|
||||
const fetchKnowledgeBases = async (baseUrl) => {
|
||||
// 用已保存的账号密码静默重新登录，刷新缓存 token。
|
||||
// saved 缺省用当前表单（重登时表单已填好）；首次加载时由调用方传入后端配置。
|
||||
// 成功返回 true；失败返回 false（不抛出，由调用方决定后续）。
|
||||
const autoLogin = async (saved = form) => {
|
||||
try {
|
||||
if (!saved.platformUrl || !saved.username || !saved.password) return false
|
||||
const { token, data } = await wanchuanLogin(saved.platformUrl, saved.username, saved.password)
|
||||
if (!token) return false
|
||||
setModules(data.modules || [])
|
||||
setConnected(true)
|
||||
return true
|
||||
} catch {
|
||||
return false
|
||||
}
|
||||
}
|
||||
|
||||
const fetchKnowledgeBases = async (baseUrl, { allowRelogin = false, savedKbId, credsForRelogin } = {}) => {
|
||||
// 首次加载时 selectedKbId 状态可能还没刷新到，用传入的 savedKbId 兜底
|
||||
const targetKbId = savedKbId !== undefined ? savedKbId : selectedKbId
|
||||
setKbLoading(true)
|
||||
setKbError('')
|
||||
try {
|
||||
const { list } = await getWanchuanKnowledgeBases(baseUrl, { relationType: 'position', page: 1, pageSize: 100 })
|
||||
let list
|
||||
try {
|
||||
({ list } = await getWanchuanKnowledgeBases(baseUrl, { relationType: 'position', page: 1, pageSize: 100 }))
|
||||
} catch (e) {
|
||||
// token 可能已过期：用已保存凭证自动重登一次后重试。
|
||||
// credsForRelogin 用于首次加载场景——此时 form 状态尚未刷新，
|
||||
// 必须显式传入后端读到的凭证，否则 autoLogin 拿到的是空表单。
|
||||
const isAuthErr = /\b401\b|\b403\b/.test(e.message || '')
|
||||
if (allowRelogin && isAuthErr && await autoLogin(credsForRelogin)) {
|
||||
({ list } = await getWanchuanKnowledgeBases(baseUrl, { relationType: 'position', page: 1, pageSize: 100 }))
|
||||
} else {
|
||||
throw e
|
||||
}
|
||||
}
|
||||
setKnowledgeBases(list)
|
||||
setConnected(true)
|
||||
// 登录后恢复之前保存的已选知识库（按统一的 kbKey 匹配）
|
||||
const keys = new Set(list.map(kb => kbKey(kb)))
|
||||
if (selectedKbId && keys.has(selectedKbId)) {
|
||||
// 已选项仍在列表中：用完整信息重新持久化（登录时曾因列表为空被写成 null）
|
||||
handleSave(selectedKbId, list)
|
||||
} else if (selectedKbId) {
|
||||
if (targetKbId && keys.has(targetKbId)) {
|
||||
// 已选项仍在列表中：用完整信息重新持久化
|
||||
setSelectedKbId(targetKbId)
|
||||
handleSave(targetKbId, list)
|
||||
} else if (targetKbId) {
|
||||
// 兼容旧数据：之前可能保存的是 kb.id，尝试映射到 datasetId
|
||||
const matchById = list.find(kb => String(kb.id) === selectedKbId)
|
||||
const matchById = list.find(kb => String(kb.id) === targetKbId)
|
||||
if (matchById) {
|
||||
const newKey = kbKey(matchById)
|
||||
setSelectedKbId(newKey)
|
||||
@@ -389,7 +562,8 @@ function WanchuanPlatformForm() {
|
||||
}
|
||||
|
||||
const handleRefreshKB = () => {
|
||||
if (form.platformUrl) fetchKnowledgeBases(form.platformUrl)
|
||||
// 手动刷新也允许 token 过期时自动重登，避免必须重新点「测试连接」
|
||||
if (form.platformUrl) fetchKnowledgeBases(form.platformUrl, { allowRelogin: true })
|
||||
}
|
||||
|
||||
const kbTypeLabel = (type) => ({
|
||||
@@ -488,6 +662,11 @@ function WanchuanPlatformForm() {
|
||||
<span style={{ display: 'flex', alignItems: 'center', gap: 4, fontSize: 12, color: 'var(--danger, #dc2626)' }}>
|
||||
<AlertCircle size={12} /> {connectError}
|
||||
</span>
|
||||
)}
|
||||
{modelSyncMsg && (
|
||||
<span style={{ display: 'flex', alignItems: 'center', gap: 4, fontSize: 12, color: modelSyncError ? 'var(--danger, #dc2626)' : 'var(--success, #10b981)' }}>
|
||||
{modelSyncError ? <AlertCircle size={12} /> : <Check size={12} />} {modelSyncMsg}
|
||||
</span>
|
||||
)}
|
||||
{kbExpanded && !kbLoading && knowledgeBases.length === 0 && (
|
||||
<div style={{ padding: '20px 16px', textAlign: 'center', fontSize: 12, color: 'var(--text-muted)' }}>
|
||||
@@ -638,28 +817,9 @@ function WanchuanPlatformForm() {
|
||||
)
|
||||
}
|
||||
|
||||
// 导出供外部调用获取已绑定知识库信息
|
||||
export function getBoundKnowledgeBase() {
|
||||
try {
|
||||
const STORAGE_KEY = 'chatlab_wanchuan_config'
|
||||
const raw = localStorage.getItem(STORAGE_KEY)
|
||||
if (!raw) return null
|
||||
const saved = JSON.parse(raw)
|
||||
// 返回完整的知识库信息，包括保存的详细信息
|
||||
if (!saved.selectedKbId) return null
|
||||
return {
|
||||
id: saved.selectedKbInfo?.kbDatasetId || saved.selectedKbId,
|
||||
platformUrl: saved.platformUrl,
|
||||
kbName: saved.selectedKbInfo?.kbName || saved.selectedKbId,
|
||||
kbDatasetId: saved.selectedKbInfo?.kbDatasetId || saved.selectedKbId,
|
||||
kbType: saved.selectedKbInfo?.kbType,
|
||||
}
|
||||
} catch {
|
||||
return null
|
||||
}
|
||||
}
|
||||
|
||||
export default function SettingsPage() {
|
||||
// 万川平台同步模型配置后递增此值，触发 AISettingsForm 重新读取后端 AI 配置
|
||||
const [aiRefreshKey, setAiRefreshKey] = useState(0)
|
||||
return (
|
||||
<div style={{ flex: 1, overflowY: 'auto', padding: '28px 36px' }}>
|
||||
<div style={{ maxWidth: 720 }}>
|
||||
@@ -668,11 +828,11 @@ export default function SettingsPage() {
|
||||
系统各服务地址及 AI 配置管理。
|
||||
</div>
|
||||
|
||||
{/* AI 配置表单 */}
|
||||
<AISettingsForm />
|
||||
{/* 万川 AI 平台对接：放最上面，登录后自动获取下方的 AI 模型配置 */}
|
||||
<WanchuanPlatformForm onModelSynced={() => setAiRefreshKey(k => k + 1)} />
|
||||
|
||||
{/* 万川 AI 平台对接 */}
|
||||
<WanchuanPlatformForm />
|
||||
{/* AI 配置表单：由上方万川平台登录后自动填充，也可手动修改 */}
|
||||
<AISettingsForm refreshKey={aiRefreshKey} />
|
||||
|
||||
{CONFIG_ITEMS.map((group) => (
|
||||
<div key={group.group} style={{ marginBottom: 28 }}>
|
||||
|
||||
@@ -22,7 +22,7 @@ import {
|
||||
import MessageBubble from '../components/MessageBubble'
|
||||
import ReportDocumentView from '../components/ReportDocumentView'
|
||||
import { exportWordDoc } from '../utils/wordExport'
|
||||
import { uploadToKnowledgeBase, getWanchuanKnowledgeBases } from '../api/wanchuan'
|
||||
import { uploadToKnowledgeBase, getWanchuanKnowledgeBases, getBoundKnowledgeBase } from '../api/wanchuan'
|
||||
|
||||
const TYPE_MAP = {
|
||||
1: 0,
|
||||
@@ -648,19 +648,13 @@ export default function TopicsPage({ sessions = [], onToast }) {
|
||||
if (uploadingToKb) return
|
||||
setUploadingToKb(true)
|
||||
try {
|
||||
const raw = localStorage.getItem('chatlab_wanchuan_config')
|
||||
if (!raw) return
|
||||
const bound = JSON.parse(raw)
|
||||
const kbInfo = bound?.selectedKbInfo
|
||||
// 上传接口必须使用 datasetId。selectedKbId 现已统一存为 datasetId，
|
||||
// selectedKbInfo.kbDatasetId 为冗余备份，两者任一可用。
|
||||
const datasetId = kbInfo?.kbDatasetId || bound?.selectedKbId
|
||||
if (!bound?.platformUrl || !datasetId) return
|
||||
const bound = await getBoundKnowledgeBase()
|
||||
if (!bound) return
|
||||
// 上传接口必须使用 datasetId
|
||||
const datasetId = bound.kbDatasetId || bound.id
|
||||
if (!bound.platformUrl || !datasetId) return
|
||||
|
||||
const safeTitle = (reportTitle || 'report').replace(/[^a-zA-Z0-9\u4e00-\u9fa5_-]/g, '_').slice(0, 50)
|
||||
// 上传接口必须使用 datasetId，而不是知识库对象的 id
|
||||
console.log('[上传KB] datasetId:', datasetId, 'kbName:', kbInfo?.kbName || 'N/A')
|
||||
const blob = new Blob([content], { type: 'text/markdown;charset=utf-8' })
|
||||
const file = new File([blob], `${safeTitle}.md`, { type: 'text/markdown' })
|
||||
|
||||
// 上传接口使用 datasetId
|
||||
|
||||
@@ -30,6 +30,14 @@ class Settings(BaseSettings):
|
||||
summary_model: str = "" # 不设默认值，必须由用户在设置页配置
|
||||
voice_model: str = "" # 不设默认值，必须由用户在设置页配置
|
||||
vision_model: str = "" # 不设默认值，必须由用户在设置页配置
|
||||
# 语音/视觉/报告生成可使用与话题分析不同的网关与密钥（如万川平台不同 code）。
|
||||
# 留空则回退到 ai_base_url / ai_api_key。
|
||||
voice_base_url: str = ""
|
||||
voice_api_key: str = ""
|
||||
vision_base_url: str = ""
|
||||
vision_api_key: str = ""
|
||||
summary_base_url: str = ""
|
||||
summary_api_key: str = ""
|
||||
data_dir: str = _default_data_dir()
|
||||
static_dir: str = _default_static_dir()
|
||||
db_path: str = str(Path(_default_data_dir()) / "data" / "knowledge.db")
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
import aiosqlite
|
||||
import asyncio
|
||||
import httpx
|
||||
import logging
|
||||
import time
|
||||
from pathlib import Path
|
||||
from config import settings
|
||||
from services.http_client import shared_client
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
@@ -44,9 +44,9 @@ async def get_current_wxid(force: bool = False):
|
||||
return _resolved_wxid
|
||||
# 重新解析当前 wxid
|
||||
base = settings.chatlog_base_url
|
||||
async with httpx.AsyncClient(trust_env=False, timeout=10.0) as client:
|
||||
client = shared_client()
|
||||
try:
|
||||
r = await client.get(f"{base}/api/v1/chatlog", params={"talker": "filehelper", "limit": 100, "time": "1970-01-01,2099-12-31", "format": "json"})
|
||||
r = await client.get(f"{base}/api/v1/chatlog", params={"talker": "filehelper", "limit": 100, "time": "1970-01-01,2099-12-31", "format": "json"}, timeout=10.0)
|
||||
if r.status_code == 200:
|
||||
data = r.json()
|
||||
for msg in data.get("items", []):
|
||||
@@ -58,12 +58,12 @@ async def get_current_wxid(force: bool = False):
|
||||
pass
|
||||
|
||||
try:
|
||||
r = await client.get(f"{base}/api/v1/chatroom", params={"limit": 10, "format": "json"})
|
||||
r = await client.get(f"{base}/api/v1/chatroom", params={"limit": 10, "format": "json"}, timeout=10.0)
|
||||
if r.status_code == 200:
|
||||
rooms = r.json().get("items", [])
|
||||
for room in rooms:
|
||||
room_id = room.get("name")
|
||||
r2 = await client.get(f"{base}/api/v1/chatlog", params={"talker": room_id, "limit": 50, "time": "1970-01-01,2099-12-31", "format": "json"})
|
||||
r2 = await client.get(f"{base}/api/v1/chatlog", params={"talker": room_id, "limit": 50, "time": "1970-01-01,2099-12-31", "format": "json"}, timeout=10.0)
|
||||
if r2.status_code == 200:
|
||||
data2 = r2.json()
|
||||
for msg in data2.get("items", []):
|
||||
@@ -85,6 +85,10 @@ async def update_db_path(force: bool = False):
|
||||
log.info(f"Switching database to {new_path}")
|
||||
_current_db_path = new_path
|
||||
await init_db(new_path)
|
||||
# 账号切换：清掉 chatlog 客户端的 contact 库路径与头像缓存，避免显示上一个账号的头像。
|
||||
# 局部导入避免潜在的模块循环引用。
|
||||
from services.chatlog_client import chatlog_client
|
||||
chatlog_client.reset_account_cache()
|
||||
return _current_db_path
|
||||
|
||||
def get_active_db_path():
|
||||
|
||||
@@ -7,7 +7,6 @@ from contextlib import asynccontextmanager
|
||||
import asyncio
|
||||
import logging
|
||||
from pathlib import Path
|
||||
import httpx
|
||||
from database import get_active_db_path, get_current_wxid, init_db, reset_wxid_cache, update_db_path
|
||||
from scheduler import start_scheduler
|
||||
from config import settings
|
||||
@@ -15,6 +14,7 @@ from routers import search, groups, topics, knowledge, ai, sse, files, chatlog_p
|
||||
from routers import settings as settings_router
|
||||
from services.chatlog_context import get_chatlog_context, update_chatlog_context
|
||||
from services.media_resolver import diagnose_media
|
||||
from services.http_client import shared_client, close_shared_client
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
@@ -49,6 +49,7 @@ async def lifespan(app: FastAPI):
|
||||
await task
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
await close_shared_client()
|
||||
|
||||
app = FastAPI(lifespan=lifespan)
|
||||
|
||||
@@ -79,8 +80,8 @@ async def health():
|
||||
chatlog_ok = False
|
||||
chatlog_error = ""
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=3.0, trust_env=False) as client:
|
||||
resp = await client.get(f"{settings.chatlog_base_url}/api/v1/session", params={"limit": 1, "format": "json"})
|
||||
client = shared_client()
|
||||
resp = await client.get(f"{settings.chatlog_base_url}/api/v1/session", params={"limit": 1, "format": "json"}, timeout=3.0)
|
||||
chatlog_ok = resp.status_code == 200
|
||||
if not chatlog_ok:
|
||||
chatlog_error = f"HTTP {resp.status_code}"
|
||||
@@ -101,6 +102,9 @@ async def health():
|
||||
@app.post("/api/system/refresh-account")
|
||||
async def refresh_account():
|
||||
reset_wxid_cache()
|
||||
# 用户主动点“重新识别账号”=要最新数据，无条件清掉头像/contact 库缓存
|
||||
from services.chatlog_client import chatlog_client
|
||||
chatlog_client.reset_account_cache()
|
||||
db_path = await update_db_path(force=True)
|
||||
wxid = await get_current_wxid()
|
||||
return {"ok": True, "wxid": wxid, "db_path": db_path}
|
||||
|
||||
@@ -5,6 +5,7 @@ from fastapi import APIRouter, Request
|
||||
from fastapi.responses import Response, StreamingResponse
|
||||
|
||||
from config import settings
|
||||
from services.http_client import shared_client
|
||||
|
||||
router = APIRouter(tags=["chatlog-proxy"])
|
||||
|
||||
@@ -41,12 +42,13 @@ async def _proxy_chatlog(request: Request, upstream_path: str) -> Response:
|
||||
if key.lower() not in HOP_BY_HOP_HEADERS and key.lower() != "host"
|
||||
}
|
||||
|
||||
async with httpx.AsyncClient(timeout=None, trust_env=False, follow_redirects=True) as client:
|
||||
client = shared_client()
|
||||
upstream = await client.request(
|
||||
request.method,
|
||||
target,
|
||||
content=body if body else None,
|
||||
headers=headers,
|
||||
timeout=None,
|
||||
)
|
||||
|
||||
response_headers = _copy_headers(upstream.headers)
|
||||
|
||||
@@ -7,12 +7,12 @@ import tempfile
|
||||
from pathlib import Path
|
||||
from urllib.parse import quote
|
||||
|
||||
import httpx
|
||||
from fastapi import APIRouter, HTTPException, Query
|
||||
from fastapi.responses import FileResponse, StreamingResponse
|
||||
|
||||
from config import settings
|
||||
from services.chatlog_client import chatlog_client
|
||||
from services.http_client import shared_client
|
||||
|
||||
router = APIRouter(prefix="/api/files", tags=["files"])
|
||||
|
||||
@@ -61,8 +61,8 @@ def _guess_media_type(filename: str, fallback: str = "") -> str:
|
||||
async def _proxy_chatlog_file(md5: str, filename: str = ""):
|
||||
url = f"{settings.chatlog_base_url}/file/{quote(md5, safe='')}"
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=30, trust_env=False, follow_redirects=True) as client:
|
||||
resp = await client.get(url)
|
||||
client = shared_client()
|
||||
resp = await client.get(url, timeout=30)
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
from fastapi import APIRouter, Depends
|
||||
from pydantic import BaseModel
|
||||
from typing import Optional
|
||||
from typing import Optional, Any
|
||||
import json
|
||||
import aiosqlite
|
||||
from database import get_db
|
||||
|
||||
@@ -9,8 +10,18 @@ router = APIRouter(prefix="/api/settings", tags=["settings"])
|
||||
EDITABLE_KEYS = [
|
||||
"ai_base_url", "ai_api_key", "ai_model", "summary_model",
|
||||
"vision_model", "voice_model", "topic_analysis_prompt",
|
||||
"voice_base_url", "voice_api_key", "vision_base_url", "vision_api_key",
|
||||
"summary_base_url", "summary_api_key",
|
||||
]
|
||||
|
||||
# 需要脱敏（GET 返回打码）并在 PUT 时跳过含 `*` 占位值的密钥字段。
|
||||
SECRET_KEYS = {"ai_api_key", "voice_api_key", "vision_api_key", "summary_api_key"}
|
||||
|
||||
# 万川 AI 平台对接配置整体作为一条 JSON 存储，独立于上面的 AI 模型配置。
|
||||
# 存到后端 SQLite（app_settings 表）后，配置不再依赖前端 localStorage 的 origin，
|
||||
# 桌面应用（exe）即便后端端口/origin 变化也能跨次启动恢复平台地址、账号、密码与已选知识库。
|
||||
WANCHUAN_KEY = "wanchuan_config"
|
||||
|
||||
|
||||
def _mask_key(value: str) -> str:
|
||||
if not value or len(value) <= 8:
|
||||
@@ -29,7 +40,7 @@ async def get_settings(db: aiosqlite.Connection = Depends(get_db)):
|
||||
rows = await cur.fetchall()
|
||||
for row in rows:
|
||||
k, v = row["key"], row["value"]
|
||||
result[k] = _mask_key(v) if k == "ai_api_key" else v
|
||||
result[k] = _mask_key(v) if k in SECRET_KEYS else v
|
||||
for k in EDITABLE_KEYS:
|
||||
if k not in result:
|
||||
result[k] = ""
|
||||
@@ -44,6 +55,12 @@ class SettingsUpdate(BaseModel):
|
||||
vision_model: Optional[str] = None
|
||||
voice_model: Optional[str] = None
|
||||
topic_analysis_prompt: Optional[str] = None
|
||||
voice_base_url: Optional[str] = None
|
||||
voice_api_key: Optional[str] = None
|
||||
vision_base_url: Optional[str] = None
|
||||
vision_api_key: Optional[str] = None
|
||||
summary_base_url: Optional[str] = None
|
||||
summary_api_key: Optional[str] = None
|
||||
|
||||
|
||||
@router.put("")
|
||||
@@ -52,7 +69,8 @@ async def update_settings(body: SettingsUpdate, db: aiosqlite.Connection = Depen
|
||||
for k, v in updates.items():
|
||||
if k not in EDITABLE_KEYS:
|
||||
continue
|
||||
if k == "ai_api_key" and "*" in v:
|
||||
# 密钥字段含 `*` 说明是 GET 返回的打码值，未被用户真正修改，跳过避免覆盖真实值
|
||||
if k in SECRET_KEYS and "*" in v:
|
||||
continue
|
||||
await db.execute(
|
||||
"INSERT INTO app_settings (key, value) VALUES (?, ?) ON CONFLICT(key) DO UPDATE SET value = ?",
|
||||
@@ -62,3 +80,32 @@ async def update_settings(body: SettingsUpdate, db: aiosqlite.Connection = Depen
|
||||
from services.runtime_settings import invalidate_cache
|
||||
invalidate_cache()
|
||||
return {"status": "ok"}
|
||||
|
||||
|
||||
# ── 万川 AI 平台对接配置（整体 JSON 持久化） ──────────────────
|
||||
# 前端把 { platformUrl, username, password, selectedKbId, selectedKbInfo } 整体存这里。
|
||||
# 桌面应用 origin 变化不影响该配置（不依赖 localStorage）。
|
||||
|
||||
@router.get("/wanchuan")
|
||||
async def get_wanchuan_config(db: aiosqlite.Connection = Depends(get_db)):
|
||||
async with db.execute(
|
||||
"SELECT value FROM app_settings WHERE key = ?", (WANCHUAN_KEY,)
|
||||
) as cur:
|
||||
row = await cur.fetchone()
|
||||
if not row or not row["value"]:
|
||||
return {}
|
||||
try:
|
||||
return json.loads(row["value"])
|
||||
except (ValueError, TypeError):
|
||||
return {}
|
||||
|
||||
|
||||
@router.put("/wanchuan")
|
||||
async def update_wanchuan_config(body: dict[str, Any], db: aiosqlite.Connection = Depends(get_db)):
|
||||
value = json.dumps(body, ensure_ascii=False)
|
||||
await db.execute(
|
||||
"INSERT INTO app_settings (key, value) VALUES (?, ?) ON CONFLICT(key) DO UPDATE SET value = ?",
|
||||
(WANCHUAN_KEY, value, value),
|
||||
)
|
||||
await db.commit()
|
||||
return {"status": "ok"}
|
||||
|
||||
@@ -3,29 +3,44 @@ from openai import AsyncOpenAI
|
||||
|
||||
from services.runtime_settings import get_ai_settings
|
||||
|
||||
# 按 (base_url, api_key) 缓存客户端：聊天/视觉/语音可能指向不同网关与密钥，
|
||||
# 各自一个 pair，最多累积 3 个，有界。配置变更（settings PUT 会 invalidate
|
||||
# runtime_settings 缓存）后，新的 pair 会自然生成新客户端；旧的留存无副作用。
|
||||
_client_cache: dict[tuple[str, str], AsyncOpenAI] = {}
|
||||
_http_client_cache: dict[tuple[str, str], httpx.AsyncClient] = {}
|
||||
|
||||
|
||||
async def get_openai_client() -> tuple[AsyncOpenAI, dict]:
|
||||
settings = await get_ai_settings()
|
||||
cache_key = (
|
||||
settings.get("ai_base_url") or "",
|
||||
settings.get("ai_api_key") or "",
|
||||
)
|
||||
|
||||
def _get_client(base_url: str, api_key: str) -> AsyncOpenAI:
|
||||
cache_key = (base_url or "", api_key or "")
|
||||
if cache_key not in _client_cache:
|
||||
for http_client in _http_client_cache.values():
|
||||
await http_client.aclose()
|
||||
_client_cache.clear()
|
||||
_http_client_cache.clear()
|
||||
|
||||
http_client = httpx.AsyncClient(timeout=httpx.Timeout(600.0, connect=30.0))
|
||||
_http_client_cache[cache_key] = http_client
|
||||
_client_cache[cache_key] = AsyncOpenAI(
|
||||
api_key=settings.get("ai_api_key") or "missing",
|
||||
base_url=settings.get("ai_base_url"),
|
||||
api_key=api_key or "missing",
|
||||
base_url=base_url or None,
|
||||
http_client=http_client,
|
||||
)
|
||||
return _client_cache[cache_key]
|
||||
|
||||
return _client_cache[cache_key], settings
|
||||
|
||||
async def get_openai_client() -> tuple[AsyncOpenAI, dict]:
|
||||
"""聊天调用方（话题/报告/总结/对话）复用：用全局 ai_base_url / ai_api_key。"""
|
||||
settings = await get_ai_settings()
|
||||
client = _get_client(
|
||||
settings.get("ai_base_url") or "",
|
||||
settings.get("ai_api_key") or "",
|
||||
)
|
||||
return client, settings
|
||||
|
||||
|
||||
async def get_client_for(purpose: str) -> tuple[AsyncOpenAI, dict]:
|
||||
"""按用途取客户端：purpose 为 'voice' / 'vision'。
|
||||
|
||||
优先用 {purpose}_base_url / {purpose}_api_key，为空则回退到全局
|
||||
ai_base_url / ai_api_key（单网关场景无需重复配置）。
|
||||
"""
|
||||
settings = await get_ai_settings()
|
||||
base_url = settings.get(f"{purpose}_base_url") or settings.get("ai_base_url") or ""
|
||||
api_key = settings.get(f"{purpose}_api_key") or settings.get("ai_api_key") or ""
|
||||
client = _get_client(base_url, api_key)
|
||||
return client, settings
|
||||
|
||||
@@ -2,6 +2,7 @@ import httpx
|
||||
import asyncio
|
||||
from typing import List
|
||||
from config import settings
|
||||
from services.http_client import shared_client
|
||||
|
||||
|
||||
class ChatlogHTTPError(RuntimeError):
|
||||
@@ -21,11 +22,19 @@ class ChatlogClient:
|
||||
def __init__(self):
|
||||
self.base = settings.chatlog_base_url
|
||||
self._contact_db_file = None
|
||||
# 进程级头像缓存：wxid -> url。同一账号下同一 wxid 只查一次 chatlog SQL，
|
||||
# 避免打开群聊时几十个发言人各打一次 /api/v1/db/query 头像查询。
|
||||
self._avatar_cache: dict[str, str] = {}
|
||||
|
||||
def reset_account_cache(self):
|
||||
"""账号切换时调用：清掉 contact 库路径与头像缓存，避免显示上一个账号的数据。"""
|
||||
self._contact_db_file = None
|
||||
self._avatar_cache.clear()
|
||||
|
||||
async def _get(self, path: str, params: dict, timeout: float = 30.0) -> dict:
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=timeout, trust_env=False) as client:
|
||||
r = await client.get(f"{self.base}{path}", params=params)
|
||||
client = shared_client()
|
||||
r = await client.get(f"{self.base}{path}", params=params, timeout=timeout)
|
||||
r.raise_for_status()
|
||||
return r.json()
|
||||
except httpx.TimeoutException:
|
||||
@@ -38,8 +47,8 @@ class ChatlogClient:
|
||||
|
||||
async def _post(self, path: str, body: dict, timeout: float = 30.0) -> dict:
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=timeout, trust_env=False) as client:
|
||||
r = await client.post(f"{self.base}{path}", json=body)
|
||||
client = shared_client()
|
||||
r = await client.post(f"{self.base}{path}", json=body, timeout=timeout)
|
||||
r.raise_for_status()
|
||||
return r.json()
|
||||
except httpx.TimeoutException:
|
||||
@@ -128,10 +137,11 @@ class ChatlogClient:
|
||||
|
||||
async def get_message(self, talker: str, seq: int) -> dict | None:
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=10.0, trust_env=False) as client:
|
||||
client = shared_client()
|
||||
r = await client.get(
|
||||
f"{self.base}/api/v1/chatlog/message",
|
||||
params={"talker": talker, "seq": seq},
|
||||
timeout=10.0,
|
||||
)
|
||||
if r.status_code == 404:
|
||||
return None
|
||||
@@ -174,6 +184,11 @@ class ChatlogClient:
|
||||
|
||||
|
||||
async def get_avatar_url(self, wxid: str) -> str:
|
||||
if not wxid:
|
||||
return ""
|
||||
cached = self._avatar_cache.get(wxid)
|
||||
if cached is not None:
|
||||
return cached
|
||||
if self._contact_db_file is None:
|
||||
try:
|
||||
db_list = await self._get("/api/v1/db", {})
|
||||
@@ -185,15 +200,17 @@ class ChatlogClient:
|
||||
safe_wxid = wxid.replace("'", "''")
|
||||
sql = f"SELECT small_head_url, big_head_url FROM contact WHERE username='{safe_wxid}' LIMIT 1"
|
||||
params = {"group": "contact", "file": self._contact_db_file, "sql": sql}
|
||||
url = ""
|
||||
try:
|
||||
rows = await self._get("/api/v1/db/query", params, timeout=5.0)
|
||||
if rows:
|
||||
url = rows[0].get("small_head_url") or rows[0].get("big_head_url") or ""
|
||||
if url:
|
||||
return url
|
||||
except Exception:
|
||||
pass
|
||||
# 查询失败不写缓存，下次仍可重试
|
||||
return ""
|
||||
# 命中（含确定无头像的空串）都缓存，避免重复查询
|
||||
self._avatar_cache[wxid] = url
|
||||
return url
|
||||
|
||||
async def get_db_paths(self) -> dict:
|
||||
data = await self._get("/api/v1/db", {}, timeout=10.0)
|
||||
|
||||
46
chatlog_fastAPI/services/http_client.py
Normal file
46
chatlog_fastAPI/services/http_client.py
Normal file
@@ -0,0 +1,46 @@
|
||||
"""共享的 httpx.AsyncClient。
|
||||
|
||||
历史问题：后端每次访问 chatlog.exe（127.0.0.1:5030）都新建一个 AsyncClient，
|
||||
用完即关，没有 keep-alive。打开一个群聊会瞬间产生几十条短连接（图片代理、
|
||||
头像查询等），在 Windows 上会堆积 TIME_WAIT / 耗尽临时端口，导致“用一会儿就卡”。
|
||||
|
||||
这里改为全后端共享一个带连接池的 client，复用 keep-alive 连接，连接建立开销
|
||||
和端口占用都大幅下降。在 lifespan 关闭时统一释放。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import httpx
|
||||
|
||||
_client: httpx.AsyncClient | None = None
|
||||
|
||||
|
||||
def shared_client() -> httpx.AsyncClient:
|
||||
"""返回进程级共享的 AsyncClient（惰性创建）。
|
||||
|
||||
- trust_env=False：与原各处调用保持一致，不读系统代理，避免本地回环被代理拦截。
|
||||
- follow_redirects=True：媒体/文件接口需要；普通 api 调用无重定向，无副作用。
|
||||
- limits：保持 keep-alive 连接，避免每请求新建连接。
|
||||
单次请求可通过 client.get(..., timeout=...) 覆盖超时。
|
||||
"""
|
||||
global _client
|
||||
if _client is None or _client.is_closed:
|
||||
_client = httpx.AsyncClient(
|
||||
trust_env=False,
|
||||
follow_redirects=True,
|
||||
timeout=httpx.Timeout(30.0, connect=5.0),
|
||||
limits=httpx.Limits(
|
||||
max_keepalive_connections=32,
|
||||
max_connections=128,
|
||||
keepalive_expiry=30.0,
|
||||
),
|
||||
)
|
||||
return _client
|
||||
|
||||
|
||||
async def close_shared_client() -> None:
|
||||
"""在应用关闭时释放共享 client。"""
|
||||
global _client
|
||||
if _client is not None and not _client.is_closed:
|
||||
await _client.aclose()
|
||||
_client = None
|
||||
@@ -1,18 +1,19 @@
|
||||
import asyncio
|
||||
import base64
|
||||
import logging
|
||||
|
||||
import httpx
|
||||
from fastapi import HTTPException
|
||||
|
||||
from services.ai_client import get_openai_client
|
||||
from services.ai_client import get_client_for
|
||||
from services.media_resolver import resolve_media
|
||||
from services.runtime_settings import get_ai_settings
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
|
||||
async def _get_ai_client():
|
||||
return await get_openai_client()
|
||||
# 语音异步 ASR 默认网关（阿里云）。voice_base_url 为空时回退到此；
|
||||
# 提交任务/轮询的子路径由代码自动拼接，配置只需填到 .../api/v1 这一层。
|
||||
DEFAULT_ASR_BASE_URL = "https://dashscope.aliyuncs.com/api/v1"
|
||||
|
||||
|
||||
async def parse_media(kind: str, key: str) -> dict:
|
||||
@@ -28,11 +29,16 @@ async def parse_media(kind: str, key: str) -> dict:
|
||||
raise HTTPException(400, "媒体 key 不能为空")
|
||||
|
||||
ai = await get_ai_settings()
|
||||
if not ai.get("ai_api_key"):
|
||||
raise HTTPException(503, "AI 服务未配置，请在设置页填写 AI API Key")
|
||||
if kind == "voice" and not ai.get("voice_model"):
|
||||
# voice/vision 各自有独立 url/key，为空则回退全局 ai_api_key
|
||||
if kind == "voice":
|
||||
if not (ai.get("voice_api_key") or ai.get("ai_api_key")):
|
||||
raise HTTPException(503, "AI 服务未配置，请在设置页填写语音密钥或 AI API Key")
|
||||
if not ai.get("voice_model"):
|
||||
raise HTTPException(503, "语音模型未配置，请在设置页填写语音模型名称，例如 paraformer-v2")
|
||||
if kind in ("image", "video") and not ai.get("vision_model"):
|
||||
if kind in ("image", "video"):
|
||||
if not (ai.get("vision_api_key") or ai.get("ai_api_key")):
|
||||
raise HTTPException(503, "AI 服务未配置，请在设置页填写视觉密钥或 AI API Key")
|
||||
if not ai.get("vision_model"):
|
||||
raise HTTPException(503, "视觉模型未配置，请在设置页填写视觉模型名称，例如 qwen-vl-plus")
|
||||
|
||||
media = await resolve_media(kind, key)
|
||||
@@ -41,56 +47,90 @@ async def parse_media(kind: str, key: str) -> dict:
|
||||
return {"text": await _parse_visual(kind, media.bytes, media.content_type)}
|
||||
|
||||
|
||||
async def _parse_voice(media_bytes: bytes, content_type: str) -> str:
|
||||
b64_audio = base64.b64encode(media_bytes).decode()
|
||||
audio_ct = content_type.lower()
|
||||
if "silk" in audio_ct or "x-silk" in audio_ct:
|
||||
audio_mime = "audio/silk"
|
||||
elif "amr" in audio_ct:
|
||||
audio_mime = "audio/amr"
|
||||
elif "ogg" in audio_ct or "opus" in audio_ct:
|
||||
audio_mime = "audio/ogg"
|
||||
elif "wav" in audio_ct:
|
||||
audio_mime = "audio/wav"
|
||||
else:
|
||||
audio_mime = "audio/mpeg"
|
||||
def _audio_mime(content_type: str) -> str:
|
||||
"""由 chatlog 返回的 content_type 推断音频 MIME（用于 data URI）。"""
|
||||
ct = content_type.lower()
|
||||
if "silk" in ct or "x-silk" in ct:
|
||||
return "audio/silk"
|
||||
if "amr" in ct:
|
||||
return "audio/amr"
|
||||
if "ogg" in ct or "opus" in ct:
|
||||
return "audio/ogg"
|
||||
if "wav" in ct:
|
||||
return "audio/wav"
|
||||
return "audio/mpeg"
|
||||
|
||||
data_uri = f"data:{audio_mime};base64,{b64_audio}"
|
||||
_, ai = await _get_ai_client()
|
||||
|
||||
def _asr_json(resp: httpx.Response, url: str) -> dict:
|
||||
"""安全解析 ASR 响应为 JSON。
|
||||
|
||||
响应非 JSON（空响应 / HTML 错误页 / 网关 404）时，原来直接 .json() 会抛
|
||||
JSONDecodeError，把真实原因（HTTP 状态码 + 正文）掩盖掉。这里改成抛出
|
||||
带状态码与正文片段的 HTTPException，便于排查（如地址填成 compatible-mode/v1）。
|
||||
"""
|
||||
try:
|
||||
return resp.json()
|
||||
except Exception:
|
||||
body = (resp.text or "").strip()[:300]
|
||||
raise HTTPException(
|
||||
500,
|
||||
f"ASR 接口返回非 JSON (HTTP {resp.status_code}) @ {url}：{body or '（空响应）'}。"
|
||||
"请检查语音接口地址是否为异步 ASR 网关（如 .../api/v1）及密钥是否正确。",
|
||||
)
|
||||
|
||||
|
||||
async def _parse_voice(media_bytes: bytes, content_type: str) -> str:
|
||||
"""语音转文字：阿里云异步 ASR 协议（提交任务 → 轮询 → 取结果）。
|
||||
|
||||
接口地址动态：base = voice_base_url（为空直接用默认阿里云原生网关，不回退 ai_base_url），
|
||||
提交端点 = {base}/services/audio/asr/transcription，轮询 = {base}/tasks/{id}，
|
||||
子路径由代码自动拼接，配置只需填到 .../api/v1 这一层。
|
||||
密钥 = voice_api_key（为空回退 ai_api_key）。
|
||||
"""
|
||||
ai = await get_ai_settings()
|
||||
# strip 防止配置/同步带入首尾空格（实测出现过 api_key 前导空格导致鉴权失败）
|
||||
# 注意：异步 ASR 走原生网关 /api/v1，与 ai_base_url（OpenAI 兼容的 chat 端点
|
||||
# .../compatible-mode/...）是两套服务，不能混用。voice_base_url 为空时应回退到
|
||||
# DEFAULT_ASR_BASE_URL，绝不能回退到 ai_base_url，否则会拼成 .../compatible-mode/.../asr 而 404。
|
||||
base = (ai.get("voice_base_url") or DEFAULT_ASR_BASE_URL).strip().rstrip("/")
|
||||
api_key = (ai.get("voice_api_key") or ai.get("ai_api_key") or "").strip()
|
||||
voice_model = (ai.get("voice_model") or "").strip()
|
||||
|
||||
b64_audio = base64.b64encode(media_bytes).decode()
|
||||
data_uri = f"data:{_audio_mime(content_type)};base64,{b64_audio}"
|
||||
asr_headers = {
|
||||
"Authorization": f"Bearer {ai['ai_api_key']}",
|
||||
"Authorization": f"Bearer {api_key}",
|
||||
"Content-Type": "application/json",
|
||||
}
|
||||
|
||||
submit_url = f"{base}/services/audio/asr/transcription"
|
||||
async with httpx.AsyncClient(timeout=60) as http:
|
||||
submit = await http.post(
|
||||
"https://dashscope.aliyuncs.com/api/v1/services/audio/asr/transcription",
|
||||
submit_url,
|
||||
headers={**asr_headers, "X-DashScope-Async": "enable"},
|
||||
json={
|
||||
"model": ai["voice_model"],
|
||||
"model": voice_model,
|
||||
"input": {"file_urls": [data_uri]},
|
||||
"parameters": {"language_hints": ["zh", "en"]},
|
||||
},
|
||||
timeout=30,
|
||||
)
|
||||
submit_data = submit.json()
|
||||
submit_data = _asr_json(submit, submit_url)
|
||||
if submit.status_code not in (200, 201):
|
||||
raise HTTPException(500, f"提交识别任务失败: {submit_data.get('message', submit_data)}")
|
||||
raise HTTPException(500, f"提交识别任务失败 (HTTP {submit.status_code}): {submit_data.get('message', submit_data)}")
|
||||
|
||||
task_id = submit_data.get("output", {}).get("task_id")
|
||||
if not task_id:
|
||||
raise HTTPException(500, f"未获取到 task_id: {submit_data}")
|
||||
|
||||
for _ in range(30):
|
||||
import asyncio
|
||||
|
||||
await asyncio.sleep(1)
|
||||
poll = await http.get(
|
||||
f"https://dashscope.aliyuncs.com/api/v1/tasks/{task_id}",
|
||||
f"{base}/tasks/{task_id}",
|
||||
headers=asr_headers,
|
||||
timeout=10,
|
||||
)
|
||||
poll_data = poll.json()
|
||||
poll_data = _asr_json(poll, f"{base}/tasks/{task_id}")
|
||||
status = poll_data.get("output", {}).get("task_status", "")
|
||||
if status == "SUCCEEDED":
|
||||
results = poll_data.get("output", {}).get("results", [])
|
||||
@@ -125,7 +165,7 @@ async def _parse_visual(kind: str, media_bytes: bytes, content_type: str) -> str
|
||||
data_url = f"data:{mime};base64,{b64}"
|
||||
prompt = "请用中文简洁描述这张图片的内容。" if kind == "image" else "请用中文简洁描述这个视频截图的内容。"
|
||||
|
||||
client, ai = await _get_ai_client()
|
||||
client, ai = await get_client_for("vision")
|
||||
resp_ai = await client.chat.completions.create(
|
||||
model=ai["vision_model"],
|
||||
messages=[
|
||||
|
||||
@@ -10,6 +10,7 @@ from fastapi import HTTPException
|
||||
|
||||
from config import settings
|
||||
from services.chatlog_context import get_chatlog_context
|
||||
from services.http_client import shared_client
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
@@ -100,9 +101,9 @@ async def diagnose_media(kind: str, key: str) -> dict:
|
||||
"chatlog_context": get_chatlog_context(),
|
||||
}
|
||||
|
||||
async with httpx.AsyncClient(timeout=20, trust_env=False, follow_redirects=True) as client:
|
||||
client = shared_client()
|
||||
try:
|
||||
resp = await client.get(url)
|
||||
resp = await client.get(url, timeout=20)
|
||||
content_type = resp.headers.get("content-type", "")
|
||||
result.update(
|
||||
{
|
||||
@@ -132,9 +133,9 @@ async def resolve_media(kind: str, key: str) -> ResolvedMedia:
|
||||
raise HTTPException(400, "媒体 key 不能为空")
|
||||
|
||||
url = _media_url(kind, key, thumb=kind in {"image", "video"})
|
||||
async with httpx.AsyncClient(timeout=60, trust_env=False, follow_redirects=True) as client:
|
||||
client = shared_client()
|
||||
try:
|
||||
resp = await client.get(url)
|
||||
resp = await client.get(url, timeout=60)
|
||||
resp.raise_for_status()
|
||||
except httpx.HTTPStatusError as exc:
|
||||
diagnostics = await diagnose_media(kind, key)
|
||||
|
||||
@@ -27,6 +27,13 @@ async def get_ai_settings() -> dict:
|
||||
"vision_model": "",
|
||||
"voice_model": "",
|
||||
"topic_analysis_prompt": "",
|
||||
# 语音/视觉/报告生成独立网关与密钥；留空则由调用方回退到 ai_base_url / ai_api_key
|
||||
"voice_base_url": "",
|
||||
"voice_api_key": "",
|
||||
"vision_base_url": "",
|
||||
"vision_api_key": "",
|
||||
"summary_base_url": "",
|
||||
"summary_api_key": "",
|
||||
}
|
||||
|
||||
try:
|
||||
|
||||
@@ -13,7 +13,7 @@ import aiosqlite
|
||||
from urllib.parse import quote
|
||||
|
||||
from database import get_active_db_path
|
||||
from services.ai_client import get_openai_client
|
||||
from services.ai_client import get_client_for
|
||||
from services.fts import tokenize
|
||||
from services.message_formatter import append_quote_text, extract_contents, extract_quote
|
||||
from services.report_learning import build_report_learning_context
|
||||
@@ -25,7 +25,8 @@ SUMMARY_LLM_TIMEOUT_SECONDS = 300
|
||||
|
||||
|
||||
async def _get_client():
|
||||
return await get_openai_client()
|
||||
# 报告生成走独立网关 summary_base_url/summary_api_key（为空回退 ai_*）
|
||||
return await get_client_for("summary")
|
||||
|
||||
|
||||
def _message_line(item: dict, fallback_seq: int = 0) -> tuple[int, str] | None:
|
||||
|
||||
@@ -159,7 +159,8 @@
|
||||
height: 660px;
|
||||
transform: translate(-50%, -50%);
|
||||
pointer-events: none;
|
||||
filter: drop-shadow(0 48px 55px rgba(0,0,0,0.78));
|
||||
/* 原本的 drop-shadow 滤镜会随手臂无限动画每帧重算整块阴影，极耗 GPU。
|
||||
SVG 内部已有一个静态阴影椭圆，这里移除滤镜即可。 */
|
||||
}
|
||||
|
||||
.robot {
|
||||
@@ -203,9 +204,9 @@
|
||||
padding: 26px;
|
||||
border: 1px solid var(--line-strong);
|
||||
border-radius: 24px;
|
||||
background: linear-gradient(180deg, rgba(27,29,34,0.94), rgba(15,16,19,0.9));
|
||||
/* 面板背景已接近不透明，去掉 backdrop-filter:blur 避免 GPU 每帧重模糊（动画背景下尤其卡） */
|
||||
background: linear-gradient(180deg, rgba(29,31,37,0.97), rgba(16,17,21,0.96));
|
||||
box-shadow: var(--shadow);
|
||||
backdrop-filter: blur(14px);
|
||||
}
|
||||
|
||||
.panel-title {
|
||||
@@ -467,8 +468,8 @@
|
||||
z-index: 100;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
background: rgba(0, 0, 0, 0.72);
|
||||
backdrop-filter: blur(8px);
|
||||
/* 去掉 backdrop-filter:blur，改用更深的纯遮罩，避免弹窗出现时整屏每帧重模糊 */
|
||||
background: rgba(0, 0, 0, 0.82);
|
||||
}
|
||||
|
||||
.modal-overlay.show {
|
||||
@@ -751,13 +752,26 @@
|
||||
let startingAll = false;
|
||||
let refreshingAccount = false;
|
||||
|
||||
let robotRafPending = false
|
||||
let robotNextX = 0
|
||||
let robotNextY = 0
|
||||
|
||||
launcher.addEventListener('mousemove', (event) => {
|
||||
const rect = launcher.getBoundingClientRect();
|
||||
const x = ((event.clientX - rect.left) / rect.width - 0.5) * 2;
|
||||
const y = ((event.clientY - rect.top) / rect.height - 0.5) * 2;
|
||||
const clampedX = Math.max(-1, Math.min(1, x));
|
||||
const clampedY = Math.max(-1, Math.min(1, y));
|
||||
robotHead.style.transform = `translate(${clampedX * 9}px, ${clampedY * 6}px) rotate(${clampedX * 7}deg)`;
|
||||
// 缓存目标坐标，用 rAF 合并到每帧最多一次写入，避免每个 mousemove 事件
|
||||
// 都触发 getBoundingClientRect()（强制同步布局）和 robotHead 滤镜重算。
|
||||
robotNextX = event.clientX
|
||||
robotNextY = event.clientY
|
||||
if (robotRafPending) return
|
||||
robotRafPending = true
|
||||
requestAnimationFrame(() => {
|
||||
robotRafPending = false
|
||||
const rect = launcher.getBoundingClientRect()
|
||||
const x = ((robotNextX - rect.left) / rect.width - 0.5) * 2
|
||||
const y = ((robotNextY - rect.top) / rect.height - 0.5) * 2
|
||||
const clampedX = Math.max(-1, Math.min(1, x))
|
||||
const clampedY = Math.max(-1, Math.min(1, y))
|
||||
robotHead.style.transform = `translate(${clampedX * 9}px, ${clampedY * 6}px) rotate(${clampedX * 7}deg)`
|
||||
})
|
||||
});
|
||||
|
||||
launcher.addEventListener('mouseleave', () => {
|
||||
|
||||
@@ -44,6 +44,22 @@ function chatlogExePath() {
|
||||
return resourcePath('chatlog.exe');
|
||||
}
|
||||
|
||||
// 启动 chatlog.exe 前确认文件确实存在。
|
||||
// 该文件常被杀毒软件（尤其 Windows Defender）误判为风险程序而静默隔离/删除，
|
||||
// 导致安装后“有时候”运行就报 spawn ... ENOENT。这里把底层的 ENOENT
|
||||
// 提前转成一句用户能看懂、能自救的中文提示。
|
||||
function ensureChatlogExe() {
|
||||
const exePath = chatlogExePath();
|
||||
if (!fs.existsSync(exePath)) {
|
||||
throw new Error(
|
||||
`未找到 chatlog.exe（应在：${exePath}）。` +
|
||||
`该文件可能被杀毒软件误删或隔离。请将安装目录加入杀毒软件白名单，` +
|
||||
`从隔离区恢复 chatlog.exe，或重新安装本应用后再试。`
|
||||
);
|
||||
}
|
||||
return exePath;
|
||||
}
|
||||
|
||||
function backendExePath() {
|
||||
return resourcePath('backend', 'ChatLabBackend.exe');
|
||||
}
|
||||
@@ -688,7 +704,13 @@ function selectMainWeChatProcess(processes) {
|
||||
|
||||
function runChatlogKey(args, label) {
|
||||
return new Promise((resolve, reject) => {
|
||||
const exePath = chatlogExePath();
|
||||
let exePath;
|
||||
try {
|
||||
exePath = ensureChatlogExe();
|
||||
} catch (e) {
|
||||
reject(e);
|
||||
return;
|
||||
}
|
||||
const keyProcess = spawn(exePath, args, {
|
||||
cwd: projectRoot(),
|
||||
windowsHide: true,
|
||||
|
||||
26526
release/manifest.txt
26526
release/manifest.txt
File diff suppressed because it is too large
Load Diff
@@ -11,7 +11,8 @@
|
||||
[string]$PublisherName,
|
||||
[string]$TimestampServer = "http://timestamp.digicert.com",
|
||||
[string[]]$VoiceSmokeKeys = @(),
|
||||
[switch]$ForceSign
|
||||
[switch]$ForceSign,
|
||||
[switch]$CleanBackend
|
||||
)
|
||||
|
||||
$ErrorActionPreference = "Stop"
|
||||
@@ -160,7 +161,16 @@ if (-not $SkipFrontend) {
|
||||
|
||||
if (-not $SkipBackend) {
|
||||
Push-Location $Backend
|
||||
Invoke-Python312 @("-m", "PyInstaller", "ChatLabBackend.spec", "--noconfirm", "--clean")
|
||||
# 默认复用 PyInstaller 的分析缓存（build\ 目录），代码未大改时增量打包可从 ~9 分钟降到 1-2 分钟。
|
||||
# 仅在升级依赖或缓存异常时用 -CleanBackend 做一次全量重建。
|
||||
$pyArgs = @("-m", "PyInstaller", "ChatLabBackend.spec", "--noconfirm")
|
||||
if ($CleanBackend) {
|
||||
$pyArgs += "--clean"
|
||||
Write-Host "Backend: full clean rebuild (--clean)."
|
||||
} else {
|
||||
Write-Host "Backend: incremental build (reusing cache). Use -CleanBackend for a full rebuild."
|
||||
}
|
||||
Invoke-Python312 $pyArgs
|
||||
Pop-Location
|
||||
}
|
||||
|
||||
|
||||
217
自动定时生成报告-方案设计.md
Normal file
217
自动定时生成报告-方案设计.md
Normal file
@@ -0,0 +1,217 @@
|
||||
# 自动定时生成报告 — 方案设计
|
||||
|
||||
> **需求（已锁定）**：前端提供「手动 / 自动」两种模式。
|
||||
> - **手动**：保持现状不变 —— 用户选时间段，点一下，跑 AI 分析 + 生成报告。
|
||||
> - **自动**：用户设置一个间隔（如每 30 分钟 / 每小时 / 每天），系统到点**自动**跑「AI 分析 + 生成报告」全流程，无需人工点。
|
||||
|
||||
---
|
||||
|
||||
## 一、关键结论：地基已经存在，不用从零搭
|
||||
|
||||
调研现有代码后发现，自动模式的基础设施**已经埋好了，只是之前被主动关掉**：
|
||||
|
||||
| 已有的东西 | 位置 | 现状 |
|
||||
|-----------|------|------|
|
||||
| 后台调度器 APScheduler | [scheduler.py](chatlog_fastAPI/scheduler.py) | 已在 `lifespan` 启动（[main.py:43](chatlog_fastAPI/main.py#L43)），目前只做账号/数据库切换检测 |
|
||||
| `groups.poll_interval` 字段 | [database.py:117](chatlog_fastAPI/database.py#L117) | 默认 300 秒，建群时可传（[groups.py:12](chatlog_fastAPI/routers/groups.py#L12)），但**目前未被使用** —— 正好用来存「多久自动一次」 |
|
||||
| `groups.cursor_seq` 字段 | [database.py:115](chatlog_fastAPI/database.py#L115) | 为增量游标预留，**目前未被使用** |
|
||||
| `register_poll_job` 轮询注册 | [scheduler.py:16](chatlog_fastAPI/scheduler.py#L16) | **已废弃成空函数** |
|
||||
|
||||
**最关键的一条线索** —— [scheduler.py](chatlog_fastAPI/scheduler.py) 顶部注释：
|
||||
|
||||
> APScheduler — 仅保留 wxid/数据库切换检测。
|
||||
> （不再运行任何 AI 分类轮询：AI 分析改为用户手动按时间窗口触发）
|
||||
|
||||
这说明自动轮询曾经跑过，后来被撤掉、改成了现在的「手动」模式。**撤掉的原因，正是新自动模式必须解决的坑。**
|
||||
|
||||
---
|
||||
|
||||
## 二、手动模式现在是怎么跑的（自动模式要复用它）
|
||||
|
||||
理解现状是设计自动模式的前提。手动模式的完整链路：
|
||||
|
||||
```
|
||||
前端 ChatlogPage 选时间段 → POST /api/groups/{id}/init {start_time, end_time}
|
||||
└─ run_classify_window(group_id, start_ts, end_ts) [topic_engine.py:960]
|
||||
1. 全量拉取该时间段的消息
|
||||
2. _delete_ai_topics 删掉旧 AI 话题 ← 注意这里是「全删重建」
|
||||
3. 分批 LLM 分类 → 合并 → 补充分配 → 落库 topics
|
||||
（报告仍需用户在前端对每个话题点「生成」）
|
||||
└─ run_summarize(topic_id) [summary_engine.py:471]
|
||||
从 chatlog 拉回话题消息原文 → LLM 生成 Markdown → 写 knowledge_docs
|
||||
```
|
||||
|
||||
> 注意：手动模式目前**分类和报告是分两步**的。分类出话题后，报告要用户在前端逐个点。
|
||||
> 自动模式的「全自动」就体现在：分类完，自动把需要的话题也一并生成报告，不用人点。
|
||||
|
||||
---
|
||||
|
||||
## 三、必须正视的三个约束（也是当年撤掉自动轮询的原因）
|
||||
|
||||
### 约束 ① 分类是全局串行的，一次只能跑一个群
|
||||
|
||||
[topic_engine.py:26-27](chatlog_fastAPI/services/topic_engine.py#L26-L27)：
|
||||
|
||||
```python
|
||||
_classify_lock = asyncio.Lock()
|
||||
_classifying_group: int | None = None
|
||||
```
|
||||
|
||||
[groups.py:70-72](chatlog_fastAPI/routers/groups.py#L70-L72)：只要有群在分析，手动 `/init` 直接返回 `409 已有群正在分析`。
|
||||
|
||||
**影响**：多个群各自定时，会互相撞锁。
|
||||
**对策**：自动调度**串成一条队列**，并且**自动任务要让位于手动**（用户手点时不被自动任务卡住）。
|
||||
|
||||
### 约束 ② 分类是「全量重跑」，不是增量
|
||||
|
||||
[topic_engine.py:1033](chatlog_fastAPI/services/topic_engine.py#L1033)：每次分类先 `_delete_ai_topics` 把旧 AI 话题**全删重建**。
|
||||
|
||||
**影响**：自动模式如果每轮都对一个大窗口（如最近 7 天）重跑，每轮都把这些消息**全部重新喂 LLM**。间隔越短，成本越爆。**这几乎可以肯定是当年撤掉自动轮询的主因。**
|
||||
**对策**：见第四节的窗口策略 —— 这是本方案唯一需要你拍板的关键点。
|
||||
|
||||
### 约束 ③ 自动跑报告 = 话题数 × LLM 调用
|
||||
|
||||
全自动生成报告时，每个有更新的话题都要调一次 LLM。
|
||||
**对策**：报告只对**本轮真正有新消息变动的话题**生成/更新，不要把所有历史话题每轮重刷。
|
||||
|
||||
---
|
||||
|
||||
## 四、唯一需要你拍板的点：自动模式每轮分析「哪段消息」
|
||||
|
||||
这是整个方案的核心，直接决定**成本**和**改动量**。三个候选：
|
||||
|
||||
### 方案 A：自上次运行至今（增量按时间）—— **推荐**
|
||||
|
||||
每轮窗口 = `[上次运行时间, 现在]`，只分析这段时间的新消息。
|
||||
|
||||
- **成本**：低且稳定，只跟「新消息量」相关，跟历史总量无关。
|
||||
- **要解决的冲突**：现有 `run_classify_window` 会 `_delete_ai_topics` 全删。增量模式下**不能删历史话题**，要把新消息**补充归并**到已有话题或新建话题（复用现有 `_supplement_assignments` / `_coalesce_device_issue_topics` 的思路）。
|
||||
- **改动量**：中。是本方案最核心的改造。
|
||||
|
||||
### 方案 B：固定回看窗口（每轮全量重跑最近 N 天）
|
||||
|
||||
每轮窗口 = `[现在 - N 天, 现在]`，直接复用现有 `run_classify_window`（含全删重建）。
|
||||
|
||||
- **成本**：随间隔频率线性上升，且每轮重复分析窗口内所有消息。
|
||||
- **优点**：几乎不用改分类逻辑，最快跑通。
|
||||
- **代价**：① 成本高；② 超出窗口的历史话题会被删掉（因为全删重建只覆盖窗口内消息）。**不适合做知识库沉淀。**
|
||||
|
||||
### 方案 C：混合（增量为主 + 每天全量重跑一次）
|
||||
|
||||
平时走方案 A 增量；每天凌晨全量重跑一次，重组当天话题、提升分类精度。
|
||||
|
||||
- **成本**：介于 A、B 之间。
|
||||
- **优点**：兼顾成本与分类质量（增量归并精度略低于全量重组，每天校正一次）。
|
||||
- **改动量**：最大（A 的全部 + 一个定时全量任务）。
|
||||
|
||||
> **我的建议**：先按**方案 A** 落地（成本可控、能沉淀知识库、符合"定时增量出报告"的直觉）。
|
||||
> 若后续发现增量归并的分类精度不够，再加方案 C 的每日全量校正。
|
||||
> **方案 B 不建议**，除非你只想快速验证、且能接受历史被覆盖。
|
||||
|
||||
---
|
||||
|
||||
## 五、推荐架构（按方案 A：增量 + 全自动 + 每群独立间隔）
|
||||
|
||||
核心思路：**一个 tick 调度器扫表，挑到期的群丢进串行队列，逐个增量处理并出报告。**
|
||||
|
||||
```
|
||||
APScheduler 每 60 秒 tick 一次（单个 job，不是每群一个）
|
||||
│
|
||||
├─ 扫描所有 groups，挑出满足条件的群：
|
||||
│ auto_enabled = 1 且 now - last_run_at >= poll_interval
|
||||
│
|
||||
└─ 把到期的群按顺序逐个 await 处理（串行，复用 _classify_lock）
|
||||
│
|
||||
└─ 对每个群 run_auto_analyze(group_id)：
|
||||
1. 读 groups.cursor_seq，只拉游标之后的新消息（get_messages 已支持 min_seq）
|
||||
2. 无新消息 → 更新 last_run_at，结束
|
||||
3. 增量分类：把新消息归并进已有话题 / 新建话题（不删历史）
|
||||
4. 对「本轮有新增消息的话题」逐个 run_summarize 生成/更新报告
|
||||
5. 更新 cursor_seq = 本轮最大 seq，last_run_at = now
|
||||
```
|
||||
|
||||
### 为什么这样设计
|
||||
|
||||
| 设计点 | 原因 |
|
||||
|--------|------|
|
||||
| **单 tick job 扫表**，而非每群一个 APScheduler job | 新增/删群、改间隔都不用动调度器；天然串行，避开并发撞锁 |
|
||||
| **用 `cursor_seq` 增量** | 字段现成，从根本上解决「全量重跑」的成本问题 |
|
||||
| **串行队列 + 自动让位手动** | 符合现有 `_classify_lock` 全局串行约束，用户手点时优先 |
|
||||
| **报告只生成有变动的话题** | 避免每轮把所有历史话题重刷 LLM |
|
||||
| **间隔存 `groups.poll_interval`** | 字段现成，天然支持「每群单独设多久自动一次」 |
|
||||
|
||||
---
|
||||
|
||||
## 六、具体改动清单
|
||||
|
||||
### 6.1 数据库 schema（[database.py](chatlog_fastAPI/database.py)）
|
||||
|
||||
`groups` 表加一个字段（用现有 `PRAGMA table_info` 迁移模式补列）：
|
||||
|
||||
```sql
|
||||
auto_enabled INTEGER DEFAULT 0 -- 是否开启该群的自动分析
|
||||
last_run_at DATETIME -- 上次自动跑的时间（判断是否到期）
|
||||
```
|
||||
|
||||
`poll_interval`（间隔）、`cursor_seq`（增量游标）已存在，直接复用。
|
||||
|
||||
### 6.2 调度器（[scheduler.py](chatlog_fastAPI/scheduler.py)）
|
||||
|
||||
- 新增 `_auto_analyze_tick()` job，`interval` 60 秒。
|
||||
- tick 内扫 `groups`，挑 `auto_enabled=1 AND (last_run_at IS NULL OR now - last_run_at >= poll_interval)`。
|
||||
- 到期的群**逐个 `await`** 处理（串行，不用 `create_task` 并发）。
|
||||
- 处理前检查 `get_classifying_group()`，若手动任务在跑则本轮跳过，下轮再来（让位手动）。
|
||||
|
||||
### 6.3 增量分析逻辑（[topic_engine.py](chatlog_fastAPI/services/topic_engine.py)）
|
||||
|
||||
新增 `run_auto_analyze(group_id)`（与现有 `run_classify_window` 并存，不破坏手动）：
|
||||
|
||||
- 读 `cursor_seq`，调 `chatlog_client.get_messages(talker, min_seq=cursor_seq+1, ...)` 只拉新消息（`get_messages` 已支持 `min_seq`，见 [chatlog_client.py:77](chatlog_fastAPI/services/chatlog_client.py#L77)）。
|
||||
- **不调用** `_delete_ai_topics`（这是和手动模式的关键区别）。
|
||||
- 把新消息走「补充分配」逻辑归入已有话题或新建话题（复用 `_supplement_assignments`、`_coalesce_device_issue_topics`）。
|
||||
- 收集本轮被改动 / 新建的 `topic_id` 列表。
|
||||
- 更新 `cursor_seq`、`last_run_at`。
|
||||
|
||||
### 6.4 自动报告（[summary_engine.py](chatlog_fastAPI/services/summary_engine.py)）
|
||||
|
||||
- `run_auto_analyze` 分类完后，对「本轮有变动的话题」逐个 `await run_summarize(topic_id, topic)`。
|
||||
- `run_summarize` 已支持「已存在则更新」（[summary_engine.py:444-454](chatlog_fastAPI/services/summary_engine.py#L444-L454)），直接复用。
|
||||
|
||||
### 6.5 接口（[groups.py](chatlog_fastAPI/routers/groups.py)）
|
||||
|
||||
- `GroupPatch` 增加 `auto_enabled`、`poll_interval`，`patch_group` 支持更新。
|
||||
- （可选）`GET /api/groups/{id}/auto-status`：返回 `auto_enabled / poll_interval / last_run_at / 下次预计运行时间`，给前端展示状态。
|
||||
|
||||
### 6.6 前端（群管理 / [SettingsPage.jsx](chatlab-web/frontend/src/pages/SettingsPage.jsx)）
|
||||
|
||||
- 每个群一个「自动分析」开关 + 间隔下拉（如 30 分钟 / 1 小时 / 3 小时 / 每天）。
|
||||
- 手动模式 UI 保持不变（现有「查询时间段 + 手动分析」照旧）。
|
||||
- 调用 `patchGroup(groupId, { auto_enabled, poll_interval })`（[api/index.js:232](chatlab-web/frontend/src/api/index.js#L232) 已有 `patchGroup`）。
|
||||
- （可选）展示「上次自动分析时间 / 下次预计时间」。
|
||||
|
||||
---
|
||||
|
||||
## 七、风险与注意点
|
||||
|
||||
1. **LLM 成本是头号风险**：间隔越短、群越多，成本越高。方案 A 增量 + 报告只刷变动话题已是最省的组合；前端间隔下拉**建议最小 30 分钟**，不要给「每 5 分钟」这类档位。
|
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2. **自动要让位手动**：tick 处理前必须检查 `get_classifying_group()`，避免占着锁让用户手动分析报 409。
|
||||
3. **chatlog 服务可能未就绪**：tick 要捕获 `MessageIndexNotReady`（[chatlog_client.py:17](chatlog_fastAPI/services/chatlog_client.py#L17)），跳过本轮、**不更新游标**，下轮重试。
|
||||
4. **账号切换**：`cursor_seq` 按 `groups` 表存，数据库随微信账号切换（[database.py:80](chatlog_fastAPI/database.py#L80)），游标天然隔离。但要确认 tick 用的是 `get_active_db_path()` 当前库。
|
||||
5. **增量分类精度权衡**：增量归并（新消息往已有话题靠）比全量重组精度略低。若要求高，用方案 C 每日全量校正一次。
|
||||
6. **首次开启自动的冷启动**：群刚开自动且 `cursor_seq=0` 时，第一轮会把全部历史拉进来分析。建议开启自动时让用户先手动跑一次（或把 `cursor_seq` 初始化为当前最新 seq），避免首轮巨量分析。
|
||||
|
||||
---
|
||||
|
||||
## 八、建议的落地顺序
|
||||
|
||||
1. **先确认第四节的窗口策略**（推荐方案 A）。
|
||||
2. schema 加字段 + `groups.py` 接口打通（小改动，先让「开关 + 间隔」能存能读）。
|
||||
3. 调度器接 tick + 串行队列骨架（先只 `log`「本应处理 group X」，不真跑 LLM，验证调度和让位逻辑正确）。
|
||||
4. 接增量分类 `run_auto_analyze`（先不出报告，验证话题增量归并正确）。
|
||||
5. 接自动报告。
|
||||
6. 前端配置 UI（自动开关 + 间隔下拉）。
|
||||
7. 小间隔实测 LLM 成本与分类质量，再决定前端开放哪些间隔档位、是否需要方案 C。
|
||||
|
||||
---
|
||||
|
||||
*本文档为方案设计，未改动任何代码。确认第四节窗口策略后即可进入实现。*
|
||||
Reference in New Issue
Block a user