feat: 接入在线 LLM 聊天能力

.umirc.ts

@@ -38,6 +38,11 @@ export default defineConfig({
       changeOrigin: true,
       pathRewrite: { '^/api': '' },
     },
+    '/llm-api': {
+      target: 'https://lingsuan.top',
+      changeOrigin: true,
+      pathRewrite: { '^/llm-api': '' },
+    },
     '/myapi/upload_invoices': {
       target: 'http://1.94.111.115:5173',
       changeOrigin: true,

src/pages/Chat/invoice.jsx

@@ -13,9 +13,9 @@ import {
   Bubble,
   Prompts,
   Sender,
+  XStream,
   useXAgent,
   useXChat,
-  XStream,
 } from '@ant-design/x';
 import { App, Button, Flex, Space, Spin, Steps, Tag, Typography } from 'antd';
 import { createStyles } from 'antd-style';
@@ -34,8 +34,11 @@ import {
 import {
   callMcpTool,
   createConversation,
+  extractLlmChunkContent,
+  extractLlmChunkReasoning,
   llmChat,
   queryMcps,
+  resolveLlmModel,
   updateConversation,
 } from '../../services/DataService';
 
@@ -71,6 +74,14 @@ const SYSTEM_PROMPT = `你叫Mia，是一个有用的的发票智能助手。如
 如果你不知道答案，请回答你只能根据上下文的内容回答问题。在识别完文件之后，需要调用mcp工具生成用户可能需要的问题选项。用户确认数量没问题之后，需要生成一个表单供用户填写。接收到的是html的时候，assistent角色回复的消息需要是html格式，只要用户发送了表单信息，那么无论表单信息内容是否为空，都需要调用mcp工具对表单信息进行标准化处理
 `;
 
+const INVOICE_TOOL_API_KEY =
+  process.env.UMI_APP_INVOICE_TOOL_API_KEY ||
+  process.env.VITE_INVOICE_TOOL_API_KEY;
+const INVOICE_TOOL_BASE_URL =
+  process.env.UMI_APP_INVOICE_TOOL_BASE_URL ||
+  process.env.VITE_INVOICE_TOOL_BASE_URL ||
+  'https://api.deepseek.com';
+
 const useStyle = createStyles(({ token, css, responsive }) => {
   return {
     chat: css`
@@ -437,8 +448,8 @@ const InvoiceAssistant = forwardRef((props, ref) => {
         return originMessage;
       }
 
-      currentThink = messageData?.choices?.[0]?.delta?.reasoning_content || '';
-      currentContent = messageData?.choices?.[0]?.delta?.content || '';
+      currentThink = extractLlmChunkReasoning(messageData);
+      currentContent = extractLlmChunkContent(messageData);
       messageData?.choices?.[0]?.delta?.tool_calls?.forEach((call) => {
         const index = call.index;
         const toolCall = (toolCalls[index] = toolCalls[index] || {
@@ -729,7 +740,10 @@ const InvoiceAssistant = forwardRef((props, ref) => {
         }
         const content = JSON.stringify(requestMessages);
         const long = content.length > 900000;
-        const model = long ? 'qwen-long' : 'qwen-plus';
+        const model = await resolveLlmModel({
+          long,
+          thinking: false,
+        });
         const enable_search = false;
         const enable_thinking = false;
 
@@ -800,8 +814,10 @@ const InvoiceAssistant = forwardRef((props, ref) => {
             ) {
               mcpArguments = {
                 ...mcpArguments,
-                apikey: 'sk-bf3698390f3b40dd8435cb73169a7437',
-                baseurl: 'https://api.deepseek.com',
+                ...(INVOICE_TOOL_API_KEY
+                  ? { apikey: INVOICE_TOOL_API_KEY }
+                  : {}),
+                baseurl: INVOICE_TOOL_BASE_URL,
               };
             }
 

src/pages/Home/index.jsx

@@ -535,7 +535,12 @@ function buildConversationItems(conversations = []) {
   return conversations.map((c) => ({
     key: c.name,
     label: c.metadata.label || '新会话',
-    icon: c.metadata.topic ? <BorderlessTableOutlined /> : <CommentOutlined />,
+    icon:
+      c.metadata.topic || c.metadata.topicConfig ? (
+        <BorderlessTableOutlined />
+      ) : (
+        <CommentOutlined />
+      ),
     pinned: c.metadata.pinned,
     group: '历史会话',
   }));
@@ -545,7 +550,12 @@ function buildCompactConversationItems(conversations = []) {
   return conversations.slice(0, 6).map((c) => ({
     key: c.name,
     label: c.metadata.label || '新会话',
-    icon: c.metadata.topic ? <BorderlessTableOutlined /> : <CommentOutlined />,
+    icon:
+      c.metadata.topic || c.metadata.topicConfig ? (
+        <BorderlessTableOutlined />
+      ) : (
+        <CommentOutlined />
+      ),
     pinned: c.metadata.pinned,
   }));
 }

src/services/DataService.js

@@ -4,7 +4,117 @@ import { request } from '@umijs/max';
 
 const SUBJECT_CONVERSATION = 'ai_conversation';
 const SUBJECT_TOPIC = 'ai_topic';
-const SUBJECT_KNOWLEDGEBASE = 'ai_knowledgebase';
+const LLM_API_BASE =
+  process.env.UMI_APP_LLM_API_BASE ||
+  process.env.VITE_LLM_API_BASE ||
+  '/llm-api';
+const LLM_CHAT_PATH =
+  process.env.UMI_APP_LLM_CHAT_PATH ||
+  process.env.VITE_LLM_CHAT_PATH ||
+  '/v1/chat/completions';
+const LLM_API_KEY =
+  process.env.UMI_APP_LLM_API_KEY || process.env.VITE_LLM_API_KEY;
+const LLM_MODEL =
+  process.env.UMI_APP_LLM_MODEL || process.env.VITE_LLM_MODEL || 'gpt-5.5';
+
+function joinUrl(baseUrl, path) {
+  return `${baseUrl.replace(/\/+$/, '')}/${path.replace(/^\/+/, '')}`;
+}
+
+export function extractLlmChunkContent(value) {
+  if (typeof value === 'string') {
+    return value;
+  }
+  if (!value || typeof value !== 'object') {
+    return '';
+  }
+
+  if (typeof value.content === 'string') {
+    return value.content;
+  }
+  if (typeof value.text === 'string') {
+    return value.text;
+  }
+
+  const delta = value.delta;
+  if (delta && typeof delta === 'object') {
+    const deltaContent = extractLlmChunkContent(delta);
+    if (deltaContent) {
+      return deltaContent;
+    }
+  }
+
+  const message = value.message;
+  if (message && typeof message === 'object') {
+    const messageContent = extractLlmChunkContent(message);
+    if (messageContent) {
+      return messageContent;
+    }
+  }
+
+  const data = value.data;
+  if (data && typeof data === 'object') {
+    const dataContent = extractLlmChunkContent(data);
+    if (dataContent) {
+      return dataContent;
+    }
+  }
+
+  const choices = value.choices;
+  if (Array.isArray(choices)) {
+    return choices.map((choice) => extractLlmChunkContent(choice)).join('');
+  }
+
+  return '';
+}
+
+export function extractLlmChunkReasoning(value) {
+  if (!value || typeof value !== 'object') {
+    return '';
+  }
+
+  const reasoning =
+    value.reasoning_content ||
+    value.reasoning ||
+    value.thinking_content ||
+    value.thought ||
+    value.thoughts;
+
+  if (typeof reasoning === 'string') {
+    return reasoning;
+  }
+
+  const delta = value.delta;
+  if (delta && typeof delta === 'object') {
+    const deltaReasoning = extractLlmChunkReasoning(delta);
+    if (deltaReasoning) {
+      return deltaReasoning;
+    }
+  }
+
+  const message = value.message;
+  if (message && typeof message === 'object') {
+    const messageReasoning = extractLlmChunkReasoning(message);
+    if (messageReasoning) {
+      return messageReasoning;
+    }
+  }
+
+  const data = value.data;
+  if (data && typeof data === 'object') {
+    const dataReasoning = extractLlmChunkReasoning(data);
+    if (dataReasoning) {
+      return dataReasoning;
+    }
+  }
+
+  const choices = value.choices;
+  if (Array.isArray(choices)) {
+    return choices.map((choice) => extractLlmChunkReasoning(choice)).join('');
+  }
+
+  return '';
+}
 
 export function getApplication(name) {
   return request(`/api/application/${name}`, {
@@ -158,14 +268,37 @@ export async function callMcpTool(mcpName, toolName, data = {}) {
 }
 
 export async function llmChat(data, signal = undefined) {
-  return fetch(`/api/llm/chat`, {
-    method: 'post',
-    headers: {
-      'Content-Type': 'application/json',
-    },
+  if (!LLM_API_KEY) {
+    throw new Error('缺少 UMI_APP_LLM_API_KEY，请配置线上模型服务 API Key。');
+  }
+
+  const headers = new Headers({
+    Accept: 'text/event-stream',
+    Authorization: `Bearer ${LLM_API_KEY}`,
+    'Content-Type': 'application/json',
+  });
+
+  const response = await fetch(joinUrl(LLM_API_BASE, LLM_CHAT_PATH), {
+    method: 'POST',
+    headers,
     signal,
-    body: JSON.stringify(data),
+    body: JSON.stringify({
+      ...data,
+      model: LLM_MODEL,
+      stream: true,
+    }),
   });
+
+  if (!response.ok) {
+    const message = await response.text();
+    throw new Error(message || `HTTP ${response.status}`);
+  }
+
+  return response;
+}
+
+export async function resolveLlmModel(options = {}) {
+  return LLM_MODEL;
 }
 
 export async function queryConversations(query) {
@@ -179,7 +312,7 @@ export async function queryConversations(query) {
     page_size: 20,
     mode: 'basic',
     select:
-      'metadata.label metadata.pinned metadata.agent metadata.avatar metadata.topic',
+      'metadata.label metadata.pinned metadata.agent metadata.agentConfig metadata.avatar metadata.topic metadata.topicConfig metadata.knowledgeBases',
     sort: '-metadata.pinned -created_at',
     filter: {
       'metadata.user': userInfo._id,