feat: 替换redis搜索

src/pages/api/openapi/chat/lafGpt.ts

@@ -7,12 +7,15 @@ import { ChatCompletionRequestMessage, ChatCompletionRequestMessageRoleEnum } fr
 import { ChatItemType } from '@/types/chat';
 import { jsonRes } from '@/service/response';
 import { PassThrough } from 'stream';
-import { ChatModelNameEnum, modelList, ChatModelNameMap } from '@/constants/model';
+import {
+  ChatModelNameEnum,
+  modelList,
+  ChatModelNameMap,
+  ModelVectorSearchModeMap
+} from '@/constants/model';
 import { pushChatBill } from '@/service/events/pushBill';
-import { connectRedis } from '@/service/redis';
-import { VecModelDataPrefix } from '@/constants/redis';
-import { vectorToBuffer } from '@/utils/tools';
 import { openaiCreateEmbedding, gpt35StreamResponse } from '@/service/utils/openai';
+import { PgClient } from '@/service/pg';
 
 /* 发送提示词 */
 export default async function handler(req: NextApiRequest, res: NextApiResponse) {
@@ -46,7 +49,6 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
     }
 
     await connectToDatabase();
-    const redis = await connectRedis();
     let startTime = Date.now();
 
     /* 凭证校验 */
@@ -144,39 +146,29 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
     // 读取对话内容
     const prompts = [prompt];
 
-    // 搜索系统提示词, 按相似度从 redis 中搜出相关的 q 和 text
-    const redisData: any[] = await redis.sendCommand([
-      'FT.SEARCH',
-      `idx:${VecModelDataPrefix}:hash`,
-      `@modelId:{${String(model._id)}}=>[KNN 20 @vector $blob AS score]`,
-      'RETURN',
-      '1',
-      'text',
-      'SORTBY',
-      'score',
-      'PARAMS',
-      '2',
-      'blob',
-      vectorToBuffer(promptVector),
-      'DIALECT',
-      '2'
-    ]);
+    // 相似度搜索
+    const similarity = ModelVectorSearchModeMap[model.search.mode]?.similarity || 0.22;
+    const vectorSearch = await PgClient.select<{ id: string; q: string; a: string }>('modelData', {
+      fields: ['id', 'q', 'a'],
+      order: [{ field: 'vector', mode: `<=> '[${promptVector}]'` }],
+      where: [
+        ['model_id', model._id],
+        'AND',
+        ['user_id', userId],
+        'AND',
+        `vector <=> '[${promptVector}]' < ${similarity}`
+      ],
+      limit: 30
+    });
 
-    // 格式化响应值，获取 qa
-    const formatRedisPrompt: string[] = [];
-    for (let i = 2; i < 42; i += 2) {
-      const text = redisData[i]?.[1];
-      if (text) {
-        formatRedisPrompt.push(text);
-      }
-    }
+    const formatRedisPrompt: string[] = vectorSearch.rows.map((item) => `${item.q}\n${item.a}`);
 
     // textArr 筛选，最多 3000 tokens
     const systemPrompt = systemPromptFilter(formatRedisPrompt, 3000);
 
     prompts.unshift({
       obj: 'SYSTEM',
-      value: `${model.systemPrompt} 知识库内容是最新的，知识库内容为: "${systemPrompt}"`
+      value: `${model.systemPrompt} 知识库是最新的,下面是知识库内容:${systemPrompt}`
     });
 
     // 控制在 tokens 数量，防止超出