feat: app module

client/src/pages/api/openapi/modules/kb/search.ts

@@ -0,0 +1,115 @@
+import type { NextApiRequest, NextApiResponse } from 'next';
+import { jsonRes } from '@/service/response';
+import { PgClient } from '@/service/pg';
+import { withNextCors } from '@/service/utils/tools';
+import type { ChatItemType } from '@/types/chat';
+import { ChatRoleEnum } from '@/constants/chat';
+import { openaiEmbedding_system } from '../../plugin/openaiEmbedding';
+import { modelToolMap } from '@/utils/plugin';
+
+export type QuoteItemType = {
+  id: string;
+  q: string;
+  a: string;
+  source?: string;
+};
+type Props = {
+  kb_ids: string[];
+  history: ChatItemType[];
+  similarity: number;
+  limit: number;
+  maxToken: number;
+  userChatInput: string;
+  stream?: boolean;
+};
+type Response = {
+  rawSearch: QuoteItemType[];
+  isEmpty?: boolean;
+  quotePrompt: string;
+};
+
+export default withNextCors(async function handler(req: NextApiRequest, res: NextApiResponse<any>) {
+  try {
+    const {
+      kb_ids = [],
+      history = [],
+      similarity,
+      limit,
+      maxToken,
+      userChatInput
+    } = req.body as Props;
+
+    if (!similarity || !Array.isArray(kb_ids)) {
+      throw new Error('params is error');
+    }
+
+    const result = await appKbSearch({
+      kb_ids,
+      history,
+      similarity,
+      limit,
+      maxToken,
+      userChatInput
+    });
+
+    jsonRes<Response>(res, {
+      data: result
+    });
+  } catch (err) {
+    console.log(err);
+    jsonRes(res, {
+      code: 500,
+      error: err
+    });
+  }
+});
+
+export async function appKbSearch({
+  kb_ids = [],
+  history = [],
+  similarity = 0.8,
+  limit = 5,
+  maxToken = 2500,
+  userChatInput
+}: Props): Promise<Response> {
+  // get vector
+  const promptVector = await openaiEmbedding_system({
+    input: [userChatInput]
+  });
+
+  // search kb
+  const res: any = await PgClient.query(
+    `BEGIN;
+    SET LOCAL ivfflat.probes = ${global.systemEnv.pgIvfflatProbe || 10};
+    select id,q,a,source from modelData where kb_id IN (${kb_ids
+      .map((item) => `'${item}'`)
+      .join(',')}) AND vector <#> '[${promptVector[0]}]' < -${similarity} order by vector <#> '[${
+      promptVector[0]
+    }]' limit ${limit};
+    COMMIT;`
+  );
+
+  const searchRes: QuoteItemType[] = res?.[2]?.rows || [];
+
+  // filter part quote by maxToken
+  const sliceResult = modelToolMap['gpt-3.5-turbo']
+    .tokenSlice({
+      maxToken,
+      messages: searchRes.map((item, i) => ({
+        obj: ChatRoleEnum.System,
+        value: `${i + 1}: [${item.q}\n${item.a}]`
+      }))
+    })
+    .map((item) => item.value)
+    .join('\n')
+    .trim();
+
+  // slice filterSearch
+  const rawSearch = searchRes.slice(0, sliceResult.length);
+
+  return {
+    isEmpty: rawSearch.length === 0,
+    rawSearch,
+    quotePrompt: sliceResult ? `知识库:\n${sliceResult}` : ''
+  };
+}