V4.12.0 features (#5435)

* add logs chart (#5352) * charts * chart data * log chart * delete * rename api * fix * move api * fix * fix * pro config * fix * feat: Repository interaction (#5356) * feat: 1好像功能没问题了，明天再测 * feat: 2 解决了昨天遗留的bug,但全选按钮又bug了 * feat: 3 第三版，解决了全选功能bug * feat: 4 第四版，下面改小细节 * feat: 5 我勒个痘 * feat: 6 * feat: 6 pr * feat: 7 * feat: 8 * feat: 9 * feat: 10 * feat: 11 * feat: 12 * perf: checkbox ui * refactor: tweak login loyout (#5357) Co-authored-by: Archer <545436317@qq.com> * login ui * app chat log chart pro display (#5392) * app chat log chart pro display * add canopen props * perf: pro tag tip * perf: pro tag tip * feat: openrouter provider (#5406) * perf: login ui * feat: openrouter provider * provider * perf: custom error throw * perf: emb batch (#5407) * perf: emb batch * perf: vector retry * doc * doc (#5411) * doc * fix: team folder will add to workflow * fix: generateToc shell * Tool price (#5376) * resolve conflicts for cherry-pick * fix i18n * Enhance system plugin template data structure and update ToolSelectModal to include CostTooltip component * refactor: update systemKeyCost type to support array of objects in plugin and workflow types * refactor: simplify systemKeyCost type across plugin and workflow types to a single number * refactor: streamline systemKeyCost handling in plugin and workflow components * fix * fix * perf: toolset price config;fix: workflow array selector ui (#5419) * fix: workflow array selector ui * update default model tip * perf: toolset price config * doc * fix: test * Refactor/chat (#5418) * refactor: add homepage configuration; add home chat page; add side bar animated collapse and layout * fix: fix lint rules * chore: improve logics and code * chore: more clearer logics * chore: adjust api --------- Co-authored-by: Archer <545436317@qq.com> * perf: chat setting code * del history * logo image * perf: home chat ui * feat: enhance chat response handling with external links and user info (#5427) * feat: enhance chat response handling with external links and user info * fix * cite code * perf: toolset add in workflow * fix: test * fix: search paraentId * Fix/chat (#5434) * wip: rebase了upstream * wip: adapt mobile UI * fix: fix chat page logic and UI * fix: fix UI and improve some logics * fix: model selector missing logo; vision model to retrieve file * perf: role selector * fix: chat ui * optimize export app chat log (#5436) * doc * chore: move components to proper directory; fix the api to get app list (#5437) * chore: improve team app panel display form (#5438) * feat: add home chat log tab * chore: improve team app panel display form * chore: improve log panel * fix: spec * doc * fix: log permission * fix: dataset schema required * add loading status * remove ui weight * manage log * fix: log detail per * doc * fix: log menu * rename permission * bg color * fix: app log per * fix: log key selector * fix: log * doc --------- Co-authored-by: heheer <zhiyu44@qq.com> Co-authored-by: colnii <1286949794@qq.com> Co-authored-by: 伍闲犬 <76519998+xqvvu@users.noreply.github.com> Co-authored-by: Ctrlz <143257420+ctrlz526@users.noreply.github.com> Co-authored-by: 伍闲犬 <whoeverimf5@gmail.com> Co-authored-by: heheer <heheer@sealos.io>
2025-10-15 07:31:19 +00:00 · 2025-08-12 22:22:18 +08:00
parent c6e58291f7
commit c51395b2c8
239 changed files with 9336 additions and 3128 deletions
--- a/packages/service/common/api/plusRequest.ts
+++ b/packages/service/common/api/plusRequest.ts
@@ -5,6 +5,7 @@ import axios, {
  type AxiosRequestConfig
 } from 'axios';
 import { FastGPTProUrl } from '../system/constants';
+import { UserError } from '@fastgpt/global/common/error/utils';

 interface ConfigType {
  headers?: { [key: string]: string };
@@ -78,7 +79,7 @@ instance.interceptors.response.use(responseSuccess, (err) => Promise.reject(err)
 export function request(url: string, data: any, config: ConfigType, method: Method): any {
  if (!FastGPTProUrl) {
    console.log('未部署商业版接口', url);
-    return Promise.reject('The The request was denied...');
+    return Promise.reject(new UserError('The request was denied...'));
  }

  /* 去空 */
--- a/packages/service/common/file/gridfs/controller.ts
+++ b/packages/service/common/file/gridfs/controller.ts
@@ -151,10 +151,6 @@ export async function getFileById({
    _id: new Types.ObjectId(fileId)
  });

-  // if (!file) {
-  //   return Promise.reject('File not found');
-  // }
-
  return file || undefined;
 }

--- a/packages/service/common/file/image/controller.ts
+++ b/packages/service/common/file/image/controller.ts
@@ -11,6 +11,7 @@ import { UserError } from '@fastgpt/global/common/error/utils';

 export const maxImgSize = 1024 * 1024 * 12;
 const base64MimeRegex = /data:image\/([^\)]+);base64/;
+
 export async function uploadMongoImg({
  base64Img,
  teamId,
@@ -22,13 +23,13 @@ export async function uploadMongoImg({
  forever?: Boolean;
 }) {
  if (base64Img.length > maxImgSize) {
-    return Promise.reject('Image too large');
+    return Promise.reject(new UserError('Image too large'));
  }

  const [base64Mime, base64Data] = base64Img.split(',');
  // Check if mime type is valid
  if (!base64MimeRegex.test(base64Mime)) {
-    return Promise.reject('Invalid image base64');
+    return Promise.reject(new UserError('Invalid image base64'));
  }

  const mime = `image/${base64Mime.match(base64MimeRegex)?.[1] ?? 'image/jpeg'}`;
@@ -39,7 +40,7 @@ export async function uploadMongoImg({
  }

  if (!extension || !imageFileType.includes(`.${extension}`)) {
-    return Promise.reject(`Invalid image file type: ${mime}`);
+    return Promise.reject(new UserError(`Invalid image file type: ${mime}`));
  }

  const { _id } = await retryFn(() =>
--- a/packages/service/common/file/multer.ts
+++ b/packages/service/common/file/multer.ts
@@ -4,6 +4,7 @@ import path from 'path';
 import type { BucketNameEnum } from '@fastgpt/global/common/file/constants';
 import { bucketNameMap } from '@fastgpt/global/common/file/constants';
 import { getNanoid } from '@fastgpt/global/common/string/tools';
+import { UserError } from '@fastgpt/global/common/error/utils';

 export type FileType = {
  fieldname: string;
@@ -61,7 +62,7 @@ export const getUploadModel = ({ maxSize = 500 }: { maxSize?: number }) => {
          // check bucket name
          const bucketName = (req.body?.bucketName || originBucketName) as `${BucketNameEnum}`;
          if (bucketName && !bucketNameMap[bucketName]) {
-            return reject('BucketName is invalid');
+            return reject(new UserError('BucketName is invalid'));
          }

          // @ts-ignore
--- a/packages/service/common/vectorDB/controller.d.ts
+++ b/packages/service/common/vectorDB/controller.d.ts
@@ -17,8 +17,7 @@ export type InsertVectorProps = {
  collectionId: string;
 };
 export type InsertVectorControllerProps = InsertVectorProps & {
-  vector: number[];
-  retry?: number;
+  vectors: number[][];
 };

 export type EmbeddingRecallProps = {
--- a/packages/service/common/vectorDB/controller.ts
+++ b/packages/service/common/vectorDB/controller.ts
@@ -2,7 +2,12 @@
 import { PgVectorCtrl } from './pg';
 import { ObVectorCtrl } from './oceanbase';
 import { getVectorsByText } from '../../core/ai/embedding';
-import { type DelDatasetVectorCtrlProps, type InsertVectorProps } from './controller.d';
+import type {
+  EmbeddingRecallCtrlProps} from './controller.d';
+import {
+  type DelDatasetVectorCtrlProps,
+  type InsertVectorProps
+} from './controller.d';
 import { type EmbeddingModelItemType } from '@fastgpt/global/core/ai/model.d';
 import { MILVUS_ADDRESS, PG_ADDRESS, OCEANBASE_ADDRESS } from './constants';
 import { MilvusCtrl } from './milvus';
@@ -35,7 +40,8 @@ const onIncrCache = (teamId: string) => incrValueToCache(getChcheKey(teamId), 1)
 const Vector = getVectorObj();

 export const initVectorStore = Vector.init;
-export const recallFromVectorStore = Vector.embRecall;
+export const recallFromVectorStore = (props: EmbeddingRecallCtrlProps) =>
+  retryFn(() => Vector.embRecall(props));
 export const getVectorDataByTime = Vector.getVectorDataByTime;

 export const getVectorCountByTeamId = async (teamId: string) => {
@@ -58,34 +64,34 @@ export const getVectorCountByCollectionId = Vector.getVectorCountByCollectionId;

 export const insertDatasetDataVector = async ({
  model,
-  query,
+  inputs,
  ...props
 }: InsertVectorProps & {
-  query: string;
+  inputs: string[];
  model: EmbeddingModelItemType;
 }) => {
-  return retryFn(async () => {
-    const { vectors, tokens } = await getVectorsByText({
-      model,
-      input: query,
-      type: 'db'
-    });
-    const { insertId } = await Vector.insert({
-      ...props,
-      vector: vectors[0]
-    });
-
-    onIncrCache(props.teamId);
-
-    return {
-      tokens,
-      insertId
-    };
+  const { vectors, tokens } = await getVectorsByText({
+    model,
+    input: inputs,
+    type: 'db'
  });
+  const { insertIds } = await retryFn(() =>
+    Vector.insert({
+      ...props,
+      vectors
+    })
+  );
+
+  onIncrCache(props.teamId);
+
+  return {
+    tokens,
+    insertIds
+  };
 };

 export const deleteDatasetDataVector = async (props: DelDatasetVectorCtrlProps) => {
-  const result = await Vector.delete(props);
+  const result = await retryFn(() => Vector.delete(props));
  onDelCache(props.teamId);
  return result;
 };
--- a/packages/service/common/vectorDB/milvus/index.ts
+++ b/packages/service/common/vectorDB/milvus/index.ts
@@ -11,7 +11,7 @@ import type {
  EmbeddingRecallResponse,
  InsertVectorControllerProps
 } from '../controller.d';
-import { delay } from '@fastgpt/global/common/system/utils';
+import { delay, retryFn } from '@fastgpt/global/common/system/utils';
 import { addLog } from '../../system/log';
 import { customNanoid } from '@fastgpt/global/common/string/tools';

@@ -27,6 +27,7 @@ export class MilvusCtrl {
      address: MILVUS_ADDRESS,
      token: MILVUS_TOKEN
    });
+    await global.milvusClient.connectPromise;

    addLog.info(`Milvus connected`);

@@ -124,9 +125,9 @@ export class MilvusCtrl {
    }
  };

-  insert = async (props: InsertVectorControllerProps): Promise<{ insertId: string }> => {
+  insert = async (props: InsertVectorControllerProps): Promise<{ insertIds: string[] }> => {
    const client = await this.getClient();
-    const { teamId, datasetId, collectionId, vector, retry = 3 } = props;
+    const { teamId, datasetId, collectionId, vectors } = props;

    const generateId = () => {
      // in js, the max safe integer is 2^53 - 1: 9007199254740991
@@ -136,45 +137,32 @@ export class MilvusCtrl {
      const restDigits = customNanoid('1234567890', 15);
      return Number(`${firstDigit}${restDigits}`);
    };
-    const id = generateId();
-    try {
-      const result = await client.insert({
-        collection_name: DatasetVectorTableName,
-        data: [
-          {
-            id,
-            vector,
-            teamId: String(teamId),
-            datasetId: String(datasetId),
-            collectionId: String(collectionId),
-            createTime: Date.now()
-          }
-        ]
-      });

-      const insertId = (() => {
-        if ('int_id' in result.IDs) {
-          return `${result.IDs.int_id.data?.[0]}`;
-        }
-        return `${result.IDs.str_id.data?.[0]}`;
-      })();
+    const result = await client.insert({
+      collection_name: DatasetVectorTableName,
+      data: vectors.map((vector) => ({
+        id: generateId(),
+        vector,
+        teamId: String(teamId),
+        datasetId: String(datasetId),
+        collectionId: String(collectionId),
+        createTime: Date.now()
+      }))
+    });

-      return {
-        insertId: insertId
-      };
-    } catch (error) {
-      if (retry <= 0) {
-        return Promise.reject(error);
+    const insertIds = (() => {
+      if ('int_id' in result.IDs) {
+        return result.IDs.int_id.data.map((id) => String(id));
      }
-      await delay(500);
-      return this.insert({
-        ...props,
-        retry: retry - 1
-      });
-    }
+      return result.IDs.str_id.data.map((id) => String(id));
+    })();
+
+    return {
+      insertIds
+    };
  };
  delete = async (props: DelDatasetVectorCtrlProps): Promise<any> => {
-    const { teamId, retry = 2 } = props;
+    const { teamId } = props;
    const client = await this.getClient();

    const teamIdWhere = `(teamId=="${String(teamId)}")`;
@@ -206,33 +194,15 @@ export class MilvusCtrl {

    const concatWhere = `${teamIdWhere} and ${where}`;

-    try {
-      await client.delete({
-        collection_name: DatasetVectorTableName,
-        filter: concatWhere
-      });
-    } catch (error) {
-      if (retry <= 0) {
-        return Promise.reject(error);
-      }
-      await delay(500);
-      return this.delete({
-        ...props,
-        retry: retry - 1
-      });
-    }
+    await client.delete({
+      collection_name: DatasetVectorTableName,
+      filter: concatWhere
+    });
  };
  embRecall = async (props: EmbeddingRecallCtrlProps): Promise<EmbeddingRecallResponse> => {
    const client = await this.getClient();
-    const {
-      teamId,
-      datasetIds,
-      vector,
-      limit,
-      forbidCollectionIdList,
-      filterCollectionIdList,
-      retry = 2
-    } = props;
+    const { teamId, datasetIds, vector, limit, forbidCollectionIdList, filterCollectionIdList } =
+      props;

    // Forbid collection
    const formatForbidCollectionIdList = (() => {
@@ -262,37 +232,29 @@ export class MilvusCtrl {
      return { results: [] };
    }

-    try {
-      const { results } = await client.search({
+    const { results } = await retryFn(() =>
+      client.search({
        collection_name: DatasetVectorTableName,
        data: vector,
        limit,
        filter: `(teamId == "${teamId}") and (datasetId in [${datasetIds.map((id) => `"${id}"`).join(',')}]) ${collectionIdQuery} ${forbidColQuery}`,
        output_fields: ['collectionId']
-      });
+      })
+    );

-      const rows = results as {
-        score: number;
-        id: string;
-        collectionId: string;
-      }[];
+    const rows = results as {
+      score: number;
+      id: string;
+      collectionId: string;
+    }[];

-      return {
-        results: rows.map((item) => ({
-          id: String(item.id),
-          collectionId: item.collectionId,
-          score: item.score
-        }))
-      };
-    } catch (error) {
-      if (retry <= 0) {
-        return Promise.reject(error);
-      }
-      return this.embRecall({
-        ...props,
-        retry: retry - 1
-      });
-    }
+    return {
+      results: rows.map((item) => ({
+        id: String(item.id),
+        collectionId: item.collectionId,
+        score: item.score
+      }))
+    };
  };

  getVectorCountByTeamId = async (teamId: string) => {
--- a/packages/service/common/vectorDB/oceanbase/index.ts
+++ b/packages/service/common/vectorDB/oceanbase/index.ts
@@ -1,6 +1,6 @@
 /* oceanbase vector crud */
 import { DatasetVectorTableName } from '../constants';
-import { delay } from '@fastgpt/global/common/system/utils';
+import { delay, retryFn } from '@fastgpt/global/common/system/utils';
 import { ObClient } from './controller';
 import { type RowDataPacket } from 'mysql2/promise';
 import {
@@ -42,41 +42,30 @@ export class ObVectorCtrl {
      addLog.error('init oceanbase error', error);
    }
  };
-  insert = async (props: InsertVectorControllerProps): Promise<{ insertId: string }> => {
-    const { teamId, datasetId, collectionId, vector, retry = 3 } = props;
+  insert = async (props: InsertVectorControllerProps): Promise<{ insertIds: string[] }> => {
+    const { teamId, datasetId, collectionId, vectors } = props;

-    try {
-      const { rowCount, rows } = await ObClient.insert(DatasetVectorTableName, {
-        values: [
-          [
-            { key: 'vector', value: `[${vector}]` },
-            { key: 'team_id', value: String(teamId) },
-            { key: 'dataset_id', value: String(datasetId) },
-            { key: 'collection_id', value: String(collectionId) }
-          ]
-        ]
-      });
+    const values = vectors.map((vector) => [
+      { key: 'vector', value: `[${vector}]` },
+      { key: 'team_id', value: String(teamId) },
+      { key: 'dataset_id', value: String(datasetId) },
+      { key: 'collection_id', value: String(collectionId) }
+    ]);

-      if (rowCount === 0) {
-        return Promise.reject('insertDatasetData: no insert');
-      }
+    const { rowCount, rows } = await ObClient.insert(DatasetVectorTableName, {
+      values
+    });

-      return {
-        insertId: rows[0].id
-      };
-    } catch (error) {
-      if (retry <= 0) {
-        return Promise.reject(error);
-      }
-      await delay(500);
-      return this.insert({
-        ...props,
-        retry: retry - 1
-      });
+    if (rowCount === 0) {
+      return Promise.reject('insertDatasetData: no insert');
    }
+
+    return {
+      insertIds: rows.map((row) => row.id)
+    };
  };
  delete = async (props: DelDatasetVectorCtrlProps): Promise<any> => {
-    const { teamId, retry = 2 } = props;
+    const { teamId } = props;

    const teamIdWhere = `team_id='${String(teamId)}' AND`;

@@ -106,31 +95,13 @@ export class ObVectorCtrl {

    if (!where) return;

-    try {
-      await ObClient.delete(DatasetVectorTableName, {
-        where: [where]
-      });
-    } catch (error) {
-      if (retry <= 0) {
-        return Promise.reject(error);
-      }
-      await delay(500);
-      return this.delete({
-        ...props,
-        retry: retry - 1
-      });
-    }
+    await ObClient.delete(DatasetVectorTableName, {
+      where: [where]
+    });
  };
  embRecall = async (props: EmbeddingRecallCtrlProps): Promise<EmbeddingRecallResponse> => {
-    const {
-      teamId,
-      datasetIds,
-      vector,
-      limit,
-      forbidCollectionIdList,
-      filterCollectionIdList,
-      retry = 2
-    } = props;
+    const { teamId, datasetIds, vector, limit, forbidCollectionIdList, filterCollectionIdList } =
+      props;

    // Get forbid collection
    const formatForbidCollectionIdList = (() => {
@@ -161,15 +132,14 @@ export class ObVectorCtrl {
      return { results: [] };
    }

-    try {
-      const rows = await ObClient.query<
-        ({
-          id: string;
-          collection_id: string;
-          score: number;
-        } & RowDataPacket)[][]
-      >(
-        `BEGIN;
+    const rows = await ObClient.query<
+      ({
+        id: string;
+        collection_id: string;
+        score: number;
+      } & RowDataPacket)[][]
+    >(
+      `BEGIN;
          SET ob_hnsw_ef_search = ${global.systemEnv?.hnswEfSearch || 100};
          SELECT id, collection_id, inner_product(vector, [${vector}]) AS score
            FROM ${DatasetVectorTableName}
@@ -179,24 +149,15 @@ export class ObVectorCtrl {
              ${forbidCollectionSql}
            ORDER BY score desc APPROXIMATE LIMIT ${limit};
        COMMIT;`
-      ).then(([rows]) => rows[2]);
+    ).then(([rows]) => rows[2]);

-      return {
-        results: rows.map((item) => ({
-          id: String(item.id),
-          collectionId: item.collection_id,
-          score: item.score
-        }))
-      };
-    } catch (error) {
-      if (retry <= 0) {
-        return Promise.reject(error);
-      }
-      return this.embRecall({
-        ...props,
-        retry: retry - 1
-      });
-    }
+    return {
+      results: rows.map((item) => ({
+        id: String(item.id),
+        collectionId: item.collection_id,
+        score: item.score
+      }))
+    };
  };
  getVectorDataByTime = async (start: Date, end: Date) => {
    const rows = await ObClient.query<
--- a/packages/service/common/vectorDB/pg/index.ts
+++ b/packages/service/common/vectorDB/pg/index.ts
@@ -1,6 +1,6 @@
 /* pg vector crud */
 import { DatasetVectorTableName } from '../constants';
-import { delay } from '@fastgpt/global/common/system/utils';
+import { delay, retryFn } from '@fastgpt/global/common/system/utils';
 import { PgClient, connectPg } from './controller';
 import { type PgSearchRawType } from '@fastgpt/global/core/dataset/api';
 import type {
@@ -65,41 +65,30 @@ export class PgVectorCtrl {
      addLog.error('init pg error', error);
    }
  };
-  insert = async (props: InsertVectorControllerProps): Promise<{ insertId: string }> => {
-    const { teamId, datasetId, collectionId, vector, retry = 3 } = props;
+  insert = async (props: InsertVectorControllerProps): Promise<{ insertIds: string[] }> => {
+    const { teamId, datasetId, collectionId, vectors } = props;

-    try {
-      const { rowCount, rows } = await PgClient.insert(DatasetVectorTableName, {
-        values: [
-          [
-            { key: 'vector', value: `[${vector}]` },
-            { key: 'team_id', value: String(teamId) },
-            { key: 'dataset_id', value: String(datasetId) },
-            { key: 'collection_id', value: String(collectionId) }
-          ]
-        ]
-      });
+    const values = vectors.map((vector) => [
+      { key: 'vector', value: `[${vector}]` },
+      { key: 'team_id', value: String(teamId) },
+      { key: 'dataset_id', value: String(datasetId) },
+      { key: 'collection_id', value: String(collectionId) }
+    ]);

-      if (rowCount === 0) {
-        return Promise.reject('insertDatasetData: no insert');
-      }
+    const { rowCount, rows } = await PgClient.insert(DatasetVectorTableName, {
+      values
+    });

-      return {
-        insertId: rows[0].id
-      };
-    } catch (error) {
-      if (retry <= 0) {
-        return Promise.reject(error);
-      }
-      await delay(500);
-      return this.insert({
-        ...props,
-        retry: retry - 1
-      });
+    if (rowCount === 0) {
+      return Promise.reject('insertDatasetData: no insert');
    }
+
+    return {
+      insertIds: rows.map((row) => row.id)
+    };
  };
  delete = async (props: DelDatasetVectorCtrlProps): Promise<any> => {
-    const { teamId, retry = 2 } = props;
+    const { teamId } = props;

    const teamIdWhere = `team_id='${String(teamId)}' AND`;

@@ -129,31 +118,13 @@ export class PgVectorCtrl {

    if (!where) return;

-    try {
-      await PgClient.delete(DatasetVectorTableName, {
-        where: [where]
-      });
-    } catch (error) {
-      if (retry <= 0) {
-        return Promise.reject(error);
-      }
-      await delay(500);
-      return this.delete({
-        ...props,
-        retry: retry - 1
-      });
-    }
+    await PgClient.delete(DatasetVectorTableName, {
+      where: [where]
+    });
  };
  embRecall = async (props: EmbeddingRecallCtrlProps): Promise<EmbeddingRecallResponse> => {
-    const {
-      teamId,
-      datasetIds,
-      vector,
-      limit,
-      forbidCollectionIdList,
-      filterCollectionIdList,
-      retry = 2
-    } = props;
+    const { teamId, datasetIds, vector, limit, forbidCollectionIdList, filterCollectionIdList } =
+      props;

    // Get forbid collection
    const formatForbidCollectionIdList = (() => {
@@ -184,9 +155,8 @@ export class PgVectorCtrl {
      return { results: [] };
    }

-    try {
-      const results: any = await PgClient.query(
-        `BEGIN;
+    const results: any = await PgClient.query(
+      `BEGIN;
          SET LOCAL hnsw.ef_search = ${global.systemEnv?.hnswEfSearch || 100};
          SET LOCAL hnsw.max_scan_tuples = ${global.systemEnv?.hnswMaxScanTuples || 100000};
          SET LOCAL hnsw.iterative_scan = relaxed_order;
@@ -199,31 +169,22 @@ export class PgVectorCtrl {
              order by score limit ${limit}
          ) SELECT id, collection_id, score FROM relaxed_results ORDER BY score;
        COMMIT;`
-      );
-      const rows = results?.[results.length - 2]?.rows as PgSearchRawType[];
-
-      if (!Array.isArray(rows)) {
-        return {
-          results: []
-        };
-      }
+    );
+    const rows = results?.[results.length - 2]?.rows as PgSearchRawType[];

+    if (!Array.isArray(rows)) {
      return {
-        results: rows.map((item) => ({
-          id: String(item.id),
-          collectionId: item.collection_id,
-          score: item.score * -1
-        }))
+        results: []
      };
-    } catch (error) {
-      if (retry <= 0) {
-        return Promise.reject(error);
-      }
-      return this.embRecall({
-        ...props,
-        retry: retry - 1
-      });
    }
+
+    return {
+      results: rows.map((item) => ({
+        id: String(item.id),
+        collectionId: item.collection_id,
+        score: item.score * -1
+      }))
+    };
  };
  getVectorDataByTime = async (start: Date, end: Date) => {
    const { rows } = await PgClient.query<{
--- a/packages/service/core/ai/audio/transcriptions.ts
+++ b/packages/service/core/ai/audio/transcriptions.ts
@@ -3,6 +3,7 @@ import { getAxiosConfig } from '../config';
 import axios from 'axios';
 import FormData from 'form-data';
 import { type STTModelType } from '@fastgpt/global/core/ai/model.d';
+import { UserError } from '@fastgpt/global/common/error/utils';

 export const aiTranscriptions = async ({
  model: modelData,
@@ -14,7 +15,7 @@ export const aiTranscriptions = async ({
  headers?: Record<string, string>;
 }) => {
  if (!modelData) {
-    return Promise.reject('no model');
+    return Promise.reject(new UserError('no model'));
  }

  const data = new FormData();
--- a/packages/service/core/ai/embedding/index.ts
+++ b/packages/service/core/ai/embedding/index.ts
@@ -6,7 +6,7 @@ import { addLog } from '../../../common/system/log';

 type GetVectorProps = {
  model: EmbeddingModelItemType;
-  input: string;
+  input: string[] | string;
  type?: `${EmbeddingTypeEnm}`;
  headers?: Record<string, string>;
 };
@@ -19,60 +19,85 @@ export async function getVectorsByText({ model, input, type, headers }: GetVecto
      message: 'input is empty'
    });
  }
+  const ai = getAIApi();
+
+  const formatInput = Array.isArray(input) ? input : [input];
+
+  // 20 size every request
+  const chunkSize = 20;
+  const chunks = [];
+  for (let i = 0; i < formatInput.length; i += chunkSize) {
+    chunks.push(formatInput.slice(i, i + chunkSize));
+  }

  try {
-    const ai = getAIApi();
+    // Process chunks sequentially
+    let totalTokens = 0;
+    const allVectors: number[][] = [];

-    // input text to vector
-    const result = await ai.embeddings
-      .create(
-        {
-          ...model.defaultConfig,
-          ...(type === EmbeddingTypeEnm.db && model.dbConfig),
-          ...(type === EmbeddingTypeEnm.query && model.queryConfig),
-          model: model.model,
-          input: [input]
-        },
-        model.requestUrl
-          ? {
-              path: model.requestUrl,
-              headers: {
-                ...(model.requestAuth ? { Authorization: `Bearer ${model.requestAuth}` } : {}),
-                ...headers
+    for (const chunk of chunks) {
+      // input text to vector
+      const result = await ai.embeddings
+        .create(
+          {
+            ...model.defaultConfig,
+            ...(type === EmbeddingTypeEnm.db && model.dbConfig),
+            ...(type === EmbeddingTypeEnm.query && model.queryConfig),
+            model: model.model,
+            input: chunk
+          },
+          model.requestUrl
+            ? {
+                path: model.requestUrl,
+                headers: {
+                  ...(model.requestAuth ? { Authorization: `Bearer ${model.requestAuth}` } : {}),
+                  ...headers
+                }
              }
-            }
-          : { headers }
-      )
-      .then(async (res) => {
-        if (!res.data) {
-          addLog.error('Embedding API is not responding', res);
-          return Promise.reject('Embedding API is not responding');
-        }
-        if (!res?.data?.[0]?.embedding) {
-          console.log(res);
-          // @ts-ignore
-          return Promise.reject(res.data?.err?.message || 'Embedding API Error');
-        }
+            : { headers }
+        )
+        .then(async (res) => {
+          if (!res.data) {
+            addLog.error('Embedding API is not responding', res);
+            return Promise.reject('Embedding API is not responding');
+          }
+          if (!res?.data?.[0]?.embedding) {
+            console.log(res);
+            // @ts-ignore
+            return Promise.reject(res.data?.err?.message || 'Embedding API Error');
+          }

-        const [tokens, vectors] = await Promise.all([
-          countPromptTokens(input),
-          Promise.all(
-            res.data
-              .map((item) => unityDimensional(item.embedding))
-              .map((item) => {
-                if (model.normalization) return normalization(item);
-                return item;
-              })
-          )
-        ]);
+          const [tokens, vectors] = await Promise.all([
+            (async () => {
+              if (res.usage) return res.usage.total_tokens;

-        return {
-          tokens,
-          vectors
-        };
-      });
+              const tokens = await Promise.all(chunk.map((item) => countPromptTokens(item)));
+              return tokens.reduce((sum, item) => sum + item, 0);
+            })(),
+            Promise.all(
+              res.data
+                .map((item) => unityDimensional(item.embedding))
+                .map((item) => {
+                  if (model.normalization) return normalization(item);
+                  return item;
+                })
+            )
+          ]);

-    return result;
+          return {
+            tokens,
+            vectors
+          };
+        });
+
+      totalTokens += result.tokens;
+      allVectors.push(...result.vectors);
+    }
+
+    return {
+      tokens: totalTokens,
+      vectors: allVectors
+    };
  } catch (error) {
    addLog.error(`Embedding Error`, error);

--- a/packages/service/core/app/logs/chatLogsSchema.ts
+++ b/packages/service/core/app/logs/chatLogsSchema.ts
@@ -0,0 +1,77 @@
+import type { AppChatLogSchema } from '@fastgpt/global/core/app/logs/type';
+import { getMongoLogModel, Schema } from '../../../common/mongo';
+import { AppCollectionName } from '../schema';
+
+export const ChatLogCollectionName = 'app_chat_logs';
+
+const ChatLogSchema = new Schema({
+  appId: {
+    type: Schema.Types.ObjectId,
+    ref: AppCollectionName,
+    required: true
+  },
+  teamId: {
+    type: Schema.Types.ObjectId,
+    required: true
+  },
+  chatId: {
+    type: String,
+    required: true
+  },
+  userId: {
+    type: String,
+    required: true
+  },
+  source: {
+    type: String,
+    required: true
+  },
+  sourceName: {
+    type: String
+  },
+  createTime: {
+    type: Date,
+    required: true
+  },
+  updateTime: {
+    type: Date,
+    required: true
+  },
+  // 累计统计字段
+  chatItemCount: {
+    type: Number,
+    default: 0
+  },
+  errorCount: {
+    type: Number,
+    default: 0
+  },
+  totalPoints: {
+    type: Number,
+    default: 0
+  },
+  goodFeedbackCount: {
+    type: Number,
+    default: 0
+  },
+  badFeedbackCount: {
+    type: Number,
+    default: 0
+  },
+  totalResponseTime: {
+    type: Number,
+    default: 0
+  },
+  isFirstChat: {
+    type: Boolean,
+    default: false
+  }
+});
+
+ChatLogSchema.index({ teamId: 1, appId: 1, source: 1, updateTime: -1 });
+ChatLogSchema.index({ userId: 1, appId: 1, source: 1, createTime: -1 });
+
+export const MongoAppChatLog = getMongoLogModel<AppChatLogSchema>(
+  ChatLogCollectionName,
+  ChatLogSchema
+);
--- a/packages/service/core/app/plugin/controller.ts
+++ b/packages/service/core/app/plugin/controller.ts
@@ -46,6 +46,7 @@ import { getMCPParentId, getMCPToolRuntimeNode } from '@fastgpt/global/core/app/
 import { AppTypeEnum } from '@fastgpt/global/core/app/constants';
 import { getMCPChildren } from '../mcp';
 import { cloneDeep } from 'lodash';
+import { UserError } from '@fastgpt/global/common/error/utils';

 type ChildAppType = SystemPluginTemplateItemType & {
  teamId?: string;
@@ -80,7 +81,7 @@ export const getSystemPluginByIdAndVersionId = async (
          app
        })
      : await getAppLatestVersion(plugin.associatedPluginId, app);
-    if (!version.versionId) return Promise.reject('App version not found');
+    if (!version.versionId) return Promise.reject(new UserError('App version not found'));
    const isLatest = version.versionId
      ? await checkIsLatestVersion({
          appId: plugin.associatedPluginId,
@@ -119,7 +120,7 @@ export const getSystemPluginByIdAndVersionId = async (
  const versionList = (plugin.versionList as SystemPluginTemplateItemType['versionList']) || [];

  if (versionList.length === 0) {
-    return Promise.reject('Can not find plugin version list');
+    return Promise.reject(new UserError('Can not find plugin version list'));
  }

  const version = versionId
@@ -304,11 +305,13 @@ export async function getChildAppPreviewNode({
              ? {
                  systemToolSet: {
                    toolId: app.id,
-                    toolList: children.map((item) => ({
-                      toolId: item.id,
-                      name: parseI18nString(item.name, lang),
-                      description: parseI18nString(item.intro, lang)
-                    }))
+                    toolList: children
+                      .filter((item) => item.isActive !== false)
+                      .map((item) => ({
+                        toolId: item.id,
+                        name: parseI18nString(item.name, lang),
+                        description: parseI18nString(item.intro, lang)
+                      }))
                  }
                }
              : { systemTool: { toolId: app.id } })
@@ -378,8 +381,10 @@ export async function getChildAppPreviewNode({
    showTargetHandle: true,

    currentCost: app.currentCost,
+    systemKeyCost: app.systemKeyCost,
    hasTokenFee: app.hasTokenFee,
    hasSystemSecret: app.hasSystemSecret,
+    isFolder: app.isFolder,

    ...nodeIOConfig,
    outputs: nodeIOConfig.outputs.some((item) => item.type === FlowNodeOutputTypeEnum.error)
@@ -432,6 +437,7 @@ export async function getChildAppRuntimeById({

        originCost: 0,
        currentCost: 0,
+        systemKeyCost: 0,
        hasTokenFee: false,
        pluginOrder: 0
      };
@@ -448,6 +454,7 @@ export async function getChildAppRuntimeById({
    avatar: app.avatar || '',
    showStatus: true,
    currentCost: app.currentCost,
+    systemKeyCost: app.systemKeyCost,
    nodes: app.workflow.nodes,
    edges: app.workflow.edges,
    hasTokenFee: app.hasTokenFee
@@ -474,6 +481,7 @@ const dbPluginFormat = (item: SystemPluginConfigSchemaType): SystemPluginTemplat
    currentCost: item.currentCost,
    hasTokenFee: item.hasTokenFee,
    pluginOrder: item.pluginOrder,
+    systemKeyCost: item.systemKeyCost,
    associatedPluginId,
    userGuide,
    workflow: {
@@ -515,61 +523,63 @@ export const getSystemTools = async (): Promise<SystemPluginTemplateItemType[]>
    const tools = await APIGetSystemToolList();

    // 从数据库里加载插件配置进行替换
-    const systemPluginsArray = await MongoSystemPlugin.find({}).lean();
-    const systemPlugins = new Map(systemPluginsArray.map((plugin) => [plugin.pluginId, plugin]));
+    const systemToolsArray = await MongoSystemPlugin.find({}).lean();
+    const systemTools = new Map(systemToolsArray.map((plugin) => [plugin.pluginId, plugin]));

-    tools.forEach((tool) => {
-      // 如果有插件的配置信息，则需要进行替换
-      const dbPluginConfig = systemPlugins.get(tool.id);
+    // tools.forEach((tool) => {
+    //   // 如果有插件的配置信息，则需要进行替换
+    //   const dbPluginConfig = systemTools.get(tool.id);

-      if (dbPluginConfig) {
-        const children = tools.filter((item) => item.parentId === tool.id);
-        const list = [tool, ...children];
-        list.forEach((item) => {
-          item.isActive = dbPluginConfig.isActive ?? item.isActive ?? true;
-          item.originCost = dbPluginConfig.originCost ?? 0;
-          item.currentCost = dbPluginConfig.currentCost ?? 0;
-          item.hasTokenFee = dbPluginConfig.hasTokenFee ?? false;
-          item.pluginOrder = dbPluginConfig.pluginOrder ?? 0;
-        });
-      }
-    });
+    //   if (dbPluginConfig) {
+    //     const children = tools.filter((item) => item.parentId === tool.id);
+    //     const list = [tool, ...children];
+    //     list.forEach((item) => {
+    //       item.isActive = dbPluginConfig.isActive ?? item.isActive ?? true;
+    //       item.originCost = dbPluginConfig.originCost ?? 0;
+    //       item.currentCost = dbPluginConfig.currentCost ?? 0;
+    //       item.hasTokenFee = dbPluginConfig.hasTokenFee ?? false;
+    //       item.pluginOrder = dbPluginConfig.pluginOrder ?? 0;
+    //     });
+    //   }
+    // });

    const formatTools = tools.map<SystemPluginTemplateItemType>((item) => {
-      const dbPluginConfig = systemPlugins.get(item.id);
+      const dbPluginConfig = systemTools.get(item.id);
+      const isFolder = tools.some((tool) => tool.parentId === item.id);

      const versionList = (item.versionList as SystemPluginTemplateItemType['versionList']) || [];

      return {
        id: item.id,
        parentId: item.parentId,
-        isFolder: tools.some((tool) => tool.parentId === item.id),
-
+        isFolder,
        name: item.name,
        avatar: item.avatar,
        intro: item.description,
-
        author: item.author,
        courseUrl: item.courseUrl,
        weight: item.weight,
-
        workflow: {
          nodes: [],
          edges: []
        },
        versionList,
-
        templateType: item.templateType,
        showStatus: true,
-
-        isActive: item.isActive,
+        isActive: dbPluginConfig?.isActive ?? item.isActive ?? true,
        inputList: item?.secretInputConfig,
-        hasSystemSecret: !!dbPluginConfig?.inputListVal
+        hasSystemSecret: !!dbPluginConfig?.inputListVal,
+
+        originCost: dbPluginConfig?.originCost ?? 0,
+        currentCost: dbPluginConfig?.currentCost ?? 0,
+        systemKeyCost: dbPluginConfig?.systemKeyCost ?? 0,
+        hasTokenFee: dbPluginConfig?.hasTokenFee ?? false,
+        pluginOrder: dbPluginConfig?.pluginOrder
      };
    });

    // TODO: Check the app exists
-    const dbPlugins = systemPluginsArray
+    const dbPlugins = systemToolsArray
      .filter((item) => item.customConfig?.associatedPluginId)
      .map((item) => dbPluginFormat(item));

--- a/packages/service/core/app/plugin/systemPluginSchema.ts
+++ b/packages/service/core/app/plugin/systemPluginSchema.ts
@@ -27,6 +27,10 @@ const SystemPluginSchema = new Schema({
  pluginOrder: {
    type: Number
  },
+  systemKeyCost: {
+    type: Number,
+    default: 0
+  },
  customConfig: Object,
  inputListVal: Object,

--- a/packages/service/core/app/plugin/type.d.ts
+++ b/packages/service/core/app/plugin/type.d.ts
@@ -1,6 +1,7 @@
 import { SystemPluginListItemType } from '@fastgpt/global/core/app/type';
 import { FlowNodeTemplateTypeEnum } from '@fastgpt/global/core/workflow/constants';
 import type { WorkflowTemplateBasicType } from '@fastgpt/global/core/workflow/type';
+import type { InputConfigType } from '@fastgpt/global/core/workflow/type/io';

 export type SystemPluginConfigSchemaType = {
  pluginId: string;
@@ -10,6 +11,7 @@ export type SystemPluginConfigSchemaType = {
  hasTokenFee: boolean;
  isActive: boolean;
  pluginOrder?: number;
+  systemKeyCost?: number;

  customConfig?: {
    name: string;
--- a/packages/service/core/app/utils.ts
+++ b/packages/service/core/app/utils.ts
@@ -82,8 +82,10 @@ export async function rewriteAppWorkflowToDetail({
        node.version = preview.version;

        node.currentCost = preview.currentCost;
+        node.systemKeyCost = preview.systemKeyCost;
        node.hasTokenFee = preview.hasTokenFee;
        node.hasSystemSecret = preview.hasSystemSecret;
+        node.isFolder = preview.isFolder;

        node.toolConfig = preview.toolConfig;

--- a/packages/service/core/chat/chatSchema.ts
+++ b/packages/service/core/chat/chatSchema.ts
@@ -1,7 +1,7 @@
 import { connectionMongo, getMongoModel } from '../../common/mongo';
 const { Schema } = connectionMongo;
 import { type ChatSchema as ChatType } from '@fastgpt/global/core/chat/type.d';
-import { ChatSourceEnum, ChatSourceMap } from '@fastgpt/global/core/chat/constants';
+import { ChatSourceEnum } from '@fastgpt/global/core/chat/constants';
 import {
  TeamCollectionName,
  TeamMemberCollectionName
--- a/packages/service/core/chat/controller.ts
+++ b/packages/service/core/chat/controller.ts
@@ -4,6 +4,7 @@ import { addLog } from '../../common/system/log';
 import { delFileByFileIdList, getGFSCollection } from '../../common/file/gridfs/controller';
 import { BucketNameEnum } from '@fastgpt/global/common/file/constants';
 import { MongoChat } from './chatSchema';
+import { UserError } from '@fastgpt/global/common/error/utils';

 export async function getChatItems({
  appId,
@@ -72,7 +73,8 @@ export const deleteChatFiles = async ({
  chatIdList?: string[];
  appId?: string;
 }) => {
-  if (!appId && !chatIdList) return Promise.reject('appId or chatIdList is required');
+  if (!appId && !chatIdList)
+    return Promise.reject(new UserError('appId or chatIdList is required'));

  const appChatIdList = await (async () => {
    if (appId) {
--- a/packages/service/core/chat/saveChat.ts
+++ b/packages/service/core/chat/saveChat.ts
@@ -14,6 +14,7 @@ import { pushChatLog } from './pushChatLog';
 import { FlowNodeTypeEnum } from '@fastgpt/global/core/workflow/node/constant';
 import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
 import { extractDeepestInteractive } from '@fastgpt/global/core/workflow/runtime/utils';
+import { MongoAppChatLog } from '../app/logs/chatLogsSchema';

 type Props = {
  chatId: string;
@@ -163,6 +164,62 @@ export async function saveChat({
      });
    });

+    try {
+      const userId = outLinkUid || tmbId;
+      const now = new Date();
+      const fifteenMinutesAgo = new Date(now.getTime() - 15 * 60 * 1000);
+
+      const aiResponse = processedContent.find((item) => item.obj === ChatRoleEnum.AI);
+      const errorCount = aiResponse?.responseData?.some((item) => item.errorText) ? 1 : 0;
+      const totalPoints =
+        aiResponse?.responseData?.reduce(
+          (sum: number, item: any) => sum + (item.totalPoints || 0),
+          0
+        ) || 0;
+
+      const hasHistoryChat = await MongoAppChatLog.exists({
+        appId,
+        userId,
+        createTime: { $lt: now }
+      });
+
+      await MongoAppChatLog.updateOne(
+        {
+          chatId,
+          appId,
+          updateTime: { $gte: fifteenMinutesAgo }
+        },
+        {
+          $inc: {
+            chatItemCount: 1,
+            errorCount,
+            totalPoints,
+            totalResponseTime: durationSeconds
+          },
+          $set: {
+            updateTime: now,
+            sourceName
+          },
+          $setOnInsert: {
+            appId,
+            teamId,
+            chatId,
+            userId,
+            source,
+            createTime: now,
+            goodFeedbackCount: 0,
+            badFeedbackCount: 0,
+            isFirstChat: !hasHistoryChat
+          }
+        },
+        {
+          upsert: true
+        }
+      );
+    } catch (error) {
+      addLog.error('update chat log error', error);
+    }
+
    if (isUpdateUseTime) {
      await MongoApp.findByIdAndUpdate(appId, {
        updateTime: new Date()
--- a/packages/service/core/chat/setting/schema.ts
+++ b/packages/service/core/chat/setting/schema.ts
@@ -0,0 +1,37 @@
+import { connectionMongo, getMongoModel } from '../../../common/mongo';
+import { type ChatSettingSchema as ChatSettingType } from '@fastgpt/global/core/chat/setting/type';
+import { TeamCollectionName } from '@fastgpt/global/support/user/team/constant';
+import { AppCollectionName } from '../../app/schema';
+
+const { Schema } = connectionMongo;
+
+export const ChatSettingCollectionName = 'chat_settings';
+
+const ChatSettingSchema = new Schema({
+  teamId: {
+    type: Schema.Types.ObjectId,
+    ref: TeamCollectionName,
+    required: true
+  },
+  appId: {
+    type: Schema.Types.ObjectId,
+    ref: AppCollectionName,
+    required: true
+  },
+  slogan: String,
+  dialogTips: String,
+  selectedTools: {
+    type: Array,
+    default: []
+  },
+  homeTabTitle: String,
+  wideLogoUrl: String,
+  squareLogoUrl: String
+});
+
+ChatSettingSchema.index({ teamId: 1 });
+
+export const MongoChatSetting = getMongoModel<ChatSettingType>(
+  ChatSettingCollectionName,
+  ChatSettingSchema
+);
--- a/packages/service/core/dataset/controller.ts
+++ b/packages/service/core/dataset/controller.ts
@@ -14,6 +14,7 @@ import { MongoDatasetCollectionTags } from './tag/schema';
 import { removeDatasetSyncJobScheduler } from './datasetSync';
 import { mongoSessionRun } from '../../common/mongo/sessionRun';
 import { removeImageByPath } from '../../common/file/image/controller';
+import { UserError } from '@fastgpt/global/common/error/utils';

 /* ============= dataset ========== */
 /* find all datasetId by top datasetId */
@@ -50,7 +51,7 @@ export async function findDatasetAndAllChildren({
  ]);

  if (!dataset) {
-    return Promise.reject('Dataset not found');
+    return Promise.reject(new UserError('Dataset not found'));
  }

  return [dataset, ...childDatasets];
@@ -79,7 +80,7 @@ export async function delDatasetRelevantData({
  const teamId = datasets[0].teamId;

  if (!teamId) {
-    return Promise.reject('TeamId is required');
+    return Promise.reject(new UserError('TeamId is required'));
  }

  const datasetIds = datasets.map((item) => item._id);
--- a/packages/service/core/dataset/read.ts
+++ b/packages/service/core/dataset/read.ts
@@ -16,6 +16,7 @@ import { text2Chunks } from '../../worker/function';
 import { addLog } from '../../common/system/log';
 import { retryFn } from '@fastgpt/global/common/system/utils';
 import { getFileMaxSize } from '../../common/file/utils';
+import { UserError } from '@fastgpt/global/common/error/utils';

 export const readFileRawTextByUrl = async ({
  teamId,
@@ -200,7 +201,7 @@ export const readDatasetSourceRawText = async ({
      rawText: content
    };
  } else if (type === DatasetSourceReadTypeEnum.externalFile) {
-    if (!externalFileId) return Promise.reject('FileId not found');
+    if (!externalFileId) return Promise.reject(new UserError('FileId not found'));
    const rawText = await readFileRawTextByUrl({
      teamId,
      tmbId,
--- a/packages/service/core/dataset/search/controller.ts
+++ b/packages/service/core/dataset/search/controller.ts
@@ -7,6 +7,10 @@ import { recallFromVectorStore } from '../../../common/vectorDB/controller';
 import { getVectorsByText } from '../../ai/embedding';
 import { getEmbeddingModel, getDefaultRerankModel, getLLMModel } from '../../ai/model';
 import { MongoDatasetData } from '../data/schema';
+import type {
+  DatasetCollectionSchemaType,
+  DatasetDataSchemaType
+} from '@fastgpt/global/core/dataset/type';
 import {
  type DatasetDataTextSchemaType,
  type SearchDataResponseItemType
@@ -27,7 +31,6 @@ import { type ChatItemType } from '@fastgpt/global/core/chat/type';
 import type { NodeInputKeyEnum } from '@fastgpt/global/core/workflow/constants';
 import { datasetSearchQueryExtension } from './utils';
 import type { RerankModelItemType } from '@fastgpt/global/core/ai/model.d';
-import { addLog } from '../../../common/system/log';
 import { formatDatasetDataValue } from '../data/controller';

 export type SearchDatasetDataProps = {
@@ -435,214 +438,114 @@ export async function searchDatasetData(
    } catch (error) {}
  };
  const embeddingRecall = async ({
-    query,
+    queries,
    limit,
    forbidCollectionIdList,
    filterCollectionIdList
  }: {
-    query: string;
+    queries: string[];
    limit: number;
    forbidCollectionIdList: string[];
    filterCollectionIdList?: string[];
-  }) => {
+  }): Promise<{
+    embeddingRecallResults: SearchDataResponseItemType[][];
+    tokens: number;
+  }> => {
+    if (limit === 0) {
+      return {
+        embeddingRecallResults: [],
+        tokens: 0
+      };
+    }
+
    const { vectors, tokens } = await getVectorsByText({
      model: getEmbeddingModel(model),
-      input: query,
+      input: queries,
      type: 'query'
    });

-    const { results } = await recallFromVectorStore({
-      teamId,
-      datasetIds,
-      vector: vectors[0],
-      limit,
-      forbidCollectionIdList,
-      filterCollectionIdList
-    });
+    const recallResults = await Promise.all(
+      vectors.map(async (vector) => {
+        return await recallFromVectorStore({
+          teamId,
+          datasetIds,
+          vector,
+          limit,
+          forbidCollectionIdList,
+          filterCollectionIdList
+        });
+      })
+    );

    // Get data and collections
-    const collectionIdList = Array.from(new Set(results.map((item) => item.collectionId)));
-    const [dataList, collections] = await Promise.all([
+    const collectionIdList = Array.from(
+      new Set(recallResults.map((item) => item.results.map((item) => item.collectionId)).flat())
+    );
+    const indexDataIds = Array.from(
+      new Set(recallResults.map((item) => item.results.map((item) => item.id?.trim())).flat())
+    );
+
+    const [dataMaps, collectionMaps] = await Promise.all([
      MongoDatasetData.find(
        {
          teamId,
          datasetId: { $in: datasetIds },
          collectionId: { $in: collectionIdList },
-          'indexes.dataId': { $in: results.map((item) => item.id?.trim()) }
+          'indexes.dataId': { $in: indexDataIds }
        },
        datasetDataSelectField,
        { ...readFromSecondary }
-      ).lean(),
+      )
+        .lean()
+        .then((res) => {
+          const map = new Map<string, DatasetDataSchemaType>();
+
+          res.forEach((item) => {
+            item.indexes.forEach((index) => {
+              map.set(String(index.dataId), item);
+            });
+          });
+
+          return map;
+        }),
      MongoDatasetCollection.find(
        {
          _id: { $in: collectionIdList }
        },
        datsaetCollectionSelectField,
        { ...readFromSecondary }
-      ).lean()
+      )
+        .lean()
+        .then((res) => {
+          const map = new Map<string, DatasetCollectionSchemaType>();
+
+          res.forEach((item) => {
+            map.set(String(item._id), item);
+          });
+
+          return map;
+        })
    ]);

-    const set = new Set<string>();
-    const formatResult = results
-      .map((item, index) => {
-        const collection = collections.find((col) => String(col._id) === String(item.collectionId));
-        if (!collection) {
-          console.log('Collection is not found', item);
-          return;
-        }
-        const data = dataList.find((data) =>
-          data.indexes.some((index) => index.dataId === item.id)
-        );
-        if (!data) {
-          console.log('Data is not found', item);
-          return;
-        }
-
-        const result: SearchDataResponseItemType = {
-          id: String(data._id),
-          updateTime: data.updateTime,
-          ...formatDatasetDataValue({
-            teamId,
-            datasetId: data.datasetId,
-            q: data.q,
-            a: data.a,
-            imageId: data.imageId,
-            imageDescMap: data.imageDescMap
-          }),
-          chunkIndex: data.chunkIndex,
-          datasetId: String(data.datasetId),
-          collectionId: String(data.collectionId),
-          ...getCollectionSourceData(collection),
-          score: [{ type: SearchScoreTypeEnum.embedding, value: item?.score || 0, index }]
-        };
-
-        return result;
-      })
-      .filter((item) => {
-        if (!item) return false;
-        if (set.has(item.id)) return false;
-        set.add(item.id);
-        return true;
-      })
-      .map((item, index) => {
-        if (!item) return;
-        return {
-          ...item,
-          score: item.score.map((item) => ({ ...item, index }))
-        };
-      }) as SearchDataResponseItemType[];
-
-    return {
-      embeddingRecallResults: formatResult,
-      tokens
-    };
-  };
-  const fullTextRecall = async ({
-    query,
-    limit,
-    filterCollectionIdList,
-    forbidCollectionIdList
-  }: {
-    query: string;
-    limit: number;
-    filterCollectionIdList?: string[];
-    forbidCollectionIdList: string[];
-  }): Promise<{
-    fullTextRecallResults: SearchDataResponseItemType[];
-    tokenLen: number;
-  }> => {
-    if (limit === 0) {
-      return {
-        fullTextRecallResults: [],
-        tokenLen: 0
-      };
-    }
-
-    try {
-      const searchResults = (await MongoDatasetDataText.aggregate(
-        [
-          {
-            $match: {
-              teamId: new Types.ObjectId(teamId),
-              $text: { $search: await jiebaSplit({ text: query }) },
-              datasetId: { $in: datasetIds.map((id) => new Types.ObjectId(id)) },
-              ...(filterCollectionIdList
-                ? {
-                    collectionId: {
-                      $in: filterCollectionIdList
-                        .filter((id) => !forbidCollectionIdList.includes(id))
-                        .map((id) => new Types.ObjectId(id))
-                    }
-                  }
-                : forbidCollectionIdList?.length
-                  ? {
-                      collectionId: {
-                        $nin: forbidCollectionIdList.map((id) => new Types.ObjectId(id))
-                      }
-                    }
-                  : {})
-            }
-          },
-          {
-            $sort: {
-              score: { $meta: 'textScore' }
-            }
-          },
-          {
-            $limit: limit
-          },
-          {
-            $project: {
-              _id: 1,
-              collectionId: 1,
-              dataId: 1,
-              score: { $meta: 'textScore' }
-            }
-          }
-        ],
-        {
-          ...readFromSecondary
-        }
-      )) as (DatasetDataTextSchemaType & { score: number })[];
-
-      // Get data and collections
-      const [dataList, collections] = await Promise.all([
-        MongoDatasetData.find(
-          {
-            _id: { $in: searchResults.map((item) => item.dataId) }
-          },
-          datasetDataSelectField,
-          { ...readFromSecondary }
-        ).lean(),
-        MongoDatasetCollection.find(
-          {
-            _id: { $in: searchResults.map((item) => item.collectionId) }
-          },
-          datsaetCollectionSelectField,
-          { ...readFromSecondary }
-        ).lean()
-      ]);
-
-      return {
-        fullTextRecallResults: searchResults
+    const embeddingRecallResults = recallResults.map((item) => {
+      const set = new Set<string>();
+      return (
+        item.results
          .map((item, index) => {
-            const collection = collections.find(
-              (col) => String(col._id) === String(item.collectionId)
-            );
+            const collection = collectionMaps.get(String(item.collectionId));
            if (!collection) {
              console.log('Collection is not found', item);
              return;
            }
-            const data = dataList.find((data) => String(data._id) === String(item.dataId));
+
+            const data = dataMaps.get(String(item.id));
            if (!data) {
              console.log('Data is not found', item);
              return;
            }

-            return {
+            const result: SearchDataResponseItemType = {
              id: String(data._id),
-              datasetId: String(data.datasetId),
-              collectionId: String(data.collectionId),
              updateTime: data.updateTime,
              ...formatDatasetDataValue({
                teamId,
@@ -653,37 +556,204 @@ export async function searchDatasetData(
                imageDescMap: data.imageDescMap
              }),
              chunkIndex: data.chunkIndex,
-              indexes: data.indexes,
+              datasetId: String(data.datasetId),
+              collectionId: String(data.collectionId),
              ...getCollectionSourceData(collection),
-              score: [
-                {
-                  type: SearchScoreTypeEnum.fullText,
-                  value: item.score || 0,
-                  index
-                }
-              ]
+              score: [{ type: SearchScoreTypeEnum.embedding, value: item?.score || 0, index }]
            };
+
+            return result;
          })
+          // 多个向量对应一个数据，每一路召回，保障数据只有一份，并且取最高排名
          .filter((item) => {
            if (!item) return false;
+            if (set.has(item.id)) return false;
+            set.add(item.id);
            return true;
          })
          .map((item, index) => {
-            if (!item) return;
            return {
-              ...item,
-              score: item.score.map((item) => ({ ...item, index }))
+              ...item!,
+              score: item!.score.map((item) => ({ ...item, index }))
            };
-          }) as SearchDataResponseItemType[],
-        tokenLen: 0
-      };
-    } catch (error) {
-      addLog.error('Full text search error', error);
+          }) as SearchDataResponseItemType[]
+      );
+    });
+
+    return {
+      embeddingRecallResults,
+      tokens
+    };
+  };
+  const fullTextRecall = async ({
+    queries,
+    limit,
+    filterCollectionIdList,
+    forbidCollectionIdList
+  }: {
+    queries: string[];
+    limit: number;
+    filterCollectionIdList?: string[];
+    forbidCollectionIdList: string[];
+  }): Promise<{
+    fullTextRecallResults: SearchDataResponseItemType[][];
+  }> => {
+    if (limit === 0) {
      return {
-        fullTextRecallResults: [],
-        tokenLen: 0
+        fullTextRecallResults: []
      };
    }
+
+    const recallResults = await Promise.all(
+      queries.map(async (query) => {
+        return (await MongoDatasetDataText.aggregate(
+          [
+            {
+              $match: {
+                teamId: new Types.ObjectId(teamId),
+                $text: { $search: await jiebaSplit({ text: query }) },
+                datasetId: { $in: datasetIds.map((id) => new Types.ObjectId(id)) },
+                ...(filterCollectionIdList
+                  ? {
+                      collectionId: {
+                        $in: filterCollectionIdList
+                          .filter((id) => !forbidCollectionIdList.includes(id))
+                          .map((id) => new Types.ObjectId(id))
+                      }
+                    }
+                  : forbidCollectionIdList?.length
+                    ? {
+                        collectionId: {
+                          $nin: forbidCollectionIdList.map((id) => new Types.ObjectId(id))
+                        }
+                      }
+                    : {})
+              }
+            },
+            {
+              $sort: {
+                score: { $meta: 'textScore' }
+              }
+            },
+            {
+              $limit: limit
+            },
+            {
+              $project: {
+                _id: 1,
+                collectionId: 1,
+                dataId: 1,
+                score: { $meta: 'textScore' }
+              }
+            }
+          ],
+          {
+            ...readFromSecondary
+          }
+        )) as (DatasetDataTextSchemaType & { score: number })[];
+      })
+    );
+
+    const dataIds = Array.from(
+      new Set(recallResults.map((item) => item.map((item) => item.dataId)).flat())
+    );
+    const collectionIds = Array.from(
+      new Set(recallResults.map((item) => item.map((item) => item.collectionId)).flat())
+    );
+
+    // Get data and collections
+    const [dataMaps, collectionMaps] = await Promise.all([
+      MongoDatasetData.find(
+        {
+          _id: { $in: dataIds }
+        },
+        datasetDataSelectField,
+        { ...readFromSecondary }
+      )
+        .lean()
+        .then((res) => {
+          const map = new Map<string, DatasetDataSchemaType>();
+
+          res.forEach((item) => {
+            map.set(String(item._id), item);
+          });
+
+          return map;
+        }),
+      MongoDatasetCollection.find(
+        {
+          _id: { $in: collectionIds }
+        },
+        datsaetCollectionSelectField,
+        { ...readFromSecondary }
+      )
+        .lean()
+        .then((res) => {
+          const map = new Map<string, DatasetCollectionSchemaType>();
+
+          res.forEach((item) => {
+            map.set(String(item._id), item);
+          });
+
+          return map;
+        })
+    ]);
+
+    const fullTextRecallResults = recallResults.map((item) => {
+      return item
+        .map((item, index) => {
+          const collection = collectionMaps.get(String(item.collectionId));
+          if (!collection) {
+            console.log('Collection is not found', item);
+            return;
+          }
+
+          const data = dataMaps.get(String(item.dataId));
+          if (!data) {
+            console.log('Data is not found', item);
+            return;
+          }
+
+          return {
+            id: String(data._id),
+            datasetId: String(data.datasetId),
+            collectionId: String(data.collectionId),
+            updateTime: data.updateTime,
+            ...formatDatasetDataValue({
+              teamId,
+              datasetId: data.datasetId,
+              q: data.q,
+              a: data.a,
+              imageId: data.imageId,
+              imageDescMap: data.imageDescMap
+            }),
+            chunkIndex: data.chunkIndex,
+            indexes: data.indexes,
+            ...getCollectionSourceData(collection),
+            score: [
+              {
+                type: SearchScoreTypeEnum.fullText,
+                value: item.score || 0,
+                index
+              }
+            ]
+          };
+        })
+        .filter((item) => {
+          if (!item) return false;
+          return true;
+        })
+        .map((item, index) => {
+          return {
+            ...item,
+            score: item!.score.map((item) => ({ ...item, index }))
+          };
+        }) as SearchDataResponseItemType[];
+    });
+
+    return {
+      fullTextRecallResults
+    };
  };
  const multiQueryRecall = async ({
    embeddingLimit,
@@ -692,50 +762,36 @@ export async function searchDatasetData(
    embeddingLimit: number;
    fullTextLimit: number;
  }) => {
-    // multi query recall
-    const embeddingRecallResList: SearchDataResponseItemType[][] = [];
-    const fullTextRecallResList: SearchDataResponseItemType[][] = [];
-    let totalTokens = 0;
-
    const [{ forbidCollectionIdList }, filterCollectionIdList] = await Promise.all([
      getForbidData(),
      filterCollectionByMetadata()
    ]);

-    await Promise.all(
-      queries.map(async (query) => {
-        const [{ tokens, embeddingRecallResults }, { fullTextRecallResults }] = await Promise.all([
-          embeddingRecall({
-            query,
-            limit: embeddingLimit,
-            forbidCollectionIdList,
-            filterCollectionIdList
-          }),
-          // FullText tmp
-          fullTextRecall({
-            query,
-            limit: fullTextLimit,
-            filterCollectionIdList,
-            forbidCollectionIdList
-          })
-        ]);
-        totalTokens += tokens;
-
-        embeddingRecallResList.push(embeddingRecallResults);
-        fullTextRecallResList.push(fullTextRecallResults);
+    const [{ tokens, embeddingRecallResults }, { fullTextRecallResults }] = await Promise.all([
+      embeddingRecall({
+        queries,
+        limit: embeddingLimit,
+        forbidCollectionIdList,
+        filterCollectionIdList
+      }),
+      fullTextRecall({
+        queries,
+        limit: fullTextLimit,
+        filterCollectionIdList,
+        forbidCollectionIdList
      })
-    );
+    ]);

    // rrf concat
    const rrfEmbRecall = datasetSearchResultConcat(
-      embeddingRecallResList.map((list) => ({ k: 60, list }))
+      embeddingRecallResults.map((list) => ({ k: 60, list }))
    ).slice(0, embeddingLimit);
    const rrfFTRecall = datasetSearchResultConcat(
-      fullTextRecallResList.map((list) => ({ k: 60, list }))
+      fullTextRecallResults.map((list) => ({ k: 60, list }))
    ).slice(0, fullTextLimit);

    return {
-      tokens: totalTokens,
+      tokens,
      embeddingRecallResults: rrfEmbRecall,
      fullTextRecallResults: rrfFTRecall
    };
--- a/packages/service/core/dataset/training/controller.ts
+++ b/packages/service/core/dataset/training/controller.ts
@@ -53,7 +53,7 @@ export async function pushDataListToTrainingQueue({
  const { model, maxToken, weight } = await (async () => {
    if (mode === TrainingModeEnum.chunk) {
      return {
-        maxToken: getLLMMaxChunkSize(agentModelData),
+        maxToken: Infinity,
        model: vectorModelData.model,
        weight: vectorModelData.weight
      };
--- a/packages/service/core/workflow/dispatch/child/runApp.ts
+++ b/packages/service/core/workflow/dispatch/child/runApp.ts
@@ -21,6 +21,7 @@ import { ReadPermissionVal } from '@fastgpt/global/support/permission/constant';
 import { getAppVersionById } from '../../../app/version/controller';
 import { parseUrlToFileType } from '@fastgpt/global/common/file/tools';
 import { getUserChatInfoAndAuthTeamPoints } from '../../../../support/permission/auth/team';
+import { getRunningUserInfoByTmbId } from '../../../../support/user/team/utils';

 type Props = ModuleDispatchProps<{
  [NodeInputKeyEnum.userChatInput]: string;
@@ -147,6 +148,7 @@ export const dispatchRunAppNode = async (props: Props): Promise<Response> => {
        tmbId: String(appData.tmbId),
        isChildApp: true
      },
+      runningUserInfo: await getRunningUserInfoByTmbId(appData.tmbId),
      runtimeNodes,
      runtimeEdges,
      histories: chatHistories,
--- a/packages/service/core/workflow/dispatch/child/runTool.ts
+++ b/packages/service/core/workflow/dispatch/child/runTool.ts
@@ -90,6 +90,10 @@ export const dispatchRunTool = async (props: RunToolProps): Promise<RunToolRespo
        systemVar: {
          user: {
            id: variables.userId,
+            username: runningUserInfo.username,
+            contact: runningUserInfo.contact,
+            membername: runningUserInfo.memberName,
+            teamName: runningUserInfo.teamName,
            teamId: runningUserInfo.teamId,
            name: runningUserInfo.tmbId
          },
@@ -150,7 +154,7 @@ export const dispatchRunTool = async (props: RunToolProps): Promise<RunToolRespo
        if (params.system_input_config?.type !== SystemToolInputTypeEnum.system) {
          return 0;
        }
-        return tool.currentCost ?? 0;
+        return (tool.systemKeyCost ?? 0) + (tool.currentCost ?? 0);
      })();

      pushTrack.runSystemTool({
--- a/packages/service/core/workflow/dispatch/index.ts
+++ b/packages/service/core/workflow/dispatch/index.ts
@@ -152,7 +152,7 @@ export async function dispatchWorkFlow(data: Props): Promise<DispatchFlowRespons
  } = data;
  const startTime = Date.now();

-  await rewriteRuntimeWorkFlow({ nodes: runtimeNodes, edges: runtimeEdges });
+  await rewriteRuntimeWorkFlow({ nodes: runtimeNodes, edges: runtimeEdges, lang: data.lang });

  // 初始化深度和自动增加深度，避免无限嵌套
  if (!props.workflowDispatchDeep) {
--- a/packages/service/core/workflow/dispatch/utils.ts
+++ b/packages/service/core/workflow/dispatch/utils.ts
@@ -20,6 +20,7 @@ import { FlowNodeTypeEnum } from '@fastgpt/global/core/workflow/node/constant';
 import { MongoApp } from '../../../core/app/schema';
 import { getMCPChildren } from '../../../core/app/mcp';
 import { getSystemToolRunTimeNodeFromSystemToolset } from '../utils';
+import type { localeType } from '@fastgpt/global/common/i18n/type';

 export const getWorkflowResponseWrite = ({
  res,
@@ -161,10 +162,12 @@ export const formatHttpError = (error: any) => {
 */
 export const rewriteRuntimeWorkFlow = async ({
  nodes,
-  edges
+  edges,
+  lang
 }: {
  nodes: RuntimeNodeItemType[];
  edges: RuntimeEdgeItemType[];
+  lang?: localeType;
 }) => {
  const toolSetNodes = nodes.filter((node) => node.flowNodeType === FlowNodeTypeEnum.toolSet);

@@ -195,7 +198,8 @@ export const rewriteRuntimeWorkFlow = async ({
    // systemTool
    if (systemToolId) {
      const children = await getSystemToolRunTimeNodeFromSystemToolset({
-        toolSetNode
+        toolSetNode,
+        lang
      });
      children.forEach((node) => {
        nodes.push(node);
--- a/packages/service/core/workflow/utils.ts
+++ b/packages/service/core/workflow/utils.ts
@@ -6,6 +6,7 @@ import { FlowNodeTypeEnum } from '@fastgpt/global/core/workflow/node/constant';
 import { getNanoid } from '@fastgpt/global/common/string/tools';
 import { NodeInputKeyEnum } from '@fastgpt/global/core/workflow/constants';
 import { parseI18nString } from '@fastgpt/global/common/i18n/utils';
+import type { localeType } from '@fastgpt/global/common/i18n/type';

 /* filter search result */
 export const filterSearchResultsByMaxChars = async (
@@ -31,9 +32,11 @@ export const filterSearchResultsByMaxChars = async (
 };

 export async function getSystemToolRunTimeNodeFromSystemToolset({
-  toolSetNode
+  toolSetNode,
+  lang = 'en'
 }: {
  toolSetNode: RuntimeNodeItemType;
+  lang?: localeType;
 }): Promise<RuntimeNodeItemType[]> {
  const systemToolId = toolSetNode.toolConfig?.systemToolSet?.toolId!;

@@ -41,13 +44,14 @@ export async function getSystemToolRunTimeNodeFromSystemToolset({
    (item) => item.key === NodeInputKeyEnum.systemInputConfig
  );
  const tools = await getSystemTools();
-  const children = tools.filter((item) => item.parentId === systemToolId);
-
+  const children = tools.filter(
+    (item) => item.parentId === systemToolId && item.isActive !== false
+  );
  const nodes = await Promise.all(
    children.map(async (child) => {
      const toolListItem = toolSetNode.toolConfig?.systemToolSet?.toolList.find(
        (item) => item.toolId === child.id
-      )!;
+      );

      const tool = await getSystemPluginByIdAndVersionId(child.id);

@@ -63,8 +67,8 @@ export async function getSystemToolRunTimeNodeFromSystemToolset({
        ...tool,
        inputs,
        outputs: tool.outputs ?? [],
-        name: toolListItem.name ?? parseI18nString(tool.name, 'en'),
-        intro: toolListItem.description ?? parseI18nString(tool.intro, 'en'),
+        name: toolListItem?.name || parseI18nString(tool.name, lang),
+        intro: toolListItem?.description || parseI18nString(tool.intro, lang),
        flowNodeType: FlowNodeTypeEnum.tool,
        nodeId: getNanoid(),
        toolConfig: {
--- a/packages/service/package.json
+++ b/packages/service/package.json
@@ -3,7 +3,7 @@
  "version": "1.0.0",
  "type": "module",
  "dependencies": {
-    "@fastgpt-sdk/plugin": "^0.1.7",
+    "@fastgpt-sdk/plugin": "^0.1.8",
    "@fastgpt/global": "workspace:*",
    "@modelcontextprotocol/sdk": "^1.12.1",
    "@node-rs/jieba": "2.0.1",
@@ -15,7 +15,7 @@
    "@opentelemetry/winston-transport": "^0.14.0",
    "@vercel/otel": "^1.13.0",
    "@xmldom/xmldom": "^0.8.10",
-    "@zilliz/milvus2-sdk-node": "2.4.2",
+    "@zilliz/milvus2-sdk-node": "2.4.10",
    "axios": "^1.8.2",
    "bullmq": "^5.52.2",
    "chalk": "^5.3.0",
--- a/packages/service/support/permission/app/auth.ts
+++ b/packages/service/support/permission/app/auth.ts
@@ -2,17 +2,22 @@
 import { MongoApp } from '../../../core/app/schema';
 import { type AppDetailType } from '@fastgpt/global/core/app/type.d';
 import { parseHeaderCert } from '../controller';
-import { PerResourceTypeEnum } from '@fastgpt/global/support/permission/constant';
+import {
+  PerResourceTypeEnum,
+  ReadPermissionVal,
+  ReadRoleVal
+} from '@fastgpt/global/support/permission/constant';
 import { AppErrEnum } from '@fastgpt/global/common/error/code/app';
 import { getTmbInfoByTmbId } from '../../user/team/controller';
 import { getResourcePermission } from '../controller';
 import { AppPermission } from '@fastgpt/global/support/permission/app/controller';
 import { type PermissionValueType } from '@fastgpt/global/support/permission/type';
-import { AppFolderTypeList } from '@fastgpt/global/core/app/constants';
+import { AppFolderTypeList, AppTypeEnum } from '@fastgpt/global/core/app/constants';
 import { type ParentIdType } from '@fastgpt/global/common/parentFolder/type';
 import { PluginSourceEnum } from '@fastgpt/global/core/app/plugin/constants';
 import { type AuthModeType, type AuthResponseType } from '../type';
 import { splitCombinePluginId } from '@fastgpt/global/core/app/plugin/utils';
+import { AppReadChatLogPerVal } from '@fastgpt/global/support/permission/app/constant';

 export const authPluginByTmbId = async ({
  tmbId,
@@ -68,6 +73,21 @@ export const authAppByTmbId = async ({
      return Promise.reject(AppErrEnum.unAuthApp);
    }

+    if (app.type === AppTypeEnum.hidden) {
+      if (per === AppReadChatLogPerVal) {
+        if (!tmbPer.hasManagePer) {
+          return Promise.reject(AppErrEnum.unAuthApp);
+        }
+      } else if (per !== ReadPermissionVal) {
+        return Promise.reject(AppErrEnum.unAuthApp);
+      }
+
+      return {
+        ...app,
+        permission: new AppPermission({ isOwner: false, role: ReadRoleVal })
+      };
+    }
+
    const isOwner = tmbPer.isOwner || String(app.tmbId) === String(tmbId);

    const { Per } = await (async () => {
@@ -134,7 +154,7 @@ export const authApp = async ({
  appId: ParentIdType;
  per: PermissionValueType;
 }): Promise<
-  AuthResponseType & {
+  AuthResponseType<AppPermission> & {
    app: AppDetailType;
  }
 > => {
--- a/packages/service/support/user/team/utils.ts
+++ b/packages/service/support/user/team/utils.ts
@@ -0,0 +1,35 @@
+import { MongoTeamMember } from '../../user/team/teamMemberSchema';
+import { type UserModelSchema } from '@fastgpt/global/support/user/type';
+import { type TeamSchema } from '@fastgpt/global/support/user/team/type';
+import { TeamErrEnum } from '@fastgpt/global/common/error/code/team';
+
+// TODO: 数据库优化
+export async function getRunningUserInfoByTmbId(tmbId: string) {
+  if (tmbId) {
+    const tmb = await MongoTeamMember.findById(tmbId, 'teamId name userId') // team_members name is the user's name
+      .populate<{ team: TeamSchema; user: UserModelSchema }>([
+        {
+          path: 'team',
+          select: 'name'
+        },
+        {
+          path: 'user',
+          select: 'username contact'
+        }
+      ])
+      .lean();
+
+    if (!tmb) return Promise.reject(TeamErrEnum.notUser);
+
+    return {
+      username: tmb.user.username,
+      teamName: tmb.team.name,
+      memberName: tmb.name,
+      contact: tmb.user.contact || '',
+      teamId: tmb.teamId,
+      tmbId: tmb._id
+    };
+  }
+
+  return Promise.reject(TeamErrEnum.notUser);
+}