update doc ;perf: model test (#4098)

* perf: extract array

* update doc

* perf: model test

* perf: model test

packages/service/core/ai/audio/transcriptions.ts

@@ -6,10 +6,12 @@ import { getSTTModel } from '../model';
 
 export const aiTranscriptions = async ({
   model,
-  fileStream
+  fileStream,
+  headers
 }: {
   model: string;
   fileStream: fs.ReadStream;
+  headers?: Record<string, string>;
 }) => {
   const data = new FormData();
   data.append('model', model);
@@ -30,7 +32,8 @@ export const aiTranscriptions = async ({
       Authorization: modelData.requestAuth
         ? `Bearer ${modelData.requestAuth}`
         : aiAxiosConfig.authorization,
-      ...data.getHeaders()
+      ...data.getHeaders(),
+      ...headers
     },
     data: data
   });

packages/service/core/ai/config/provider/Qwen.json

@@ -76,7 +76,7 @@
     {
       "model": "qwen-max",
       "name": "Qwen-max",
-      "maxContext": 8000,
+      "maxContext": 32000,
       "maxResponse": 4000,
       "quoteMaxToken": 6000,
       "maxTemperature": 1,

packages/service/core/ai/embedding/index.ts

@@ -8,10 +8,11 @@ type GetVectorProps = {
   model: EmbeddingModelItemType;
   input: string;
   type?: `${EmbeddingTypeEnm}`;
+  headers?: Record<string, string>;
 };
 
 // text to vector
-export async function getVectorsByText({ model, input, type }: GetVectorProps) {
+export async function getVectorsByText({ model, input, type, headers }: GetVectorProps) {
   if (!input) {
     return Promise.reject({
       code: 500,
@@ -37,9 +38,10 @@ export async function getVectorsByText({ model, input, type }: GetVectorProps) {
               path: model.requestUrl,
               headers: model.requestAuth
                 ? {
-                    Authorization: `Bearer ${model.requestAuth}`
+                    Authorization: `Bearer ${model.requestAuth}`,
+                    ...headers
                   }
-                : undefined
+                : headers
             }
           : {}
       )

packages/service/core/ai/rerank/index.ts

@@ -16,11 +16,13 @@ type ReRankCallResult = { id: string; score?: number }[];
 export function reRankRecall({
   model = getDefaultRerankModel(),
   query,
-  documents
+  documents,
+  headers
 }: {
   model?: ReRankModelItemType;
   query: string;
   documents: { id: string; text: string }[];
+  headers?: Record<string, string>;
 }): Promise<ReRankCallResult> {
   if (!model) {
     return Promise.reject('no rerank model');
@@ -41,7 +43,8 @@ export function reRankRecall({
     },
     {
       headers: {
-        Authorization: model.requestAuth ? `Bearer ${model.requestAuth}` : authorization
+        Authorization: model.requestAuth ? `Bearer ${model.requestAuth}` : authorization,
+        ...headers
       },
       timeout: 30000
     }

packages/service/core/app/utils.ts

@@ -12,12 +12,12 @@ export async function listAppDatasetDataByTeamIdAndDatasetIds({
   datasetIdList: string[];
 }) {
   const myDatasets = await MongoDataset.find({
-    teamId,
-    _id: { $in: datasetIdList }
+    _id: { $in: datasetIdList },
+    ...(teamId && { teamId })
   }).lean();
 
   return myDatasets.map((item) => ({
-    datasetId: item._id,
+    datasetId: String(item._id),
     avatar: item.avatar,
     name: item.name,
     vectorModel: getEmbeddingModel(item.vectorModel)
@@ -47,7 +47,7 @@ export async function rewriteAppWorkflowToDetail({
 
     const datasetIds = Array.isArray(rawValue)
       ? rawValue.map((v) => v?.datasetId).filter((id) => !!id && typeof id === 'string')
-      : rawValue.datasetId
+      : rawValue?.datasetId
         ? [String(rawValue.datasetId)]
         : [];
 
@@ -61,38 +61,63 @@ export async function rewriteAppWorkflowToDetail({
     teamId: isRoot ? undefined : teamId,
     datasetIdList: Array.from(datasetIdSet)
   });
+
   const datasetMap = new Map(datasetList.map((ds) => [String(ds.datasetId), ds]));
 
   // Rewrite dataset ids, add dataset info to nodes
-  nodes.forEach((node) => {
-    if (node.flowNodeType !== FlowNodeTypeEnum.datasetSearchNode) return;
+  if (datasetList.length > 0) {
+    nodes.forEach((node) => {
+      if (node.flowNodeType !== FlowNodeTypeEnum.datasetSearchNode) return;
 
-    node.inputs.forEach((item) => {
-      if (item.key !== NodeInputKeyEnum.datasetSelectList) return;
+      node.inputs.forEach((item) => {
+        if (item.key !== NodeInputKeyEnum.datasetSelectList) return;
 
-      const val = item.value as undefined | { datasetId: string }[] | { datasetId: string };
+        const val = item.value as undefined | { datasetId: string }[] | { datasetId: string };
 
-      if (Array.isArray(val)) {
-        item.value = val.map((v) => {
-          const data = datasetMap.get(String(v.datasetId))!;
-          return {
-            datasetId: data.datasetId,
-            avatar: data.avatar,
-            name: data.name,
-            vectorModel: data.vectorModel
-          };
-        });
-      } else if (typeof val === 'object' && val !== null) {
-        const data = datasetMap.get(String(val.datasetId))!;
-        item.value = {
-          datasetId: data.datasetId,
-          avatar: data.avatar,
-          name: data.name,
-          vectorModel: data.vectorModel
-        };
-      }
+        if (Array.isArray(val)) {
+          item.value = val
+            .map((v) => {
+              const data = datasetMap.get(String(v.datasetId));
+              if (!data)
+                return {
+                  datasetId: v.datasetId,
+                  avatar: '',
+                  name: 'Dataset not found',
+                  vectorModel: ''
+                };
+              return {
+                datasetId: data.datasetId,
+                avatar: data.avatar,
+                name: data.name,
+                vectorModel: data.vectorModel
+              };
+            })
+            .filter(Boolean);
+        } else if (typeof val === 'object' && val !== null) {
+          const data = datasetMap.get(String(val.datasetId));
+          if (!data) {
+            item.value = [
+              {
+                datasetId: val.datasetId,
+                avatar: '',
+                name: 'Dataset not found',
+                vectorModel: ''
+              }
+            ];
+          } else {
+            item.value = [
+              {
+                datasetId: data.datasetId,
+                avatar: data.avatar,
+                name: data.name,
+                vectorModel: data.vectorModel
+              }
+            ];
+          }
+        }
+      });
     });
-  });
+  }
 
   return nodes;
 }

packages/service/core/workflow/dispatch/agent/extract.ts

@@ -202,7 +202,7 @@ ${description ? `- ${description}` : ''}
     properties[item.key] = {
       ...jsonSchema,
       description: item.desc,
-      ...(item.enum ? { enum: item.enum.split('\n') } : {})
+      ...(item.enum ? { enum: item.enum.split('\n').filter(Boolean) } : {})
     };
   });
   // function body

packages/service/core/workflow/dispatch/index.ts

@@ -21,7 +21,7 @@ import {
   FlowNodeInputTypeEnum,
   FlowNodeTypeEnum
 } from '@fastgpt/global/core/workflow/node/constant';
-import { getNanoid, replaceVariable } from '@fastgpt/global/common/string/tools';
+import { getNanoid } from '@fastgpt/global/common/string/tools';
 import { getSystemTime } from '@fastgpt/global/common/time/timezone';
 
 import { dispatchWorkflowStart } from './init/workflowStart';
@@ -426,6 +426,14 @@ export async function dispatchWorkFlow(data: Props): Promise<DispatchFlowRespons
     })();
 
     if (!nodeRunResult) return [];
+    if (res?.closed) {
+      addLog.warn('Request is closed', {
+        appId: props.runningAppInfo.id,
+        nodeId: node.nodeId,
+        nodeName: node.name
+      });
+      return [];
+    }
 
     /* 
       特殊情况：