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test-openG
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gru/projec
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18ddccb709 | ||
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05c7ba4483 | ||
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fa80ce3a77 | ||
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830358aa72 | ||
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02b214b3ec | ||
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a171c7b11c | ||
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802de11363 |
@@ -132,15 +132,15 @@ services:
|
||||
# fastgpt
|
||||
sandbox:
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container_name: sandbox
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||||
image: ghcr.io/labring/fastgpt-sandbox:v4.9.10 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.9.10 # 阿里云
|
||||
image: ghcr.io/labring/fastgpt-sandbox:v4.9.10-fix2 # git
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||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.9.10-fix2 # 阿里云
|
||||
networks:
|
||||
- fastgpt
|
||||
restart: always
|
||||
fastgpt-mcp-server:
|
||||
container_name: fastgpt-mcp-server
|
||||
image: ghcr.io/labring/fastgpt-mcp_server:v4.9.10 # git
|
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# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-mcp_server:v4.9.10 # 阿里云
|
||||
image: ghcr.io/labring/fastgpt-mcp_server:v4.9.10-fix2 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-mcp_server:v4.9.10-fix2 # 阿里云
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ports:
|
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- 3005:3000
|
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networks:
|
||||
@@ -150,8 +150,8 @@ services:
|
||||
- FASTGPT_ENDPOINT=http://fastgpt:3000
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fastgpt:
|
||||
container_name: fastgpt
|
||||
image: ghcr.io/labring/fastgpt:v4.9.10 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.9.10 # 阿里云
|
||||
image: ghcr.io/labring/fastgpt:v4.9.10-fix2 # git
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||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.9.10-fix2 # 阿里云
|
||||
ports:
|
||||
- 3000:3000
|
||||
networks:
|
||||
|
||||
@@ -109,15 +109,15 @@ services:
|
||||
# fastgpt
|
||||
sandbox:
|
||||
container_name: sandbox
|
||||
image: ghcr.io/labring/fastgpt-sandbox:v4.9.10 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.9.10 # 阿里云
|
||||
image: ghcr.io/labring/fastgpt-sandbox:v4.9.10-fix2 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.9.10-fix2 # 阿里云
|
||||
networks:
|
||||
- fastgpt
|
||||
restart: always
|
||||
fastgpt-mcp-server:
|
||||
container_name: fastgpt-mcp-server
|
||||
image: ghcr.io/labring/fastgpt-mcp_server:v4.9.10 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-mcp_server:v4.9.10 # 阿里云
|
||||
image: ghcr.io/labring/fastgpt-mcp_server:v4.9.10-fix2 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-mcp_server:v4.9.10-fix2 # 阿里云
|
||||
ports:
|
||||
- 3005:3000
|
||||
networks:
|
||||
@@ -127,8 +127,8 @@ services:
|
||||
- FASTGPT_ENDPOINT=http://fastgpt:3000
|
||||
fastgpt:
|
||||
container_name: fastgpt
|
||||
image: ghcr.io/labring/fastgpt:v4.9.10 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.9.10 # 阿里云
|
||||
image: ghcr.io/labring/fastgpt:v4.9.10-fix2 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.9.10-fix2 # 阿里云
|
||||
ports:
|
||||
- 3000:3000
|
||||
networks:
|
||||
|
||||
@@ -96,15 +96,15 @@ services:
|
||||
# fastgpt
|
||||
sandbox:
|
||||
container_name: sandbox
|
||||
image: ghcr.io/labring/fastgpt-sandbox:v4.9.10 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.9.10 # 阿里云
|
||||
image: ghcr.io/labring/fastgpt-sandbox:v4.9.10-fix2 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.9.10-fix2 # 阿里云
|
||||
networks:
|
||||
- fastgpt
|
||||
restart: always
|
||||
fastgpt-mcp-server:
|
||||
container_name: fastgpt-mcp-server
|
||||
image: ghcr.io/labring/fastgpt-mcp_server:v4.9.10 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-mcp_server:v4.9.10 # 阿里云
|
||||
image: ghcr.io/labring/fastgpt-mcp_server:v4.9.10-fix2 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-mcp_server:v4.9.10-fix2 # 阿里云
|
||||
ports:
|
||||
- 3005:3000
|
||||
networks:
|
||||
@@ -114,8 +114,8 @@ services:
|
||||
- FASTGPT_ENDPOINT=http://fastgpt:3000
|
||||
fastgpt:
|
||||
container_name: fastgpt
|
||||
image: ghcr.io/labring/fastgpt:v4.9.10 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.9.10 # 阿里云
|
||||
image: ghcr.io/labring/fastgpt:v4.9.10-fix2 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.9.10-fix2 # 阿里云
|
||||
ports:
|
||||
- 3000:3000
|
||||
networks:
|
||||
|
||||
@@ -72,15 +72,15 @@ services:
|
||||
|
||||
sandbox:
|
||||
container_name: sandbox
|
||||
image: ghcr.io/labring/fastgpt-sandbox:v4.9.10 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.9.10 # 阿里云
|
||||
image: ghcr.io/labring/fastgpt-sandbox:v4.9.10-fix2 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.9.10-fix2 # 阿里云
|
||||
networks:
|
||||
- fastgpt
|
||||
restart: always
|
||||
fastgpt-mcp-server:
|
||||
container_name: fastgpt-mcp-server
|
||||
image: ghcr.io/labring/fastgpt-mcp_server:v4.9.10 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-mcp_server:v4.9.10 # 阿里云
|
||||
image: ghcr.io/labring/fastgpt-mcp_server:v4.9.10-fix2 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-mcp_server:v4.9.10-fix2 # 阿里云
|
||||
ports:
|
||||
- 3005:3000
|
||||
networks:
|
||||
@@ -90,8 +90,8 @@ services:
|
||||
- FASTGPT_ENDPOINT=http://fastgpt:3000
|
||||
fastgpt:
|
||||
container_name: fastgpt
|
||||
image: ghcr.io/labring/fastgpt:v4.9.10 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.9.10 # 阿里云
|
||||
image: ghcr.io/labring/fastgpt:v4.9.10-fix2 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.9.10-fix2 # 阿里云
|
||||
ports:
|
||||
- 3000:3000
|
||||
networks:
|
||||
|
||||
@@ -15,8 +15,8 @@ weight: 790
|
||||
|
||||
### 2. 更新镜像 tag
|
||||
|
||||
- 更新 FastGPT 镜像 tag: v4.9.10
|
||||
- 更新 FastGPT 商业版镜像 tag: v4.9.10
|
||||
- 更新 FastGPT 镜像 tag: v4.9.10-fix2
|
||||
- 更新 FastGPT 商业版镜像 tag: v4.9.10-fix2
|
||||
- mcp_server 无需更新
|
||||
- Sandbox 无需更新
|
||||
- AIProxy 无需更新
|
||||
|
||||
@@ -10,12 +10,16 @@ weight: 789
|
||||
|
||||
## 🚀 新增内容
|
||||
|
||||
1. 工作流中,子流程版本控制,可选择“保持最新版本”,无需手动更新。
|
||||
1. 工作流中增加节点搜索功能。
|
||||
2. 工作流中,子流程版本控制,可选择“保持最新版本”,无需手动更新。
|
||||
|
||||
## ⚙️ 优化
|
||||
|
||||
|
||||
1. 原文缓存改用 gridfs 存储,提高上限。
|
||||
|
||||
## 🐛 修复
|
||||
|
||||
1. 工作流中,管理员声明的全局系统工具,无法进行版本管理。
|
||||
1. 工作流中,管理员声明的全局系统工具,无法进行版本管理。
|
||||
2. 工具调用节点前,有交互节点时,上下文异常。
|
||||
3. 修复备份导入,小于 1000 字时,无法分块问题。
|
||||
4. 自定义 PDF 解析,无法保存 base64 图片。
|
||||
1
packages/global/core/workflow/type/node.d.ts
vendored
1
packages/global/core/workflow/type/node.d.ts
vendored
@@ -125,6 +125,7 @@ export type FlowNodeItemType = FlowNodeTemplateType & {
|
||||
nodeId: string;
|
||||
parentNodeId?: string;
|
||||
isError?: boolean;
|
||||
searchedText?: string;
|
||||
debugResult?: {
|
||||
status: 'running' | 'success' | 'skipped' | 'failed';
|
||||
message?: string;
|
||||
|
||||
178
packages/service/common/buffer/rawText/controller.ts
Normal file
178
packages/service/common/buffer/rawText/controller.ts
Normal file
@@ -0,0 +1,178 @@
|
||||
import { retryFn } from '@fastgpt/global/common/system/utils';
|
||||
import { connectionMongo } from '../../mongo';
|
||||
import { MongoRawTextBufferSchema, bucketName } from './schema';
|
||||
import { addLog } from '../../system/log';
|
||||
import { setCron } from '../../system/cron';
|
||||
import { checkTimerLock } from '../../system/timerLock/utils';
|
||||
import { TimerIdEnum } from '../../system/timerLock/constants';
|
||||
|
||||
const getGridBucket = () => {
|
||||
return new connectionMongo.mongo.GridFSBucket(connectionMongo.connection.db!, {
|
||||
bucketName: bucketName
|
||||
});
|
||||
};
|
||||
|
||||
export const addRawTextBuffer = async ({
|
||||
sourceId,
|
||||
sourceName,
|
||||
text,
|
||||
expiredTime
|
||||
}: {
|
||||
sourceId: string;
|
||||
sourceName: string;
|
||||
text: string;
|
||||
expiredTime: Date;
|
||||
}) => {
|
||||
const gridBucket = getGridBucket();
|
||||
const metadata = {
|
||||
sourceId,
|
||||
sourceName,
|
||||
expiredTime
|
||||
};
|
||||
|
||||
const buffer = Buffer.from(text);
|
||||
|
||||
const fileSize = buffer.length;
|
||||
// 单块大小:尽可能大,但不超过 14MB,不小于128KB
|
||||
const chunkSizeBytes = (() => {
|
||||
// 计算理想块大小:文件大小 ÷ 目标块数(10)。 并且每个块需要小于 14MB
|
||||
const idealChunkSize = Math.min(Math.ceil(fileSize / 10), 14 * 1024 * 1024);
|
||||
|
||||
// 确保块大小至少为128KB
|
||||
const minChunkSize = 128 * 1024; // 128KB
|
||||
|
||||
// 取理想块大小和最小块大小中的较大值
|
||||
let chunkSize = Math.max(idealChunkSize, minChunkSize);
|
||||
|
||||
// 将块大小向上取整到最接近的64KB的倍数,使其更整齐
|
||||
chunkSize = Math.ceil(chunkSize / (64 * 1024)) * (64 * 1024);
|
||||
|
||||
return chunkSize;
|
||||
})();
|
||||
|
||||
const uploadStream = gridBucket.openUploadStream(sourceId, {
|
||||
metadata,
|
||||
chunkSizeBytes
|
||||
});
|
||||
|
||||
return retryFn(async () => {
|
||||
return new Promise((resolve, reject) => {
|
||||
uploadStream.end(buffer);
|
||||
uploadStream.on('finish', () => {
|
||||
resolve(uploadStream.id);
|
||||
});
|
||||
uploadStream.on('error', (error) => {
|
||||
addLog.error('addRawTextBuffer error', error);
|
||||
resolve('');
|
||||
});
|
||||
});
|
||||
});
|
||||
};
|
||||
|
||||
export const getRawTextBuffer = async (sourceId: string) => {
|
||||
const gridBucket = getGridBucket();
|
||||
|
||||
return retryFn(async () => {
|
||||
const bufferData = await MongoRawTextBufferSchema.findOne(
|
||||
{
|
||||
'metadata.sourceId': sourceId
|
||||
},
|
||||
'_id metadata'
|
||||
).lean();
|
||||
if (!bufferData) {
|
||||
return null;
|
||||
}
|
||||
|
||||
// Read file content
|
||||
const downloadStream = gridBucket.openDownloadStream(bufferData._id);
|
||||
const chunks: Buffer[] = [];
|
||||
|
||||
return new Promise<{
|
||||
text: string;
|
||||
sourceName: string;
|
||||
} | null>((resolve, reject) => {
|
||||
downloadStream.on('data', (chunk) => {
|
||||
chunks.push(chunk);
|
||||
});
|
||||
|
||||
downloadStream.on('end', () => {
|
||||
const buffer = Buffer.concat(chunks);
|
||||
const text = buffer.toString('utf8');
|
||||
resolve({
|
||||
text,
|
||||
sourceName: bufferData.metadata?.sourceName || ''
|
||||
});
|
||||
});
|
||||
|
||||
downloadStream.on('error', (error) => {
|
||||
addLog.error('getRawTextBuffer error', error);
|
||||
resolve(null);
|
||||
});
|
||||
});
|
||||
});
|
||||
};
|
||||
|
||||
export const deleteRawTextBuffer = async (sourceId: string): Promise<boolean> => {
|
||||
const gridBucket = getGridBucket();
|
||||
|
||||
return retryFn(async () => {
|
||||
const buffer = await MongoRawTextBufferSchema.findOne({ 'metadata.sourceId': sourceId });
|
||||
if (!buffer) {
|
||||
return false;
|
||||
}
|
||||
|
||||
await gridBucket.delete(buffer._id);
|
||||
return true;
|
||||
});
|
||||
};
|
||||
|
||||
export const updateRawTextBufferExpiredTime = async ({
|
||||
sourceId,
|
||||
expiredTime
|
||||
}: {
|
||||
sourceId: string;
|
||||
expiredTime: Date;
|
||||
}) => {
|
||||
return retryFn(async () => {
|
||||
return MongoRawTextBufferSchema.updateOne(
|
||||
{ 'metadata.sourceId': sourceId },
|
||||
{ $set: { 'metadata.expiredTime': expiredTime } }
|
||||
);
|
||||
});
|
||||
};
|
||||
|
||||
export const clearExpiredRawTextBufferCron = async () => {
|
||||
const clearExpiredRawTextBuffer = async () => {
|
||||
addLog.debug('Clear expired raw text buffer start');
|
||||
const gridBucket = getGridBucket();
|
||||
|
||||
return retryFn(async () => {
|
||||
const data = await MongoRawTextBufferSchema.find(
|
||||
{
|
||||
'metadata.expiredTime': { $lt: new Date() }
|
||||
},
|
||||
'_id'
|
||||
).lean();
|
||||
|
||||
for (const item of data) {
|
||||
await gridBucket.delete(item._id);
|
||||
}
|
||||
addLog.debug('Clear expired raw text buffer end');
|
||||
});
|
||||
};
|
||||
|
||||
setCron('*/10 * * * *', async () => {
|
||||
if (
|
||||
await checkTimerLock({
|
||||
timerId: TimerIdEnum.clearExpiredRawTextBuffer,
|
||||
lockMinuted: 9
|
||||
})
|
||||
) {
|
||||
try {
|
||||
await clearExpiredRawTextBuffer();
|
||||
} catch (error) {
|
||||
addLog.error('clearExpiredRawTextBufferCron error', error);
|
||||
}
|
||||
}
|
||||
});
|
||||
};
|
||||
@@ -1,33 +1,22 @@
|
||||
import { getMongoModel, Schema } from '../../mongo';
|
||||
import { type RawTextBufferSchemaType } from './type';
|
||||
import { getMongoModel, type Types, Schema } from '../../mongo';
|
||||
|
||||
export const collectionName = 'buffer_rawtexts';
|
||||
export const bucketName = 'buffer_rawtext';
|
||||
|
||||
const RawTextBufferSchema = new Schema({
|
||||
sourceId: {
|
||||
type: String,
|
||||
required: true
|
||||
},
|
||||
rawText: {
|
||||
type: String,
|
||||
default: ''
|
||||
},
|
||||
createTime: {
|
||||
type: Date,
|
||||
default: () => new Date()
|
||||
},
|
||||
metadata: Object
|
||||
metadata: {
|
||||
sourceId: { type: String, required: true },
|
||||
sourceName: { type: String, required: true },
|
||||
expiredTime: { type: Date, required: true }
|
||||
}
|
||||
});
|
||||
RawTextBufferSchema.index({ 'metadata.sourceId': 'hashed' });
|
||||
RawTextBufferSchema.index({ 'metadata.expiredTime': -1 });
|
||||
|
||||
try {
|
||||
RawTextBufferSchema.index({ sourceId: 1 });
|
||||
// 20 minutes
|
||||
RawTextBufferSchema.index({ createTime: 1 }, { expireAfterSeconds: 20 * 60 });
|
||||
} catch (error) {
|
||||
console.log(error);
|
||||
}
|
||||
|
||||
export const MongoRawTextBuffer = getMongoModel<RawTextBufferSchemaType>(
|
||||
collectionName,
|
||||
RawTextBufferSchema
|
||||
);
|
||||
export const MongoRawTextBufferSchema = getMongoModel<{
|
||||
_id: Types.ObjectId;
|
||||
metadata: {
|
||||
sourceId: string;
|
||||
sourceName: string;
|
||||
expiredTime: Date;
|
||||
};
|
||||
}>(`${bucketName}.files`, RawTextBufferSchema);
|
||||
|
||||
@@ -1,8 +0,0 @@
|
||||
export type RawTextBufferSchemaType = {
|
||||
sourceId: string;
|
||||
rawText: string;
|
||||
createTime: Date;
|
||||
metadata?: {
|
||||
filename: string;
|
||||
};
|
||||
};
|
||||
@@ -6,13 +6,13 @@ import { type DatasetFileSchema } from '@fastgpt/global/core/dataset/type';
|
||||
import { MongoChatFileSchema, MongoDatasetFileSchema } from './schema';
|
||||
import { detectFileEncoding, detectFileEncodingByPath } from '@fastgpt/global/common/file/tools';
|
||||
import { CommonErrEnum } from '@fastgpt/global/common/error/code/common';
|
||||
import { MongoRawTextBuffer } from '../../buffer/rawText/schema';
|
||||
import { readRawContentByFileBuffer } from '../read/utils';
|
||||
import { gridFsStream2Buffer, stream2Encoding } from './utils';
|
||||
import { addLog } from '../../system/log';
|
||||
import { readFromSecondary } from '../../mongo/utils';
|
||||
import { parseFileExtensionFromUrl } from '@fastgpt/global/common/string/tools';
|
||||
import { Readable } from 'stream';
|
||||
import { addRawTextBuffer, getRawTextBuffer } from '../../buffer/rawText/controller';
|
||||
import { addMinutes } from 'date-fns';
|
||||
|
||||
export function getGFSCollection(bucket: `${BucketNameEnum}`) {
|
||||
MongoDatasetFileSchema;
|
||||
@@ -225,13 +225,11 @@ export const readFileContentFromMongo = async ({
|
||||
}> => {
|
||||
const bufferId = `${fileId}-${customPdfParse}`;
|
||||
// read buffer
|
||||
const fileBuffer = await MongoRawTextBuffer.findOne({ sourceId: bufferId }, undefined, {
|
||||
...readFromSecondary
|
||||
}).lean();
|
||||
const fileBuffer = await getRawTextBuffer(bufferId);
|
||||
if (fileBuffer) {
|
||||
return {
|
||||
rawText: fileBuffer.rawText,
|
||||
filename: fileBuffer.metadata?.filename || ''
|
||||
rawText: fileBuffer.text,
|
||||
filename: fileBuffer?.sourceName
|
||||
};
|
||||
}
|
||||
|
||||
@@ -265,16 +263,13 @@ export const readFileContentFromMongo = async ({
|
||||
}
|
||||
});
|
||||
|
||||
// < 14M
|
||||
if (fileBuffers.length < 14 * 1024 * 1024 && rawText.trim()) {
|
||||
MongoRawTextBuffer.create({
|
||||
sourceId: bufferId,
|
||||
rawText,
|
||||
metadata: {
|
||||
filename: file.filename
|
||||
}
|
||||
});
|
||||
}
|
||||
// Add buffer
|
||||
addRawTextBuffer({
|
||||
sourceId: bufferId,
|
||||
sourceName: file.filename,
|
||||
text: rawText,
|
||||
expiredTime: addMinutes(new Date(), 20)
|
||||
});
|
||||
|
||||
return {
|
||||
rawText,
|
||||
|
||||
@@ -1,16 +1,16 @@
|
||||
import { Schema, getMongoModel } from '../../mongo';
|
||||
|
||||
const DatasetFileSchema = new Schema({});
|
||||
const ChatFileSchema = new Schema({});
|
||||
const DatasetFileSchema = new Schema({
|
||||
metadata: Object
|
||||
});
|
||||
const ChatFileSchema = new Schema({
|
||||
metadata: Object
|
||||
});
|
||||
|
||||
try {
|
||||
DatasetFileSchema.index({ uploadDate: -1 });
|
||||
DatasetFileSchema.index({ uploadDate: -1 });
|
||||
|
||||
ChatFileSchema.index({ uploadDate: -1 });
|
||||
ChatFileSchema.index({ 'metadata.chatId': 1 });
|
||||
} catch (error) {
|
||||
console.log(error);
|
||||
}
|
||||
ChatFileSchema.index({ uploadDate: -1 });
|
||||
ChatFileSchema.index({ 'metadata.chatId': 1 });
|
||||
|
||||
export const MongoDatasetFileSchema = getMongoModel('dataset.files', DatasetFileSchema);
|
||||
export const MongoChatFileSchema = getMongoModel('chat.files', ChatFileSchema);
|
||||
|
||||
@@ -110,7 +110,7 @@ export const readRawContentByFileBuffer = async ({
|
||||
|
||||
return {
|
||||
rawText: text,
|
||||
formatText: rawText,
|
||||
formatText: text,
|
||||
imageList
|
||||
};
|
||||
};
|
||||
|
||||
@@ -5,7 +5,8 @@ export enum TimerIdEnum {
|
||||
clearExpiredSubPlan = 'clearExpiredSubPlan',
|
||||
updateStandardPlan = 'updateStandardPlan',
|
||||
scheduleTriggerApp = 'scheduleTriggerApp',
|
||||
notification = 'notification'
|
||||
notification = 'notification',
|
||||
clearExpiredRawTextBuffer = 'clearExpiredRawTextBuffer'
|
||||
}
|
||||
|
||||
export enum LockNotificationEnum {
|
||||
|
||||
@@ -77,7 +77,10 @@ export const createCollectionAndInsertData = async ({
|
||||
const chunkSplitter = computeChunkSplitter(createCollectionParams);
|
||||
const paragraphChunkDeep = computeParagraphChunkDeep(createCollectionParams);
|
||||
|
||||
if (trainingType === DatasetCollectionDataProcessModeEnum.qa) {
|
||||
if (
|
||||
trainingType === DatasetCollectionDataProcessModeEnum.qa ||
|
||||
trainingType === DatasetCollectionDataProcessModeEnum.backup
|
||||
) {
|
||||
delete createCollectionParams.chunkTriggerType;
|
||||
delete createCollectionParams.chunkTriggerMinSize;
|
||||
delete createCollectionParams.dataEnhanceCollectionName;
|
||||
|
||||
@@ -218,6 +218,10 @@ export const rawText2Chunks = ({
|
||||
};
|
||||
};
|
||||
|
||||
if (backupParse) {
|
||||
return parseDatasetBackup2Chunks(rawText).chunks;
|
||||
}
|
||||
|
||||
// Chunk condition
|
||||
// 1. 选择最大值条件,只有超过了最大值(默认为模型的最大值*0.7),才会触发分块
|
||||
if (chunkTriggerType === ChunkTriggerConfigTypeEnum.maxSize) {
|
||||
@@ -240,10 +244,6 @@ export const rawText2Chunks = ({
|
||||
}
|
||||
}
|
||||
|
||||
if (backupParse) {
|
||||
return parseDatasetBackup2Chunks(rawText).chunks;
|
||||
}
|
||||
|
||||
const { chunks } = splitText2Chunks({
|
||||
text: rawText,
|
||||
chunkSize,
|
||||
|
||||
@@ -86,7 +86,6 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
|
||||
});
|
||||
|
||||
// Check interactive entry
|
||||
const interactiveResponse = lastInteractive;
|
||||
props.node.isEntry = false;
|
||||
const hasReadFilesTool = toolNodes.some(
|
||||
(item) => item.flowNodeType === FlowNodeTypeEnum.readFiles
|
||||
@@ -143,7 +142,7 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
|
||||
})
|
||||
}
|
||||
];
|
||||
if (interactiveResponse) {
|
||||
if (lastInteractive && isEntry) {
|
||||
return value.slice(0, -2);
|
||||
}
|
||||
return value;
|
||||
@@ -183,7 +182,7 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
|
||||
toolModel,
|
||||
maxRunToolTimes: 30,
|
||||
messages: adaptMessages,
|
||||
interactiveEntryToolParams: interactiveResponse?.toolParams
|
||||
interactiveEntryToolParams: lastInteractive?.toolParams
|
||||
});
|
||||
}
|
||||
if (toolModel.functionCall) {
|
||||
@@ -194,7 +193,7 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
|
||||
toolNodes,
|
||||
toolModel,
|
||||
messages: adaptMessages,
|
||||
interactiveEntryToolParams: interactiveResponse?.toolParams
|
||||
interactiveEntryToolParams: lastInteractive?.toolParams
|
||||
});
|
||||
}
|
||||
|
||||
@@ -224,7 +223,7 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
|
||||
toolNodes,
|
||||
toolModel,
|
||||
messages: adaptMessages,
|
||||
interactiveEntryToolParams: interactiveResponse?.toolParams
|
||||
interactiveEntryToolParams: lastInteractive?.toolParams
|
||||
});
|
||||
})();
|
||||
|
||||
|
||||
@@ -11,7 +11,6 @@ import type {
|
||||
SystemVariablesType
|
||||
} from '@fastgpt/global/core/workflow/runtime/type';
|
||||
import type { RuntimeNodeItemType } from '@fastgpt/global/core/workflow/runtime/type.d';
|
||||
import type { FlowNodeOutputItemType } from '@fastgpt/global/core/workflow/type/io.d';
|
||||
import type {
|
||||
AIChatItemValueItemType,
|
||||
ChatHistoryItemResType,
|
||||
|
||||
@@ -17,6 +17,7 @@ import { chatValue2RuntimePrompt } from '@fastgpt/global/core/chat/adapt';
|
||||
import { getPluginRunUserQuery } from '@fastgpt/global/core/workflow/utils';
|
||||
import { getPluginInputsFromStoreNodes } from '@fastgpt/global/core/app/plugin/utils';
|
||||
import type { NodeInputKeyEnum } from '@fastgpt/global/core/workflow/constants';
|
||||
import { getUserChatInfoAndAuthTeamPoints } from '../../../../support/permission/auth/team';
|
||||
|
||||
type RunPluginProps = ModuleDispatchProps<{
|
||||
[NodeInputKeyEnum.forbidStream]?: boolean;
|
||||
@@ -73,9 +74,11 @@ export const dispatchRunPlugin = async (props: RunPluginProps): Promise<RunPlugi
|
||||
};
|
||||
});
|
||||
|
||||
const { externalProvider } = await getUserChatInfoAndAuthTeamPoints(runningAppInfo.tmbId);
|
||||
const runtimeVariables = {
|
||||
...filterSystemVariables(props.variables),
|
||||
appId: String(plugin.id)
|
||||
appId: String(plugin.id),
|
||||
...(externalProvider ? externalProvider.externalWorkflowVariables : {})
|
||||
};
|
||||
const { flowResponses, flowUsages, assistantResponses, runTimes } = await dispatchWorkFlow({
|
||||
...props,
|
||||
|
||||
@@ -20,6 +20,7 @@ import { ReadPermissionVal } from '@fastgpt/global/support/permission/constant';
|
||||
import { getAppVersionById } from '../../../app/version/controller';
|
||||
import { parseUrlToFileType } from '@fastgpt/global/common/file/tools';
|
||||
import { type ChildrenInteractive } from '@fastgpt/global/core/workflow/template/system/interactive/type';
|
||||
import { getUserChatInfoAndAuthTeamPoints } from '../../../../support/permission/auth/team';
|
||||
|
||||
type Props = ModuleDispatchProps<{
|
||||
[NodeInputKeyEnum.userChatInput]: string;
|
||||
@@ -97,11 +98,13 @@ export const dispatchRunAppNode = async (props: Props): Promise<Response> => {
|
||||
|
||||
// Rewrite children app variables
|
||||
const systemVariables = filterSystemVariables(variables);
|
||||
const { externalProvider } = await getUserChatInfoAndAuthTeamPoints(appData.tmbId);
|
||||
const childrenRunVariables = {
|
||||
...systemVariables,
|
||||
...childrenAppVariables,
|
||||
histories: chatHistories,
|
||||
appId: String(appData._id)
|
||||
appId: String(appData._id),
|
||||
...(externalProvider ? externalProvider.externalWorkflowVariables : {})
|
||||
};
|
||||
|
||||
const childrenInteractive =
|
||||
|
||||
@@ -5,8 +5,6 @@ import { NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
|
||||
import { type DispatchNodeResultType } from '@fastgpt/global/core/workflow/runtime/type';
|
||||
import axios from 'axios';
|
||||
import { serverRequestBaseUrl } from '../../../../common/api/serverRequest';
|
||||
import { MongoRawTextBuffer } from '../../../../common/buffer/rawText/schema';
|
||||
import { readFromSecondary } from '../../../../common/mongo/utils';
|
||||
import { getErrText } from '@fastgpt/global/common/error/utils';
|
||||
import { detectFileEncoding, parseUrlToFileType } from '@fastgpt/global/common/file/tools';
|
||||
import { readRawContentByFileBuffer } from '../../../../common/file/read/utils';
|
||||
@@ -14,6 +12,8 @@ import { ChatRoleEnum } from '@fastgpt/global/core/chat/constants';
|
||||
import { type ChatItemType, type UserChatItemValueItemType } from '@fastgpt/global/core/chat/type';
|
||||
import { parseFileExtensionFromUrl } from '@fastgpt/global/common/string/tools';
|
||||
import { addLog } from '../../../../common/system/log';
|
||||
import { addRawTextBuffer, getRawTextBuffer } from '../../../../common/buffer/rawText/controller';
|
||||
import { addMinutes } from 'date-fns';
|
||||
|
||||
type Props = ModuleDispatchProps<{
|
||||
[NodeInputKeyEnum.fileUrlList]: string[];
|
||||
@@ -158,14 +158,12 @@ export const getFileContentFromLinks = async ({
|
||||
parseUrlList
|
||||
.map(async (url) => {
|
||||
// Get from buffer
|
||||
const fileBuffer = await MongoRawTextBuffer.findOne({ sourceId: url }, undefined, {
|
||||
...readFromSecondary
|
||||
}).lean();
|
||||
const fileBuffer = await getRawTextBuffer(url);
|
||||
if (fileBuffer) {
|
||||
return formatResponseObject({
|
||||
filename: fileBuffer.metadata?.filename || url,
|
||||
filename: fileBuffer.sourceName || url,
|
||||
url,
|
||||
content: fileBuffer.rawText
|
||||
content: fileBuffer.text
|
||||
});
|
||||
}
|
||||
|
||||
@@ -220,17 +218,12 @@ export const getFileContentFromLinks = async ({
|
||||
});
|
||||
|
||||
// Add to buffer
|
||||
try {
|
||||
if (buffer.length < 14 * 1024 * 1024 && rawText.trim()) {
|
||||
MongoRawTextBuffer.create({
|
||||
sourceId: url,
|
||||
rawText,
|
||||
metadata: {
|
||||
filename: filename
|
||||
}
|
||||
});
|
||||
}
|
||||
} catch (error) {}
|
||||
addRawTextBuffer({
|
||||
sourceId: url,
|
||||
sourceName: filename,
|
||||
text: rawText,
|
||||
expiredTime: addMinutes(new Date(), 20)
|
||||
});
|
||||
|
||||
return formatResponseObject({ filename, url, content: rawText });
|
||||
} catch (error) {
|
||||
|
||||
@@ -1,17 +1,26 @@
|
||||
import { Box } from '@chakra-ui/react';
|
||||
import React from 'react';
|
||||
import React, { useMemo } from 'react';
|
||||
|
||||
const HighlightText = ({
|
||||
rawText,
|
||||
matchText,
|
||||
color = 'primary.600'
|
||||
color = 'primary.600',
|
||||
mode = 'text'
|
||||
}: {
|
||||
rawText: string;
|
||||
matchText: string;
|
||||
color?: string;
|
||||
mode?: 'text' | 'bg';
|
||||
}) => {
|
||||
const regex = new RegExp(`(${matchText})`, 'gi');
|
||||
const parts = rawText.split(regex);
|
||||
const { parts } = useMemo(() => {
|
||||
const regx = new RegExp(`(${matchText})`, 'gi');
|
||||
const parts = rawText.split(regx);
|
||||
|
||||
return {
|
||||
regx,
|
||||
parts
|
||||
};
|
||||
}, [rawText, matchText]);
|
||||
|
||||
return (
|
||||
<Box>
|
||||
@@ -28,7 +37,17 @@ const HighlightText = ({
|
||||
}
|
||||
|
||||
return (
|
||||
<Box as="span" key={index} color={highLight ? color : 'inherit'}>
|
||||
<Box
|
||||
as="span"
|
||||
key={index}
|
||||
{...(mode === 'bg'
|
||||
? {
|
||||
bg: highLight ? color : 'transparent'
|
||||
}
|
||||
: {
|
||||
color: highLight ? color : 'inherit'
|
||||
})}
|
||||
>
|
||||
{part}
|
||||
</Box>
|
||||
);
|
||||
@@ -37,4 +56,4 @@ const HighlightText = ({
|
||||
);
|
||||
};
|
||||
|
||||
export default HighlightText;
|
||||
export default React.memo(HighlightText);
|
||||
|
||||
@@ -3,6 +3,8 @@ import { useContextSelector } from 'use-context-selector';
|
||||
|
||||
export const useSystem = () => {
|
||||
const isPc = useContextSelector(useSystemStoreContext, (state) => state.isPc);
|
||||
const isMac =
|
||||
typeof window !== 'undefined' && window.navigator.userAgent.toLocaleLowerCase().includes('mac');
|
||||
|
||||
return { isPc };
|
||||
return { isPc, isMac };
|
||||
};
|
||||
|
||||
@@ -63,6 +63,8 @@
|
||||
"field_required": "Required",
|
||||
"field_used_as_tool_input": "Used as Tool Call Parameter",
|
||||
"filter_description": "Currently supports filtering by tags and creation time. Fill in the format as follows:\n{\n \"tags\": {\n \"$and\": [\"Tag 1\",\"Tag 2\"],\n \"$or\": [\"When there are $and tags, and is effective, or is not effective\"]\n },\n \"createTime\": {\n \"$gte\": \"YYYY-MM-DD HH:mm format, collection creation time greater than this time\",\n \"$lte\": \"YYYY-MM-DD HH:mm format, collection creation time less than this time, can be used with $gte\"\n }\n}",
|
||||
"find_tip": "Find node ctrl f",
|
||||
"find_tip_mac": "Find node ⌘ f",
|
||||
"foldAll": "Collapse all",
|
||||
"form_input_result": "User complete input result",
|
||||
"form_input_result_tip": "an object containing the complete result",
|
||||
@@ -123,18 +125,23 @@
|
||||
"max_tokens": "Maximum Tokens",
|
||||
"mouse_priority": "Mouse first\n- Press the left button to drag the canvas\n- Hold down shift and left click to select batches",
|
||||
"new_context": "New Context",
|
||||
"next": "Next",
|
||||
"no_match_node": "No results",
|
||||
"no_node_found": "No node was not found",
|
||||
"not_contains": "Does Not Contain",
|
||||
"only_the_reference_type_is_supported": "Only reference type is supported",
|
||||
"optional_value_type": "Optional Value Type",
|
||||
"optional_value_type_tip": "You can specify one or more data types. When dynamically adding fields, users can only select the configured types.",
|
||||
"pan_priority": "Touchpad first\n- Click to batch select\n- Move the canvas with two fingers",
|
||||
"pass_returned_object_as_output_to_next_nodes": "Pass the object returned in the code as output to the next nodes. The variable name needs to correspond to the return key.",
|
||||
"please_enter_node_name": "Enter the node name",
|
||||
"plugin.Instruction_Tip": "You can configure an instruction to explain the purpose of the plugin. This instruction will be displayed each time the plugin is used. Supports standard Markdown syntax.",
|
||||
"plugin.Instructions": "Instructions",
|
||||
"plugin.global_file_input": "File links (deprecated)",
|
||||
"plugin_file_abandon_tip": "Plugin global file upload has been deprecated, please adjust it as soon as possible. \nRelated functions can be achieved through plug-in input and adding image type input.",
|
||||
"plugin_input": "Plugin Input",
|
||||
"plugin_output_tool": "When the plug-in is executed as a tool, whether this field responds as a result of the tool",
|
||||
"previous": "Previous",
|
||||
"question_classification": "Classify",
|
||||
"question_optimization": "Query extension",
|
||||
"quote_content_placeholder": "The structure of the reference content can be customized to better suit different scenarios. \nSome variables can be used for template configuration\n\n{{q}} - main content\n\n{{a}} - auxiliary data\n\n{{source}} - source name\n\n{{sourceId}} - source ID\n\n{{index}} - nth reference",
|
||||
@@ -177,9 +184,9 @@
|
||||
"text_content_extraction": "Text Extract",
|
||||
"text_to_extract": "Text to Extract",
|
||||
"these_variables_will_be_input_parameters_for_code_execution": "These variables will be input parameters for code execution",
|
||||
"tool.tool_result": "Tool operation results",
|
||||
"to_add_node": "to add",
|
||||
"to_connect_node": "to connect",
|
||||
"tool.tool_result": "Tool operation results",
|
||||
"tool_call_termination": "Stop ToolCall",
|
||||
"tool_custom_field": "Custom Tool",
|
||||
"tool_field": " Tool Field Parameter Configuration",
|
||||
|
||||
@@ -63,6 +63,8 @@
|
||||
"field_required": "必填",
|
||||
"field_used_as_tool_input": "作为工具调用参数",
|
||||
"filter_description": "目前支持标签和创建时间过滤,需按照以下格式填写:\n{\n \"tags\": {\n \"$and\": [\"标签 1\",\"标签 2\"],\n \"$or\": [\"有 $and 标签时,and 生效,or 不生效\"]\n },\n \"createTime\": {\n \"$gte\": \"YYYY-MM-DD HH:mm 格式即可,集合的创建时间大于该时间\",\n \"$lte\": \"YYYY-MM-DD HH:mm 格式即可,集合的创建时间小于该时间,可和 $gte 共同使用\"\n }\n}",
|
||||
"find_tip": "查找节点 ctrl f",
|
||||
"find_tip_mac": "查找节点 ⌘ f",
|
||||
"foldAll": "全部折叠",
|
||||
"form_input_result": "用户完整输入结果",
|
||||
"form_input_result_tip": "一个包含完整结果的对象",
|
||||
@@ -123,18 +125,23 @@
|
||||
"max_tokens": "最大 Tokens",
|
||||
"mouse_priority": "鼠标优先\n- 左键按下后可拖动画布\n- 按住 shift 后左键可批量选择",
|
||||
"new_context": "新的上下文",
|
||||
"next": "下一个",
|
||||
"no_match_node": "无结果",
|
||||
"no_node_found": "未搜索到节点",
|
||||
"not_contains": "不包含",
|
||||
"only_the_reference_type_is_supported": "仅支持引用类型",
|
||||
"optional_value_type": "可选的数据类型",
|
||||
"optional_value_type_tip": "可以指定 1 个或多个数据类型,用户在动态添加字段时,仅可选择配置的类型",
|
||||
"pan_priority": "触摸板优先\n- 单击批量选择\n- 双指移动画布",
|
||||
"pass_returned_object_as_output_to_next_nodes": "将代码中 return 的对象作为输出,传递给后续的节点。变量名需要对应 return 的 key",
|
||||
"please_enter_node_name": "请输入节点名称",
|
||||
"plugin.Instruction_Tip": "可以配置一段说明,以解释该插件的用途。每次使用插件前,会显示该段说明。支持标准 Markdown 语法。",
|
||||
"plugin.Instructions": "使用说明",
|
||||
"plugin.global_file_input": "文件链接(弃用)",
|
||||
"plugin_file_abandon_tip": "插件全局文件上传已弃用,请尽快调整。可以通过插件输入,添加图片类型输入来实现相关功能。",
|
||||
"plugin_input": "插件输入",
|
||||
"plugin_output_tool": "插件作为工具执行时,该字段是否作为工具响应结果",
|
||||
"previous": "上一个",
|
||||
"question_classification": "问题分类",
|
||||
"question_optimization": "问题优化",
|
||||
"quote_content_placeholder": "可以自定义引用内容的结构,以更好的适配不同场景。可以使用一些变量来进行模板配置\n{{q}} - 主要内容\n{{a}} - 辅助数据\n{{source}} - 来源名\n{{sourceId}} - 来源ID\n{{index}} - 第 n 个引用",
|
||||
|
||||
@@ -63,6 +63,8 @@
|
||||
"field_required": "必填",
|
||||
"field_used_as_tool_input": "作為工具呼叫參數",
|
||||
"filter_description": "目前支援標籤和建立時間篩選,需按照以下格式填寫:\n{\n \"tags\": {\n \"$and\": [\"標籤 1\",\"標籤 2\"],\n \"$or\": [\"當有 $and 標籤時,$and 才會生效,$or 不會生效\"]\n },\n \"createTime\": {\n \"$gte\": \"YYYY-MM-DD HH:mm 格式,資料集的建立時間大於這個時間\",\n \"$lte\": \"YYYY-MM-DD HH:mm 格式,資料集的建立時間小於這個時間,可以和 $gte 一起使用\"\n }\n}",
|
||||
"find_tip": "查找節點 ctrl f",
|
||||
"find_tip_mac": "查找節點 ⌘ f",
|
||||
"foldAll": "全部折疊",
|
||||
"form_input_result": "使用者完整輸入結果",
|
||||
"form_input_result_tip": "一個包含完整結果的物件",
|
||||
@@ -123,18 +125,23 @@
|
||||
"max_tokens": "最大 Token 數",
|
||||
"mouse_priority": "滑鼠優先\n- 按下左鍵拖曳畫布\n- 按住 Shift 鍵並點選左鍵可批次選取",
|
||||
"new_context": "新的脈絡",
|
||||
"next": "下一個",
|
||||
"no_match_node": "無結果",
|
||||
"no_node_found": "未搜索到節點",
|
||||
"not_contains": "不包含",
|
||||
"only_the_reference_type_is_supported": "僅支援引用類型",
|
||||
"optional_value_type": "可選的資料類型",
|
||||
"optional_value_type_tip": "可以指定一或多個資料類型,使用者在動態新增欄位時,只能選擇已設定的類型",
|
||||
"pan_priority": "觸控板優先\n- 點選可批次選取\n- 使用兩指移動畫布",
|
||||
"pass_returned_object_as_output_to_next_nodes": "將程式碼中 return 的物件作為輸出,傳遞給後續的節點。變數名稱需要對應 return 的鍵值",
|
||||
"please_enter_node_name": "請輸入節點名稱",
|
||||
"plugin.Instruction_Tip": "您可以設定一段說明來解釋這個外掛程式的用途。每次使用外掛程式前,都會顯示這段說明。支援標準 Markdown 語法。",
|
||||
"plugin.Instructions": "使用說明",
|
||||
"plugin.global_file_input": "檔案連結(已淘汰)",
|
||||
"plugin_file_abandon_tip": "外掛程式全域檔案上傳功能已淘汰,請儘速調整。您可以透過外掛程式輸入,新增圖片類型輸入來達成相關功能。",
|
||||
"plugin_input": "外掛程式輸入",
|
||||
"plugin_output_tool": "外掛程式作為工具執行時,這個欄位是否作為工具的回應結果",
|
||||
"previous": "上一個",
|
||||
"question_classification": "問題分類",
|
||||
"question_optimization": "問題最佳化",
|
||||
"quote_content_placeholder": "可以自訂引用內容的結構,以便更好地適應不同場景。可以使用一些變數來設定範本\n{{q}} - 主要內容\n{{a}} - 輔助資料\n{{source}} - 來源名稱\n{{sourceId}} - 來源 ID\n{{index}} - 第 n 個引用",
|
||||
@@ -177,9 +184,9 @@
|
||||
"text_content_extraction": "文字內容擷取",
|
||||
"text_to_extract": "要擷取的文字",
|
||||
"these_variables_will_be_input_parameters_for_code_execution": "這些變數會作為程式碼執行的輸入參數",
|
||||
"tool.tool_result": "工具運行結果",
|
||||
"to_add_node": "添加節點",
|
||||
"to_connect_node": "連接節點",
|
||||
"tool.tool_result": "工具運行結果",
|
||||
"tool_call_termination": "工具呼叫終止",
|
||||
"tool_custom_field": "自訂工具變數",
|
||||
"tool_field": "工具參數設定",
|
||||
|
||||
@@ -39,6 +39,12 @@ export async function register() {
|
||||
systemStartCb();
|
||||
initGlobalVariables();
|
||||
|
||||
try {
|
||||
await preLoadWorker();
|
||||
} catch (error) {
|
||||
console.error('Preload worker error', error);
|
||||
}
|
||||
|
||||
// Connect to MongoDB
|
||||
await connectMongo(connectionMongo, MONGO_URL);
|
||||
connectMongo(connectionLogMongo, MONGO_LOG_URL);
|
||||
@@ -54,12 +60,6 @@ export async function register() {
|
||||
startCron();
|
||||
startTrainingQueue(true);
|
||||
|
||||
try {
|
||||
await preLoadWorker();
|
||||
} catch (error) {
|
||||
console.error('Preload worker error', error);
|
||||
}
|
||||
|
||||
console.log('Init system success');
|
||||
}
|
||||
} catch (error) {
|
||||
|
||||
@@ -25,16 +25,20 @@ import MyModal from '@fastgpt/web/components/common/MyModal';
|
||||
import { formatTime2YMDHMS } from '@fastgpt/global/common/string/time';
|
||||
import { useToast } from '@fastgpt/web/hooks/useToast';
|
||||
import { useSystemStore } from '@/web/common/system/useSystemStore';
|
||||
import SaveButton from '../Workflow/components/SaveButton';
|
||||
import PublishHistories from '../PublishHistoriesSlider';
|
||||
import { WorkflowEventContext } from '../WorkflowComponents/context/workflowEventContext';
|
||||
import { WorkflowStatusContext } from '../WorkflowComponents/context/workflowStatusContext';
|
||||
import SaveButton from '../Workflow/components/SaveButton';
|
||||
|
||||
const Header = () => {
|
||||
const { t } = useTranslation();
|
||||
const { isPc } = useSystem();
|
||||
const router = useRouter();
|
||||
const { toast } = useToast();
|
||||
const { toast: backSaveToast } = useToast({
|
||||
containerStyle: {
|
||||
mt: '60px'
|
||||
}
|
||||
});
|
||||
|
||||
const { appDetail, onSaveApp, currentTab } = useContextSelector(AppContext, (v) => v);
|
||||
const isV2Workflow = appDetail?.version === 'v2';
|
||||
@@ -183,6 +187,7 @@ const Header = () => {
|
||||
size={'sm'}
|
||||
leftIcon={<MyIcon name={'core/workflow/debug'} w={['14px', '16px']} />}
|
||||
variant={'whitePrimary'}
|
||||
flexShrink={0}
|
||||
onClick={() => {
|
||||
const data = flowData2StoreDataAndCheck();
|
||||
if (data) {
|
||||
@@ -211,12 +216,12 @@ const Header = () => {
|
||||
onBack,
|
||||
onOpenBackConfirm,
|
||||
isV2Workflow,
|
||||
showHistoryModal,
|
||||
t,
|
||||
showHistoryModal,
|
||||
loading,
|
||||
onClickSave,
|
||||
flowData2StoreDataAndCheck,
|
||||
setShowHistoryModal,
|
||||
flowData2StoreDataAndCheck,
|
||||
setWorkflowTestData
|
||||
]);
|
||||
|
||||
@@ -229,10 +234,11 @@ const Header = () => {
|
||||
setShowHistoryModal(false);
|
||||
}}
|
||||
past={past}
|
||||
onSwitchTmpVersion={onSwitchTmpVersion}
|
||||
onSwitchCloudVersion={onSwitchCloudVersion}
|
||||
onSwitchTmpVersion={onSwitchTmpVersion}
|
||||
/>
|
||||
)}
|
||||
|
||||
<MyModal
|
||||
isOpen={isOpenBackConfirm}
|
||||
onClose={onCloseBackConfirm}
|
||||
@@ -254,7 +260,7 @@ const Header = () => {
|
||||
await onClickSave({});
|
||||
onCloseBackConfirm();
|
||||
onBack();
|
||||
toast({
|
||||
backSaveToast({
|
||||
status: 'success',
|
||||
title: t('app:saved_success'),
|
||||
position: 'top-right'
|
||||
|
||||
@@ -13,7 +13,7 @@ import { useTranslation } from 'next-i18next';
|
||||
|
||||
import MyIcon from '@fastgpt/web/components/common/Icon';
|
||||
import { useContextSelector } from 'use-context-selector';
|
||||
import { WorkflowContext } from '../WorkflowComponents/context';
|
||||
import { WorkflowContext, type WorkflowSnapshotsType } from '../WorkflowComponents/context';
|
||||
import { AppContext, TabEnum } from '../context';
|
||||
import RouteTab from '../RouteTab';
|
||||
import { useRouter } from 'next/router';
|
||||
@@ -25,10 +25,10 @@ import MyModal from '@fastgpt/web/components/common/MyModal';
|
||||
import { formatTime2YMDHMS } from '@fastgpt/global/common/string/time';
|
||||
import { useToast } from '@fastgpt/web/hooks/useToast';
|
||||
import { useSystemStore } from '@/web/common/system/useSystemStore';
|
||||
import SaveButton from './components/SaveButton';
|
||||
import PublishHistories from '../PublishHistoriesSlider';
|
||||
import { WorkflowEventContext } from '../WorkflowComponents/context/workflowEventContext';
|
||||
import { WorkflowStatusContext } from '../WorkflowComponents/context/workflowStatusContext';
|
||||
import SaveButton from '../Workflow/components/SaveButton';
|
||||
|
||||
const Header = () => {
|
||||
const { t } = useTranslation();
|
||||
@@ -187,6 +187,7 @@ const Header = () => {
|
||||
size={'sm'}
|
||||
leftIcon={<MyIcon name={'core/workflow/debug'} w={['14px', '16px']} />}
|
||||
variant={'whitePrimary'}
|
||||
flexShrink={0}
|
||||
onClick={() => {
|
||||
const data = flowData2StoreDataAndCheck();
|
||||
if (data) {
|
||||
@@ -215,12 +216,12 @@ const Header = () => {
|
||||
onBack,
|
||||
onOpenBackConfirm,
|
||||
isV2Workflow,
|
||||
showHistoryModal,
|
||||
t,
|
||||
showHistoryModal,
|
||||
loading,
|
||||
onClickSave,
|
||||
flowData2StoreDataAndCheck,
|
||||
setShowHistoryModal,
|
||||
flowData2StoreDataAndCheck,
|
||||
setWorkflowTestData
|
||||
]);
|
||||
|
||||
@@ -228,7 +229,7 @@ const Header = () => {
|
||||
<>
|
||||
{Render}
|
||||
{showHistoryModal && isV2Workflow && currentTab === TabEnum.appEdit && (
|
||||
<PublishHistories
|
||||
<PublishHistories<WorkflowSnapshotsType>
|
||||
onClose={() => {
|
||||
setShowHistoryModal(false);
|
||||
}}
|
||||
|
||||
@@ -43,6 +43,7 @@ const SaveButton = ({
|
||||
Trigger={
|
||||
<Button
|
||||
size={'sm'}
|
||||
flexShrink={0}
|
||||
rightIcon={
|
||||
<MyIcon
|
||||
name={isSave ? 'core/chat/chevronUp' : 'core/chat/chevronDown'}
|
||||
|
||||
@@ -0,0 +1,220 @@
|
||||
import React, { useState, useCallback } from 'react';
|
||||
import { Box, Flex, Button, IconButton, type ButtonProps, Input } from '@chakra-ui/react';
|
||||
import { useTranslation } from 'next-i18next';
|
||||
import { useContextSelector } from 'use-context-selector';
|
||||
import { WorkflowNodeEdgeContext } from '../../WorkflowComponents/context/workflowInitContext';
|
||||
import { useReactFlow } from 'reactflow';
|
||||
import { useKeyPress, useThrottleEffect } from 'ahooks';
|
||||
import MyIcon from '@fastgpt/web/components/common/Icon';
|
||||
import MyTooltip from '@fastgpt/web/components/common/MyTooltip';
|
||||
import { useSystem } from '@fastgpt/web/hooks/useSystem';
|
||||
|
||||
const SearchButton = (props: ButtonProps) => {
|
||||
const { t } = useTranslation();
|
||||
const setNodes = useContextSelector(WorkflowNodeEdgeContext, (state) => state.setNodes);
|
||||
const { fitView } = useReactFlow();
|
||||
const { isMac } = useSystem();
|
||||
|
||||
const [keyword, setKeyword] = useState<string>();
|
||||
const [searchIndex, setSearchIndex] = useState<number>(0);
|
||||
const [searchedNodeCount, setSearchedNodeCount] = useState(0);
|
||||
|
||||
useKeyPress(['ctrl.f', 'meta.f'], (e) => {
|
||||
e.preventDefault();
|
||||
e.stopPropagation();
|
||||
setKeyword('');
|
||||
});
|
||||
useKeyPress(['esc'], (e) => {
|
||||
e.preventDefault();
|
||||
e.stopPropagation();
|
||||
setKeyword(undefined);
|
||||
});
|
||||
|
||||
const onSearch = useCallback(() => {
|
||||
setNodes((nodes) => {
|
||||
if (!keyword) {
|
||||
setSearchIndex(0);
|
||||
setSearchedNodeCount(0);
|
||||
return nodes.map((node) => ({
|
||||
...node,
|
||||
data: {
|
||||
...node.data,
|
||||
searchedText: undefined
|
||||
}
|
||||
}));
|
||||
}
|
||||
|
||||
const searchResult = nodes.filter((node) => {
|
||||
const nodeName = t(node.data.name as any);
|
||||
return nodeName.toLowerCase().includes(keyword.toLowerCase());
|
||||
});
|
||||
|
||||
if (searchResult.length === 0) {
|
||||
return nodes.map((node) => ({
|
||||
...node,
|
||||
data: {
|
||||
...node.data,
|
||||
searchedText: undefined
|
||||
}
|
||||
}));
|
||||
}
|
||||
|
||||
setSearchedNodeCount(searchResult.length);
|
||||
|
||||
const searchedNode = searchResult[searchIndex] ?? searchResult[0];
|
||||
|
||||
if (searchedNode) {
|
||||
fitView({ nodes: [searchedNode], padding: 0.4 });
|
||||
}
|
||||
|
||||
return nodes.map((node) => ({
|
||||
...node,
|
||||
selected: node.id === searchedNode.id,
|
||||
data: {
|
||||
...node.data,
|
||||
searchedText: searchResult.find((item) => item.id === node.id) ? keyword : undefined
|
||||
}
|
||||
}));
|
||||
});
|
||||
}, [keyword, searchIndex]);
|
||||
|
||||
useThrottleEffect(
|
||||
() => {
|
||||
onSearch();
|
||||
},
|
||||
[onSearch],
|
||||
{
|
||||
wait: 500
|
||||
}
|
||||
);
|
||||
|
||||
const goToNextMatch = useCallback(() => {
|
||||
if (searchIndex === searchedNodeCount - 1) {
|
||||
setSearchIndex(0);
|
||||
} else {
|
||||
setSearchIndex(searchIndex + 1);
|
||||
}
|
||||
}, [searchIndex, searchedNodeCount]);
|
||||
|
||||
const goToPreviousMatch = useCallback(() => {
|
||||
if (searchIndex === 0) {
|
||||
setSearchIndex(searchedNodeCount - 1);
|
||||
} else {
|
||||
setSearchIndex(searchIndex - 1);
|
||||
}
|
||||
}, [searchIndex, searchedNodeCount]);
|
||||
|
||||
const clearSearch = useCallback(() => {
|
||||
setKeyword(undefined);
|
||||
setSearchIndex(0);
|
||||
setSearchedNodeCount(0);
|
||||
}, []);
|
||||
|
||||
if (keyword === undefined) {
|
||||
return (
|
||||
<Box position={'absolute'} top={'72px'} left={6} zIndex={1}>
|
||||
<MyTooltip label={isMac ? t('workflow:find_tip_mac') : t('workflow:find_tip')}>
|
||||
<IconButton
|
||||
icon={<MyIcon name="common/searchLight" w="20px" color={'#8A95A7'} />}
|
||||
aria-label=""
|
||||
variant="whitePrimary"
|
||||
size={'mdSquare'}
|
||||
borderRadius={'50%'}
|
||||
bg={'white'}
|
||||
_hover={{ bg: 'white', borderColor: 'primary.300' }}
|
||||
boxShadow={'0px 4px 10px 0px rgba(19, 51, 107, 0.20)'}
|
||||
{...props}
|
||||
onClick={() => setKeyword('')}
|
||||
/>
|
||||
</MyTooltip>
|
||||
</Box>
|
||||
);
|
||||
}
|
||||
|
||||
return (
|
||||
<Flex
|
||||
position="absolute"
|
||||
top={3}
|
||||
left="50%"
|
||||
transform="translateX(-50%)"
|
||||
pl={5}
|
||||
pr={4}
|
||||
py={4}
|
||||
zIndex={1}
|
||||
borderRadius={'lg'}
|
||||
bg={'white'}
|
||||
alignItems={'center'}
|
||||
boxShadow={
|
||||
'0px 20px 24px -8px rgba(19, 51, 107, 0.15), 0px 0px 1px 0px rgba(19, 51, 107, 0.15)'
|
||||
}
|
||||
border={'0.5px solid rgba(0, 0, 0, 0.13)'}
|
||||
maxW={['90vw', '550px']}
|
||||
w={'100%'}
|
||||
>
|
||||
<Input
|
||||
flex="1 0 0"
|
||||
h={8}
|
||||
border={'none'}
|
||||
px={0}
|
||||
_focus={{
|
||||
border: 'none',
|
||||
boxShadow: 'none'
|
||||
}}
|
||||
fontSize={'16px'}
|
||||
value={keyword}
|
||||
placeholder={t('workflow:please_enter_node_name')}
|
||||
autoFocus
|
||||
onFocus={onSearch}
|
||||
onChange={(e) => setKeyword(e.target.value)}
|
||||
onKeyDown={(e) => {
|
||||
if (e.key === 'Enter') {
|
||||
e.preventDefault();
|
||||
e.stopPropagation();
|
||||
goToNextMatch();
|
||||
}
|
||||
}}
|
||||
/>
|
||||
|
||||
<Box fontSize="sm" color="myGray.600" whiteSpace={'nowrap'} userSelect={'none'}>
|
||||
{searchedNodeCount > 0
|
||||
? `${searchIndex + 1} / ${searchedNodeCount}`
|
||||
: t('workflow:no_match_node')}
|
||||
</Box>
|
||||
|
||||
{/* Border */}
|
||||
<Box h={5} w={'1px'} bg={'myGray.250'} ml={3} mr={2} />
|
||||
|
||||
<Button
|
||||
size="xs"
|
||||
variant="grayGhost"
|
||||
px={2}
|
||||
isDisabled={searchedNodeCount <= 1}
|
||||
onClick={goToPreviousMatch}
|
||||
>
|
||||
{t('workflow:previous')}
|
||||
</Button>
|
||||
<Button
|
||||
size="xs"
|
||||
variant="grayGhost"
|
||||
px={2}
|
||||
isDisabled={searchedNodeCount <= 1}
|
||||
onClick={goToNextMatch}
|
||||
>
|
||||
{t('workflow:next')}
|
||||
</Button>
|
||||
|
||||
<Flex
|
||||
ml={2}
|
||||
borderRadius="sm"
|
||||
_hover={{ bg: 'myGray.100' }}
|
||||
p={'1'}
|
||||
cursor="pointer"
|
||||
onClick={clearSearch}
|
||||
>
|
||||
<MyIcon name="common/closeLight" w="1.2rem" />
|
||||
</Flex>
|
||||
</Flex>
|
||||
);
|
||||
};
|
||||
|
||||
export default React.memo(SearchButton);
|
||||
@@ -1,7 +1,6 @@
|
||||
import React from 'react';
|
||||
import ReactFlow, { type NodeProps, SelectionMode } from 'reactflow';
|
||||
import { Box, IconButton, useDisclosure } from '@chakra-ui/react';
|
||||
import { SmallCloseIcon } from '@chakra-ui/icons';
|
||||
import { EDGE_TYPE, FlowNodeTypeEnum } from '@fastgpt/global/core/workflow/node/constant';
|
||||
|
||||
import dynamic from 'next/dynamic';
|
||||
@@ -20,6 +19,8 @@ import ContextMenu from './components/ContextMenu';
|
||||
import { WorkflowNodeEdgeContext, WorkflowInitContext } from '../context/workflowInitContext';
|
||||
import { WorkflowEventContext } from '../context/workflowEventContext';
|
||||
import NodeTemplatesPopover from './NodeTemplatesPopover';
|
||||
import SearchButton from '../../Workflow/components/SearchButton';
|
||||
import MyIcon from '@fastgpt/web/components/common/Icon';
|
||||
|
||||
const NodeSimple = dynamic(() => import('./nodes/NodeSimple'));
|
||||
const nodeTypes: Record<FlowNodeTypeEnum, any> = {
|
||||
@@ -113,20 +114,22 @@ const Workflow = () => {
|
||||
<>
|
||||
<IconButton
|
||||
position={'absolute'}
|
||||
top={5}
|
||||
left={5}
|
||||
top={6}
|
||||
left={6}
|
||||
size={'mdSquare'}
|
||||
borderRadius={'50%'}
|
||||
icon={<SmallCloseIcon fontSize={'26px'} />}
|
||||
transform={isOpenTemplate ? '' : 'rotate(135deg)'}
|
||||
icon={<MyIcon name="common/addLight" w={'26px'} />}
|
||||
transition={'0.2s ease'}
|
||||
aria-label={''}
|
||||
zIndex={1}
|
||||
boxShadow={'2px 2px 6px #85b1ff'}
|
||||
boxShadow={
|
||||
'0px 4px 10px 0px rgba(19, 51, 107, 0.20), 0px 0px 1px 0px rgba(19, 51, 107, 0.50)'
|
||||
}
|
||||
onClick={() => {
|
||||
isOpenTemplate ? onCloseTemplate() : onOpenTemplate();
|
||||
}}
|
||||
/>
|
||||
<SearchButton />
|
||||
<NodeTemplatesModal isOpen={isOpenTemplate} onClose={onCloseTemplate} />
|
||||
<NodeTemplatesPopover />
|
||||
</>
|
||||
|
||||
@@ -36,6 +36,7 @@ import MyTag from '@fastgpt/web/components/common/Tag/index';
|
||||
import MySelect from '@fastgpt/web/components/common/MySelect';
|
||||
import { useCreation } from 'ahooks';
|
||||
import { formatToolError } from '@fastgpt/global/core/app/utils';
|
||||
import HighlightText from '@fastgpt/web/components/common/String/HighlightText';
|
||||
|
||||
type Props = FlowNodeItemType & {
|
||||
children?: React.ReactNode | React.ReactNode[] | string;
|
||||
@@ -45,6 +46,7 @@ type Props = FlowNodeItemType & {
|
||||
w?: string | number;
|
||||
h?: string | number;
|
||||
selected?: boolean;
|
||||
searchedText?: string;
|
||||
menuForbid?: {
|
||||
debug?: boolean;
|
||||
copy?: boolean;
|
||||
@@ -70,6 +72,7 @@ const NodeCard = (props: Props) => {
|
||||
h = 'full',
|
||||
nodeId,
|
||||
selected,
|
||||
searchedText,
|
||||
menuForbid,
|
||||
isTool = false,
|
||||
isError = false,
|
||||
@@ -187,7 +190,12 @@ const NodeCard = (props: Props) => {
|
||||
h={'24px'}
|
||||
/>
|
||||
<Box ml={2} fontSize={'18px'} fontWeight={'medium'} color={'myGray.900'}>
|
||||
{t(name as any)}
|
||||
<HighlightText
|
||||
rawText={t(name as any)}
|
||||
matchText={searchedText ?? ''}
|
||||
mode={'bg'}
|
||||
color={'#ffe82d'}
|
||||
/>
|
||||
</Box>
|
||||
<Button
|
||||
display={'none'}
|
||||
@@ -280,6 +288,7 @@ const NodeCard = (props: Props) => {
|
||||
nodeId,
|
||||
isFolded,
|
||||
avatar,
|
||||
searchedText,
|
||||
t,
|
||||
name,
|
||||
showVersion,
|
||||
|
||||
@@ -138,18 +138,20 @@ async function handler(req: ApiRequestProps<ListAppBody>): Promise<AppListItemTy
|
||||
})();
|
||||
const limit = (() => {
|
||||
if (getRecentlyChat) return 15;
|
||||
if (searchKey) return 20;
|
||||
return 1000;
|
||||
if (searchKey) return 50;
|
||||
return;
|
||||
})();
|
||||
|
||||
const myApps = await MongoApp.find(
|
||||
findAppsQuery,
|
||||
'_id parentId avatar type name intro tmbId updateTime pluginData inheritPermission'
|
||||
'_id parentId avatar type name intro tmbId updateTime pluginData inheritPermission',
|
||||
{
|
||||
limit: limit
|
||||
}
|
||||
)
|
||||
.sort({
|
||||
updateTime: -1
|
||||
})
|
||||
.limit(limit)
|
||||
.lean();
|
||||
|
||||
// Add app permission and filter apps by read permission
|
||||
|
||||
@@ -4,11 +4,11 @@ import { type FileIdCreateDatasetCollectionParams } from '@fastgpt/global/core/d
|
||||
import { createCollectionAndInsertData } from '@fastgpt/service/core/dataset/collection/controller';
|
||||
import { DatasetCollectionTypeEnum } from '@fastgpt/global/core/dataset/constants';
|
||||
import { BucketNameEnum } from '@fastgpt/global/common/file/constants';
|
||||
import { MongoRawTextBuffer } from '@fastgpt/service/common/buffer/rawText/schema';
|
||||
import { NextAPI } from '@/service/middleware/entry';
|
||||
import { type ApiRequestProps } from '@fastgpt/service/type/next';
|
||||
import { WritePermissionVal } from '@fastgpt/global/support/permission/constant';
|
||||
import { type CreateCollectionResponse } from '@/global/core/dataset/api';
|
||||
import { deleteRawTextBuffer } from '@fastgpt/service/common/buffer/rawText/controller';
|
||||
|
||||
async function handler(
|
||||
req: ApiRequestProps<FileIdCreateDatasetCollectionParams>
|
||||
@@ -52,7 +52,7 @@ async function handler(
|
||||
});
|
||||
|
||||
// remove buffer
|
||||
await MongoRawTextBuffer.deleteOne({ sourceId: fileId });
|
||||
await deleteRawTextBuffer(fileId);
|
||||
|
||||
return {
|
||||
collectionId,
|
||||
|
||||
@@ -11,6 +11,7 @@ import { checkTimerLock } from '@fastgpt/service/common/system/timerLock/utils';
|
||||
import { TimerIdEnum } from '@fastgpt/service/common/system/timerLock/constants';
|
||||
import { addHours } from 'date-fns';
|
||||
import { getScheduleTriggerApp } from '@/service/core/app/utils';
|
||||
import { clearExpiredRawTextBufferCron } from '@fastgpt/service/common/buffer/rawText/controller';
|
||||
|
||||
// Try to run train every minute
|
||||
const setTrainingQueueCron = () => {
|
||||
@@ -83,4 +84,5 @@ export const startCron = () => {
|
||||
setClearTmpUploadFilesCron();
|
||||
clearInvalidDataCron();
|
||||
scheduleTriggerAppCron();
|
||||
clearExpiredRawTextBufferCron();
|
||||
};
|
||||
|
||||
128
test/cases/service/core/dataset/training/utils.test.ts
Normal file
128
test/cases/service/core/dataset/training/utils.test.ts
Normal file
@@ -0,0 +1,128 @@
|
||||
import { describe, it, expect, vi, beforeEach } from 'vitest';
|
||||
import {
|
||||
createDatasetTrainingMongoWatch,
|
||||
startTrainingQueue
|
||||
} from '@/service/core/dataset/training/utils';
|
||||
import { MongoDatasetTraining } from '@fastgpt/service/core/dataset/training/schema';
|
||||
import { generateQA } from '@/service/core/dataset/queues/generateQA';
|
||||
import { generateVector } from '@/service/core/dataset/queues/generateVector';
|
||||
import { TrainingModeEnum } from '@fastgpt/global/core/dataset/constants';
|
||||
|
||||
vi.mock('@/service/core/dataset/queues/generateQA', () => ({
|
||||
generateQA: vi.fn()
|
||||
}));
|
||||
|
||||
vi.mock('@/service/core/dataset/queues/generateVector', () => ({
|
||||
generateVector: vi.fn()
|
||||
}));
|
||||
|
||||
vi.mock('@fastgpt/service/core/dataset/training/schema', () => ({
|
||||
MongoDatasetTraining: {
|
||||
watch: vi.fn().mockReturnValue({
|
||||
on: vi.fn()
|
||||
})
|
||||
}
|
||||
}));
|
||||
|
||||
describe('dataset training utils', () => {
|
||||
beforeEach(() => {
|
||||
vi.clearAllMocks();
|
||||
});
|
||||
|
||||
describe('createDatasetTrainingMongoWatch', () => {
|
||||
it('should setup mongo watch and handle qa mode', () => {
|
||||
const mockOn = vi.fn();
|
||||
vi.mocked(MongoDatasetTraining.watch).mockReturnValue({
|
||||
on: mockOn
|
||||
});
|
||||
|
||||
createDatasetTrainingMongoWatch();
|
||||
|
||||
expect(MongoDatasetTraining.watch).toHaveBeenCalled();
|
||||
expect(mockOn).toHaveBeenCalledWith('change', expect.any(Function));
|
||||
|
||||
// Simulate change event for QA mode
|
||||
const changeHandler = mockOn.mock.calls[0][1];
|
||||
changeHandler({
|
||||
operationType: 'insert',
|
||||
fullDocument: {
|
||||
mode: TrainingModeEnum.qa
|
||||
}
|
||||
});
|
||||
|
||||
expect(generateQA).toHaveBeenCalled();
|
||||
expect(generateVector).not.toHaveBeenCalled();
|
||||
});
|
||||
|
||||
it('should handle chunk mode', () => {
|
||||
const mockOn = vi.fn();
|
||||
vi.mocked(MongoDatasetTraining.watch).mockReturnValue({
|
||||
on: mockOn
|
||||
});
|
||||
|
||||
createDatasetTrainingMongoWatch();
|
||||
|
||||
const changeHandler = mockOn.mock.calls[0][1];
|
||||
changeHandler({
|
||||
operationType: 'insert',
|
||||
fullDocument: {
|
||||
mode: TrainingModeEnum.chunk
|
||||
}
|
||||
});
|
||||
|
||||
expect(generateVector).toHaveBeenCalled();
|
||||
expect(generateQA).not.toHaveBeenCalled();
|
||||
});
|
||||
|
||||
it('should ignore non-insert operations', () => {
|
||||
const mockOn = vi.fn();
|
||||
vi.mocked(MongoDatasetTraining.watch).mockReturnValue({
|
||||
on: mockOn
|
||||
});
|
||||
|
||||
createDatasetTrainingMongoWatch();
|
||||
|
||||
const changeHandler = mockOn.mock.calls[0][1];
|
||||
changeHandler({
|
||||
operationType: 'update',
|
||||
fullDocument: {
|
||||
mode: TrainingModeEnum.qa
|
||||
}
|
||||
});
|
||||
|
||||
expect(generateQA).not.toHaveBeenCalled();
|
||||
expect(generateVector).not.toHaveBeenCalled();
|
||||
});
|
||||
});
|
||||
|
||||
describe('startTrainingQueue', () => {
|
||||
beforeEach(() => {
|
||||
global.systemEnv = {
|
||||
qaMaxProcess: 3
|
||||
};
|
||||
});
|
||||
|
||||
it('should start single process by default', () => {
|
||||
startTrainingQueue();
|
||||
|
||||
expect(generateQA).toHaveBeenCalledTimes(1);
|
||||
expect(generateVector).toHaveBeenCalledTimes(1);
|
||||
});
|
||||
|
||||
it('should start max processes when fast mode enabled', () => {
|
||||
startTrainingQueue(true);
|
||||
|
||||
expect(generateQA).toHaveBeenCalledTimes(3);
|
||||
expect(generateVector).toHaveBeenCalledTimes(3);
|
||||
});
|
||||
|
||||
it('should use default max process when not configured', () => {
|
||||
global.systemEnv = undefined;
|
||||
|
||||
startTrainingQueue(true);
|
||||
|
||||
expect(generateQA).toHaveBeenCalledTimes(10);
|
||||
expect(generateVector).toHaveBeenCalledTimes(10);
|
||||
});
|
||||
});
|
||||
});
|
||||
Reference in New Issue
Block a user