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test-openG
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gru/projec
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802de11363 |
@@ -132,15 +132,15 @@ services:
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# fastgpt
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sandbox:
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container_name: sandbox
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image: ghcr.io/labring/fastgpt-sandbox:v4.9.10 # git
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# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.9.10 # 阿里云
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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
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networks:
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- fastgpt
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restart: always
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fastgpt-mcp-server:
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container_name: fastgpt-mcp-server
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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
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image: ghcr.io/labring/fastgpt-mcp_server:v4.9.10-fix2 # git
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# 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:
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@@ -150,8 +150,8 @@ services:
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- FASTGPT_ENDPOINT=http://fastgpt:3000
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fastgpt:
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container_name: fastgpt
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image: ghcr.io/labring/fastgpt:v4.9.10 # git
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# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.9.10 # 阿里云
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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
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ports:
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- 3000:3000
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networks:
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@@ -109,15 +109,15 @@ services:
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# fastgpt
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sandbox:
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container_name: sandbox
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image: ghcr.io/labring/fastgpt-sandbox:v4.9.10 # git
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# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.9.10 # 阿里云
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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
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networks:
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- fastgpt
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restart: always
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fastgpt-mcp-server:
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container_name: fastgpt-mcp-server
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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
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image: ghcr.io/labring/fastgpt-mcp_server:v4.9.10-fix2 # git
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# 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:
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@@ -127,8 +127,8 @@ services:
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- FASTGPT_ENDPOINT=http://fastgpt:3000
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fastgpt:
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container_name: fastgpt
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image: ghcr.io/labring/fastgpt:v4.9.10 # git
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# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.9.10 # 阿里云
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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
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ports:
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- 3000:3000
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networks:
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@@ -1,218 +0,0 @@
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# 数据库的默认账号和密码仅首次运行时设置有效
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# 如果修改了账号密码，记得改数据库和项目连接参数，别只改一处~
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# 该配置文件只是给快速启动，测试使用。正式使用，记得务必修改账号密码，以及调整合适的知识库参数，共享内存等。
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# 如何无法访问 dockerhub 和 git，可以用阿里云（阿里云没有arm包）
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version: '3.3'
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services:
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# db
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gs:
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image: opengauss/opengauss:7.0.0-RC1 # docker hub
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container_name: gs
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restart: always
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# ports: # 生产环境建议不要暴露
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# - 5432:5432
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networks:
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- fastgpt
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environment:
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# 这里的配置只有首次运行生效。修改后，重启镜像是不会生效的。需要把持久化数据删除再重启，才有效果
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- GS_USER=username
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- GS_PASSWORD=password
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- GS_DB=postgres
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volumes:
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- ./opengauss/data:/var/lib/opengauss/data
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healthcheck:
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test: ['CMD-SHELL', 'netstat -lntp | grep tcp6 > /dev/null 2>&1']
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interval: 10s
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timeout: 10s
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retries: 10
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mongo:
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image: mongo:5.0.18 # dockerhub
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# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/mongo:5.0.18 # 阿里云
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# image: mongo:4.4.29 # cpu不支持AVX时候使用
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container_name: mongo
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restart: always
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# ports:
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# - 27017:27017
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networks:
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- fastgpt
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command: mongod --keyFile /data/mongodb.key --replSet rs0
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environment:
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- MONGO_INITDB_ROOT_USERNAME=myusername
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- MONGO_INITDB_ROOT_PASSWORD=mypassword
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volumes:
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- ./mongo/data:/data/db
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entrypoint:
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- bash
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- -c
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- |
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openssl rand -base64 128 > /data/mongodb.key
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chmod 400 /data/mongodb.key
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chown 999:999 /data/mongodb.key
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echo 'const isInited = rs.status().ok === 1
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if(!isInited){
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rs.initiate({
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_id: "rs0",
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members: [
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{ _id: 0, host: "mongo:27017" }
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]
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})
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}' > /data/initReplicaSet.js
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# 启动MongoDB服务
|
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exec docker-entrypoint.sh "$$@" &
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# 等待MongoDB服务启动
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until mongo -u myusername -p mypassword --authenticationDatabase admin --eval "print('waited for connection')"; do
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echo "Waiting for MongoDB to start..."
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sleep 2
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done
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# 执行初始化副本集的脚本
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mongo -u myusername -p mypassword --authenticationDatabase admin /data/initReplicaSet.js
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# 等待docker-entrypoint.sh脚本执行的MongoDB服务进程
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wait $$!
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redis:
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image: redis:7.2-alpine
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container_name: redis
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# ports:
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# - 6379:6379
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networks:
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||||
- fastgpt
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restart: always
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command: |
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redis-server --requirepass mypassword --loglevel warning --maxclients 10000 --appendonly yes --save 60 10 --maxmemory 4gb --maxmemory-policy noeviction
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healthcheck:
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test: ['CMD', 'redis-cli', '-a', 'mypassword', 'ping']
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interval: 10s
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timeout: 3s
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retries: 3
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start_period: 30s
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volumes:
|
||||
- ./redis/data:/data
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|
||||
# fastgpt
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sandbox:
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container_name: sandbox
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||||
image: ghcr.io/labring/fastgpt-sandbox:v4.9.7-fix2 # git
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||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.9.7-fix2 # 阿里云
|
||||
networks:
|
||||
- fastgpt
|
||||
restart: always
|
||||
fastgpt-mcp-server:
|
||||
container_name: fastgpt-mcp-server
|
||||
image: ghcr.io/labring/fastgpt-mcp_server:v4.9.7-fix2 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-mcp_server:v4.9.7-fix2 # 阿里云
|
||||
ports:
|
||||
- 3005:3000
|
||||
networks:
|
||||
- fastgpt
|
||||
restart: always
|
||||
environment:
|
||||
- FASTGPT_ENDPOINT=http://fastgpt:3000
|
||||
fastgpt:
|
||||
container_name: fastgpt
|
||||
image: ghcr.io/labring/fastgpt:v4.9.7-fix2 # git
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# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.9.7-fix2 # 阿里云
|
||||
# image: swr.cn-north-4.myhuaweicloud.com/ddn-k8s/ghcr.io/labring/fastgpt:v4.8.4-linuxarm64 # openGauss在arm架构上性能更好
|
||||
ports:
|
||||
- 3000:3000
|
||||
networks:
|
||||
- fastgpt
|
||||
depends_on:
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||||
- mongo
|
||||
- gs
|
||||
- sandbox
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||||
restart: always
|
||||
environment:
|
||||
# 前端外部可访问的地址，用于自动补全文件资源路径。例如 https:fastgpt.cn，不能填 localhost。这个值可以不填，不填则发给模型的图片会是一个相对路径，而不是全路径，模型可能伪造Host。
|
||||
- FE_DOMAIN=
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||||
# root 密码，用户名为: root。如果需要修改 root 密码，直接修改这个环境变量，并重启即可。
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||||
- DEFAULT_ROOT_PSW=1234
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||||
# AI Proxy 的地址，如果配了该地址，优先使用
|
||||
- AIPROXY_API_ENDPOINT=http://aiproxy:3000
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||||
# AI Proxy 的 Admin Token，与 AI Proxy 中的环境变量 ADMIN_KEY
|
||||
- AIPROXY_API_TOKEN=aiproxy
|
||||
# 数据库最大连接数
|
||||
- DB_MAX_LINK=30
|
||||
# 登录凭证密钥
|
||||
- TOKEN_KEY=any
|
||||
# root的密钥，常用于升级时候的初始化请求
|
||||
- ROOT_KEY=root_key
|
||||
# 文件阅读加密
|
||||
- FILE_TOKEN_KEY=filetoken
|
||||
# MongoDB 连接参数. 用户名myusername,密码mypassword。
|
||||
- MONGODB_URI=mongodb://myusername:mypassword@mongo:27017/fastgpt?authSource=admin
|
||||
# openGauss 连接参数
|
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- OPENGAUSS_URL=opengauss://gaussdb:Huawei12%23%24@gs:9999/test
|
||||
# Redis 连接参数
|
||||
- REDIS_URL=redis://default:mypassword@redis:6379
|
||||
# sandbox 地址
|
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- SANDBOX_URL=http://sandbox:3000
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# 日志等级: debug, info, warn, error
|
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- LOG_LEVEL=info
|
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- STORE_LOG_LEVEL=warn
|
||||
# 工作流最大运行次数
|
||||
- WORKFLOW_MAX_RUN_TIMES=1000
|
||||
# 批量执行节点，最大输入长度
|
||||
- WORKFLOW_MAX_LOOP_TIMES=100
|
||||
# 自定义跨域，不配置时，默认都允许跨域（多个域名通过逗号分割）
|
||||
- ALLOWED_ORIGINS=
|
||||
# 是否开启IP限制，默认不开启
|
||||
- USE_IP_LIMIT=false
|
||||
# 对话文件过期天数
|
||||
- CHAT_FILE_EXPIRE_TIME=7
|
||||
volumes:
|
||||
- ./config.json:/app/data/config.json
|
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|
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# AI Proxy
|
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aiproxy:
|
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image: ghcr.io/labring/aiproxy:v0.1.7
|
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# image: registry.cn-hangzhou.aliyuncs.com/labring/aiproxy:v0.1.7 # 阿里云
|
||||
container_name: aiproxy
|
||||
restart: unless-stopped
|
||||
depends_on:
|
||||
aiproxy_pg:
|
||||
condition: service_healthy
|
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networks:
|
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- fastgpt
|
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environment:
|
||||
# 对应 fastgpt 里的AIPROXY_API_TOKEN
|
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- ADMIN_KEY=aiproxy
|
||||
# 错误日志详情保存时间（小时）
|
||||
- LOG_DETAIL_STORAGE_HOURS=1
|
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# 数据库连接地址
|
||||
- SQL_DSN=postgres://postgres:aiproxy@aiproxy_pg:5432/aiproxy
|
||||
# 最大重试次数
|
||||
- RETRY_TIMES=3
|
||||
# 不需要计费
|
||||
- BILLING_ENABLED=false
|
||||
# 不需要严格检测模型
|
||||
- DISABLE_MODEL_CONFIG=true
|
||||
healthcheck:
|
||||
test: ['CMD', 'curl', '-f', 'http://localhost:3000/api/status']
|
||||
interval: 5s
|
||||
timeout: 5s
|
||||
retries: 10
|
||||
aiproxy_pg:
|
||||
image: pgvector/pgvector:0.8.0-pg15 # docker hub
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/pgvector:v0.8.0-pg15 # 阿里云
|
||||
restart: unless-stopped
|
||||
container_name: aiproxy_pg
|
||||
volumes:
|
||||
- ./aiproxy_pg:/var/lib/postgresql/data
|
||||
networks:
|
||||
- fastgpt
|
||||
environment:
|
||||
TZ: Asia/Shanghai
|
||||
POSTGRES_USER: postgres
|
||||
POSTGRES_DB: aiproxy
|
||||
POSTGRES_PASSWORD: aiproxy
|
||||
healthcheck:
|
||||
test: ['CMD', 'pg_isready', '-U', 'postgres', '-d', 'aiproxy']
|
||||
interval: 5s
|
||||
timeout: 5s
|
||||
retries: 10
|
||||
networks:
|
||||
fastgpt:
|
||||
@@ -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:
|
||||
|
||||
@@ -645,7 +645,7 @@ data 为集合的 ID。
|
||||
{{< /tab >}}
|
||||
{{< /tabs >}}
|
||||
|
||||
### 创建一个外部文件库集合（商业版）
|
||||
### 创建一个外部文件库集合（弃用）
|
||||
|
||||
{{< tabs tabTotal="3" >}}
|
||||
{{< tab tabName="请求示例" >}}
|
||||
|
||||
@@ -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
env.d.ts
vendored
1
env.d.ts
vendored
@@ -15,7 +15,6 @@ declare global {
|
||||
MONGODB_LOG_URI?: string;
|
||||
PG_URL: string;
|
||||
OCEANBASE_URL: string;
|
||||
OPENGAUSS_URL: string;
|
||||
MILVUS_ADDRESS: string;
|
||||
MILVUS_TOKEN: string;
|
||||
SANDBOX_URL: string;
|
||||
|
||||
7
packages/global/core/dataset/api.d.ts
vendored
7
packages/global/core/dataset/api.d.ts
vendored
@@ -124,13 +124,6 @@ export type PgSearchRawType = {
|
||||
collection_id: string;
|
||||
score: number;
|
||||
};
|
||||
|
||||
export type GsSearchRawType = {
|
||||
id: string;
|
||||
collection_id: string;
|
||||
score: number;
|
||||
};
|
||||
|
||||
export type PushDatasetDataChunkProps = {
|
||||
q: string; // embedding content
|
||||
a?: string; // bonus content
|
||||
|
||||
@@ -40,5 +40,6 @@ export function getSourceNameIcon({
|
||||
export const predictDataLimitLength = (mode: TrainingModeEnum, data: any[]) => {
|
||||
if (mode === TrainingModeEnum.qa) return data.length * 20;
|
||||
if (mode === TrainingModeEnum.auto) return data.length * 5;
|
||||
if (mode === TrainingModeEnum.image) return data.length * 2;
|
||||
return data.length;
|
||||
};
|
||||
|
||||
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;
|
||||
|
||||
@@ -13,6 +13,7 @@ const staticPluginList = [
|
||||
'WeWorkWebhook',
|
||||
'google',
|
||||
'bing',
|
||||
'bocha',
|
||||
'delay'
|
||||
];
|
||||
// Run in worker thread (Have npm packages)
|
||||
|
||||
677
packages/plugins/src/bocha/template.json
Normal file
677
packages/plugins/src/bocha/template.json
Normal file
@@ -0,0 +1,677 @@
|
||||
{
|
||||
"author": "",
|
||||
"name": "博查搜索",
|
||||
"avatar": "core/workflow/template/bocha",
|
||||
"intro": "使用博查AI搜索引擎进行网络搜索。",
|
||||
"showStatus": true,
|
||||
"weight": 10,
|
||||
"courseUrl": "",
|
||||
"isTool": true,
|
||||
"templateType": "search",
|
||||
"workflow": {
|
||||
"nodes": [
|
||||
{
|
||||
"nodeId": "pluginInput",
|
||||
"name": "workflow:template.plugin_start",
|
||||
"intro": "workflow:intro_plugin_input",
|
||||
"avatar": "core/workflow/template/workflowStart",
|
||||
"flowNodeType": "pluginInput",
|
||||
"showStatus": false,
|
||||
"position": {
|
||||
"x": 636.3048409085379,
|
||||
"y": -238.61714728578016
|
||||
},
|
||||
"version": "481",
|
||||
"inputs": [
|
||||
{
|
||||
"renderTypeList": [
|
||||
"input"
|
||||
],
|
||||
"selectedTypeIndex": 0,
|
||||
"valueType": "string",
|
||||
"canEdit": true,
|
||||
"key": "apiKey",
|
||||
"label": "apiKey",
|
||||
"description": "博查API密钥",
|
||||
"defaultValue": "",
|
||||
"required": true
|
||||
},
|
||||
{
|
||||
"renderTypeList": [
|
||||
"input",
|
||||
"reference"
|
||||
],
|
||||
"selectedTypeIndex": 0,
|
||||
"valueType": "string",
|
||||
"canEdit": true,
|
||||
"key": "query",
|
||||
"label": "query",
|
||||
"description": "搜索查询词",
|
||||
"defaultValue": "",
|
||||
"required": true,
|
||||
"toolDescription": "搜索查询词"
|
||||
},
|
||||
{
|
||||
"renderTypeList": [
|
||||
"input",
|
||||
"reference"
|
||||
],
|
||||
"selectedTypeIndex": 0,
|
||||
"valueType": "string",
|
||||
"canEdit": true,
|
||||
"key": "freshness",
|
||||
"label": "freshness",
|
||||
"description": "搜索指定时间范围内的网页。可填值：oneDay(一天内)、oneWeek(一周内)、oneMonth(一个月内)、oneYear(一年内)、noLimit(不限，默认)、YYYY-MM-DD..YYYY-MM-DD(日期范围)、YYYY-MM-DD(指定日期)",
|
||||
"defaultValue": "noLimit",
|
||||
"required": false,
|
||||
"toolDescription": "搜索时间范围"
|
||||
},
|
||||
{
|
||||
"renderTypeList": [
|
||||
"input",
|
||||
"reference"
|
||||
],
|
||||
"selectedTypeIndex": 0,
|
||||
"valueType": "boolean",
|
||||
"canEdit": true,
|
||||
"key": "summary",
|
||||
"label": "summary",
|
||||
"description": "是否显示文本摘要。true显示，false不显示(默认)",
|
||||
"defaultValue": false,
|
||||
"required": false,
|
||||
"toolDescription": "是否显示文本摘要"
|
||||
},
|
||||
{
|
||||
"renderTypeList": [
|
||||
"input",
|
||||
"reference"
|
||||
],
|
||||
"selectedTypeIndex": 0,
|
||||
"valueType": "string",
|
||||
"canEdit": true,
|
||||
"key": "include",
|
||||
"label": "include",
|
||||
"description": "指定搜索的site范围。多个域名使用|或,分隔，最多20个。例如：qq.com|m.163.com",
|
||||
"defaultValue": "",
|
||||
"required": false,
|
||||
"toolDescription": "指定搜索的site范围"
|
||||
},
|
||||
{
|
||||
"renderTypeList": [
|
||||
"input",
|
||||
"reference"
|
||||
],
|
||||
"selectedTypeIndex": 0,
|
||||
"valueType": "string",
|
||||
"canEdit": true,
|
||||
"key": "exclude",
|
||||
"label": "exclude",
|
||||
"description": "排除搜索的网站范围。多个域名使用|或,分隔，最多20个。例如：qq.com|m.163.com",
|
||||
"defaultValue": "",
|
||||
"required": false,
|
||||
"toolDescription": "排除搜索的网站范围"
|
||||
},
|
||||
{
|
||||
"renderTypeList": [
|
||||
"input",
|
||||
"reference"
|
||||
],
|
||||
"selectedTypeIndex": 0,
|
||||
"valueType": "number",
|
||||
"canEdit": true,
|
||||
"key": "count",
|
||||
"label": "count",
|
||||
"description": "返回结果的条数。可填范围：1-50，默认为10",
|
||||
"defaultValue": 10,
|
||||
"required": false,
|
||||
"min": 1,
|
||||
"max": 50,
|
||||
"toolDescription": "返回结果条数"
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"id": "apiKey",
|
||||
"valueType": "string",
|
||||
"key": "apiKey",
|
||||
"label": "apiKey",
|
||||
"type": "hidden"
|
||||
},
|
||||
{
|
||||
"id": "query",
|
||||
"valueType": "string",
|
||||
"key": "query",
|
||||
"label": "query",
|
||||
"type": "hidden"
|
||||
},
|
||||
{
|
||||
"id": "freshness",
|
||||
"valueType": "string",
|
||||
"key": "freshness",
|
||||
"label": "freshness",
|
||||
"type": "hidden"
|
||||
},
|
||||
{
|
||||
"id": "summary",
|
||||
"valueType": "boolean",
|
||||
"key": "summary",
|
||||
"label": "summary",
|
||||
"type": "hidden"
|
||||
},
|
||||
{
|
||||
"id": "include",
|
||||
"valueType": "string",
|
||||
"key": "include",
|
||||
"label": "include",
|
||||
"type": "hidden"
|
||||
},
|
||||
{
|
||||
"id": "exclude",
|
||||
"valueType": "string",
|
||||
"key": "exclude",
|
||||
"label": "exclude",
|
||||
"type": "hidden"
|
||||
},
|
||||
{
|
||||
"id": "count",
|
||||
"valueType": "number",
|
||||
"key": "count",
|
||||
"label": "count",
|
||||
"type": "hidden"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"nodeId": "pluginOutput",
|
||||
"name": "common:core.module.template.self_output",
|
||||
"intro": "workflow:intro_custom_plugin_output",
|
||||
"avatar": "core/workflow/template/pluginOutput",
|
||||
"flowNodeType": "pluginOutput",
|
||||
"showStatus": false,
|
||||
"position": {
|
||||
"x": 2764.1105686698083,
|
||||
"y": -30.617147285780163
|
||||
},
|
||||
"version": "481",
|
||||
"inputs": [
|
||||
{
|
||||
"renderTypeList": [
|
||||
"reference"
|
||||
],
|
||||
"valueType": "object",
|
||||
"canEdit": true,
|
||||
"key": "result",
|
||||
"label": "result",
|
||||
"isToolOutput": true,
|
||||
"description": "",
|
||||
"value": [
|
||||
"nyA6oA8mF1iW",
|
||||
"httpRawResponse"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"nodeId": "pluginConfig",
|
||||
"name": "common:core.module.template.system_config",
|
||||
"intro": "",
|
||||
"avatar": "core/workflow/template/systemConfig",
|
||||
"flowNodeType": "pluginConfig",
|
||||
"position": {
|
||||
"x": 184.66337662472682,
|
||||
"y": -216.05298493910115
|
||||
},
|
||||
"version": "4811",
|
||||
"inputs": [],
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"nodeId": "nyA6oA8mF1iW",
|
||||
"name": "HTTP 请求",
|
||||
"intro": "调用博查搜索API",
|
||||
"avatar": "core/workflow/template/httpRequest",
|
||||
"flowNodeType": "httpRequest468",
|
||||
"showStatus": true,
|
||||
"position": {
|
||||
"x": 1335.0647252518884,
|
||||
"y": -455.9043948565971
|
||||
},
|
||||
"version": "481",
|
||||
"inputs": [
|
||||
{
|
||||
"key": "system_addInputParam",
|
||||
"renderTypeList": [
|
||||
"addInputParam"
|
||||
],
|
||||
"valueType": "dynamic",
|
||||
"label": "",
|
||||
"required": false,
|
||||
"description": "common:core.module.input.description.HTTP Dynamic Input",
|
||||
"customInputConfig": {
|
||||
"selectValueTypeList": [
|
||||
"string",
|
||||
"number",
|
||||
"boolean",
|
||||
"object",
|
||||
"arrayString",
|
||||
"arrayNumber",
|
||||
"arrayBoolean",
|
||||
"arrayObject",
|
||||
"arrayAny",
|
||||
"any",
|
||||
"chatHistory",
|
||||
"datasetQuote",
|
||||
"dynamic",
|
||||
"selectDataset",
|
||||
"selectApp"
|
||||
],
|
||||
"showDescription": false,
|
||||
"showDefaultValue": true
|
||||
},
|
||||
"debugLabel": "",
|
||||
"toolDescription": ""
|
||||
},
|
||||
{
|
||||
"key": "system_httpMethod",
|
||||
"renderTypeList": [
|
||||
"custom"
|
||||
],
|
||||
"valueType": "string",
|
||||
"label": "",
|
||||
"value": "POST",
|
||||
"required": true,
|
||||
"debugLabel": "",
|
||||
"toolDescription": ""
|
||||
},
|
||||
{
|
||||
"key": "system_httpTimeout",
|
||||
"renderTypeList": [
|
||||
"custom"
|
||||
],
|
||||
"valueType": "number",
|
||||
"label": "",
|
||||
"value": 30,
|
||||
"min": 5,
|
||||
"max": 600,
|
||||
"required": true,
|
||||
"debugLabel": "",
|
||||
"toolDescription": ""
|
||||
},
|
||||
{
|
||||
"key": "system_httpReqUrl",
|
||||
"renderTypeList": [
|
||||
"hidden"
|
||||
],
|
||||
"valueType": "string",
|
||||
"label": "",
|
||||
"description": "common:core.module.input.description.Http Request Url",
|
||||
"placeholder": "https://api.ai.com/getInventory",
|
||||
"required": false,
|
||||
"value": "https://api.bochaai.com/v1/web-search",
|
||||
"debugLabel": "",
|
||||
"toolDescription": ""
|
||||
},
|
||||
{
|
||||
"key": "system_httpHeader",
|
||||
"renderTypeList": [
|
||||
"custom"
|
||||
],
|
||||
"valueType": "any",
|
||||
"value": [
|
||||
{
|
||||
"key": "Authorization",
|
||||
"type": "string",
|
||||
"value": "Bearer {{$pluginInput.apiKey$}}"
|
||||
},
|
||||
{
|
||||
"key": "Content-Type",
|
||||
"type": "string",
|
||||
"value": "application/json"
|
||||
}
|
||||
],
|
||||
"label": "",
|
||||
"description": "common:core.module.input.description.Http Request Header",
|
||||
"placeholder": "common:core.module.input.description.Http Request Header",
|
||||
"required": false,
|
||||
"debugLabel": "",
|
||||
"toolDescription": ""
|
||||
},
|
||||
{
|
||||
"key": "system_httpParams",
|
||||
"renderTypeList": [
|
||||
"hidden"
|
||||
],
|
||||
"valueType": "any",
|
||||
"value": [],
|
||||
"label": "",
|
||||
"required": false,
|
||||
"debugLabel": "",
|
||||
"toolDescription": ""
|
||||
},
|
||||
{
|
||||
"key": "system_httpJsonBody",
|
||||
"renderTypeList": [
|
||||
"hidden"
|
||||
],
|
||||
"valueType": "any",
|
||||
"value": "{\n \"query\": \"{{query}}\",\n \"freshness\": \"{{freshness}}\",\n \"summary\": {{summary}},\n \"include\": \"{{include}}\",\n \"exclude\": \"{{exclude}}\",\n \"count\": {{count}}\n}",
|
||||
"label": "",
|
||||
"required": false,
|
||||
"debugLabel": "",
|
||||
"toolDescription": ""
|
||||
},
|
||||
{
|
||||
"key": "system_httpFormBody",
|
||||
"renderTypeList": [
|
||||
"hidden"
|
||||
],
|
||||
"valueType": "any",
|
||||
"value": [],
|
||||
"label": "",
|
||||
"required": false,
|
||||
"debugLabel": "",
|
||||
"toolDescription": ""
|
||||
},
|
||||
{
|
||||
"key": "system_httpContentType",
|
||||
"renderTypeList": [
|
||||
"hidden"
|
||||
],
|
||||
"valueType": "string",
|
||||
"value": "json",
|
||||
"label": "",
|
||||
"required": false,
|
||||
"debugLabel": "",
|
||||
"toolDescription": ""
|
||||
},
|
||||
{
|
||||
"valueType": "string",
|
||||
"renderTypeList": [
|
||||
"reference"
|
||||
],
|
||||
"key": "query",
|
||||
"label": "query",
|
||||
"toolDescription": "博查搜索检索词",
|
||||
"required": true,
|
||||
"canEdit": true,
|
||||
"editField": {
|
||||
"key": true,
|
||||
"description": true
|
||||
},
|
||||
"customInputConfig": {
|
||||
"selectValueTypeList": [
|
||||
"string",
|
||||
"number",
|
||||
"boolean",
|
||||
"object",
|
||||
"arrayString",
|
||||
"arrayNumber",
|
||||
"arrayBoolean",
|
||||
"arrayObject",
|
||||
"arrayAny",
|
||||
"any",
|
||||
"chatHistory",
|
||||
"datasetQuote",
|
||||
"dynamic",
|
||||
"selectApp",
|
||||
"selectDataset"
|
||||
],
|
||||
"showDescription": false,
|
||||
"showDefaultValue": true
|
||||
},
|
||||
"value": [
|
||||
"pluginInput",
|
||||
"query"
|
||||
]
|
||||
},
|
||||
{
|
||||
"valueType": "string",
|
||||
"renderTypeList": [
|
||||
"reference"
|
||||
],
|
||||
"key": "freshness",
|
||||
"label": "freshness",
|
||||
"toolDescription": "搜索时间范围",
|
||||
"required": false,
|
||||
"canEdit": true,
|
||||
"editField": {
|
||||
"key": true,
|
||||
"description": true
|
||||
},
|
||||
"customInputConfig": {
|
||||
"selectValueTypeList": [
|
||||
"string",
|
||||
"number",
|
||||
"boolean",
|
||||
"object",
|
||||
"arrayString",
|
||||
"arrayNumber",
|
||||
"arrayBoolean",
|
||||
"arrayObject",
|
||||
"arrayAny",
|
||||
"any",
|
||||
"chatHistory",
|
||||
"datasetQuote",
|
||||
"dynamic",
|
||||
"selectApp",
|
||||
"selectDataset"
|
||||
],
|
||||
"showDescription": false,
|
||||
"showDefaultValue": true
|
||||
},
|
||||
"value": [
|
||||
"pluginInput",
|
||||
"freshness"
|
||||
]
|
||||
},
|
||||
{
|
||||
"valueType": "boolean",
|
||||
"renderTypeList": [
|
||||
"reference"
|
||||
],
|
||||
"key": "summary",
|
||||
"label": "summary",
|
||||
"toolDescription": "是否显示文本摘要",
|
||||
"required": false,
|
||||
"canEdit": true,
|
||||
"editField": {
|
||||
"key": true,
|
||||
"description": true
|
||||
},
|
||||
"customInputConfig": {
|
||||
"selectValueTypeList": [
|
||||
"string",
|
||||
"number",
|
||||
"boolean",
|
||||
"object",
|
||||
"arrayString",
|
||||
"arrayNumber",
|
||||
"arrayBoolean",
|
||||
"arrayObject",
|
||||
"arrayAny",
|
||||
"any",
|
||||
"chatHistory",
|
||||
"datasetQuote",
|
||||
"dynamic",
|
||||
"selectApp",
|
||||
"selectDataset"
|
||||
],
|
||||
"showDescription": false,
|
||||
"showDefaultValue": true
|
||||
},
|
||||
"value": [
|
||||
"pluginInput",
|
||||
"summary"
|
||||
]
|
||||
},
|
||||
{
|
||||
"valueType": "string",
|
||||
"renderTypeList": [
|
||||
"reference"
|
||||
],
|
||||
"key": "include",
|
||||
"label": "include",
|
||||
"toolDescription": "指定搜索的site范围",
|
||||
"required": false,
|
||||
"canEdit": true,
|
||||
"editField": {
|
||||
"key": true,
|
||||
"description": true
|
||||
},
|
||||
"customInputConfig": {
|
||||
"selectValueTypeList": [
|
||||
"string",
|
||||
"number",
|
||||
"boolean",
|
||||
"object",
|
||||
"arrayString",
|
||||
"arrayNumber",
|
||||
"arrayBoolean",
|
||||
"arrayObject",
|
||||
"arrayAny",
|
||||
"any",
|
||||
"chatHistory",
|
||||
"datasetQuote",
|
||||
"dynamic",
|
||||
"selectApp",
|
||||
"selectDataset"
|
||||
],
|
||||
"showDescription": false,
|
||||
"showDefaultValue": true
|
||||
},
|
||||
"value": [
|
||||
"pluginInput",
|
||||
"include"
|
||||
]
|
||||
},
|
||||
{
|
||||
"valueType": "string",
|
||||
"renderTypeList": [
|
||||
"reference"
|
||||
],
|
||||
"key": "exclude",
|
||||
"label": "exclude",
|
||||
"toolDescription": "排除搜索的网站范围",
|
||||
"required": false,
|
||||
"canEdit": true,
|
||||
"editField": {
|
||||
"key": true,
|
||||
"description": true
|
||||
},
|
||||
"customInputConfig": {
|
||||
"selectValueTypeList": [
|
||||
"string",
|
||||
"number",
|
||||
"boolean",
|
||||
"object",
|
||||
"arrayString",
|
||||
"arrayNumber",
|
||||
"arrayBoolean",
|
||||
"arrayObject",
|
||||
"arrayAny",
|
||||
"any",
|
||||
"chatHistory",
|
||||
"datasetQuote",
|
||||
"dynamic",
|
||||
"selectApp",
|
||||
"selectDataset"
|
||||
],
|
||||
"showDescription": false,
|
||||
"showDefaultValue": true
|
||||
},
|
||||
"value": [
|
||||
"pluginInput",
|
||||
"exclude"
|
||||
]
|
||||
},
|
||||
{
|
||||
"valueType": "number",
|
||||
"renderTypeList": [
|
||||
"reference"
|
||||
],
|
||||
"key": "count",
|
||||
"label": "count",
|
||||
"toolDescription": "返回结果条数",
|
||||
"required": false,
|
||||
"canEdit": true,
|
||||
"editField": {
|
||||
"key": true,
|
||||
"description": true
|
||||
},
|
||||
"customInputConfig": {
|
||||
"selectValueTypeList": [
|
||||
"string",
|
||||
"number",
|
||||
"boolean",
|
||||
"object",
|
||||
"arrayString",
|
||||
"arrayNumber",
|
||||
"arrayBoolean",
|
||||
"arrayObject",
|
||||
"arrayAny",
|
||||
"any",
|
||||
"chatHistory",
|
||||
"datasetQuote",
|
||||
"dynamic",
|
||||
"selectApp",
|
||||
"selectDataset"
|
||||
],
|
||||
"showDescription": false,
|
||||
"showDefaultValue": true
|
||||
},
|
||||
"value": [
|
||||
"pluginInput",
|
||||
"count"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"id": "error",
|
||||
"key": "error",
|
||||
"label": "workflow:request_error",
|
||||
"description": "HTTP请求错误信息，成功时返回空",
|
||||
"valueType": "object",
|
||||
"type": "static"
|
||||
},
|
||||
{
|
||||
"id": "httpRawResponse",
|
||||
"key": "httpRawResponse",
|
||||
"required": true,
|
||||
"label": "workflow:raw_response",
|
||||
"description": "HTTP请求的原始响应。只能接受字符串或JSON类型响应数据。",
|
||||
"valueType": "any",
|
||||
"type": "static"
|
||||
},
|
||||
{
|
||||
"id": "system_addOutputParam",
|
||||
"key": "system_addOutputParam",
|
||||
"type": "dynamic",
|
||||
"valueType": "dynamic",
|
||||
"label": "",
|
||||
"editField": {
|
||||
"key": true,
|
||||
"valueType": true
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": "pluginInput",
|
||||
"target": "nyA6oA8mF1iW",
|
||||
"sourceHandle": "pluginInput-source-right",
|
||||
"targetHandle": "nyA6oA8mF1iW-target-left"
|
||||
},
|
||||
{
|
||||
"source": "nyA6oA8mF1iW",
|
||||
"target": "pluginOutput",
|
||||
"sourceHandle": "nyA6oA8mF1iW-source-right",
|
||||
"targetHandle": "pluginOutput-target-left"
|
||||
}
|
||||
]
|
||||
},
|
||||
"chatConfig": {}
|
||||
}
|
||||
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: `${BucketNameEnum}`) {
|
||||
MongoDatasetFileSchema;
|
||||
@@ -223,15 +223,13 @@ export const readFileContentFromMongo = async ({
|
||||
rawText: string;
|
||||
filename: string;
|
||||
}> => {
|
||||
const bufferId = `${fileId}-${customPdfParse}`;
|
||||
const bufferId = `${String(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);
|
||||
|
||||
@@ -1,5 +1,57 @@
|
||||
import { detectFileEncoding } from '@fastgpt/global/common/file/tools';
|
||||
import { PassThrough } from 'stream';
|
||||
import { getGridBucket } from './controller';
|
||||
import { type BucketNameEnum } from '@fastgpt/global/common/file/constants';
|
||||
import { retryFn } from '@fastgpt/global/common/system/utils';
|
||||
|
||||
export const createFileFromText = async ({
|
||||
bucket,
|
||||
filename,
|
||||
text,
|
||||
metadata
|
||||
}: {
|
||||
bucket: `${BucketNameEnum}`;
|
||||
filename: string;
|
||||
text: string;
|
||||
metadata: Record<string, any>;
|
||||
}) => {
|
||||
const gridBucket = getGridBucket(bucket);
|
||||
|
||||
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(filename, {
|
||||
metadata,
|
||||
chunkSizeBytes
|
||||
});
|
||||
|
||||
return retryFn(async () => {
|
||||
return new Promise<{ fileId: string }>((resolve, reject) => {
|
||||
uploadStream.end(buffer);
|
||||
uploadStream.on('finish', () => {
|
||||
resolve({ fileId: String(uploadStream.id) });
|
||||
});
|
||||
uploadStream.on('error', reject);
|
||||
});
|
||||
});
|
||||
};
|
||||
|
||||
export const gridFsStream2Buffer = (stream: NodeJS.ReadableStream) => {
|
||||
return new Promise<Buffer>((resolve, reject) => {
|
||||
|
||||
@@ -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 {
|
||||
|
||||
@@ -3,6 +3,5 @@ export const DatasetVectorTableName = 'modeldata';
|
||||
|
||||
export const PG_ADDRESS = process.env.PG_URL;
|
||||
export const OCEANBASE_ADDRESS = process.env.OCEANBASE_URL;
|
||||
export const OPENGAUSS_ADDRESS = process.env.OPENGAUSS_URL;
|
||||
export const MILVUS_ADDRESS = process.env.MILVUS_ADDRESS;
|
||||
export const MILVUS_TOKEN = process.env.MILVUS_TOKEN;
|
||||
|
||||
@@ -1,11 +1,10 @@
|
||||
/* vector crud */
|
||||
import { PgVectorCtrl } from './pg';
|
||||
import { ObVectorCtrl } from './oceanbase';
|
||||
import { GsVectorCtrl } from './opengauss';
|
||||
import { getVectorsByText } from '../../core/ai/embedding';
|
||||
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, OPENGAUSS_ADDRESS } from './constants';
|
||||
import { MILVUS_ADDRESS, PG_ADDRESS, OCEANBASE_ADDRESS } from './constants';
|
||||
import { MilvusCtrl } from './milvus';
|
||||
import { setRedisCache, getRedisCache, delRedisCache, CacheKeyEnum } from '../redis/cache';
|
||||
import { throttle } from 'lodash';
|
||||
@@ -15,7 +14,6 @@ const getVectorObj = () => {
|
||||
if (PG_ADDRESS) return new PgVectorCtrl();
|
||||
if (OCEANBASE_ADDRESS) return new ObVectorCtrl();
|
||||
if (MILVUS_ADDRESS) return new MilvusCtrl();
|
||||
if (OPENGAUSS_ADDRESS) return new GsVectorCtrl();
|
||||
|
||||
return new PgVectorCtrl();
|
||||
};
|
||||
|
||||
@@ -1,188 +0,0 @@
|
||||
import { delay } from '@fastgpt/global/common/system/utils';
|
||||
import { addLog } from '../../system/log';
|
||||
import { Pool } from 'pg';
|
||||
import type { QueryResultRow } from 'pg';
|
||||
import { OPENGAUSS_ADDRESS } from '../constants';
|
||||
|
||||
export const connectGs = async (): Promise<Pool> => {
|
||||
if (global.gsClient) {
|
||||
return global.gsClient;
|
||||
}
|
||||
|
||||
global.gsClient = new Pool({
|
||||
connectionString: OPENGAUSS_ADDRESS,
|
||||
max: Number(process.env.DB_MAX_LINK || 20),
|
||||
min: 10,
|
||||
keepAlive: true,
|
||||
idleTimeoutMillis: 600000,
|
||||
connectionTimeoutMillis: 20000,
|
||||
query_timeout: 30000,
|
||||
statement_timeout: 40000,
|
||||
idle_in_transaction_session_timeout: 60000
|
||||
});
|
||||
|
||||
global.gsClient.on('error', async (err) => {
|
||||
addLog.error(`openGauss error`, err);
|
||||
global.gsClient?.end();
|
||||
global.gsClient = null;
|
||||
|
||||
await delay(1000);
|
||||
addLog.info(`Retry connect openGauss`);
|
||||
connectGs();
|
||||
});
|
||||
|
||||
try {
|
||||
await global.gsClient.connect();
|
||||
console.log('openGauss connected');
|
||||
return global.gsClient;
|
||||
} catch (error) {
|
||||
addLog.error(`openGauss connect error`, error);
|
||||
global.gsClient?.end();
|
||||
global.gsClient = null;
|
||||
|
||||
await delay(1000);
|
||||
addLog.info(`Retry connect openGauss`);
|
||||
|
||||
return connectGs();
|
||||
}
|
||||
};
|
||||
|
||||
type WhereProps = (string | [string, string | number])[];
|
||||
type GetProps = {
|
||||
fields?: string[];
|
||||
where?: WhereProps;
|
||||
order?: { field: string; mode: 'DESC' | 'ASC' | string }[];
|
||||
limit?: number;
|
||||
offset?: number;
|
||||
};
|
||||
|
||||
type DeleteProps = {
|
||||
where: WhereProps;
|
||||
};
|
||||
|
||||
type ValuesProps = { key: string; value?: string | number }[];
|
||||
type UpdateProps = {
|
||||
values: ValuesProps;
|
||||
where: WhereProps;
|
||||
};
|
||||
type InsertProps = {
|
||||
values: ValuesProps[];
|
||||
};
|
||||
|
||||
class GsClass {
|
||||
private getWhereStr(where?: WhereProps) {
|
||||
return where
|
||||
? `WHERE ${where
|
||||
.map((item) => {
|
||||
if (typeof item === 'string') {
|
||||
return item;
|
||||
}
|
||||
const val = typeof item[1] === 'number' ? item[1] : `'${String(item[1])}'`;
|
||||
return `${item[0]}=${val}`;
|
||||
})
|
||||
.join(' ')}`
|
||||
: '';
|
||||
}
|
||||
private getUpdateValStr(values: ValuesProps) {
|
||||
return values
|
||||
.map((item) => {
|
||||
const val =
|
||||
typeof item.value === 'number'
|
||||
? item.value
|
||||
: `'${String(item.value).replace(/\'/g, '"')}'`;
|
||||
|
||||
return `${item.key}=${val}`;
|
||||
})
|
||||
.join(',');
|
||||
}
|
||||
private getInsertValStr(values: ValuesProps[]) {
|
||||
return values
|
||||
.map(
|
||||
(items) =>
|
||||
`(${items
|
||||
.map((item) =>
|
||||
typeof item.value === 'number'
|
||||
? item.value
|
||||
: `'${String(item.value).replace(/\'/g, '"')}'`
|
||||
)
|
||||
.join(',')})`
|
||||
)
|
||||
.join(',');
|
||||
}
|
||||
async select<T extends QueryResultRow = any>(table: string, props: GetProps) {
|
||||
const sql = `SELECT ${
|
||||
!props.fields || props.fields?.length === 0 ? '*' : props.fields?.join(',')
|
||||
}
|
||||
FROM ${table}
|
||||
${this.getWhereStr(props.where)}
|
||||
${
|
||||
props.order
|
||||
? `ORDER BY ${props.order.map((item) => `${item.field} ${item.mode}`).join(',')}`
|
||||
: ''
|
||||
}
|
||||
LIMIT ${props.limit || 10} OFFSET ${props.offset || 0}
|
||||
`;
|
||||
|
||||
const gs = await connectGs();
|
||||
return gs.query<T>(sql);
|
||||
}
|
||||
async count(table: string, props: GetProps) {
|
||||
const sql = `SELECT COUNT(${props?.fields?.[0] || '*'})
|
||||
FROM ${table}
|
||||
${this.getWhereStr(props.where)}
|
||||
`;
|
||||
|
||||
const gs = await connectGs();
|
||||
return gs.query(sql).then((res) => Number(res.rows[0]?.count || 0));
|
||||
}
|
||||
async delete(table: string, props: DeleteProps) {
|
||||
const sql = `DELETE FROM ${table} ${this.getWhereStr(props.where)}`;
|
||||
const gs = await connectGs();
|
||||
return gs.query(sql);
|
||||
}
|
||||
async update(table: string, props: UpdateProps) {
|
||||
if (props.values.length === 0) {
|
||||
return {
|
||||
rowCount: 0
|
||||
};
|
||||
}
|
||||
|
||||
const sql = `UPDATE ${table} SET ${this.getUpdateValStr(props.values)} ${this.getWhereStr(
|
||||
props.where
|
||||
)}`;
|
||||
const gs = await connectGs();
|
||||
return gs.query(sql);
|
||||
}
|
||||
async insert(table: string, props: InsertProps) {
|
||||
if (props.values.length === 0) {
|
||||
return {
|
||||
rowCount: 0,
|
||||
rows: []
|
||||
};
|
||||
}
|
||||
|
||||
const fields = props.values[0].map((item) => item.key).join(',');
|
||||
const sql = `INSERT INTO ${table} (${fields}) VALUES ${this.getInsertValStr(
|
||||
props.values
|
||||
)} RETURNING id`;
|
||||
|
||||
const gs = await connectGs();
|
||||
return gs.query<{ id: string }>(sql);
|
||||
}
|
||||
async query<T extends QueryResultRow = any>(sql: string) {
|
||||
const gs = await connectGs();
|
||||
const start = Date.now();
|
||||
return gs.query<T>(sql).then((res) => {
|
||||
const time = Date.now() - start;
|
||||
|
||||
if (time > 300) {
|
||||
addLog.warn(`gs query time: ${time}ms, sql: ${sql}`);
|
||||
}
|
||||
|
||||
return res;
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
export const GsClient = new GsClass();
|
||||
export const Gs = global.gsClient;
|
||||
@@ -1,253 +0,0 @@
|
||||
/* pg vector crud */
|
||||
import { DatasetVectorTableName } from '../constants';
|
||||
import { delay } from '@fastgpt/global/common/system/utils';
|
||||
import { GsClient, connectGs } from './controller';
|
||||
import { GsSearchRawType } from '@fastgpt/global/core/dataset/api';
|
||||
import type {
|
||||
DelDatasetVectorCtrlProps,
|
||||
EmbeddingRecallCtrlProps,
|
||||
EmbeddingRecallResponse,
|
||||
InsertVectorControllerProps
|
||||
} from '../controller.d';
|
||||
import dayjs from 'dayjs';
|
||||
import { addLog } from '../../system/log';
|
||||
|
||||
export class GsVectorCtrl {
|
||||
constructor() {}
|
||||
init = async () => {
|
||||
try {
|
||||
await connectGs();
|
||||
await GsClient.query(`
|
||||
CREATE EXTENSION IF NOT EXISTS vector;
|
||||
CREATE TABLE IF NOT EXISTS ${DatasetVectorTableName} (
|
||||
id BIGSERIAL PRIMARY KEY,
|
||||
vector VECTOR(1536) NOT NULL,
|
||||
team_id VARCHAR(50) NOT NULL,
|
||||
dataset_id VARCHAR(50) NOT NULL,
|
||||
collection_id VARCHAR(50) NOT NULL,
|
||||
createtime TIMESTAMP DEFAULT CURRENT_TIMESTAMP
|
||||
);
|
||||
`);
|
||||
|
||||
await GsClient.query(
|
||||
`CREATE INDEX CONCURRENTLY IF NOT EXISTS vector_index ON ${DatasetVectorTableName} USING hnsw (vector vector_ip_ops) WITH (m = 32, ef_construction = 128);`
|
||||
);
|
||||
await GsClient.query(
|
||||
`CREATE INDEX CONCURRENTLY IF NOT EXISTS team_dataset_collection_index ON ${DatasetVectorTableName} USING btree(team_id, dataset_id, collection_id);`
|
||||
);
|
||||
await GsClient.query(
|
||||
`CREATE INDEX CONCURRENTLY IF NOT EXISTS create_time_index ON ${DatasetVectorTableName} USING btree(createtime);`
|
||||
);
|
||||
|
||||
addLog.info('init pg successful');
|
||||
} catch (error) {
|
||||
addLog.error('init pg error', error);
|
||||
}
|
||||
};
|
||||
insert = async (props: InsertVectorControllerProps): Promise<{ insertId: string }> => {
|
||||
const { teamId, datasetId, collectionId, vector, retry = 3 } = props;
|
||||
|
||||
try {
|
||||
const { rowCount, rows } = await GsClient.insert(DatasetVectorTableName, {
|
||||
values: [
|
||||
[
|
||||
{ key: 'vector', value: `[${vector}]` },
|
||||
{ key: 'team_id', value: String(teamId) },
|
||||
{ key: 'dataset_id', value: String(datasetId) },
|
||||
{ key: 'collection_id', value: String(collectionId) }
|
||||
]
|
||||
]
|
||||
});
|
||||
|
||||
if (rowCount === 0) {
|
||||
return Promise.reject('insertDatasetData: no insert');
|
||||
}
|
||||
|
||||
return {
|
||||
insertId: rows[0].id
|
||||
};
|
||||
} catch (error) {
|
||||
if (retry <= 0) {
|
||||
return Promise.reject(error);
|
||||
}
|
||||
await delay(500);
|
||||
return this.insert({
|
||||
...props,
|
||||
retry: retry - 1
|
||||
});
|
||||
}
|
||||
};
|
||||
delete = async (props: DelDatasetVectorCtrlProps): Promise<any> => {
|
||||
const { teamId, retry = 2 } = props;
|
||||
|
||||
const teamIdWhere = `team_id='${String(teamId)}' AND`;
|
||||
|
||||
const where = await (() => {
|
||||
if ('id' in props && props.id) return `${teamIdWhere} id=${props.id}`;
|
||||
|
||||
if ('datasetIds' in props && props.datasetIds) {
|
||||
const datasetIdWhere = `dataset_id IN (${props.datasetIds
|
||||
.map((id) => `'${String(id)}'`)
|
||||
.join(',')})`;
|
||||
|
||||
if ('collectionIds' in props && props.collectionIds) {
|
||||
return `${teamIdWhere} ${datasetIdWhere} AND collection_id IN (${props.collectionIds
|
||||
.map((id) => `'${String(id)}'`)
|
||||
.join(',')})`;
|
||||
}
|
||||
|
||||
return `${teamIdWhere} ${datasetIdWhere}`;
|
||||
}
|
||||
|
||||
if ('idList' in props && Array.isArray(props.idList)) {
|
||||
if (props.idList.length === 0) return;
|
||||
return `${teamIdWhere} id IN (${props.idList.map((id) => String(id)).join(',')})`;
|
||||
}
|
||||
return Promise.reject('deleteDatasetData: no where');
|
||||
})();
|
||||
|
||||
if (!where) return;
|
||||
|
||||
try {
|
||||
await GsClient.delete(DatasetVectorTableName, {
|
||||
where: [where]
|
||||
});
|
||||
} catch (error) {
|
||||
if (retry <= 0) {
|
||||
return Promise.reject(error);
|
||||
}
|
||||
await delay(500);
|
||||
return this.delete({
|
||||
...props,
|
||||
retry: retry - 1
|
||||
});
|
||||
}
|
||||
};
|
||||
embRecall = async (props: EmbeddingRecallCtrlProps): Promise<EmbeddingRecallResponse> => {
|
||||
const {
|
||||
teamId,
|
||||
datasetIds,
|
||||
vector,
|
||||
limit,
|
||||
forbidCollectionIdList,
|
||||
filterCollectionIdList,
|
||||
retry = 2
|
||||
} = props;
|
||||
|
||||
// Get forbid collection
|
||||
const formatForbidCollectionIdList = (() => {
|
||||
if (!filterCollectionIdList) return forbidCollectionIdList;
|
||||
const list = forbidCollectionIdList
|
||||
.map((id) => String(id))
|
||||
.filter((id) => !filterCollectionIdList.includes(id));
|
||||
return list;
|
||||
})();
|
||||
const forbidCollectionSql =
|
||||
formatForbidCollectionIdList.length > 0
|
||||
? `AND collection_id NOT IN (${formatForbidCollectionIdList.map((id) => `'${id}'`).join(',')})`
|
||||
: '';
|
||||
|
||||
// Filter by collectionId
|
||||
const formatFilterCollectionId = (() => {
|
||||
if (!filterCollectionIdList) return;
|
||||
|
||||
return filterCollectionIdList
|
||||
.map((id) => String(id))
|
||||
.filter((id) => !forbidCollectionIdList.includes(id));
|
||||
})();
|
||||
const filterCollectionIdSql = formatFilterCollectionId
|
||||
? `AND collection_id IN (${formatFilterCollectionId.map((id) => `'${id}'`).join(',')})`
|
||||
: '';
|
||||
// Empty data
|
||||
if (formatFilterCollectionId && formatFilterCollectionId.length === 0) {
|
||||
return { results: [] };
|
||||
}
|
||||
|
||||
try {
|
||||
const results: any = await GsClient.query(
|
||||
`BEGIN;
|
||||
SET ob_hnsw_ef_search = ${global.systemEnv?.hnswEfSearch || 100};
|
||||
SELECT id, collection_id, inner_product(vector, [${vector}]) AS score
|
||||
FROM ${DatasetVectorTableName}
|
||||
WHERE team_id='${teamId}'
|
||||
AND dataset_id IN (${datasetIds.map((id) => `'${String(id)}'`).join(',')})
|
||||
${filterCollectionIdSql}
|
||||
${forbidCollectionSql}
|
||||
ORDER BY score desc APPROXIMATE LIMIT ${limit};
|
||||
COMMIT;`
|
||||
);
|
||||
const rows = results?.[3]?.rows as GsSearchRawType[];
|
||||
|
||||
if (!Array.isArray(rows)) {
|
||||
return {
|
||||
results: []
|
||||
};
|
||||
}
|
||||
|
||||
return {
|
||||
results: rows.map((item) => ({
|
||||
id: String(item.id),
|
||||
collectionId: item.collection_id,
|
||||
score: item.score * -1
|
||||
}))
|
||||
};
|
||||
} catch (error) {
|
||||
if (retry <= 0) {
|
||||
return Promise.reject(error);
|
||||
}
|
||||
return this.embRecall({
|
||||
...props,
|
||||
retry: retry - 1
|
||||
});
|
||||
}
|
||||
};
|
||||
getVectorDataByTime = async (start: Date, end: Date) => {
|
||||
const { rows } = await GsClient.query<{
|
||||
id: string;
|
||||
team_id: string;
|
||||
dataset_id: string;
|
||||
}>(`SELECT id, team_id, dataset_id
|
||||
FROM ${DatasetVectorTableName}
|
||||
WHERE createtime BETWEEN '${dayjs(start).format('YYYY-MM-DD HH:mm:ss')}' AND '${dayjs(
|
||||
end
|
||||
).format('YYYY-MM-DD HH:mm:ss')}';
|
||||
`);
|
||||
|
||||
return rows.map((item) => ({
|
||||
id: String(item.id),
|
||||
teamId: item.team_id,
|
||||
datasetId: item.dataset_id
|
||||
}));
|
||||
};
|
||||
getVectorCountByTeamId = async (teamId: string) => {
|
||||
const total = await GsClient.count(DatasetVectorTableName, {
|
||||
where: [['team_id', String(teamId)]]
|
||||
});
|
||||
|
||||
return total;
|
||||
};
|
||||
getVectorCountByDatasetId = async (teamId: string, datasetId: string) => {
|
||||
const total = await GsClient.count(DatasetVectorTableName, {
|
||||
where: [['team_id', String(teamId)], 'and', ['dataset_id', String(datasetId)]]
|
||||
});
|
||||
|
||||
return total;
|
||||
};
|
||||
getVectorCountByCollectionId = async (
|
||||
teamId: string,
|
||||
datasetId: string,
|
||||
collectionId: string
|
||||
) => {
|
||||
const total = await GsClient.count(DatasetVectorTableName, {
|
||||
where: [
|
||||
['team_id', String(teamId)],
|
||||
'and',
|
||||
['dataset_id', String(datasetId)],
|
||||
'and',
|
||||
['collection_id', String(collectionId)]
|
||||
]
|
||||
});
|
||||
|
||||
return total;
|
||||
};
|
||||
}
|
||||
1
packages/service/common/vectorDB/type.d.ts
vendored
1
packages/service/common/vectorDB/type.d.ts
vendored
@@ -6,7 +6,6 @@ declare global {
|
||||
var pgClient: Pool | null;
|
||||
var obClient: MysqlPool | null;
|
||||
var milvusClient: MilvusClient | null;
|
||||
var gsClient: Pool | null;
|
||||
}
|
||||
|
||||
export type EmbeddingRecallItemType = {
|
||||
|
||||
@@ -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) {
|
||||
|
||||
@@ -287,6 +287,7 @@ export const iconPaths = {
|
||||
'core/workflow/template/aiChat': () => import('./icons/core/workflow/template/aiChat.svg'),
|
||||
'core/workflow/template/baseChart': () => import('./icons/core/workflow/template/baseChart.svg'),
|
||||
'core/workflow/template/bing': () => import('./icons/core/workflow/template/bing.svg'),
|
||||
'core/workflow/template/bocha': () => import('./icons/core/workflow/template/bocha.svg'),
|
||||
'core/workflow/template/codeRun': () => import('./icons/core/workflow/template/codeRun.svg'),
|
||||
'core/workflow/template/customFeedback': () =>
|
||||
import('./icons/core/workflow/template/customFeedback.svg'),
|
||||
|
||||
@@ -0,0 +1,5 @@
|
||||
<svg width="113" height="97" viewBox="0 0 113 97" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<path d="M0 31.7259C1.80046 29.9255 3.82784 28.3872 5.96621 27.1988C8.10469 26.0103 10.3126 25.1947 12.4634 24.7992C14.6143 24.4037 16.6664 24.4361 18.5022 24.8938C20.2678 25.334 21.7994 26.1604 23.0183 27.3272L23.021 27.3245L47.189 51.4924L33.4778 65.2037L0 31.7259Z" fill="#C4DEFE"/>
|
||||
<path d="M9.15662 11.5625C11.3617 10.2893 13.7181 9.32825 16.0912 8.73374C18.4645 8.13923 20.8082 7.92284 22.9882 8.09751C25.1681 8.27217 27.1419 8.83457 28.7966 9.75182C30.3881 10.6341 31.6537 11.8287 32.529 13.2712L32.5316 13.2697L32.6082 13.4025C32.6162 13.4162 32.6251 13.4297 32.633 13.4435L49.886 43.3286L33.0941 53.0234L9.15662 11.5625Z" fill="#A6CBFF"/>
|
||||
<path fill-rule="evenodd" clip-rule="evenodd" d="M31.1377 0C33.6839 4.40811e-05 36.2052 0.345872 38.5576 1.01758C40.9099 1.68929 43.0472 2.67394 44.8477 3.91504C46.6482 5.15627 48.0773 6.63021 49.0518 8.25195C49.9888 9.81168 50.4867 11.4792 50.5234 13.166H50.5273V21.4072C56.6623 17.6586 63.874 15.498 71.5898 15.498C93.9304 15.4984 112.042 33.6087 112.042 55.9492C112.042 78.29 93.9305 96.401 71.5898 96.4014C49.3907 96.4014 31.3704 78.5193 31.1426 56.374H31.1377V0ZM71.9473 35.0439C60.1187 35.0441 50.5295 44.6334 50.5293 56.4619C50.5293 63.5338 53.9569 69.8057 59.2412 73.7061C66.4989 79.0625 76.5515 75.3841 85.3955 77.1592C92.613 78.608 97.2369 82.6827 98.3652 83.7686C97.3562 82.731 93.791 78.7138 92.2715 72.3291C89.8011 61.9479 94.8744 49.6043 87.5771 41.8184C83.6695 37.6493 78.1122 35.0441 71.9473 35.0439Z" fill="#006EFF"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 1.6 KiB |
@@ -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",
|
||||
|
||||
7
plugins/webcrawler/SPIDER/package-lock.json
generated
7
plugins/webcrawler/SPIDER/package-lock.json
generated
@@ -4992,9 +4992,10 @@
|
||||
}
|
||||
},
|
||||
"node_modules/tar-fs": {
|
||||
"version": "3.0.8",
|
||||
"resolved": "https://registry.npmmirror.com/tar-fs/-/tar-fs-3.0.8.tgz",
|
||||
"integrity": "sha512-ZoROL70jptorGAlgAYiLoBLItEKw/fUxg9BSYK/dF/GAGYFJOJJJMvjPAKDJraCXFwadD456FCuvLWgfhMsPwg==",
|
||||
"version": "3.0.9",
|
||||
"resolved": "https://registry.npmjs.org/tar-fs/-/tar-fs-3.0.9.tgz",
|
||||
"integrity": "sha512-XF4w9Xp+ZQgifKakjZYmFdkLoSWd34VGKcsTCwlNWM7QG3ZbaxnTsaBwnjFZqHRf/rROxaR8rXnbtwdvaDI+lA==",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"pump": "^3.0.0",
|
||||
"tar-stream": "^3.1.5"
|
||||
|
||||
@@ -29,8 +29,6 @@ MONGODB_LOG_URI=mongodb://username:password@0.0.0.0:27017/fastgpt?authSource=adm
|
||||
PG_URL=postgresql://username:password@host:port/postgres
|
||||
# OceanBase 向量库连接参数
|
||||
OCEANBASE_URL=
|
||||
# openGauss 向量库连接参数
|
||||
OPENGAUSS_URL=
|
||||
# milvus 向量库连接参数
|
||||
MILVUS_ADDRESS=
|
||||
MILVUS_TOKEN=
|
||||
|
||||
@@ -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,
|
||||
|
||||
@@ -49,7 +49,7 @@ const CustomTextInput = () => {
|
||||
createStatus: 'waiting',
|
||||
rawText: data.value,
|
||||
sourceName: data.name,
|
||||
icon: 'file/fill/manual'
|
||||
icon: 'file/fill/txt'
|
||||
}
|
||||
]);
|
||||
goToNext();
|
||||
|
||||
@@ -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,
|
||||
|
||||
@@ -6,6 +6,7 @@ import { DatasetCollectionTypeEnum } from '@fastgpt/global/core/dataset/constant
|
||||
import { NextAPI } from '@/service/middleware/entry';
|
||||
import { WritePermissionVal } from '@fastgpt/global/support/permission/constant';
|
||||
import { type CreateCollectionResponse } from '@/global/core/dataset/api';
|
||||
import { createFileFromText } from '@fastgpt/service/common/file/gridfs/utils';
|
||||
|
||||
async function handler(req: NextApiRequest): CreateCollectionResponse {
|
||||
const { name, text, ...body } = req.body as TextCreateDatasetCollectionParams;
|
||||
@@ -18,6 +19,18 @@ async function handler(req: NextApiRequest): CreateCollectionResponse {
|
||||
per: WritePermissionVal
|
||||
});
|
||||
|
||||
// 1. Create file from text
|
||||
const filename = `${name}.txt`;
|
||||
const { fileId } = await createFileFromText({
|
||||
bucket: 'dataset',
|
||||
filename,
|
||||
text,
|
||||
metadata: {
|
||||
teamId,
|
||||
uid: tmbId
|
||||
}
|
||||
});
|
||||
|
||||
const { collectionId, insertResults } = await createCollectionAndInsertData({
|
||||
dataset,
|
||||
rawText: text,
|
||||
@@ -25,9 +38,9 @@ async function handler(req: NextApiRequest): CreateCollectionResponse {
|
||||
...body,
|
||||
teamId,
|
||||
tmbId,
|
||||
type: DatasetCollectionTypeEnum.virtual,
|
||||
|
||||
name
|
||||
type: DatasetCollectionTypeEnum.file,
|
||||
fileId,
|
||||
name: filename
|
||||
}
|
||||
});
|
||||
|
||||
|
||||
@@ -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();
|
||||
};
|
||||
|
||||
@@ -2,7 +2,7 @@ import { MongoDatasetTraining } from '@fastgpt/service/core/dataset/training/sch
|
||||
import { pushQAUsage } from '@/service/support/wallet/usage/push';
|
||||
import { TrainingModeEnum } from '@fastgpt/global/core/dataset/constants';
|
||||
import { createChatCompletion } from '@fastgpt/service/core/ai/config';
|
||||
import type { ChatCompletionMessageParam, StreamChatType } from '@fastgpt/global/core/ai/type.d';
|
||||
import type { ChatCompletionMessageParam } from '@fastgpt/global/core/ai/type.d';
|
||||
import { addLog } from '@fastgpt/service/common/system/log';
|
||||
import { splitText2Chunks } from '@fastgpt/global/common/string/textSplitter';
|
||||
import { replaceVariable } from '@fastgpt/global/common/string/tools';
|
||||
@@ -1,6 +1,6 @@
|
||||
import { TeamErrEnum } from '@fastgpt/global/common/error/code/team';
|
||||
import { checkTeamAIPoints } from '@fastgpt/service/support/permission/teamLimit';
|
||||
import { sendOneInform } from '../support/user/inform/api';
|
||||
import { sendOneInform } from '../../../support/user/inform/api';
|
||||
import { lockTrainingDataByTeamId } from '@fastgpt/service/core/dataset/training/controller';
|
||||
import { InformLevelEnum } from '@fastgpt/global/support/user/inform/constants';
|
||||
|
||||
@@ -18,7 +18,7 @@ export const checkTeamAiPointsAndLock = async (teamId: string) => {
|
||||
templateParam: {},
|
||||
teamId
|
||||
});
|
||||
console.log('余额不足，暂停【向量】生成任务');
|
||||
console.log('余额不足，暂停训练生成任务');
|
||||
await lockTrainingDataByTeamId(teamId);
|
||||
} catch (error) {}
|
||||
}
|
||||
@@ -1,5 +1,5 @@
|
||||
import { generateQA } from '@/service/events/generateQA';
|
||||
import { generateVector } from '@/service/events/generateVector';
|
||||
import { generateQA } from '@/service/core/dataset/queues/generateQA';
|
||||
import { generateVector } from '@/service/core/dataset/queues/generateVector';
|
||||
import { TrainingModeEnum } from '@fastgpt/global/core/dataset/constants';
|
||||
import { type DatasetTrainingSchemaType } from '@fastgpt/global/core/dataset/type';
|
||||
import { MongoDatasetTraining } from '@fastgpt/service/core/dataset/training/schema';
|
||||
|
||||
128
test/cases/service/core/chat/utils.test.ts
Normal file
128
test/cases/service/core/chat/utils.test.ts
Normal file
@@ -0,0 +1,128 @@
|
||||
import { describe, it, expect } from 'vitest';
|
||||
import { processChatTimeFilter } from '@/service/core/chat/utils';
|
||||
import type { DatasetCiteItemType } from '@fastgpt/global/core/dataset/type';
|
||||
|
||||
describe('processChatTimeFilter', () => {
|
||||
const baseTime = new Date('2025-01-01');
|
||||
|
||||
it('should return original item if no history', () => {
|
||||
const item: DatasetCiteItemType = {
|
||||
id: '1',
|
||||
q: 'original q',
|
||||
a: 'original a',
|
||||
updateTime: baseTime.getTime(),
|
||||
history: undefined
|
||||
};
|
||||
|
||||
const result = processChatTimeFilter([item], baseTime);
|
||||
expect(result).toEqual([item]);
|
||||
});
|
||||
|
||||
it('should return original item if updateTime <= chatTime', () => {
|
||||
const item: DatasetCiteItemType = {
|
||||
id: '1',
|
||||
q: 'original q',
|
||||
a: 'original a',
|
||||
updateTime: baseTime.getTime() - 1000,
|
||||
history: [
|
||||
{
|
||||
q: 'history q',
|
||||
a: 'history a',
|
||||
updateTime: baseTime.getTime() - 2000
|
||||
}
|
||||
]
|
||||
};
|
||||
|
||||
const result = processChatTimeFilter([item], baseTime);
|
||||
expect(result).toEqual([item]);
|
||||
});
|
||||
|
||||
it('should return history item if one exists before chatTime', () => {
|
||||
const item: DatasetCiteItemType = {
|
||||
id: '1',
|
||||
q: 'original q',
|
||||
a: 'original a',
|
||||
updateTime: baseTime.getTime() + 2000,
|
||||
history: [
|
||||
{
|
||||
q: 'history q',
|
||||
a: 'history a',
|
||||
updateTime: baseTime.getTime() - 1000
|
||||
}
|
||||
]
|
||||
};
|
||||
|
||||
const result = processChatTimeFilter([item], baseTime);
|
||||
expect(result).toEqual([
|
||||
{
|
||||
...item,
|
||||
q: 'history q',
|
||||
a: 'history a',
|
||||
updateTime: baseTime.getTime() - 1000,
|
||||
updated: true
|
||||
}
|
||||
]);
|
||||
});
|
||||
|
||||
it('should return original item if no history before chatTime', () => {
|
||||
const item: DatasetCiteItemType = {
|
||||
id: '1',
|
||||
q: 'original q',
|
||||
a: 'original a',
|
||||
updateTime: baseTime.getTime() + 2000,
|
||||
history: [
|
||||
{
|
||||
q: 'history q',
|
||||
a: 'history a',
|
||||
updateTime: baseTime.getTime() + 1000
|
||||
}
|
||||
]
|
||||
};
|
||||
|
||||
const result = processChatTimeFilter([item], baseTime);
|
||||
expect(result).toEqual([item]);
|
||||
});
|
||||
|
||||
it('should handle multiple items', () => {
|
||||
const items: DatasetCiteItemType[] = [
|
||||
{
|
||||
id: '1',
|
||||
q: 'original q1',
|
||||
a: 'original a1',
|
||||
updateTime: baseTime.getTime() + 2000,
|
||||
history: [
|
||||
{
|
||||
q: 'history q1',
|
||||
a: 'history a1',
|
||||
updateTime: baseTime.getTime() - 1000
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
id: '2',
|
||||
q: 'original q2',
|
||||
a: 'original a2',
|
||||
updateTime: baseTime.getTime() - 1000,
|
||||
history: [
|
||||
{
|
||||
q: 'history q2',
|
||||
a: 'history a2',
|
||||
updateTime: baseTime.getTime() - 2000
|
||||
}
|
||||
]
|
||||
}
|
||||
];
|
||||
|
||||
const result = processChatTimeFilter(items, baseTime);
|
||||
expect(result).toEqual([
|
||||
{
|
||||
...items[0],
|
||||
q: 'history q1',
|
||||
a: 'history a1',
|
||||
updateTime: baseTime.getTime() - 1000,
|
||||
updated: true
|
||||
},
|
||||
items[1]
|
||||
]);
|
||||
});
|
||||
});
|
||||
Reference in New Issue
Block a user