Compare commits
51 Commits
v4.8.21
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v4.8.23-fi
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@@ -68,14 +68,3 @@ jobs:
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SEALOS_TYPE: 'pr_comment'
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SEALOS_FILENAME: 'report.md'
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SEALOS_REPLACE_TAG: 'DEFAULT_REPLACE_DEPLOY'
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|
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helm-check:
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runs-on: ubuntu-20.04
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steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v3
|
||||
- name: Helm Check
|
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run: |
|
||||
helm dependency update files/helm/fastgpt
|
||||
helm lint files/helm/fastgpt
|
||||
helm package files/helm/fastgpt
|
||||
4
.github/workflows/helm-release.yaml
vendored
4
.github/workflows/helm-release.yaml
vendored
@@ -24,6 +24,6 @@ jobs:
|
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export APP_VERSION=${{ steps.vars.outputs.tag }}
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export HELM_VERSION=${{ steps.vars.outputs.tag }}
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export HELM_REPO=ghcr.io/${{ github.repository_owner }}
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helm dependency update files/helm/fastgpt
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helm package files/helm/fastgpt --version ${HELM_VERSION}-helm --app-version ${APP_VERSION} -d bin
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helm dependency update deploy/helm/fastgpt
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helm package deploy/helm/fastgpt --version ${HELM_VERSION}-helm --app-version ${APP_VERSION} -d bin
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helm push bin/fastgpt-${HELM_VERSION}-helm.tgz oci://${HELM_REPO}
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2
.vscode/nextapi.code-snippets
vendored
2
.vscode/nextapi.code-snippets
vendored
@@ -58,7 +58,7 @@
|
||||
"body": [
|
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"import '@/pages/api/__mocks__/base';",
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"import { root } from '@/pages/api/__mocks__/db/init';",
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"import { getTestRequest } from '@/test/utils';",
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"import { getTestRequest } from '@fastgpt/service/test/utils'; ;",
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"import { AppErrEnum } from '@fastgpt/global/common/error/code/app';",
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"import handler from './demo';",
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"",
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@@ -114,15 +114,15 @@ 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:v4.8.20-fix2 # git
|
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# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.8.20-fix2 # 阿里云
|
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image: ghcr.io/labring/fastgpt-sandbox:v4.8.23-fix # git
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# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.8.23-fix # 阿里云
|
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networks:
|
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- fastgpt
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restart: always
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fastgpt:
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container_name: fastgpt
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image: ghcr.io/labring/fastgpt:v4.8.20-fix2 # git
|
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# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.8.20-fix2 # 阿里云
|
||||
image: ghcr.io/labring/fastgpt:v4.8.23-fix # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.8.23-fix # 阿里云
|
||||
ports:
|
||||
- 3000:3000
|
||||
networks:
|
||||
@@ -133,7 +133,7 @@ services:
|
||||
- sandbox
|
||||
restart: always
|
||||
environment:
|
||||
# 前端访问地址: http://localhost:3000
|
||||
# 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=
|
||||
# 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。
|
||||
- DEFAULT_ROOT_PSW=1234
|
||||
@@ -72,15 +72,15 @@ services:
|
||||
# fastgpt
|
||||
sandbox:
|
||||
container_name: sandbox
|
||||
image: ghcr.io/labring/fastgpt-sandbox:v4.8.20-fix2 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.8.20-fix2 # 阿里云
|
||||
image: ghcr.io/labring/fastgpt-sandbox:v4.8.23-fix # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.8.23-fix # 阿里云
|
||||
networks:
|
||||
- fastgpt
|
||||
restart: always
|
||||
fastgpt:
|
||||
container_name: fastgpt
|
||||
image: ghcr.io/labring/fastgpt:v4.8.20-fix2 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.8.20-fix2 # 阿里云
|
||||
image: ghcr.io/labring/fastgpt:v4.8.23-fix # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.8.23-fix # 阿里云
|
||||
ports:
|
||||
- 3000:3000
|
||||
networks:
|
||||
@@ -91,7 +91,7 @@ services:
|
||||
- sandbox
|
||||
restart: always
|
||||
environment:
|
||||
# 前端访问地址: http://localhost:3000
|
||||
# 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=
|
||||
# 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。
|
||||
- DEFAULT_ROOT_PSW=1234
|
||||
@@ -53,15 +53,15 @@ services:
|
||||
wait $$!
|
||||
sandbox:
|
||||
container_name: sandbox
|
||||
image: ghcr.io/labring/fastgpt-sandbox:v4.8.20-fix2 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.8.20-fix2 # 阿里云
|
||||
image: ghcr.io/labring/fastgpt-sandbox:v4.8.23-fix # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.8.23-fix # 阿里云
|
||||
networks:
|
||||
- fastgpt
|
||||
restart: always
|
||||
fastgpt:
|
||||
container_name: fastgpt
|
||||
image: ghcr.io/labring/fastgpt:v4.8.20-fix2 # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.8.20-fix2 # 阿里云
|
||||
image: ghcr.io/labring/fastgpt:v4.8.23-fix # git
|
||||
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.8.23-fix # 阿里云
|
||||
ports:
|
||||
- 3000:3000
|
||||
networks:
|
||||
@@ -71,7 +71,7 @@ services:
|
||||
- sandbox
|
||||
restart: always
|
||||
environment:
|
||||
# 前端访问地址: http://localhost:3000
|
||||
# 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=
|
||||
# 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。
|
||||
- DEFAULT_ROOT_PSW=1234
|
||||
BIN
docSite/assets/imgs/appid.png
Normal file
BIN
docSite/assets/imgs/appid.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 332 KiB |
@@ -31,9 +31,9 @@ weight: 920
|
||||
|
||||
3 个模型代码分别为:
|
||||
|
||||
1. [https://github.com/labring/FastGPT/tree/main/python/bge-rerank/bge-reranker-base](https://github.com/labring/FastGPT/tree/main/python/bge-rerank/bge-reranker-base)
|
||||
2. [https://github.com/labring/FastGPT/tree/main/python/bge-rerank/bge-reranker-large](https://github.com/labring/FastGPT/tree/main/python/bge-rerank/bge-reranker-large)
|
||||
3. [https://github.com/labring/FastGPT/tree/main/python/bge-rerank/bge-reranker-v2-m3](https://github.com/labring/FastGPT/tree/main/python/bge-rerank/bge-reranker-v2-m3)
|
||||
1. [https://github.com/labring/FastGPT/tree/main/plugins/rerank-bge/bge-reranker-base](https://github.com/labring/FastGPT/tree/main/plugins/rerank-bge/bge-reranker-base)
|
||||
2. [https://github.com/labring/FastGPT/tree/main/plugins/rerank-bge/bge-reranker-large](https://github.com/labring/FastGPT/tree/main/plugins/rerank-bge/bge-reranker-large)
|
||||
3. [https://github.com/labring/FastGPT/tree/main/plugins/rerank-bge/bge-reranker-v2-m3](https://github.com/labring/FastGPT/tree/main/plugins/rerank-bge/bge-reranker-v2-m3)
|
||||
|
||||
### 3. 安装依赖
|
||||
|
||||
|
||||
@@ -19,7 +19,7 @@ PDF 是一个相对复杂的文件格式,在 FastGPT 内置的 pdf 解析器
|
||||
|
||||
### 1. 按照 Marker
|
||||
|
||||
参考文档 [Marker 安装教程](https://github.com/labring/FastGPT/tree/main/python/pdf-marker),安装 Marker 模型。封装的 API 已经适配了 FastGPT 自定义解析服务。
|
||||
参考文档 [Marker 安装教程](https://github.com/labring/FastGPT/tree/main/plugins/model/pdf-marker),安装 Marker 模型。封装的 API 已经适配了 FastGPT 自定义解析服务。
|
||||
|
||||
这里介绍快速 Docker 安装的方法:
|
||||
|
||||
|
||||
@@ -118,7 +118,7 @@ brew install orbstack
|
||||
非 Linux 环境或无法访问外网环境,可手动创建一个目录,并下载配置文件和对应版本的`docker-compose.yml`,在这个文件夹中依据下载的配置文件运行docker,若作为本地开发使用推荐`docker-compose-pgvector`版本,并且自行拉取并运行`sandbox`和`fastgpt`,并在docker配置文件中注释掉`sandbox`和`fastgpt`的部分
|
||||
|
||||
- [config.json](https://raw.githubusercontent.com/labring/FastGPT/refs/heads/main/projects/app/data/config.json)
|
||||
- [docker-compose.yml](https://github.com/labring/FastGPT/blob/main/files/docker) (注意,不同向量库版本的文件不一样)
|
||||
- [docker-compose.yml](https://github.com/labring/FastGPT/blob/main/deploy/docker) (注意,不同向量库版本的文件不一样)
|
||||
|
||||
{{% alert icon="🤖" context="success" %}}
|
||||
|
||||
@@ -134,11 +134,11 @@ cd fastgpt
|
||||
curl -O https://raw.githubusercontent.com/labring/FastGPT/main/projects/app/data/config.json
|
||||
|
||||
# pgvector 版本(测试推荐,简单快捷)
|
||||
curl -o docker-compose.yml https://raw.githubusercontent.com/labring/FastGPT/main/files/docker/docker-compose-pgvector.yml
|
||||
curl -o docker-compose.yml https://raw.githubusercontent.com/labring/FastGPT/main/deploy/docker/docker-compose-pgvector.yml
|
||||
# milvus 版本
|
||||
# curl -o docker-compose.yml https://raw.githubusercontent.com/labring/FastGPT/main/files/docker/docker-compose-milvus.yml
|
||||
# curl -o docker-compose.yml https://raw.githubusercontent.com/labring/FastGPT/main/deploy/docker/docker-compose-milvus.yml
|
||||
# zilliz 版本
|
||||
# curl -o docker-compose.yml https://raw.githubusercontent.com/labring/FastGPT/main/files/docker/docker-compose-zilliz.yml
|
||||
# curl -o docker-compose.yml https://raw.githubusercontent.com/labring/FastGPT/main/deploy/docker/docker-compose-zilliz.yml
|
||||
```
|
||||
|
||||
### 2. 修改环境变量
|
||||
@@ -201,6 +201,8 @@ docker restart oneapi
|
||||
|
||||
在OneApi中添加合适的AI模型渠道。[点击查看相关教程](/docs/development/modelconfig/one-api/)
|
||||
|
||||
只需要添加模型即可,模板已经配置好了oneapi的连接地址和令牌,无需变更。
|
||||
|
||||
### 5. 访问 FastGPT
|
||||
|
||||
目前可以通过 `ip:3000` 直接访问(注意防火墙)。登录用户名为 `root`,密码为`docker-compose.yml`环境变量里设置的 `DEFAULT_ROOT_PSW`。
|
||||
@@ -211,7 +213,7 @@ docker restart oneapi
|
||||
|
||||
### 6. 配置模型
|
||||
|
||||
务必先配置至少一组模型,否则系统无法正常使用。
|
||||
登录FastGPT后,进入模型配置页面,务必先配置至少一个语言模型和一个向量模型,否则系统无法正常使用。
|
||||
|
||||
[点击查看模型配置教程](/docs/development/modelConfig/intro/)
|
||||
|
||||
|
||||
@@ -142,6 +142,10 @@ OneAPI 的 API Key 配置错误,需要修改`OPENAI_API_KEY`环境变量,并
|
||||
3. ....
|
||||
|
||||
|
||||
### Tiktoken 下载失败
|
||||
|
||||
由于 OneAPI 会在启动时从网络下载一个 tiktoken 的依赖,如果网络异常,就会导致启动失败。可以参考[OneAPI 离线部署](https://blog.csdn.net/wanh/article/details/139039216)解决。
|
||||
|
||||
## 四、常见模型问题
|
||||
|
||||
### 如何检查模型可用性问题
|
||||
|
||||
@@ -15,8 +15,8 @@ weight: 705
|
||||
|
||||
- [Git](http://git-scm.com/)
|
||||
- [Docker](https://www.docker.com/)(构建镜像)
|
||||
- [Node.js v18.17 / v20.x](http://nodejs.org)(版本尽量一样,可以使用nvm管理node版本)
|
||||
- [pnpm](https://pnpm.io/) 版本 8.6.0 (目前官方的开发环境)
|
||||
- [Node.js v20.14.0](http://nodejs.org)(版本尽量一样,可以使用nvm管理node版本)
|
||||
- [pnpm](https://pnpm.io/) 推荐版本 9.4.0 (目前官方的开发环境)
|
||||
- make命令: 根据不同平台,百度安装 (官方是GNU Make 4.3)
|
||||
|
||||
## 开始本地开发
|
||||
@@ -77,8 +77,6 @@ Mongo 数据库需要注意,需要注意在连接地址中增加 `directConnec
|
||||
可参考项目根目录下的 `dev.md`,第一次编译运行可能会有点慢,需要点耐心哦
|
||||
|
||||
```bash
|
||||
# 给自动化脚本代码执行权限(非 linux 系统, 可以手动执行里面的 postinstall.sh 文件内容)
|
||||
chmod -R +x ./scripts/
|
||||
# 代码根目录下执行,会安装根 package、projects 和 packages 内所有依赖
|
||||
# 如果提示 isolate-vm 安装失败,可以参考:https://github.com/laverdet/isolated-vm?tab=readme-ov-file#requirements
|
||||
pnpm i
|
||||
|
||||
@@ -23,7 +23,7 @@ weight: 744
|
||||
|
||||
{{% alert icon=" " context="info" %}}
|
||||
- [SiliconCloud(硅基流动)](https://cloud.siliconflow.cn/i/TR9Ym0c4): 提供开源模型调用的平台。
|
||||
- [Sealos AIProxy](https://hzh.sealos.run/?openapp=system-aiproxy): 提供国内各家模型代理,无需逐一申请 api。
|
||||
- [Sealos AIProxy](https://cloud.sealos.run/?uid=fnWRt09fZP&openapp=system-aiproxy): 提供国内各家模型代理,无需逐一申请 api。
|
||||
{{% /alert %}}
|
||||
|
||||
在 OneAPI 配置好模型后,你就可以打开 FastGPT 页面,启用对应模型了。
|
||||
@@ -43,8 +43,7 @@ weight: 744
|
||||
{{% alert icon="🤖 " context="success" %}}
|
||||
注意:
|
||||
1. 目前语音识别模型和重排模型仅会生效一个,所以配置时候,只需要配置一个即可。
|
||||
2. 系统必须至少有一个语言模型和一个索引模型才能正常使用。
|
||||
3. 使用知识库功能,至少要有一个语言模型,用于知识库文件处理(可以在模型配置时候打开该开关),否则知识库会报错。
|
||||
2. 系统至少需要一个语言模型和一个索引模型才能正常使用。
|
||||
{{% /alert %}}
|
||||
|
||||
#### 核心配置
|
||||
@@ -468,4 +467,4 @@ OneAPI 的语言识别接口,无法正确的识别其他模型(会始终识
|
||||
"charsPointsPrice": 0
|
||||
}
|
||||
}
|
||||
```
|
||||
```
|
||||
|
||||
@@ -7,6 +7,12 @@ toc: true
|
||||
weight: 852
|
||||
---
|
||||
|
||||
# 如何获取 AppId
|
||||
|
||||
可在应用详情的路径里获取 AppId。
|
||||
|
||||

|
||||
|
||||
# 发起对话
|
||||
|
||||
{{% alert icon="🤖 " context="success" %}}
|
||||
@@ -102,8 +108,8 @@ curl --location --request POST 'http://localhost:3000/api/v1/chat/completions' \
|
||||
{{% alert context="info" %}}
|
||||
- headers.Authorization: Bearer {{apikey}}
|
||||
- chatId: string | undefined 。
|
||||
- 为 `undefined` 时(不传入),不使用 FastGpt 提供的上下文功能,完全通过传入的 messages 构建上下文。 不会将你的记录存储到数据库中,你也无法在记录汇总中查阅到。
|
||||
- 为`非空字符串`时,意味着使用 chatId 进行对话,自动从 FastGpt 数据库取历史记录,并使用 messages 数组最后一个内容作为用户问题。请自行确保 chatId 唯一,长度小于250,通常可以是自己系统的对话框ID。
|
||||
- 为 `undefined` 时(不传入),不使用 FastGpt 提供的上下文功能,完全通过传入的 messages 构建上下文。
|
||||
- 为`非空字符串`时,意味着使用 chatId 进行对话,自动从 FastGpt 数据库取历史记录,并使用 messages 数组最后一个内容作为用户问题,其余 message 会被忽略。请自行确保 chatId 唯一,长度小于250,通常可以是自己系统的对话框ID。
|
||||
- messages: 结构与 [GPT接口](https://platform.openai.com/docs/api-reference/chat/object) chat模式一致。
|
||||
- responseChatItemId: string | undefined 。如果传入,则会将该值作为本次对话的响应消息的 ID,FastGPT 会自动将该 ID 存入数据库。请确保,在当前`chatId`下,`responseChatItemId`是唯一的。
|
||||
- detail: 是否返回中间值(模块状态,响应的完整结果等),`stream模式`下会通过`event`进行区分,`非stream模式`结果保存在`responseData`中。
|
||||
|
||||
@@ -735,7 +735,7 @@ data 为集合的 ID。
|
||||
|
||||
**4.8.19+**
|
||||
```bash
|
||||
curl --location --request POST 'http://localhost:3000/api/core/dataset/collection/listv2' \
|
||||
curl --location --request POST 'http://localhost:3000/api/core/dataset/collection/listV2' \
|
||||
--header 'Authorization: Bearer {{authorization}}' \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data-raw '{
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
---
|
||||
title: 'Api Key 使用与鉴权'
|
||||
description: 'FastGPT Api Key 使用与鉴权'
|
||||
title: 'OpenAPI 介绍'
|
||||
description: 'FastGPT OpenAPI 介绍'
|
||||
icon: 'key'
|
||||
draft: false
|
||||
toc: true
|
||||
@@ -27,6 +27,7 @@ FastGPT 的 API Key **有 2 类**,一类是全局通用的 key (无法直接
|
||||
| --------------------- | --------------------- |
|
||||
|  |  |
|
||||
|
||||
|
||||
## 基本配置
|
||||
|
||||
OpenAPI 中,所有的接口都通过 Header.Authorization 进行鉴权。
|
||||
@@ -1,5 +1,5 @@
|
||||
---
|
||||
title: 'V4.8.18'
|
||||
title: 'V4.8.18(包含升级脚本)'
|
||||
description: 'FastGPT V4.8.18 更新说明'
|
||||
icon: 'upgrade'
|
||||
draft: false
|
||||
|
||||
@@ -20,7 +20,7 @@ SANDBOX_URL=内网地址
|
||||
|
||||
## Docker 部署
|
||||
|
||||
可以拉取最新 [docker-compose.yml](https://github.com/labring/FastGPT/blob/main/files/docker/docker-compose.yml) 文件参考
|
||||
可以拉取最新 [docker-compose.yml](https://github.com/labring/FastGPT/blob/main/deploy/docker/docker-compose.yml) 文件参考
|
||||
|
||||
1. 新增一个容器 `sandbox`
|
||||
2. fastgpt 和 fastgpt-pro(商业版) 容器新增环境变量: `SANDBOX_URL`
|
||||
|
||||
@@ -1,13 +1,21 @@
|
||||
---
|
||||
title: 'V4.8.21(进行中)'
|
||||
title: 'V4.8.21'
|
||||
description: 'FastGPT V4.8.21 更新说明'
|
||||
icon: 'upgrade'
|
||||
draft: false
|
||||
toc: true
|
||||
weight: 804
|
||||
weight: 803
|
||||
---
|
||||
|
||||
## 更新指南
|
||||
|
||||
### 1. 做好数据库备份
|
||||
|
||||
### 2. 更新镜像:
|
||||
|
||||
- 更新 fastgpt 镜像 tag: v4.8.21-fix
|
||||
- 更新 fastgpt-pro 商业版镜像 tag: v4.8.21-fix
|
||||
- Sandbox 镜像无需更新
|
||||
|
||||
## 完整更新内容
|
||||
|
||||
|
||||
61
docSite/content/zh-cn/docs/development/upgrading/4822.md
Normal file
61
docSite/content/zh-cn/docs/development/upgrading/4822.md
Normal file
@@ -0,0 +1,61 @@
|
||||
---
|
||||
title: 'V4.8.22(包含升级脚本)'
|
||||
description: 'FastGPT V4.8.22 更新说明'
|
||||
icon: 'upgrade'
|
||||
draft: false
|
||||
toc: true
|
||||
weight: 802
|
||||
---
|
||||
|
||||
## 🌟更新指南
|
||||
|
||||
### 1. 做好数据库备份
|
||||
|
||||
### 2. 更新镜像:
|
||||
|
||||
- 更新 fastgpt 镜像 tag: v4.8.22
|
||||
- 更新 fastgpt-pro 商业版镜像 tag: v4.8.22
|
||||
- Sandbox 镜像无需更新
|
||||
|
||||
### 3. 运行升级脚本
|
||||
|
||||
仅商业版,并提供 Saas 服务的用户需要运行该升级脚本。
|
||||
|
||||
从任意终端,发起 1 个 HTTP 请求。其中 {{rootkey}} 替换成环境变量里的 `rootkey`;{{host}} 替换成**FastGPT 域名**。
|
||||
|
||||
```bash
|
||||
curl --location --request POST 'https://{{host}}/api/admin/initv4822' \
|
||||
--header 'rootkey: {{rootkey}}' \
|
||||
--header 'Content-Type: application/json'
|
||||
```
|
||||
|
||||
会迁移联系方式到对应用户表中。
|
||||
|
||||
## 🚀 新增内容
|
||||
|
||||
1. AI 对话节点解析 `<think></think>` 标签内容作为思考链,便于各类模型进行思考链输出。需主动开启模型输出思考。
|
||||
2. 对话 API 优化,无论是否传递 chatId,都会保存对话日志。未传递 chatId,则随机生成一个 chatId 来进行存储。
|
||||
3. ppio 模型提供商
|
||||
|
||||
## ⚙️ 优化
|
||||
|
||||
1. 模型未配置时提示,减少冲突提示。
|
||||
2. 使用记录代码。
|
||||
3. 内容提取节点,字段描述过长时换行。同时修改其输出名用 key,而不是 description。
|
||||
4. 团队管理交互。
|
||||
5. 对话接口,非流响应,增加报错字段。
|
||||
|
||||
## 🐛 修复
|
||||
|
||||
1. 思考内容未进入到输出 Tokens.
|
||||
2. 思考链流输出时,有时与正文顺序偏差。
|
||||
3. API 调用工作流,如果传递的图片不支持 Head 检测时,图片会被过滤。已增加该类错误检测,避免被错误过滤。
|
||||
4. 模板市场部分模板错误。
|
||||
5. 免登录窗口无法正常判断语言识别是否开启。
|
||||
6. 对话日志导出,未兼容 sub path。
|
||||
7. 切换团队时未刷新成员列表
|
||||
8. list 接口在联查 member 时,存在空指针可能性。
|
||||
9. 工作流基础节点无法升级。
|
||||
10. 向量检索结果未去重。
|
||||
11. 用户选择节点无法正常连线。
|
||||
12. 对话记录保存时,source 未正常记录。
|
||||
54
docSite/content/zh-cn/docs/development/upgrading/4823.md
Normal file
54
docSite/content/zh-cn/docs/development/upgrading/4823.md
Normal file
@@ -0,0 +1,54 @@
|
||||
---
|
||||
title: 'V4.8.23'
|
||||
description: 'FastGPT V4.8.23 更新说明'
|
||||
icon: 'upgrade'
|
||||
draft: false
|
||||
toc: true
|
||||
weight: 802
|
||||
---
|
||||
|
||||
## 更新指南
|
||||
|
||||
### 1. 做好数据库备份
|
||||
|
||||
### 2. 更新镜像:
|
||||
|
||||
- 更新 fastgpt 镜像 tag: v4.8.23-fix
|
||||
- 更新 fastgpt-pro 商业版镜像 tag: v4.8.23-fix
|
||||
- Sandbox 镜像无需更新
|
||||
|
||||
### 3. 运行升级脚本
|
||||
|
||||
从任意终端,发起 1 个 HTTP 请求。其中 {{rootkey}} 替换成环境变量里的 `rootkey`;{{host}} 替换成**FastGPT 域名**。
|
||||
|
||||
```bash
|
||||
curl --location --request POST 'https://{{host}}/api/admin/initv4823' \
|
||||
--header 'rootkey: {{rootkey}}' \
|
||||
--header 'Content-Type: application/json'
|
||||
```
|
||||
|
||||
脚本会清理一些知识库脏数据,主要是多余的全文索引。
|
||||
|
||||
## 🚀 新增内容
|
||||
|
||||
1. 增加默认“知识库文本理解模型”配置
|
||||
2. AI proxy V1版,可替换 OneAPI使用,同时提供完整模型调用日志,便于排查问题。
|
||||
3. 增加工单入口支持。
|
||||
|
||||
## ⚙️ 优化
|
||||
|
||||
1. 模型配置表单,增加必填项校验。
|
||||
2. 集合列表数据统计方式,提高大数据量统计性能。
|
||||
3. 优化数学公式,转义 Latex 格式成 Markdown 格式。
|
||||
4. 解析文档图片,图片太大时,自动忽略。
|
||||
5. 时间选择器,当天开始时间自动设0,结束设置设 23:59:59,避免 UI 与实际逻辑偏差。
|
||||
6. 升级 mongoose 库版本依赖。
|
||||
|
||||
## 🐛 修复
|
||||
|
||||
1. 标签过滤时,子文件夹未成功过滤。
|
||||
2. 暂时移除 md 阅读优化,避免链接分割错误。
|
||||
3. 离开团队时,未刷新成员列表。
|
||||
4. PPTX 编码错误,导致解析失败。
|
||||
5. 删除知识库单条数据时,全文索引未跟随删除。
|
||||
6. 修复 Mongo Dataset text 索引在查询数据时未生效。
|
||||
@@ -15,7 +15,7 @@ weight: 821
|
||||
|
||||
## V4.8.3 更新说明
|
||||
|
||||
1. 新增 - 支持 Milvus 数据库, 可参考最新的 [docker-compose-milvus.yml](https://github.com/labring/FastGPT/blob/main/files/docker/docker-compose-milvus.yml).
|
||||
1. 新增 - 支持 Milvus 数据库, 可参考最新的 [docker-compose-milvus.yml](https://github.com/labring/FastGPT/blob/main/deploy/docker/docker-compose-milvus.yml).
|
||||
2. 新增 - 给 chat 接口 empty answer 增加 log,便于排查模型问题。
|
||||
3. 新增 - ifelse判断器,字符串支持正则。
|
||||
4. 新增 - 代码运行支持 console.log 输出调试。
|
||||
|
||||
@@ -7,11 +7,11 @@ toc: true
|
||||
weight: 102
|
||||
---
|
||||
|
||||
更多使用技巧,[查看视屏教程](https://www.bilibili.com/video/BV1sH4y1T7s9)
|
||||
更多使用技巧,[查看视频教程](https://www.bilibili.com/video/BV1sH4y1T7s9)
|
||||
|
||||
## 知识库
|
||||
|
||||
开始前,请准备一份测试电子文档,WORD,PDF,TXT,excel,markdown 都可以,比如公司休假制度,不涉密的销售说辞,产品知识等等。
|
||||
开始前,请准备一份测试电子文档,WORD、PDF、TXT、excel、markdown 都可以,比如公司休假制度、不涉密的销售说辞、产品知识等等。
|
||||
|
||||
这里使用 FastGPT 中文 README 文件为例。
|
||||
|
||||
@@ -31,7 +31,7 @@ weight: 102
|
||||
|
||||

|
||||
|
||||
点击上传后我们需要等待数据处理完成,等到我们上传的文件状态为可用。
|
||||
点击上传后我们需要等待数据处理完成,直到我们上传的文件状态为可用。
|
||||
|
||||

|
||||
|
||||
|
||||
@@ -20,7 +20,7 @@ weight: 502
|
||||

|
||||
|
||||
{{% alert icon="🍅" context="success" %}}
|
||||
Tips: 安全起见,你可以设置一个额度或者过期时间,放置 key 被滥用。
|
||||
Tips: 安全起见,你可以设置一个额度或者过期时间,防止 key 被滥用。
|
||||
{{% /alert %}}
|
||||
|
||||
|
||||
|
||||
@@ -7,7 +7,7 @@
|
||||
"format-code": "prettier --config \"./.prettierrc.js\" --write \"./**/src/**/*.{ts,tsx,scss}\"",
|
||||
"format-doc": "zhlint --dir ./docSite *.md --fix",
|
||||
"gen:theme-typings": "chakra-cli tokens packages/web/styles/theme.ts --out node_modules/.pnpm/node_modules/@chakra-ui/styled-system/dist/theming.types.d.ts",
|
||||
"postinstall": "sh ./scripts/postinstall.sh",
|
||||
"postinstall": "pnpm gen:theme-typings",
|
||||
"initIcon": "node ./scripts/icon/init.js",
|
||||
"previewIcon": "node ./scripts/icon/index.js",
|
||||
"api:gen": "tsc ./scripts/openapi/index.ts && node ./scripts/openapi/index.js && npx @redocly/cli build-docs ./scripts/openapi/openapi.json -o ./projects/app/public/openapi/index.html",
|
||||
|
||||
3
packages/README.md
Normal file
3
packages/README.md
Normal file
@@ -0,0 +1,3 @@
|
||||
# 目录说明
|
||||
|
||||
该目录为 FastGPT 的依赖包,多端复用。
|
||||
@@ -4,6 +4,7 @@ import { ErrType } from '../errorCode';
|
||||
/* dataset: 501000 */
|
||||
export enum DatasetErrEnum {
|
||||
unExist = 'unExistDataset',
|
||||
unExistCollection = 'unExistCollection',
|
||||
unAuthDataset = 'unAuthDataset',
|
||||
unCreateCollection = 'unCreateCollection',
|
||||
unAuthDatasetCollection = 'unAuthDatasetCollection',
|
||||
@@ -28,6 +29,10 @@ const datasetErr = [
|
||||
statusText: DatasetErrEnum.unExist,
|
||||
message: 'core.dataset.error.unExistDataset'
|
||||
},
|
||||
{
|
||||
statusText: DatasetErrEnum.unExistCollection,
|
||||
message: i18nT('common:error_collection_not_exist')
|
||||
},
|
||||
{
|
||||
statusText: DatasetErrEnum.unAuthDataset,
|
||||
message: 'core.dataset.error.unAuthDataset'
|
||||
|
||||
@@ -20,4 +20,4 @@ export const ReadFileBaseUrl = `${process.env.FILE_DOMAIN || process.env.FE_DOMA
|
||||
|
||||
export const documentFileType = '.txt, .docx, .csv, .xlsx, .pdf, .md, .html, .pptx';
|
||||
export const imageFileType =
|
||||
'.jpg, .jpeg, .png, .gif, .bmp, .webp, .svg, .tiff, .tif, .ico, .heic, .heif, .avif';
|
||||
'.jpg, .jpeg, .png, .gif, .bmp, .webp, .svg, .tiff, .tif, .ico, .heic, .heif, .avif, .raw, .cr2, .nef, .arw, .dng, .psd, .ai, .eps, .emf, .wmf, .jfif, .exif, .pgm, .ppm, .pbm, .jp2, .j2k, .jpf, .jpx, .jpm, .mj2, .xbm, .pcx';
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import { detect } from 'jschardet';
|
||||
import { documentFileType, imageFileType } from './constants';
|
||||
import { documentFileType } from './constants';
|
||||
import { ChatFileTypeEnum } from '../../core/chat/constants';
|
||||
import { UserChatItemValueItemType } from '../../core/chat/type';
|
||||
import * as fs from 'fs';
|
||||
@@ -25,6 +25,7 @@ export const detectFileEncodingByPath = async (path: string) => {
|
||||
const fd = await fs.promises.open(path, 'r');
|
||||
try {
|
||||
// Read file head
|
||||
// @ts-ignore
|
||||
const { bytesRead } = await fd.read(buffer, 0, MAX_BYTES, 0);
|
||||
const actualBuffer = buffer.slice(0, bytesRead);
|
||||
|
||||
@@ -37,40 +38,49 @@ export const detectFileEncodingByPath = async (path: string) => {
|
||||
// Url => user upload file type
|
||||
export const parseUrlToFileType = (url: string): UserChatItemValueItemType['file'] | undefined => {
|
||||
if (typeof url !== 'string') return;
|
||||
const parseUrl = new URL(url, 'https://locaohost:3000');
|
||||
|
||||
const filename = (() => {
|
||||
// Check base64 image
|
||||
if (url.startsWith('data:image/')) {
|
||||
const mime = url.split(',')[0].split(':')[1].split(';')[0];
|
||||
return `image.${mime.split('/')[1]}`;
|
||||
}
|
||||
// Old version file url: https://xxx.com/file/read?filename=xxx.pdf
|
||||
const filenameQuery = parseUrl.searchParams.get('filename');
|
||||
if (filenameQuery) return filenameQuery;
|
||||
// Handle base64 image
|
||||
if (url.startsWith('data:')) {
|
||||
const matches = url.match(/^data:([^;]+);base64,/);
|
||||
if (!matches) return;
|
||||
|
||||
// Common file: https://xxx.com/xxx.pdf?xxxx=xxx
|
||||
const pathname = parseUrl.pathname;
|
||||
if (pathname) return pathname.split('/').pop();
|
||||
})();
|
||||
const mimeType = matches[1].toLowerCase();
|
||||
if (!mimeType.startsWith('image/')) return;
|
||||
|
||||
if (!filename) return;
|
||||
|
||||
const extension = filename.split('.').pop()?.toLowerCase() || '';
|
||||
|
||||
if (!extension) return;
|
||||
|
||||
if (documentFileType.includes(extension)) {
|
||||
const extension = mimeType.split('/')[1];
|
||||
return {
|
||||
type: ChatFileTypeEnum.file,
|
||||
name: filename,
|
||||
type: ChatFileTypeEnum.image,
|
||||
name: `image.${extension}`,
|
||||
url
|
||||
};
|
||||
}
|
||||
if (imageFileType.includes(extension)) {
|
||||
|
||||
try {
|
||||
const parseUrl = new URL(url, 'https://localhost:3000');
|
||||
|
||||
// Get filename from URL
|
||||
const filename = parseUrl.searchParams.get('filename') || parseUrl.pathname.split('/').pop();
|
||||
const extension = filename?.split('.').pop()?.toLowerCase() || '';
|
||||
|
||||
// If it's a document type, return as file, otherwise treat as image
|
||||
if (extension && documentFileType.includes(extension)) {
|
||||
return {
|
||||
type: ChatFileTypeEnum.file,
|
||||
name: filename || 'null',
|
||||
url
|
||||
};
|
||||
}
|
||||
|
||||
// Default to image type for non-document files
|
||||
return {
|
||||
type: ChatFileTypeEnum.image,
|
||||
name: filename,
|
||||
name: filename || 'null.png',
|
||||
url
|
||||
};
|
||||
} catch (error) {
|
||||
return {
|
||||
type: ChatFileTypeEnum.image,
|
||||
name: 'invalid.png',
|
||||
url
|
||||
};
|
||||
}
|
||||
|
||||
@@ -7,12 +7,14 @@ import { i18nT } from '../../../web/i18n/utils';
|
||||
dayjs.extend(utc);
|
||||
dayjs.extend(timezone);
|
||||
|
||||
export const formatTime2YMDHMW = (time?: Date) => dayjs(time).format('YYYY-MM-DD HH:mm:ss dddd');
|
||||
export const formatTime2YMDHMS = (time?: Date) =>
|
||||
export const formatTime2YMDHMW = (time?: Date | number) =>
|
||||
dayjs(time).format('YYYY-MM-DD HH:mm:ss dddd');
|
||||
export const formatTime2YMDHMS = (time?: Date | number) =>
|
||||
time ? dayjs(time).format('YYYY-MM-DD HH:mm:ss') : '';
|
||||
export const formatTime2YMDHM = (time?: Date) =>
|
||||
export const formatTime2YMDHM = (time?: Date | number) =>
|
||||
time ? dayjs(time).format('YYYY-MM-DD HH:mm') : '';
|
||||
export const formatTime2YMD = (time?: Date) => (time ? dayjs(time).format('YYYY-MM-DD') : '');
|
||||
export const formatTime2YMD = (time?: Date | number) =>
|
||||
time ? dayjs(time).format('YYYY-MM-DD') : '';
|
||||
export const formatTime2HM = (time: Date = new Date()) => dayjs(time).format('HH:mm');
|
||||
|
||||
/**
|
||||
|
||||
@@ -41,6 +41,7 @@ export type FastGPTConfigFileType = {
|
||||
};
|
||||
|
||||
export type FastGPTFeConfigsType = {
|
||||
show_workorder?: boolean;
|
||||
show_emptyChat?: boolean;
|
||||
register_method?: ['email' | 'phone' | 'sync'];
|
||||
login_method?: ['email' | 'phone']; // Attention: login method is diffrent with oauth
|
||||
@@ -53,6 +54,7 @@ export type FastGPTFeConfigsType = {
|
||||
show_promotion?: boolean;
|
||||
show_team_chat?: boolean;
|
||||
show_compliance_copywriting?: boolean;
|
||||
show_aiproxy?: boolean;
|
||||
concatMd?: string;
|
||||
|
||||
docUrl?: string;
|
||||
|
||||
2
packages/global/core/ai/model.d.ts
vendored
2
packages/global/core/ai/model.d.ts
vendored
@@ -17,6 +17,8 @@ type BaseModelItemType = {
|
||||
isActive?: boolean;
|
||||
isCustom?: boolean;
|
||||
isDefault?: boolean;
|
||||
isDefaultDatasetTextModel?: boolean;
|
||||
isDefaultDatasetImageModel?: boolean;
|
||||
|
||||
// If has requestUrl, it will request the model directly
|
||||
requestUrl?: string;
|
||||
|
||||
@@ -22,6 +22,7 @@ export type ModelProviderIdType =
|
||||
| 'StepFun'
|
||||
| 'Yi'
|
||||
| 'Siliconflow'
|
||||
| 'PPIO'
|
||||
| 'Ollama'
|
||||
| 'BAAI'
|
||||
| 'FishAudio'
|
||||
@@ -71,11 +72,6 @@ export const ModelProviderList: ModelProviderType[] = [
|
||||
name: 'Groq',
|
||||
avatar: 'model/groq'
|
||||
},
|
||||
{
|
||||
id: 'AliCloud',
|
||||
name: i18nT('common:model_alicloud'),
|
||||
avatar: 'model/alicloud'
|
||||
},
|
||||
{
|
||||
id: 'Qwen',
|
||||
name: i18nT('common:model_qwen'),
|
||||
@@ -86,6 +82,11 @@ export const ModelProviderList: ModelProviderType[] = [
|
||||
name: i18nT('common:model_doubao'),
|
||||
avatar: 'model/doubao'
|
||||
},
|
||||
{
|
||||
id: 'DeepSeek',
|
||||
name: 'DeepSeek',
|
||||
avatar: 'model/deepseek'
|
||||
},
|
||||
{
|
||||
id: 'ChatGLM',
|
||||
name: i18nT('common:model_chatglm'),
|
||||
@@ -96,11 +97,6 @@ export const ModelProviderList: ModelProviderType[] = [
|
||||
name: i18nT('common:model_ernie'),
|
||||
avatar: 'model/ernie'
|
||||
},
|
||||
{
|
||||
id: 'DeepSeek',
|
||||
name: 'DeepSeek',
|
||||
avatar: 'model/deepseek'
|
||||
},
|
||||
{
|
||||
id: 'Moonshot',
|
||||
name: i18nT('common:model_moonshot'),
|
||||
@@ -162,11 +158,21 @@ export const ModelProviderList: ModelProviderType[] = [
|
||||
name: i18nT('common:model_moka'),
|
||||
avatar: 'model/moka'
|
||||
},
|
||||
{
|
||||
id: 'AliCloud',
|
||||
name: i18nT('common:model_alicloud'),
|
||||
avatar: 'model/alicloud'
|
||||
},
|
||||
{
|
||||
id: 'Siliconflow',
|
||||
name: i18nT('common:model_siliconflow'),
|
||||
avatar: 'model/siliconflow'
|
||||
},
|
||||
{
|
||||
id: 'PPIO',
|
||||
name: i18nT('common:model_ppio'),
|
||||
avatar: 'model/ppio'
|
||||
},
|
||||
{
|
||||
id: 'Other',
|
||||
name: i18nT('common:model_other'),
|
||||
|
||||
8
packages/global/core/ai/type.d.ts
vendored
8
packages/global/core/ai/type.d.ts
vendored
@@ -1,14 +1,12 @@
|
||||
import openai from 'openai';
|
||||
import type {
|
||||
ChatCompletionMessageToolCall,
|
||||
ChatCompletionChunk,
|
||||
ChatCompletionMessageParam as SdkChatCompletionMessageParam,
|
||||
ChatCompletionToolMessageParam,
|
||||
ChatCompletionContentPart as SdkChatCompletionContentPart,
|
||||
ChatCompletionUserMessageParam as SdkChatCompletionUserMessageParam,
|
||||
ChatCompletionToolMessageParam as SdkChatCompletionToolMessageParam,
|
||||
ChatCompletionAssistantMessageParam as SdkChatCompletionAssistantMessageParam,
|
||||
ChatCompletionContentPartText
|
||||
ChatCompletionAssistantMessageParam as SdkChatCompletionAssistantMessageParam
|
||||
} from 'openai/resources';
|
||||
import { ChatMessageTypeEnum } from './constants';
|
||||
import { WorkflowInteractiveResponseType } from '../workflow/template/system/interactive/type';
|
||||
@@ -48,6 +46,7 @@ export type ChatCompletionMessageParam = (
|
||||
| CustomChatCompletionToolMessageParam
|
||||
| CustomChatCompletionAssistantMessageParam
|
||||
) & {
|
||||
reasoning_text?: string;
|
||||
dataId?: string;
|
||||
hideInUI?: boolean;
|
||||
};
|
||||
@@ -71,7 +70,8 @@ export type ChatCompletionMessageFunctionCall =
|
||||
};
|
||||
|
||||
// Stream response
|
||||
export type StreamChatType = Stream<ChatCompletionChunk>;
|
||||
export type StreamChatType = Stream<openai.Chat.Completions.ChatCompletionChunk>;
|
||||
export type UnStreamChatType = openai.Chat.Completions.ChatCompletion;
|
||||
|
||||
export default openai;
|
||||
export * from 'openai';
|
||||
|
||||
@@ -46,7 +46,16 @@ export const chats2GPTMessages = ({
|
||||
|
||||
messages.forEach((item) => {
|
||||
const dataId = reserveId ? item.dataId : undefined;
|
||||
if (item.obj === ChatRoleEnum.Human) {
|
||||
if (item.obj === ChatRoleEnum.System) {
|
||||
const content = item.value?.[0]?.text?.content;
|
||||
if (content) {
|
||||
results.push({
|
||||
dataId,
|
||||
role: ChatCompletionRequestMessageRoleEnum.System,
|
||||
content
|
||||
});
|
||||
}
|
||||
} else if (item.obj === ChatRoleEnum.Human) {
|
||||
const value = item.value
|
||||
.map((item) => {
|
||||
if (item.type === ChatItemValueTypeEnum.text) {
|
||||
@@ -80,15 +89,6 @@ export const chats2GPTMessages = ({
|
||||
role: ChatCompletionRequestMessageRoleEnum.User,
|
||||
content: simpleUserContentPart(value)
|
||||
});
|
||||
} else if (item.obj === ChatRoleEnum.System) {
|
||||
const content = item.value?.[0]?.text?.content;
|
||||
if (content) {
|
||||
results.push({
|
||||
dataId,
|
||||
role: ChatCompletionRequestMessageRoleEnum.System,
|
||||
content
|
||||
});
|
||||
}
|
||||
} else {
|
||||
const aiResults: ChatCompletionMessageParam[] = [];
|
||||
|
||||
@@ -349,7 +349,7 @@ export const chatValue2RuntimePrompt = (value: ChatItemValueItemType[]): Runtime
|
||||
};
|
||||
value.forEach((item) => {
|
||||
if (item.type === 'file' && item.file) {
|
||||
prompt.files?.push(item.file);
|
||||
prompt.files.push(item.file);
|
||||
} else if (item.text) {
|
||||
prompt.text += item.text.content;
|
||||
}
|
||||
|
||||
1
packages/global/core/dataset/type.d.ts
vendored
1
packages/global/core/dataset/type.d.ts
vendored
@@ -192,6 +192,7 @@ export type DatasetCollectionItemType = CollectionWithDatasetType & {
|
||||
sourceId?: string;
|
||||
file?: DatasetFileSchema;
|
||||
permission: DatasetPermission;
|
||||
indexAmount: number;
|
||||
};
|
||||
|
||||
/* ================= data ===================== */
|
||||
|
||||
@@ -10,6 +10,7 @@ import { FlowNodeOutputItemType, ReferenceValueType } from '../type/io';
|
||||
import { ChatItemType, NodeOutputItemType } from '../../../core/chat/type';
|
||||
import { ChatItemValueTypeEnum, ChatRoleEnum } from '../../../core/chat/constants';
|
||||
import { replaceVariable, valToStr } from '../../../common/string/tools';
|
||||
import { ChatCompletionChunk } from 'openai/resources';
|
||||
|
||||
export const getMaxHistoryLimitFromNodes = (nodes: StoreNodeItemType[]): number => {
|
||||
let limit = 10;
|
||||
|
||||
10
packages/global/support/user/api.d.ts
vendored
10
packages/global/support/user/api.d.ts
vendored
@@ -1,5 +1,9 @@
|
||||
import { MemberGroupSchemaType, MemberGroupType } from 'support/permission/memberGroup/type';
|
||||
import { OAuthEnum } from './constant';
|
||||
import { TrackRegisterParams } from './login/api';
|
||||
import { TeamMemberStatusEnum } from './team/constant';
|
||||
import { OrgType } from './team/org/type';
|
||||
import { TeamMemberItemType } from './team/type';
|
||||
|
||||
export type PostLoginProps = {
|
||||
username: string;
|
||||
@@ -21,3 +25,9 @@ export type FastLoginProps = {
|
||||
token: string;
|
||||
code: string;
|
||||
};
|
||||
|
||||
export type SearchResult = {
|
||||
members: Omit<TeamMemberItemType, 'teamId' | 'permission'>[];
|
||||
orgs: Omit<OrgType, 'permission' | 'members'>[];
|
||||
groups: MemberGroupSchemaType[];
|
||||
};
|
||||
|
||||
@@ -13,6 +13,7 @@ export type CreateTeamProps = {
|
||||
defaultTeam?: boolean;
|
||||
memberName?: string;
|
||||
memberAvatar?: string;
|
||||
notificationAccount?: string;
|
||||
};
|
||||
export type UpdateTeamProps = Omit<ThirdPartyAccountType, 'externalWorkflowVariable'> & {
|
||||
name?: string;
|
||||
@@ -39,6 +40,12 @@ export type UpdateInviteProps = {
|
||||
tmbId: string;
|
||||
status: TeamMemberSchema['status'];
|
||||
};
|
||||
|
||||
export type UpdateStatusProps = {
|
||||
tmbId: string;
|
||||
status: TeamMemberSchema['status'];
|
||||
};
|
||||
|
||||
export type InviteMemberResponse = Record<
|
||||
'invite' | 'inValid' | 'inTeam',
|
||||
{ username: string; userId: string }[]
|
||||
|
||||
5
packages/global/support/user/team/type.d.ts
vendored
5
packages/global/support/user/team/type.d.ts
vendored
@@ -34,6 +34,7 @@ export type TeamTagSchema = TeamTagItemType & {
|
||||
_id: string;
|
||||
teamId: string;
|
||||
createTime: Date;
|
||||
updateTime?: Date;
|
||||
};
|
||||
|
||||
export type TeamMemberSchema = {
|
||||
@@ -41,6 +42,7 @@ export type TeamMemberSchema = {
|
||||
teamId: string;
|
||||
userId: string;
|
||||
createTime: Date;
|
||||
updateTime?: Date;
|
||||
name: string;
|
||||
role: `${TeamMemberRoleEnum}`;
|
||||
status: `${TeamMemberStatusEnum}`;
|
||||
@@ -79,6 +81,9 @@ export type TeamMemberItemType = {
|
||||
role: `${TeamMemberRoleEnum}`;
|
||||
status: `${TeamMemberStatusEnum}`;
|
||||
permission: TeamPermission;
|
||||
contact?: string;
|
||||
createTime: Date;
|
||||
updateTime?: Date;
|
||||
};
|
||||
|
||||
export type TeamTagItemType = {
|
||||
|
||||
3
packages/global/support/user/type.d.ts
vendored
3
packages/global/support/user/type.d.ts
vendored
@@ -17,6 +17,7 @@ export type UserModelSchema = {
|
||||
fastgpt_sem?: {
|
||||
keyword: string;
|
||||
};
|
||||
contact?: string;
|
||||
};
|
||||
|
||||
export type UserType = {
|
||||
@@ -26,9 +27,9 @@ export type UserType = {
|
||||
timezone: string;
|
||||
promotionRate: UserModelSchema['promotionRate'];
|
||||
team: TeamTmbItemType;
|
||||
standardInfo?: standardInfoType;
|
||||
notificationAccount?: string;
|
||||
permission: TeamPermission;
|
||||
contact?: string;
|
||||
};
|
||||
|
||||
export type SourceMemberType = {
|
||||
|
||||
4
packages/service/common/file/csv.ts
Normal file
4
packages/service/common/file/csv.ts
Normal file
@@ -0,0 +1,4 @@
|
||||
export const generateCsv = (headers: string[], data: string[][]) => {
|
||||
const csv = [headers.join(','), ...data.map((row) => row.join(','))].join('\n');
|
||||
return csv;
|
||||
};
|
||||
@@ -18,10 +18,10 @@ export function getGFSCollection(bucket: `${BucketNameEnum}`) {
|
||||
MongoDatasetFileSchema;
|
||||
MongoChatFileSchema;
|
||||
|
||||
return connectionMongo.connection.db.collection(`${bucket}.files`);
|
||||
return connectionMongo.connection.db!.collection(`${bucket}.files`);
|
||||
}
|
||||
export function getGridBucket(bucket: `${BucketNameEnum}`) {
|
||||
return new connectionMongo.mongo.GridFSBucket(connectionMongo.connection.db, {
|
||||
return new connectionMongo.mongo.GridFSBucket(connectionMongo.connection.db!, {
|
||||
bucketName: bucket,
|
||||
// @ts-ignore
|
||||
readPreference: ReadPreference.SECONDARY_PREFERRED // Read from secondary node
|
||||
|
||||
@@ -26,15 +26,18 @@ export async function uploadMongoImg({
|
||||
const [base64Mime, base64Data] = base64Img.split(',');
|
||||
// Check if mime type is valid
|
||||
if (!base64MimeRegex.test(base64Mime)) {
|
||||
return Promise.reject('Invalid image mime type');
|
||||
return Promise.reject('Invalid image base64');
|
||||
}
|
||||
|
||||
const mime = `image/${base64Mime.match(base64MimeRegex)?.[1] ?? 'image/jpeg'}`;
|
||||
const binary = Buffer.from(base64Data, 'base64');
|
||||
const extension = mime.split('/')[1];
|
||||
let extension = mime.split('/')[1];
|
||||
if (extension.startsWith('x-')) {
|
||||
extension = extension.substring(2); // Remove 'x-' prefix
|
||||
}
|
||||
|
||||
if (!imageFileType.includes(`.${extension}`)) {
|
||||
return Promise.reject('Invalid image file type');
|
||||
if (!extension || !imageFileType.includes(`.${extension}`)) {
|
||||
return Promise.reject(`Invalid image file type: ${mime}`);
|
||||
}
|
||||
|
||||
const { _id } = await MongoImage.create({
|
||||
@@ -115,7 +118,7 @@ export async function delImgByRelatedId({
|
||||
}: {
|
||||
teamId: string;
|
||||
relateIds: string[];
|
||||
session: ClientSession;
|
||||
session?: ClientSession;
|
||||
}) {
|
||||
if (relateIds.length === 0) return;
|
||||
|
||||
|
||||
@@ -111,15 +111,21 @@ export const readRawContentByFileBuffer = async ({
|
||||
// markdown data format
|
||||
if (imageList) {
|
||||
await batchRun(imageList, async (item) => {
|
||||
const src = await uploadMongoImg({
|
||||
base64Img: `data:${item.mime};base64,${item.base64}`,
|
||||
teamId,
|
||||
// expiredTime: addHours(new Date(), 1),
|
||||
metadata: {
|
||||
...metadata,
|
||||
mime: item.mime
|
||||
const src = await (async () => {
|
||||
try {
|
||||
return await uploadMongoImg({
|
||||
base64Img: `data:${item.mime};base64,${item.base64}`,
|
||||
teamId,
|
||||
// expiredTime: addHours(new Date(), 1),
|
||||
metadata: {
|
||||
...metadata,
|
||||
mime: item.mime
|
||||
}
|
||||
});
|
||||
} catch (error) {
|
||||
return '';
|
||||
}
|
||||
});
|
||||
})();
|
||||
rawText = rawText.replace(item.uuid, src);
|
||||
if (formatText) {
|
||||
formatText = formatText.replace(item.uuid, src);
|
||||
|
||||
@@ -38,10 +38,12 @@ const addCommonMiddleware = (schema: mongoose.Schema) => {
|
||||
schema.post(op, function (this: any, result: any, next) {
|
||||
if (this._startTime) {
|
||||
const duration = Date.now() - this._startTime;
|
||||
|
||||
const warnLogData = {
|
||||
query: this._query,
|
||||
op,
|
||||
collectionName: this.collection?.name,
|
||||
op: this.op,
|
||||
...(this._query && { query: this._query }),
|
||||
...(this._update && { update: this._update }),
|
||||
...(this._delete && { delete: this._delete }),
|
||||
duration
|
||||
};
|
||||
|
||||
|
||||
@@ -16,16 +16,30 @@ export async function connectMongo(): Promise<Mongoose> {
|
||||
|
||||
console.log('mongo start connect');
|
||||
try {
|
||||
connectionMongo.set('strictQuery', true);
|
||||
// Remove existing listeners to prevent duplicates
|
||||
connectionMongo.connection.removeAllListeners('error');
|
||||
connectionMongo.connection.removeAllListeners('disconnected');
|
||||
connectionMongo.set('strictQuery', false);
|
||||
|
||||
connectionMongo.connection.on('error', async (error) => {
|
||||
console.log('mongo error', error);
|
||||
await connectionMongo.disconnect();
|
||||
await delay(1000);
|
||||
connectMongo();
|
||||
try {
|
||||
if (connectionMongo.connection.readyState !== 0) {
|
||||
await connectionMongo.disconnect();
|
||||
await delay(1000);
|
||||
await connectMongo();
|
||||
}
|
||||
} catch (error) {}
|
||||
});
|
||||
connectionMongo.connection.on('disconnected', () => {
|
||||
connectionMongo.connection.on('disconnected', async () => {
|
||||
console.log('mongo disconnected');
|
||||
try {
|
||||
if (connectionMongo.connection.readyState !== 0) {
|
||||
await connectionMongo.disconnect();
|
||||
await delay(1000);
|
||||
await connectMongo();
|
||||
}
|
||||
} catch (error) {}
|
||||
});
|
||||
|
||||
await connectionMongo.connect(process.env.MONGODB_URI as string, {
|
||||
|
||||
@@ -25,7 +25,7 @@ export const countGptMessagesTokens = async (
|
||||
number
|
||||
>({
|
||||
name: WorkerNameEnum.countGptMessagesTokens,
|
||||
maxReservedThreads: global.systemEnv?.tokenWorkers || 50
|
||||
maxReservedThreads: global.systemEnv?.tokenWorkers || 30
|
||||
});
|
||||
|
||||
const total = await workerController.run({ messages, tools, functionCall });
|
||||
|
||||
@@ -1 +1,4 @@
|
||||
export const FastGPTProUrl = process.env.PRO_URL ? `${process.env.PRO_URL}/api` : '';
|
||||
export const isFastGPTMainService = !!process.env.PRO_URL;
|
||||
// @ts-ignore
|
||||
export const isFastGPTProService = () => !!global.systemConfig;
|
||||
|
||||
@@ -21,6 +21,7 @@ export const recallFromVectorStore = Vector.embRecall;
|
||||
export const getVectorDataByTime = Vector.getVectorDataByTime;
|
||||
export const getVectorCountByTeamId = Vector.getVectorCountByTeamId;
|
||||
export const getVectorCountByDatasetId = Vector.getVectorCountByDatasetId;
|
||||
export const getVectorCountByCollectionId = Vector.getVectorCountByCollectionId;
|
||||
|
||||
export const insertDatasetDataVector = async ({
|
||||
model,
|
||||
|
||||
@@ -321,6 +321,23 @@ export class MilvusCtrl {
|
||||
|
||||
return total;
|
||||
};
|
||||
getVectorCountByCollectionId = async (
|
||||
teamId: string,
|
||||
datasetId: string,
|
||||
collectionId: string
|
||||
) => {
|
||||
const client = await this.getClient();
|
||||
|
||||
const result = await client.query({
|
||||
collection_name: DatasetVectorTableName,
|
||||
output_fields: ['count(*)'],
|
||||
filter: `(teamId == "${String(teamId)}") and (datasetId == "${String(datasetId)}") and (collectionId == "${String(collectionId)}")`
|
||||
});
|
||||
|
||||
const total = result.data?.[0]?.['count(*)'] as number;
|
||||
|
||||
return total;
|
||||
};
|
||||
|
||||
getVectorDataByTime = async (start: Date, end: Date) => {
|
||||
const client = await this.getClient();
|
||||
|
||||
@@ -240,6 +240,23 @@ export class PgVectorCtrl {
|
||||
where: [['team_id', String(teamId)], 'and', ['dataset_id', String(datasetId)]]
|
||||
});
|
||||
|
||||
return total;
|
||||
};
|
||||
getVectorCountByCollectionId = async (
|
||||
teamId: string,
|
||||
datasetId: string,
|
||||
collectionId: string
|
||||
) => {
|
||||
const total = await PgClient.count(DatasetVectorTableName, {
|
||||
where: [
|
||||
['team_id', String(teamId)],
|
||||
'and',
|
||||
['dataset_id', String(datasetId)],
|
||||
'and',
|
||||
['collection_id', String(collectionId)]
|
||||
]
|
||||
});
|
||||
|
||||
return total;
|
||||
};
|
||||
}
|
||||
|
||||
@@ -1,7 +1,9 @@
|
||||
import OpenAI from '@fastgpt/global/core/ai';
|
||||
import {
|
||||
ChatCompletionCreateParamsNonStreaming,
|
||||
ChatCompletionCreateParamsStreaming
|
||||
ChatCompletionCreateParamsStreaming,
|
||||
StreamChatType,
|
||||
UnStreamChatType
|
||||
} from '@fastgpt/global/core/ai/type';
|
||||
import { getErrText } from '@fastgpt/global/common/error/utils';
|
||||
import { addLog } from '../../common/system/log';
|
||||
@@ -9,14 +11,17 @@ import { i18nT } from '../../../web/i18n/utils';
|
||||
import { OpenaiAccountType } from '@fastgpt/global/support/user/team/type';
|
||||
import { getLLMModel } from './model';
|
||||
|
||||
export const openaiBaseUrl = process.env.OPENAI_BASE_URL || 'https://api.openai.com/v1';
|
||||
const aiProxyBaseUrl = process.env.AIPROXY_API_ENDPOINT
|
||||
? `${process.env.AIPROXY_API_ENDPOINT}/v1`
|
||||
: undefined;
|
||||
const openaiBaseUrl = aiProxyBaseUrl || process.env.OPENAI_BASE_URL || 'https://api.openai.com/v1';
|
||||
const openaiBaseKey = process.env.AIPROXY_API_TOKEN || process.env.CHAT_API_KEY || '';
|
||||
|
||||
export const getAIApi = (props?: { userKey?: OpenaiAccountType; timeout?: number }) => {
|
||||
const { userKey, timeout } = props || {};
|
||||
|
||||
const baseUrl = userKey?.baseUrl || global?.systemEnv?.oneapiUrl || openaiBaseUrl;
|
||||
const apiKey = userKey?.key || global?.systemEnv?.chatApiKey || process.env.CHAT_API_KEY || '';
|
||||
|
||||
const apiKey = userKey?.key || global?.systemEnv?.chatApiKey || openaiBaseKey;
|
||||
return new OpenAI({
|
||||
baseURL: baseUrl,
|
||||
apiKey,
|
||||
@@ -30,7 +35,7 @@ export const getAxiosConfig = (props?: { userKey?: OpenaiAccountType }) => {
|
||||
const { userKey } = props || {};
|
||||
|
||||
const baseUrl = userKey?.baseUrl || global?.systemEnv?.oneapiUrl || openaiBaseUrl;
|
||||
const apiKey = userKey?.key || global?.systemEnv?.chatApiKey || process.env.CHAT_API_KEY || '';
|
||||
const apiKey = userKey?.key || global?.systemEnv?.chatApiKey || openaiBaseKey;
|
||||
|
||||
return {
|
||||
baseUrl,
|
||||
@@ -38,29 +43,30 @@ export const getAxiosConfig = (props?: { userKey?: OpenaiAccountType }) => {
|
||||
};
|
||||
};
|
||||
|
||||
type CompletionsBodyType =
|
||||
| ChatCompletionCreateParamsNonStreaming
|
||||
| ChatCompletionCreateParamsStreaming;
|
||||
type InferResponseType<T extends CompletionsBodyType> =
|
||||
T extends ChatCompletionCreateParamsStreaming
|
||||
? OpenAI.Chat.Completions.ChatCompletionChunk
|
||||
: OpenAI.Chat.Completions.ChatCompletion;
|
||||
|
||||
export const createChatCompletion = async <T extends CompletionsBodyType>({
|
||||
export const createChatCompletion = async ({
|
||||
body,
|
||||
userKey,
|
||||
timeout,
|
||||
options
|
||||
}: {
|
||||
body: T;
|
||||
body: ChatCompletionCreateParamsNonStreaming | ChatCompletionCreateParamsStreaming;
|
||||
userKey?: OpenaiAccountType;
|
||||
timeout?: number;
|
||||
options?: OpenAI.RequestOptions;
|
||||
}): Promise<{
|
||||
response: InferResponseType<T>;
|
||||
isStreamResponse: boolean;
|
||||
getEmptyResponseTip: () => string;
|
||||
}> => {
|
||||
}): Promise<
|
||||
{
|
||||
getEmptyResponseTip: () => string;
|
||||
} & (
|
||||
| {
|
||||
response: StreamChatType;
|
||||
isStreamResponse: true;
|
||||
}
|
||||
| {
|
||||
response: UnStreamChatType;
|
||||
isStreamResponse: false;
|
||||
}
|
||||
)
|
||||
> => {
|
||||
try {
|
||||
const modelConstantsData = getLLMModel(body.model);
|
||||
|
||||
@@ -69,6 +75,7 @@ export const createChatCompletion = async <T extends CompletionsBodyType>({
|
||||
userKey,
|
||||
timeout: formatTimeout
|
||||
});
|
||||
|
||||
const response = await ai.chat.completions.create(body, {
|
||||
...options,
|
||||
...(modelConstantsData.requestUrl ? { path: modelConstantsData.requestUrl } : {}),
|
||||
@@ -96,9 +103,17 @@ export const createChatCompletion = async <T extends CompletionsBodyType>({
|
||||
return i18nT('chat:LLM_model_response_empty');
|
||||
};
|
||||
|
||||
if (isStreamResponse) {
|
||||
return {
|
||||
response,
|
||||
isStreamResponse: true,
|
||||
getEmptyResponseTip
|
||||
};
|
||||
}
|
||||
|
||||
return {
|
||||
response: response as InferResponseType<T>,
|
||||
isStreamResponse,
|
||||
response,
|
||||
isStreamResponse: false,
|
||||
getEmptyResponseTip
|
||||
};
|
||||
} catch (error) {
|
||||
|
||||
@@ -1,4 +1,10 @@
|
||||
{
|
||||
"provider": "AliCloud",
|
||||
"list": []
|
||||
}
|
||||
"list": [
|
||||
{
|
||||
"model": "SenseVoiceSmall",
|
||||
"name": "SenseVoiceSmall",
|
||||
"type": "stt"
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
@@ -1,6 +1,30 @@
|
||||
{
|
||||
"provider": "Claude",
|
||||
"list": [
|
||||
{
|
||||
"model": "claude-3-7-sonnet-20250219",
|
||||
"name": "claude-3-7-sonnet-20250219",
|
||||
"maxContext": 200000,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 100000,
|
||||
"maxTemperature": 1,
|
||||
"showTopP": true,
|
||||
"showStopSign": true,
|
||||
"vision": true,
|
||||
"toolChoice": true,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm"
|
||||
},
|
||||
{
|
||||
"model": "claude-3-5-haiku-20241022",
|
||||
"name": "claude-3-5-haiku-20241022",
|
||||
@@ -10,7 +34,7 @@
|
||||
"maxTemperature": 1,
|
||||
"showTopP": true,
|
||||
"showStopSign": true,
|
||||
"vision": false,
|
||||
"vision": true,
|
||||
"toolChoice": true,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
@@ -98,4 +122,4 @@
|
||||
"type": "llm"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,6 +1,54 @@
|
||||
{
|
||||
"provider": "Gemini",
|
||||
"list": [
|
||||
{
|
||||
"model": "gemini-2.0-flash",
|
||||
"name": "gemini-2.0-flash",
|
||||
"maxContext": 1000000,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 60000,
|
||||
"maxTemperature": 1,
|
||||
"vision": true,
|
||||
"toolChoice": true,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
},
|
||||
{
|
||||
"model": "gemini-2.0-pro-exp",
|
||||
"name": "gemini-2.0-pro-exp",
|
||||
"maxContext": 2000000,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 100000,
|
||||
"maxTemperature": 1,
|
||||
"vision": true,
|
||||
"toolChoice": true,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
},
|
||||
{
|
||||
"model": "gemini-1.5-flash",
|
||||
"name": "gemini-1.5-flash",
|
||||
@@ -153,4 +201,4 @@
|
||||
"type": "embedding"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,4 +1,29 @@
|
||||
{
|
||||
"provider": "Grok",
|
||||
"list": []
|
||||
}
|
||||
"list": [
|
||||
{
|
||||
"model": "grok-3",
|
||||
"name": "grok-3",
|
||||
"maxContext": 128000,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 128000,
|
||||
"maxTemperature": 1,
|
||||
"showTopP": true,
|
||||
"showStopSign": true,
|
||||
"vision": false,
|
||||
"toolChoice": false,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm"
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
4
packages/service/core/ai/config/provider/PPIO.json
Normal file
4
packages/service/core/ai/config/provider/PPIO.json
Normal file
@@ -0,0 +1,4 @@
|
||||
{
|
||||
"provider": "PPIO",
|
||||
"list": []
|
||||
}
|
||||
@@ -52,6 +52,12 @@ export const loadSystemModels = async (init = false) => {
|
||||
if (model.isDefault) {
|
||||
global.systemDefaultModel.llm = model;
|
||||
}
|
||||
if (model.isDefaultDatasetTextModel) {
|
||||
global.systemDefaultModel.datasetTextLLM = model;
|
||||
}
|
||||
if (model.isDefaultDatasetImageModel) {
|
||||
global.systemDefaultModel.datasetImageLLM = model;
|
||||
}
|
||||
} else if (model.type === ModelTypeEnum.embedding) {
|
||||
global.embeddingModelMap.set(model.model, model);
|
||||
global.embeddingModelMap.set(model.name, model);
|
||||
@@ -134,6 +140,16 @@ export const loadSystemModels = async (init = false) => {
|
||||
if (!global.systemDefaultModel.llm) {
|
||||
global.systemDefaultModel.llm = Array.from(global.llmModelMap.values())[0];
|
||||
}
|
||||
if (!global.systemDefaultModel.datasetTextLLM) {
|
||||
global.systemDefaultModel.datasetTextLLM = Array.from(global.llmModelMap.values()).find(
|
||||
(item) => item.datasetProcess
|
||||
);
|
||||
}
|
||||
if (!global.systemDefaultModel.datasetImageLLM) {
|
||||
global.systemDefaultModel.datasetImageLLM = Array.from(global.llmModelMap.values()).find(
|
||||
(item) => item.vision
|
||||
);
|
||||
}
|
||||
if (!global.systemDefaultModel.embedding) {
|
||||
global.systemDefaultModel.embedding = Array.from(global.embeddingModelMap.values())[0];
|
||||
}
|
||||
|
||||
3
packages/service/core/ai/type.d.ts
vendored
3
packages/service/core/ai/type.d.ts
vendored
@@ -22,6 +22,9 @@ export type SystemModelItemType =
|
||||
|
||||
export type SystemDefaultModelType = {
|
||||
[ModelTypeEnum.llm]?: LLMModelItemType;
|
||||
datasetTextLLM?: LLMModelItemType;
|
||||
datasetImageLLM?: LLMModelItemType;
|
||||
|
||||
[ModelTypeEnum.embedding]?: EmbeddingModelItemType;
|
||||
[ModelTypeEnum.tts]?: TTSModelType;
|
||||
[ModelTypeEnum.stt]?: STTModelType;
|
||||
|
||||
@@ -37,25 +37,26 @@ export const computedTemperature = ({
|
||||
return temperature;
|
||||
};
|
||||
|
||||
type CompletionsBodyType = (
|
||||
type CompletionsBodyType =
|
||||
| ChatCompletionCreateParamsNonStreaming
|
||||
| ChatCompletionCreateParamsStreaming
|
||||
) & {
|
||||
response_format?: any;
|
||||
json_schema?: string;
|
||||
stop?: string;
|
||||
};
|
||||
| ChatCompletionCreateParamsStreaming;
|
||||
type InferCompletionsBody<T> = T extends { stream: true }
|
||||
? ChatCompletionCreateParamsStreaming
|
||||
: ChatCompletionCreateParamsNonStreaming;
|
||||
: T extends { stream: false }
|
||||
? ChatCompletionCreateParamsNonStreaming
|
||||
: ChatCompletionCreateParamsNonStreaming | ChatCompletionCreateParamsStreaming;
|
||||
|
||||
export const llmCompletionsBodyFormat = <T extends CompletionsBodyType>(
|
||||
body: T,
|
||||
body: T & {
|
||||
response_format?: any;
|
||||
json_schema?: string;
|
||||
stop?: string;
|
||||
},
|
||||
model: string | LLMModelItemType
|
||||
): InferCompletionsBody<T> => {
|
||||
const modelData = typeof model === 'string' ? getLLMModel(model) : model;
|
||||
if (!modelData) {
|
||||
return body as InferCompletionsBody<T>;
|
||||
return body as unknown as InferCompletionsBody<T>;
|
||||
}
|
||||
|
||||
const response_format = body.response_format;
|
||||
@@ -91,16 +92,148 @@ export const llmCompletionsBodyFormat = <T extends CompletionsBodyType>(
|
||||
});
|
||||
}
|
||||
|
||||
// console.log(requestBody);
|
||||
|
||||
return requestBody as InferCompletionsBody<T>;
|
||||
return requestBody as unknown as InferCompletionsBody<T>;
|
||||
};
|
||||
|
||||
export const llmStreamResponseToText = async (response: StreamChatType) => {
|
||||
export const llmStreamResponseToAnswerText = async (response: StreamChatType) => {
|
||||
let answer = '';
|
||||
for await (const part of response) {
|
||||
const content = part.choices?.[0]?.delta?.content || '';
|
||||
answer += content;
|
||||
}
|
||||
return answer;
|
||||
return parseReasoningContent(answer)[1];
|
||||
};
|
||||
|
||||
// Parse <think></think> tags to think and answer - unstream response
|
||||
export const parseReasoningContent = (text: string): [string, string] => {
|
||||
const regex = /<think>([\s\S]*?)<\/think>/;
|
||||
const match = text.match(regex);
|
||||
|
||||
if (!match) {
|
||||
return ['', text];
|
||||
}
|
||||
|
||||
const thinkContent = match[1].trim();
|
||||
|
||||
// Add answer (remaining text after think tag)
|
||||
const answerContent = text.slice(match.index! + match[0].length);
|
||||
|
||||
return [thinkContent, answerContent];
|
||||
};
|
||||
|
||||
// Parse <think></think> tags to think and answer - stream response
|
||||
export const parseReasoningStreamContent = () => {
|
||||
let isInThinkTag: boolean | undefined;
|
||||
|
||||
const startTag = '<think>';
|
||||
let startTagBuffer = '';
|
||||
|
||||
const endTag = '</think>';
|
||||
let endTagBuffer = '';
|
||||
|
||||
/*
|
||||
parseReasoning - 只控制是否主动解析 <think></think>,如果接口已经解析了,仍然会返回 think 内容。
|
||||
*/
|
||||
const parsePart = (
|
||||
part: {
|
||||
choices: {
|
||||
delta: {
|
||||
content?: string;
|
||||
reasoning_content?: string;
|
||||
};
|
||||
}[];
|
||||
},
|
||||
parseReasoning = false
|
||||
): [string, string] => {
|
||||
const content = part.choices?.[0]?.delta?.content || '';
|
||||
|
||||
// @ts-ignore
|
||||
const reasoningContent = part.choices?.[0]?.delta?.reasoning_content || '';
|
||||
if (reasoningContent || !parseReasoning) {
|
||||
isInThinkTag = false;
|
||||
return [reasoningContent, content];
|
||||
}
|
||||
|
||||
if (!content) {
|
||||
return ['', ''];
|
||||
}
|
||||
|
||||
// 如果不在 think 标签中,或者有 reasoningContent(接口已解析),则返回 reasoningContent 和 content
|
||||
if (isInThinkTag === false) {
|
||||
return ['', content];
|
||||
}
|
||||
|
||||
// 检测是否为 think 标签开头的数据
|
||||
if (isInThinkTag === undefined) {
|
||||
// Parse content think and answer
|
||||
startTagBuffer += content;
|
||||
// 太少内容时候,暂时不解析
|
||||
if (startTagBuffer.length < startTag.length) {
|
||||
return ['', ''];
|
||||
}
|
||||
|
||||
if (startTagBuffer.startsWith(startTag)) {
|
||||
isInThinkTag = true;
|
||||
return [startTagBuffer.slice(startTag.length), ''];
|
||||
}
|
||||
|
||||
// 如果未命中 think 标签,则认为不在 think 标签中,返回 buffer 内容作为 content
|
||||
isInThinkTag = false;
|
||||
return ['', startTagBuffer];
|
||||
}
|
||||
|
||||
// 确认是 think 标签内容,开始返回 think 内容,并实时检测 </think>
|
||||
/*
|
||||
检测 </think> 方案。
|
||||
存储所有疑似 </think> 的内容,直到检测到完整的 </think> 标签或超出 </think> 长度。
|
||||
content 返回值包含以下几种情况:
|
||||
abc - 完全未命中尾标签
|
||||
abc<th - 命中一部分尾标签
|
||||
abc</think> - 完全命中尾标签
|
||||
abc</think>abc - 完全命中尾标签
|
||||
</think>abc - 完全命中尾标签
|
||||
k>abc - 命中一部分尾标签
|
||||
*/
|
||||
// endTagBuffer 专门用来记录疑似尾标签的内容
|
||||
if (endTagBuffer) {
|
||||
endTagBuffer += content;
|
||||
if (endTagBuffer.includes(endTag)) {
|
||||
isInThinkTag = false;
|
||||
const answer = endTagBuffer.slice(endTag.length);
|
||||
return ['', answer];
|
||||
} else if (endTagBuffer.length >= endTag.length) {
|
||||
// 缓存内容超出尾标签长度,且仍未命中 </think>,则认为本次猜测 </think> 失败,仍处于 think 阶段。
|
||||
const tmp = endTagBuffer;
|
||||
endTagBuffer = '';
|
||||
return [tmp, ''];
|
||||
}
|
||||
return ['', ''];
|
||||
} else if (content.includes(endTag)) {
|
||||
// 返回内容,完整命中</think>,直接结束
|
||||
isInThinkTag = false;
|
||||
const [think, answer] = content.split(endTag);
|
||||
return [think, answer];
|
||||
} else {
|
||||
// 无 buffer,且未命中 </think>,开始疑似 </think> 检测。
|
||||
for (let i = 1; i < endTag.length; i++) {
|
||||
const partialEndTag = endTag.slice(0, i);
|
||||
// 命中一部分尾标签
|
||||
if (content.endsWith(partialEndTag)) {
|
||||
const think = content.slice(0, -partialEndTag.length);
|
||||
endTagBuffer += partialEndTag;
|
||||
return [think, ''];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// 完全未命中尾标签,还是 think 阶段。
|
||||
return [content, ''];
|
||||
};
|
||||
|
||||
const getStartTagBuffer = () => startTagBuffer;
|
||||
|
||||
return {
|
||||
parsePart,
|
||||
getStartTagBuffer
|
||||
};
|
||||
};
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
import { connectionMongo, getMongoModel } from '../../common/mongo';
|
||||
const { Schema } = connectionMongo;
|
||||
import { ChatSchema as ChatType } from '@fastgpt/global/core/chat/type.d';
|
||||
import { ChatSourceMap } from '@fastgpt/global/core/chat/constants';
|
||||
import { ChatSourceEnum, ChatSourceMap } from '@fastgpt/global/core/chat/constants';
|
||||
import {
|
||||
TeamCollectionName,
|
||||
TeamMemberCollectionName
|
||||
@@ -52,8 +52,10 @@ const ChatSchema = new Schema({
|
||||
},
|
||||
source: {
|
||||
type: String,
|
||||
required: true
|
||||
required: true,
|
||||
enum: Object.values(ChatSourceEnum)
|
||||
},
|
||||
sourceName: String,
|
||||
shareId: {
|
||||
type: String
|
||||
},
|
||||
|
||||
@@ -1,6 +1,10 @@
|
||||
import type { AIChatItemType, UserChatItemType } from '@fastgpt/global/core/chat/type.d';
|
||||
import { MongoApp } from '../app/schema';
|
||||
import { ChatItemValueTypeEnum, ChatRoleEnum } from '@fastgpt/global/core/chat/constants';
|
||||
import {
|
||||
ChatItemValueTypeEnum,
|
||||
ChatRoleEnum,
|
||||
ChatSourceEnum
|
||||
} from '@fastgpt/global/core/chat/constants';
|
||||
import { MongoChatItem } from './chatItemSchema';
|
||||
import { MongoChat } from './chatSchema';
|
||||
import { addLog } from '../../common/system/log';
|
||||
@@ -22,7 +26,8 @@ type Props = {
|
||||
variables?: Record<string, any>;
|
||||
isUpdateUseTime: boolean;
|
||||
newTitle: string;
|
||||
source: string;
|
||||
source: `${ChatSourceEnum}`;
|
||||
sourceName?: string;
|
||||
shareId?: string;
|
||||
outLinkUid?: string;
|
||||
content: [UserChatItemType & { dataId?: string }, AIChatItemType & { dataId?: string }];
|
||||
@@ -40,6 +45,7 @@ export async function saveChat({
|
||||
isUpdateUseTime,
|
||||
newTitle,
|
||||
source,
|
||||
sourceName,
|
||||
shareId,
|
||||
outLinkUid,
|
||||
content,
|
||||
@@ -96,6 +102,7 @@ export async function saveChat({
|
||||
pluginInputs,
|
||||
title: newTitle,
|
||||
source,
|
||||
sourceName,
|
||||
shareId,
|
||||
outLinkUid,
|
||||
metadata: metadataUpdate,
|
||||
|
||||
@@ -197,7 +197,11 @@ export const loadRequestMessages = async ({
|
||||
addLog.info(`Filter invalid image: ${imgUrl}`);
|
||||
return;
|
||||
}
|
||||
} catch (error) {
|
||||
} catch (error: any) {
|
||||
if (error?.response?.status === 405) {
|
||||
return item;
|
||||
}
|
||||
addLog.warn(`Filter invalid image: ${imgUrl}`, { error });
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
@@ -25,6 +25,7 @@ import { MongoImage } from '../../../common/file/image/schema';
|
||||
import { hashStr } from '@fastgpt/global/common/string/tools';
|
||||
import { addDays } from 'date-fns';
|
||||
import { MongoDatasetDataText } from '../data/dataTextSchema';
|
||||
import { delay, retryFn } from '@fastgpt/global/common/system/utils';
|
||||
|
||||
export const createCollectionAndInsertData = async ({
|
||||
dataset,
|
||||
@@ -216,7 +217,7 @@ export async function createOneCollection({
|
||||
nextSyncTime
|
||||
}
|
||||
],
|
||||
{ session }
|
||||
{ session, ordered: true }
|
||||
);
|
||||
|
||||
return collection;
|
||||
@@ -227,8 +228,14 @@ export const delCollectionRelatedSource = async ({
|
||||
collections,
|
||||
session
|
||||
}: {
|
||||
collections: DatasetCollectionSchemaType[];
|
||||
session: ClientSession;
|
||||
collections: {
|
||||
teamId: string;
|
||||
fileId?: string;
|
||||
metadata?: {
|
||||
relatedImgId?: string;
|
||||
};
|
||||
}[];
|
||||
session?: ClientSession;
|
||||
}) => {
|
||||
if (collections.length === 0) return;
|
||||
|
||||
@@ -259,11 +266,13 @@ export const delCollectionRelatedSource = async ({
|
||||
export async function delCollection({
|
||||
collections,
|
||||
session,
|
||||
delRelatedSource
|
||||
delImg = true,
|
||||
delFile = true
|
||||
}: {
|
||||
collections: DatasetCollectionSchemaType[];
|
||||
session: ClientSession;
|
||||
delRelatedSource: boolean;
|
||||
delImg: boolean;
|
||||
delFile: boolean;
|
||||
}) {
|
||||
if (collections.length === 0) return;
|
||||
|
||||
@@ -274,83 +283,55 @@ export async function delCollection({
|
||||
const datasetIds = Array.from(new Set(collections.map((item) => String(item.datasetId))));
|
||||
const collectionIds = collections.map((item) => String(item._id));
|
||||
|
||||
// Delete training data
|
||||
await MongoDatasetTraining.deleteMany({
|
||||
teamId,
|
||||
datasetIds: { $in: datasetIds },
|
||||
collectionId: { $in: collectionIds }
|
||||
await retryFn(async () => {
|
||||
await Promise.all([
|
||||
// Delete training data
|
||||
MongoDatasetTraining.deleteMany({
|
||||
teamId,
|
||||
datasetId: { $in: datasetIds },
|
||||
collectionId: { $in: collectionIds }
|
||||
}),
|
||||
// Delete dataset_data_texts
|
||||
MongoDatasetDataText.deleteMany({
|
||||
teamId,
|
||||
datasetId: { $in: datasetIds },
|
||||
collectionId: { $in: collectionIds }
|
||||
}),
|
||||
// Delete dataset_datas
|
||||
MongoDatasetData.deleteMany({
|
||||
teamId,
|
||||
datasetId: { $in: datasetIds },
|
||||
collectionId: { $in: collectionIds }
|
||||
}),
|
||||
...(delImg
|
||||
? [
|
||||
delImgByRelatedId({
|
||||
teamId,
|
||||
relateIds: collections
|
||||
.map((item) => item?.metadata?.relatedImgId || '')
|
||||
.filter(Boolean)
|
||||
})
|
||||
]
|
||||
: []),
|
||||
...(delFile
|
||||
? [
|
||||
delFileByFileIdList({
|
||||
bucketName: BucketNameEnum.dataset,
|
||||
fileIdList: collections.map((item) => item?.fileId || '').filter(Boolean)
|
||||
})
|
||||
]
|
||||
: []),
|
||||
// Delete vector data
|
||||
deleteDatasetDataVector({ teamId, datasetIds, collectionIds })
|
||||
]);
|
||||
|
||||
// delete collections
|
||||
await MongoDatasetCollection.deleteMany(
|
||||
{
|
||||
teamId,
|
||||
_id: { $in: collectionIds }
|
||||
},
|
||||
{ session }
|
||||
);
|
||||
});
|
||||
|
||||
/* file and imgs */
|
||||
if (delRelatedSource) {
|
||||
await delCollectionRelatedSource({ collections, session });
|
||||
}
|
||||
|
||||
// Delete dataset_datas
|
||||
await MongoDatasetData.deleteMany(
|
||||
{ teamId, datasetIds: { $in: datasetIds }, collectionId: { $in: collectionIds } },
|
||||
{ session }
|
||||
);
|
||||
// Delete dataset_data_texts
|
||||
await MongoDatasetDataText.deleteMany(
|
||||
{ teamId, datasetIds: { $in: datasetIds }, collectionId: { $in: collectionIds } },
|
||||
{ session }
|
||||
);
|
||||
|
||||
// delete collections
|
||||
await MongoDatasetCollection.deleteMany(
|
||||
{
|
||||
teamId,
|
||||
_id: { $in: collectionIds }
|
||||
},
|
||||
{ session }
|
||||
);
|
||||
|
||||
// no session delete: delete files, vector data
|
||||
await deleteDatasetDataVector({ teamId, datasetIds, collectionIds });
|
||||
}
|
||||
|
||||
/**
|
||||
* delete delOnlyCollection
|
||||
*/
|
||||
export async function delOnlyCollection({
|
||||
collections,
|
||||
session
|
||||
}: {
|
||||
collections: DatasetCollectionSchemaType[];
|
||||
session: ClientSession;
|
||||
}) {
|
||||
if (collections.length === 0) return;
|
||||
|
||||
const teamId = collections[0].teamId;
|
||||
|
||||
if (!teamId) return Promise.reject('teamId is not exist');
|
||||
|
||||
const datasetIds = Array.from(new Set(collections.map((item) => String(item.datasetId))));
|
||||
const collectionIds = collections.map((item) => String(item._id));
|
||||
|
||||
// delete training data
|
||||
await MongoDatasetTraining.deleteMany({
|
||||
teamId,
|
||||
datasetIds: { $in: datasetIds },
|
||||
collectionId: { $in: collectionIds }
|
||||
});
|
||||
|
||||
// delete dataset.datas
|
||||
await MongoDatasetData.deleteMany(
|
||||
{ teamId, datasetIds: { $in: datasetIds }, collectionId: { $in: collectionIds } },
|
||||
{ session }
|
||||
);
|
||||
|
||||
// delete collections
|
||||
await MongoDatasetCollection.deleteMany(
|
||||
{
|
||||
teamId,
|
||||
_id: { $in: collectionIds }
|
||||
},
|
||||
{ session }
|
||||
);
|
||||
|
||||
// no session delete: delete files, vector data
|
||||
await deleteDatasetDataVector({ teamId, datasetIds, collectionIds });
|
||||
}
|
||||
|
||||
@@ -97,7 +97,7 @@ export const createOrGetCollectionTags = async ({
|
||||
datasetId,
|
||||
tag: tagContent
|
||||
})),
|
||||
{ session }
|
||||
{ session, ordered: true }
|
||||
);
|
||||
|
||||
return [...existingTags.map((tag) => tag._id), ...newTags.map((tag) => tag._id)];
|
||||
@@ -174,6 +174,14 @@ export const syncCollection = async (collection: CollectionWithDatasetType) => {
|
||||
}
|
||||
|
||||
await mongoSessionRun(async (session) => {
|
||||
// Delete old collection
|
||||
await delCollection({
|
||||
collections: [collection],
|
||||
delImg: false,
|
||||
delFile: false,
|
||||
session
|
||||
});
|
||||
|
||||
// Create new collection
|
||||
await createCollectionAndInsertData({
|
||||
session,
|
||||
@@ -208,13 +216,6 @@ export const syncCollection = async (collection: CollectionWithDatasetType) => {
|
||||
updateTime: new Date()
|
||||
}
|
||||
});
|
||||
|
||||
// Delete old collection
|
||||
await delCollection({
|
||||
collections: [collection],
|
||||
delRelatedSource: false,
|
||||
session
|
||||
});
|
||||
});
|
||||
|
||||
return DatasetCollectionSyncResultEnum.success;
|
||||
|
||||
@@ -7,6 +7,8 @@ import { MongoDatasetTraining } from './training/schema';
|
||||
import { MongoDatasetData } from './data/schema';
|
||||
import { deleteDatasetDataVector } from '../../common/vectorStore/controller';
|
||||
import { MongoDatasetDataText } from './data/dataTextSchema';
|
||||
import { DatasetErrEnum } from '@fastgpt/global/common/error/code/dataset';
|
||||
import { retryFn } from '@fastgpt/global/common/system/utils';
|
||||
|
||||
/* ============= dataset ========== */
|
||||
/* find all datasetId by top datasetId */
|
||||
@@ -54,7 +56,7 @@ export async function getCollectionWithDataset(collectionId: string) {
|
||||
.populate<{ dataset: DatasetSchemaType }>('dataset')
|
||||
.lean();
|
||||
if (!data) {
|
||||
return Promise.reject('Collection is not exist');
|
||||
return Promise.reject(DatasetErrEnum.unExistCollection);
|
||||
}
|
||||
return data;
|
||||
}
|
||||
@@ -77,40 +79,39 @@ export async function delDatasetRelevantData({
|
||||
|
||||
const datasetIds = datasets.map((item) => item._id);
|
||||
|
||||
// delete training data
|
||||
await MongoDatasetTraining.deleteMany({
|
||||
teamId,
|
||||
datasetId: { $in: datasetIds }
|
||||
});
|
||||
|
||||
// Get _id, teamId, fileId, metadata.relatedImgId for all collections
|
||||
const collections = await MongoDatasetCollection.find(
|
||||
{
|
||||
teamId,
|
||||
datasetId: { $in: datasetIds }
|
||||
},
|
||||
'_id teamId datasetId fileId metadata',
|
||||
{ session }
|
||||
'_id teamId datasetId fileId metadata'
|
||||
).lean();
|
||||
|
||||
// Delete Image and file
|
||||
await delCollectionRelatedSource({ collections, session });
|
||||
await retryFn(async () => {
|
||||
await Promise.all([
|
||||
// delete training data
|
||||
MongoDatasetTraining.deleteMany({
|
||||
teamId,
|
||||
datasetId: { $in: datasetIds }
|
||||
}),
|
||||
//Delete dataset_data_texts
|
||||
MongoDatasetDataText.deleteMany({
|
||||
teamId,
|
||||
datasetId: { $in: datasetIds }
|
||||
}),
|
||||
//delete dataset_datas
|
||||
MongoDatasetData.deleteMany({ teamId, datasetId: { $in: datasetIds } }),
|
||||
// Delete Image and file
|
||||
delCollectionRelatedSource({ collections }),
|
||||
// Delete vector data
|
||||
deleteDatasetDataVector({ teamId, datasetIds })
|
||||
]);
|
||||
});
|
||||
|
||||
// delete collections
|
||||
await MongoDatasetCollection.deleteMany({
|
||||
teamId,
|
||||
datasetId: { $in: datasetIds }
|
||||
}).session(session);
|
||||
|
||||
// No session delete:
|
||||
// Delete dataset_data_texts
|
||||
await MongoDatasetDataText.deleteMany({
|
||||
teamId,
|
||||
datasetId: { $in: datasetIds }
|
||||
});
|
||||
// delete dataset_datas
|
||||
await MongoDatasetData.deleteMany({ teamId, datasetId: { $in: datasetIds } });
|
||||
|
||||
// Delete vector data
|
||||
await deleteDatasetDataVector({ teamId, datasetIds });
|
||||
}
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
import { connectionMongo, getMongoModel } from '../../../common/mongo';
|
||||
const { Schema } = connectionMongo;
|
||||
import { DatasetDataSchemaType } from '@fastgpt/global/core/dataset/type.d';
|
||||
import { DatasetDataTextSchemaType } from '@fastgpt/global/core/dataset/type.d';
|
||||
import { TeamCollectionName } from '@fastgpt/global/support/user/team/constant';
|
||||
import { DatasetCollectionName } from '../schema';
|
||||
import { DatasetColCollectionName } from '../collection/schema';
|
||||
@@ -40,12 +40,13 @@ try {
|
||||
default_language: 'none'
|
||||
}
|
||||
);
|
||||
DatasetDataTextSchema.index({ teamId: 1, datasetId: 1, collectionId: 1 });
|
||||
DatasetDataTextSchema.index({ dataId: 1 }, { unique: true });
|
||||
} catch (error) {
|
||||
console.log(error);
|
||||
}
|
||||
|
||||
export const MongoDatasetDataText = getMongoModel<DatasetDataSchemaType>(
|
||||
export const MongoDatasetDataText = getMongoModel<DatasetDataTextSchemaType>(
|
||||
DatasetDataTextCollectionName,
|
||||
DatasetDataTextSchema
|
||||
);
|
||||
|
||||
@@ -200,6 +200,7 @@ export async function searchDatasetData(
|
||||
forbidCollectionIdList: collections.map((item) => String(item._id))
|
||||
};
|
||||
};
|
||||
|
||||
/*
|
||||
Collection metadata filter
|
||||
标签过滤:
|
||||
@@ -207,6 +208,63 @@ export async function searchDatasetData(
|
||||
2. and 标签和 null 不能共存,否则返回空数组
|
||||
*/
|
||||
const filterCollectionByMetadata = async (): Promise<string[] | undefined> => {
|
||||
const getAllCollectionIds = async ({
|
||||
parentCollectionIds
|
||||
}: {
|
||||
parentCollectionIds?: string[];
|
||||
}): Promise<string[] | undefined> => {
|
||||
if (!parentCollectionIds) return;
|
||||
if (parentCollectionIds.length === 0) {
|
||||
return [];
|
||||
}
|
||||
|
||||
const collections = await MongoDatasetCollection.find(
|
||||
{
|
||||
teamId,
|
||||
datasetId: { $in: datasetIds },
|
||||
_id: { $in: parentCollectionIds }
|
||||
},
|
||||
'_id type',
|
||||
{
|
||||
...readFromSecondary
|
||||
}
|
||||
).lean();
|
||||
|
||||
const resultIds = new Set<string>();
|
||||
collections.forEach((item) => {
|
||||
if (item.type !== 'folder') {
|
||||
resultIds.add(String(item._id));
|
||||
}
|
||||
});
|
||||
|
||||
const folderIds = collections
|
||||
.filter((item) => item.type === 'folder')
|
||||
.map((item) => String(item._id));
|
||||
|
||||
// Get all child collection ids
|
||||
if (folderIds.length) {
|
||||
const childCollections = await MongoDatasetCollection.find(
|
||||
{
|
||||
teamId,
|
||||
datasetId: { $in: datasetIds },
|
||||
parentId: { $in: folderIds }
|
||||
},
|
||||
'_id type',
|
||||
{
|
||||
...readFromSecondary
|
||||
}
|
||||
).lean();
|
||||
|
||||
const childIds = await getAllCollectionIds({
|
||||
parentCollectionIds: childCollections.map((item) => String(item._id))
|
||||
});
|
||||
|
||||
childIds?.forEach((id) => resultIds.add(id));
|
||||
}
|
||||
|
||||
return Array.from(resultIds);
|
||||
};
|
||||
|
||||
if (!collectionFilterMatch || !global.feConfigs.isPlus) return;
|
||||
|
||||
let tagCollectionIdList: string[] | undefined = undefined;
|
||||
@@ -326,13 +384,19 @@ export async function searchDatasetData(
|
||||
}
|
||||
|
||||
// Concat tag and time
|
||||
if (tagCollectionIdList && createTimeCollectionIdList) {
|
||||
return tagCollectionIdList.filter((id) => createTimeCollectionIdList!.includes(id));
|
||||
} else if (tagCollectionIdList) {
|
||||
return tagCollectionIdList;
|
||||
} else if (createTimeCollectionIdList) {
|
||||
return createTimeCollectionIdList;
|
||||
}
|
||||
const collectionIds = (() => {
|
||||
if (tagCollectionIdList && createTimeCollectionIdList) {
|
||||
return tagCollectionIdList.filter((id) =>
|
||||
(createTimeCollectionIdList as string[]).includes(id)
|
||||
);
|
||||
}
|
||||
|
||||
return tagCollectionIdList || createTimeCollectionIdList;
|
||||
})();
|
||||
|
||||
return await getAllCollectionIds({
|
||||
parentCollectionIds: collectionIds
|
||||
});
|
||||
} catch (error) {}
|
||||
};
|
||||
const embeddingRecall = async ({
|
||||
@@ -383,6 +447,7 @@ export async function searchDatasetData(
|
||||
).lean()
|
||||
]);
|
||||
|
||||
const set = new Map<string, number>();
|
||||
const formatResult = results
|
||||
.map((item, index) => {
|
||||
const collection = collections.find((col) => String(col._id) === String(item.collectionId));
|
||||
@@ -398,8 +463,6 @@ export async function searchDatasetData(
|
||||
return;
|
||||
}
|
||||
|
||||
const score = item?.score || 0;
|
||||
|
||||
const result: SearchDataResponseItemType = {
|
||||
id: String(data._id),
|
||||
updateTime: data.updateTime,
|
||||
@@ -409,12 +472,24 @@ export async function searchDatasetData(
|
||||
datasetId: String(data.datasetId),
|
||||
collectionId: String(data.collectionId),
|
||||
...getCollectionSourceData(collection),
|
||||
score: [{ type: SearchScoreTypeEnum.embedding, value: score, index }]
|
||||
score: [{ type: SearchScoreTypeEnum.embedding, value: item?.score || 0, index }]
|
||||
};
|
||||
|
||||
return result;
|
||||
})
|
||||
.filter(Boolean) as SearchDataResponseItemType[];
|
||||
.filter((item) => {
|
||||
if (!item) return false;
|
||||
if (set.has(item.id)) return false;
|
||||
set.set(item.id, 1);
|
||||
return true;
|
||||
})
|
||||
.map((item, index) => {
|
||||
if (!item) return;
|
||||
return {
|
||||
...item,
|
||||
score: item.score.map((item) => ({ ...item, index }))
|
||||
};
|
||||
}) as SearchDataResponseItemType[];
|
||||
|
||||
return {
|
||||
embeddingRecallResults: formatResult,
|
||||
@@ -717,11 +792,12 @@ export const defaultSearchDatasetData = async ({
|
||||
? getLLMModel(datasetSearchExtensionModel)
|
||||
: undefined;
|
||||
|
||||
const { concatQueries, rewriteQuery, aiExtensionResult } = await datasetSearchQueryExtension({
|
||||
query,
|
||||
extensionModel,
|
||||
extensionBg: datasetSearchExtensionBg
|
||||
});
|
||||
const { concatQueries, extensionQueries, rewriteQuery, aiExtensionResult } =
|
||||
await datasetSearchQueryExtension({
|
||||
query,
|
||||
extensionModel,
|
||||
extensionBg: datasetSearchExtensionBg
|
||||
});
|
||||
|
||||
const result = await searchDatasetData({
|
||||
...props,
|
||||
@@ -736,7 +812,7 @@ export const defaultSearchDatasetData = async ({
|
||||
model: aiExtensionResult.model,
|
||||
inputTokens: aiExtensionResult.inputTokens,
|
||||
outputTokens: aiExtensionResult.outputTokens,
|
||||
query: concatQueries.join('\n')
|
||||
query: extensionQueries.join('\n')
|
||||
}
|
||||
: undefined
|
||||
};
|
||||
|
||||
@@ -72,12 +72,15 @@ Human: ${query}
|
||||
if (result.extensionQueries?.length === 0) return;
|
||||
return result;
|
||||
})();
|
||||
|
||||
const extensionQueries = filterSamQuery(aiExtensionResult?.extensionQueries || []);
|
||||
if (aiExtensionResult) {
|
||||
queries = filterSamQuery(queries.concat(aiExtensionResult.extensionQueries));
|
||||
queries = filterSamQuery(queries.concat(extensionQueries));
|
||||
rewriteQuery = queries.join('\n');
|
||||
}
|
||||
|
||||
return {
|
||||
extensionQueries,
|
||||
concatQueries: queries,
|
||||
rewriteQuery,
|
||||
aiExtensionResult
|
||||
|
||||
@@ -334,7 +334,7 @@ const getMultiInput = async ({
|
||||
|
||||
return {
|
||||
documentQuoteText: text,
|
||||
userFiles: fileLinks.map((url) => parseUrlToFileType(url))
|
||||
userFiles: fileLinks.map((url) => parseUrlToFileType(url)).filter(Boolean)
|
||||
};
|
||||
};
|
||||
|
||||
|
||||
@@ -4,12 +4,9 @@ import type { ChatItemType, UserChatItemValueItemType } from '@fastgpt/global/co
|
||||
import { ChatRoleEnum } from '@fastgpt/global/core/chat/constants';
|
||||
import { SseResponseEventEnum } from '@fastgpt/global/core/workflow/runtime/constants';
|
||||
import { textAdaptGptResponse } from '@fastgpt/global/core/workflow/runtime/utils';
|
||||
import { parseReasoningContent, parseReasoningStreamContent } from '../../../ai/utils';
|
||||
import { createChatCompletion } from '../../../ai/config';
|
||||
import type {
|
||||
ChatCompletion,
|
||||
ChatCompletionMessageParam,
|
||||
StreamChatType
|
||||
} from '@fastgpt/global/core/ai/type.d';
|
||||
import type { ChatCompletionMessageParam, StreamChatType } from '@fastgpt/global/core/ai/type.d';
|
||||
import { formatModelChars2Points } from '../../../../support/wallet/usage/utils';
|
||||
import type { LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
|
||||
import { postTextCensor } from '../../../../common/api/requestPlusApi';
|
||||
@@ -195,7 +192,13 @@ export const dispatchChatCompletion = async (props: ChatProps): Promise<ChatResp
|
||||
});
|
||||
|
||||
const { answerText, reasoningText } = await (async () => {
|
||||
if (res && isStreamResponse) {
|
||||
if (isStreamResponse) {
|
||||
if (!res) {
|
||||
return {
|
||||
answerText: '',
|
||||
reasoningText: ''
|
||||
};
|
||||
}
|
||||
// sse response
|
||||
const { answer, reasoning } = await streamResponse({
|
||||
res,
|
||||
@@ -210,34 +213,49 @@ export const dispatchChatCompletion = async (props: ChatProps): Promise<ChatResp
|
||||
reasoningText: reasoning
|
||||
};
|
||||
} else {
|
||||
const unStreamResponse = response as ChatCompletion;
|
||||
const answer = unStreamResponse.choices?.[0]?.message?.content || '';
|
||||
// @ts-ignore
|
||||
const reasoning = unStreamResponse.choices?.[0]?.message?.reasoning_content || '';
|
||||
const { content, reasoningContent } = (() => {
|
||||
const content = response.choices?.[0]?.message?.content || '';
|
||||
// @ts-ignore
|
||||
const reasoningContent: string = response.choices?.[0]?.message?.reasoning_content || '';
|
||||
|
||||
// API already parse reasoning content
|
||||
if (reasoningContent || !aiChatReasoning) {
|
||||
return {
|
||||
content,
|
||||
reasoningContent
|
||||
};
|
||||
}
|
||||
|
||||
const [think, answer] = parseReasoningContent(content);
|
||||
return {
|
||||
content: answer,
|
||||
reasoningContent: think
|
||||
};
|
||||
})();
|
||||
|
||||
// Some models do not support streaming
|
||||
if (stream) {
|
||||
if (isResponseAnswerText && answer) {
|
||||
if (aiChatReasoning && reasoningContent) {
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.fastAnswer,
|
||||
data: textAdaptGptResponse({
|
||||
text: answer
|
||||
reasoning_content: reasoningContent
|
||||
})
|
||||
});
|
||||
}
|
||||
if (aiChatReasoning && reasoning) {
|
||||
if (isResponseAnswerText && content) {
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.fastAnswer,
|
||||
data: textAdaptGptResponse({
|
||||
reasoning_content: reasoning
|
||||
text: content
|
||||
})
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
return {
|
||||
answerText: answer,
|
||||
reasoningText: reasoning
|
||||
answerText: content,
|
||||
reasoningText: reasoningContent
|
||||
};
|
||||
}
|
||||
})();
|
||||
@@ -249,7 +267,8 @@ export const dispatchChatCompletion = async (props: ChatProps): Promise<ChatResp
|
||||
const AIMessages: ChatCompletionMessageParam[] = [
|
||||
{
|
||||
role: ChatCompletionRequestMessageRoleEnum.Assistant,
|
||||
content: answerText
|
||||
content: answerText,
|
||||
reasoning_text: reasoningText // reasoning_text is only recorded for response, but not for request
|
||||
}
|
||||
];
|
||||
|
||||
@@ -267,7 +286,7 @@ export const dispatchChatCompletion = async (props: ChatProps): Promise<ChatResp
|
||||
});
|
||||
|
||||
return {
|
||||
answerText,
|
||||
answerText: answerText.trim(),
|
||||
reasoningText,
|
||||
[DispatchNodeResponseKeyEnum.nodeResponse]: {
|
||||
totalPoints: externalProvider.openaiAccount?.key ? 0 : totalPoints,
|
||||
@@ -386,7 +405,7 @@ async function getMultiInput({
|
||||
|
||||
return {
|
||||
documentQuoteText: text,
|
||||
userFiles: fileLinks.map((url) => parseUrlToFileType(url))
|
||||
userFiles: fileLinks.map((url) => parseUrlToFileType(url)).filter(Boolean)
|
||||
};
|
||||
}
|
||||
|
||||
@@ -500,26 +519,18 @@ async function streamResponse({
|
||||
});
|
||||
let answer = '';
|
||||
let reasoning = '';
|
||||
const { parsePart, getStartTagBuffer } = parseReasoningStreamContent();
|
||||
|
||||
for await (const part of stream) {
|
||||
if (res.closed) {
|
||||
stream.controller?.abort();
|
||||
break;
|
||||
}
|
||||
|
||||
const content = part.choices?.[0]?.delta?.content || '';
|
||||
const [reasoningContent, content] = parsePart(part, aiChatReasoning);
|
||||
answer += content;
|
||||
if (isResponseAnswerText && content) {
|
||||
workflowStreamResponse?.({
|
||||
write,
|
||||
event: SseResponseEventEnum.answer,
|
||||
data: textAdaptGptResponse({
|
||||
text: content
|
||||
})
|
||||
});
|
||||
}
|
||||
|
||||
const reasoningContent = part.choices?.[0]?.delta?.reasoning_content || '';
|
||||
reasoning += reasoningContent;
|
||||
|
||||
if (aiChatReasoning && reasoningContent) {
|
||||
workflowStreamResponse?.({
|
||||
write,
|
||||
@@ -529,6 +540,21 @@ async function streamResponse({
|
||||
})
|
||||
});
|
||||
}
|
||||
|
||||
if (isResponseAnswerText && content) {
|
||||
workflowStreamResponse?.({
|
||||
write,
|
||||
event: SseResponseEventEnum.answer,
|
||||
data: textAdaptGptResponse({
|
||||
text: content
|
||||
})
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// if answer is empty, try to get value from startTagBuffer. (Cause: The response content is too short to exceed the minimum parse length)
|
||||
if (answer === '') {
|
||||
answer = getStartTagBuffer();
|
||||
}
|
||||
|
||||
return { answer, reasoning };
|
||||
|
||||
@@ -232,9 +232,14 @@ export async function dispatchWorkFlow(data: Props): Promise<DispatchFlowRespons
|
||||
chatNodeUsages = chatNodeUsages.concat(nodeDispatchUsages);
|
||||
}
|
||||
|
||||
if (toolResponses !== undefined) {
|
||||
if (toolResponses !== undefined && toolResponses !== null) {
|
||||
if (Array.isArray(toolResponses) && toolResponses.length === 0) return;
|
||||
if (typeof toolResponses === 'object' && Object.keys(toolResponses).length === 0) return;
|
||||
if (
|
||||
!Array.isArray(toolResponses) &&
|
||||
typeof toolResponses === 'object' &&
|
||||
Object.keys(toolResponses).length === 0
|
||||
)
|
||||
return;
|
||||
toolRunResponse = toolResponses;
|
||||
}
|
||||
|
||||
@@ -243,12 +248,17 @@ export async function dispatchWorkFlow(data: Props): Promise<DispatchFlowRespons
|
||||
chatAssistantResponse = chatAssistantResponse.concat(assistantResponses);
|
||||
} else {
|
||||
if (reasoningText) {
|
||||
chatAssistantResponse.push({
|
||||
type: ChatItemValueTypeEnum.reasoning,
|
||||
reasoning: {
|
||||
content: reasoningText
|
||||
}
|
||||
});
|
||||
const isResponseReasoningText = inputs.find(
|
||||
(item) => item.key === NodeInputKeyEnum.aiChatReasoning
|
||||
)?.value;
|
||||
if (isResponseReasoningText) {
|
||||
chatAssistantResponse.push({
|
||||
type: ChatItemValueTypeEnum.reasoning,
|
||||
reasoning: {
|
||||
content: reasoningText
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
if (answerText) {
|
||||
// save assistant text response
|
||||
|
||||
@@ -53,7 +53,7 @@ export const dispatchRunAppNode = async (props: Props): Promise<Response> => {
|
||||
|
||||
const userInputFiles = (() => {
|
||||
if (fileUrlList) {
|
||||
return fileUrlList.map((url) => parseUrlToFileType(url));
|
||||
return fileUrlList.map((url) => parseUrlToFileType(url)).filter(Boolean);
|
||||
}
|
||||
// Adapt version 4.8.13 upgrade
|
||||
return files;
|
||||
|
||||
@@ -398,41 +398,6 @@ async function fetchData({
|
||||
};
|
||||
}
|
||||
|
||||
// function replaceVariable(text: string, obj: Record<string, any>) {
|
||||
// for (const [key, value] of Object.entries(obj)) {
|
||||
// if (value === undefined) {
|
||||
// text = text.replace(new RegExp(`{{(${key})}}`, 'g'), UNDEFINED_SIGN);
|
||||
// } else {
|
||||
// const replacement = JSON.stringify(value);
|
||||
// const unquotedReplacement =
|
||||
// replacement.startsWith('"') && replacement.endsWith('"')
|
||||
// ? replacement.slice(1, -1)
|
||||
// : replacement;
|
||||
// text = text.replace(new RegExp(`{{(${key})}}`, 'g'), () => unquotedReplacement);
|
||||
// }
|
||||
// }
|
||||
// return text || '';
|
||||
// }
|
||||
// function removeUndefinedSign(obj: Record<string, any>) {
|
||||
// for (const key in obj) {
|
||||
// if (obj[key] === UNDEFINED_SIGN) {
|
||||
// obj[key] = undefined;
|
||||
// } else if (Array.isArray(obj[key])) {
|
||||
// obj[key] = obj[key].map((item: any) => {
|
||||
// if (item === UNDEFINED_SIGN) {
|
||||
// return undefined;
|
||||
// } else if (typeof item === 'object') {
|
||||
// removeUndefinedSign(item);
|
||||
// }
|
||||
// return item;
|
||||
// });
|
||||
// } else if (typeof obj[key] === 'object') {
|
||||
// removeUndefinedSign(obj[key]);
|
||||
// }
|
||||
// }
|
||||
// return obj;
|
||||
// }
|
||||
|
||||
// Replace some special response from system plugin
|
||||
async function replaceSystemPluginResponse({
|
||||
response,
|
||||
|
||||
@@ -24,7 +24,7 @@
|
||||
"jsonwebtoken": "^9.0.2",
|
||||
"lodash": "^4.17.21",
|
||||
"mammoth": "^1.6.0",
|
||||
"mongoose": "^7.0.2",
|
||||
"mongoose": "^8.10.1",
|
||||
"multer": "1.4.5-lts.1",
|
||||
"next": "14.2.5",
|
||||
"nextjs-cors": "^2.2.0",
|
||||
|
||||
@@ -178,7 +178,7 @@ export const getClbsAndGroupsWithInfo = async ({
|
||||
]);
|
||||
|
||||
export const delResourcePermissionById = (id: string) => {
|
||||
return MongoResourcePermission.findByIdAndRemove(id);
|
||||
return MongoResourcePermission.findByIdAndDelete(id);
|
||||
};
|
||||
export const delResourcePermission = ({
|
||||
session,
|
||||
|
||||
@@ -196,7 +196,8 @@ export async function syncCollaborators({
|
||||
permission: item.permission
|
||||
})),
|
||||
{
|
||||
session
|
||||
session,
|
||||
ordered: true
|
||||
}
|
||||
);
|
||||
}
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user