mirror of
https://github.com/labring/FastGPT.git
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Add OpenAPI docs;Correct the glm document (#346)
This commit is contained in:
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@@ -20,20 +20,24 @@ weight: 20
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1. 自定义 title 和 logo
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2. 用户注册,支付 (已有微信扫码支付,后续会补充支付方式)
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3. 团队空间 (下期开发)
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4. 完善的 OpenAPI
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5. 高级编排额外插件
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6. 后台管理系统 (已有,持续更新)
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3. API 访问限制,可配置:额度、过期时间
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4. 团队空间 (计划)
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5. 完善的 OpenAPI(计划)
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6. 高级编排额外插件(计划)
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7. 后台管理系统
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a. 查询:用户、支付、应用、知识库
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b. 变更:用户
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c. 新增:用户
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{{% /alert %}}
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### 商业版定价
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#### 交付费用
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+ 使用 [Sealos 公有云](https://sealos.io)交付:1w/年/套 (直接在 Sealos 公有云充值,便可**免费获取 FastGPT 商业版 License**,同时您充值的金额可用于部署其他云资源,相当于白嫖了一个 FastGPT 商业版)。
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+ 渠道商使用 Sealos 交付:返现 20% 成交额。
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+ 私有服务器交付:2w/年/套(如需部署支持,按技术服务费计算)
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+ 渠道商私有服务器交付:1.3w/年/套(渠道商合同单独约谈,累计 5 套以上可签)
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+ 使用 [Sealos 公有云](https://sealos.io)部署:1万元/年/套 (无部署费用。赠送 8000 sealos 公有云额度,可用于 FastGPT 或其他云资源)。
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+ 渠道商使用 Sealos 部署:返现 20% 成交额。
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+ 私有服务器部署:2万元/年/套(如需部署支持,按技术服务费计算)
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+ 渠道商私有服务器部署:1.3万元/年/套(渠道商合同单独约谈,累计 5 套以上可签)
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#### 用户注册数量费用(按注册量算,不计量分享和 API)
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@@ -69,7 +73,8 @@ weight: 20
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## 联系方式
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通过邮箱联系 yujinlong@sealos.io
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微信: allence1004
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邮箱: yujinlong@sealos.io
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## QA
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@@ -77,7 +82,7 @@ weight: 20
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完整版应用 = 开源版镜像 + 商业版镜像
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我们会提供一个商业版镜像给你使用,还会提供一个简单的后台管理系统(目前只设置了简单的查询功能)
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我们会提供一个商业版镜像给你使用,该镜像需要一个 license 启动,license 有效期为 1 年。此外,还会提供一个简单的后台管理系统(目前只设置了简单的查询功能)
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2. 二次开发如何操作?
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|
@@ -62,19 +62,18 @@ Authorization 为 sk-aaabbbcccdddeeefffggghhhiiijjjkkk。model 为刚刚在 One
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修改 config.json 配置文件,在 VectorModels 中加入 chatglm2 和 M3E 模型:
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```json
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"ChatModels": [
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//已有模型
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{
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"model": "chatglm2",
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"name": "chatglm2",
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"contextMaxToken": 8000,
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"quoteMaxToken": 4000,
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"maxTemperature": 1.2,
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"price": 0,
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"defaultSystem": ""
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}
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],
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"ChatModels": [
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//已有模型
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{
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"model": "chatglm2",
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"name": "chatglm2",
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"contextMaxToken": 8000,
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"quoteMaxToken": 4000,
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"maxTemperature": 1.2,
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"price": 0,
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"defaultSystem": ""
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}
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],
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"VectorModels": [
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{
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"model": "text-embedding-ada-002",
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|
@@ -99,21 +99,21 @@ Authorization 为 sk-aaabbbcccdddeeefffggghhhiiijjjkkk。model 为刚刚在 One
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## 接入 FastGPT
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修改 config.json 配置文件,在 VectorModels 中加入 chatglm2 和 M3E 模型:
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修改 config.json 配置文件,在 VectorModels 中加入 chatglm2 模型:
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```json
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"ChatModels": [
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//已有模型
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{
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"model": "chatglm2",
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"name": "chatglm2",
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"contextMaxToken": 8000,
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"quoteMaxToken": 4000,
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"maxTemperature": 1.2,
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"price": 0,
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"defaultSystem": ""
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}
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],
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"ChatModels": [
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//已有模型
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{
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"model": "chatglm2",
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"name": "chatglm2",
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"contextMaxToken": 8000,
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"quoteMaxToken": 4000,
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"maxTemperature": 1.2,
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"price": 0,
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"defaultSystem": ""
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}
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]
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```
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## 测试使用
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|
@@ -66,7 +66,7 @@ Authorization 为 sk-key。model 为刚刚在 One API 填写的自定义模型
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"defaultToken": 500,
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"maxToken": 1800
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}
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],
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]
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```
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## 测试使用
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|
@@ -22,17 +22,6 @@ weight: 520
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这里介绍一些基础的配置字段:
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```json
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// 这个配置会控制前端的一些样式
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"FeConfig": {
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"show_emptyChat": true, // 对话页面,空内容时,是否展示介绍页
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"show_register": false, // 是否展示注册按键(包括忘记密码,注册账号和三方登录)
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"show_appStore": false, // 是否展示应用市场(不过目前权限还没做好,放开也没用)
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"show_userDetail": false, // 是否展示用户详情(账号余额、OpenAI 绑定)
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"show_git": true, // 是否展示 Git
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"systemTitle": "FastGPT", // 系统的 title
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"authorText": "Made by FastGPT Team.", // 签名
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},
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...
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...
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// 这个配置文件是系统级参数
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"SystemParams": {
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@@ -47,22 +36,11 @@ weight: 520
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```json
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{
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"FeConfig": {
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"show_emptyChat": true,
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"show_register": false,
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"show_appStore": false,
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"show_userDetail": false,
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"show_git": true,
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"systemTitle": "FastGPT",
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"authorText": "Made by FastGPT Team.",
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"scripts": []
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},
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"SystemParams": {
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"vectorMaxProcess": 15,
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"qaMaxProcess": 15,
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"pgIvfflatProbe": 20
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},
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"plugins": {},
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"ChatModels": [
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{
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"model": "gpt-3.5-turbo",
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@@ -92,12 +70,6 @@ weight: 520
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"defaultSystem": ""
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}
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],
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"QAModel": {
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"model": "gpt-3.5-turbo-16k",
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"name": "GPT35-16k",
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"maxToken": 16000,
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"price": 0
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},
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"VectorModels": [
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{
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"model": "text-embedding-ada-002",
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@@ -106,6 +78,28 @@ weight: 520
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"defaultToken": 500,
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"maxToken": 3000
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}
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]
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],
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"QAModel": {
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"model": "gpt-3.5-turbo-16k",
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"name": "GPT35-16k",
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"maxToken": 0,
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"price": 0
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},
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"ExtractModel": {
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"model": "gpt-3.5-turbo-16k",
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"functionCall": true,
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"name": "GPT35-16k",
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"maxToken": 0,
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"price": 0,
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"prompt": ""
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},
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"CQModel": {
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"model": "gpt-3.5-turbo-16k",
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"functionCall": true,
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"name": "GPT35-16k",
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"maxToken": 0,
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"price": 0,
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"prompt": ""
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}
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}
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```
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|
@@ -41,8 +41,9 @@ weight: 510
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git clone git@github.com:<github_username>/FastGPT.git
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```
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projects 目录下为 FastGPT 应用代码。NextJS 框架前后端放在一起,API 服务位于 `src/pages/api` 目录内。
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packages 目录为相关的共用包。
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**projects 目录下为 FastGPT 应用代码。NextJS 框架前后端放在一起,API 服务位于 `src/pages/api` 目录内。**
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**packages 目录为相关的共用包。**
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### 安装数据库
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@@ -69,15 +70,15 @@ packages 目录为相关的共用包。
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### 运行
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```bash
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cd client
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pnpm i
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cd projects/app # FastGPT 主程序
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pnpm dev
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```
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### 镜像打包
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```bash
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docker build -t dockername/fastgpt .
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docker build -t dockername/fastgpt --build-arg name=app .
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```
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## 创建拉取请求
|
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|
378
docSite/content/docs/development/openApi.md
Normal file
378
docSite/content/docs/development/openApi.md
Normal file
@@ -0,0 +1,378 @@
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---
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title: 'OpenAPI 使用'
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description: 'FastGPT OpenAPI 文档'
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icon: 'api'
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draft: false
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toc: true
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weight: 512
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---
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# 基本配置
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```
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baseUrl: "https://fastgpt.run/api"
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headers: {
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Authorization: "Bearer apikey"
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}
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```
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# 如何获取 API Key
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FastGPT 的 API Key 有 2 类,一类是全局通用的 key;一类是携带了 AppId 也就是有应用标记的 key。
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|
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| 通用key | 应用特定 key |
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| --------------------- | --------------------- |
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|  |  |
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# 接口
|
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## 发起对话
|
||||
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{{% alert icon="🤖 " context="success" %}}
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该接口 API Key 需使用应用特定的 key,否则会报错。
|
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{{% /alert %}}
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|
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|
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对话接口兼容 openai 的接口!如果你有第三方项目,可以直接通过修改 BaseUrl 和 Authorization 来访问 FastGpt 应用。缺点是你无法获取到响应的token值。
|
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|
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请求内容
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- headers.Authorization: Bearer apikey
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- chatId: string | undefined 。
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- 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。
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- 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。并拼接 messages 数组最后一个内容作为完整请求。(自行确保 chatid 唯一,长度不限)
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- messages: 与 openai gpt 接口完全一致。
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- detail: 是否返回详细值(模块状态,响应的完整结果),会通过event进行区分
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- variables: 变量。一个对象,效果同全局变量。
|
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|
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**请求示例:**
|
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|
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```bash
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curl --location --request POST 'https://fastgpt.run/api/openapi/v1/chat/completions' \
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--header 'Authorization: Bearer apikey' \
|
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--header 'Content-Type: application/json' \
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--data-raw '{
|
||||
"chatId":"111",
|
||||
"stream":false,
|
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"detail": false,
|
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"variables": {
|
||||
"cTime": "2022/2/2 22:22"
|
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},
|
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"messages": [
|
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{
|
||||
"content": "导演是谁",
|
||||
"role": "user"
|
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}
|
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]
|
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}'
|
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```
|
||||
|
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|
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{{< tabs tabTotal="3" >}}
|
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{{< tab tabName="detail=false 响应" >}}
|
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{{< markdownify >}}
|
||||
|
||||
```bash
|
||||
data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":""},"index":0,"finish_reason":null}]}
|
||||
|
||||
data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":"电"},"index":0,"finish_reason":null}]}
|
||||
|
||||
data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":"影"},"index":0,"finish_reason":null}]}
|
||||
|
||||
data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":"《"},"index":0,"finish_reason":null}]}
|
||||
```
|
||||
|
||||
{{< /markdownify >}}
|
||||
{{< /tab >}}
|
||||
|
||||
{{< tab tabName="detail=true 响应" >}}
|
||||
{{< markdownify >}}
|
||||
|
||||
```bash
|
||||
event: answer
|
||||
data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":""},"index":0,"finish_reason":null}]}
|
||||
|
||||
event: answer
|
||||
data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":"电"},"index":0,"finish_reason":null}]}
|
||||
|
||||
event: answer
|
||||
data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":"影"},"index":0,"finish_reason":null}]}
|
||||
|
||||
event: answer
|
||||
data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":"《"},"index":0,"finish_reason":null}]}
|
||||
|
||||
event: answer
|
||||
data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":"铃"},"index":0,"finish_reason":null}]}
|
||||
|
||||
event: answer
|
||||
data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":"芽"},"index":0,"finish_reason":null}]}
|
||||
|
||||
event: answer
|
||||
data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":"。"},"index":0,"finish_reason":null}]}
|
||||
|
||||
event: answer
|
||||
data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":""},"index":0,"finish_reason":null}]}
|
||||
|
||||
event: answer
|
||||
data: {"id":"","object":"","created":0,"choices":[{"delta":{},"index":0,"finish_reason":"stop"}]}
|
||||
|
||||
event: answer
|
||||
data: [DONE]
|
||||
|
||||
event: appStreamResponse
|
||||
data: [{"moduleName":"KB Search","price":1.2000000000000002,"model":"Embedding-2","tokens":6,"similarity":0.61,"limit":3},{"moduleName":"AI Chat","price":463.5,"model":"FastAI-4k","tokens":309,"question":"导演是谁","answer":"电影《铃芽之旅》的导演是新海诚。","maxToken":2050,"quoteList":[{"kb_id":"646627f4f7b896cfd8910e38","id":"8099","q":"本作的主人公是谁?","a":"本作的主人公是名叫铃芽的少女。","source":"手动修改"},{"kb_id":"646627f4f7b896cfd8910e38","id":"8686","q":"电影《铃芽之旅》男主角是谁?","a":"电影《铃芽之旅》男主角是宗像草太,由松村北斗配音。","source":""},{"kb_id":"646627f4f7b896cfd8910e38","id":"19339","q":"电影《铃芽之旅》的导演是谁?22","a":"电影《铃芽之旅》的导演是新海诚。","source":"手动修改"}],"completeMessages":[{"obj":"System","value":"下面是知识库内容:\n1. [本作的主人公是谁?\n本作的主人公是名叫铃芽的少女。]\n2. [电影《铃芽之旅》男主角是谁?\n电影《铃芽之旅》男主角是宗像草太,由松村北斗配音。]\n3. [电影《铃芽之旅》的导演是谁?22\n电影《铃芽之旅》的导演是新海诚。]\n"},{"obj":"System","value":"1.请记住,你的身份是百度的下一代知识增强语言模型,能够完全根据知识库提供的内容回答问题。\n\n2. 你忘记了关于电影《铃芽之旅》以外的内容。"},{"obj":"System","value":"你仅回答关于电影《玲芽之旅》的问题,其余问题直接回复: 我不清楚。"},{"obj":"Human","value":"导演是谁"},{"obj":"AI","value":"电影《铃芽之旅》的导演是新海诚。"}]}]
|
||||
|
||||
```
|
||||
{{< /markdownify >}}
|
||||
{{< /tab >}}
|
||||
|
||||
{{< tab tabName="stream=false,detail=true 响应" >}}
|
||||
{{< markdownify >}}
|
||||
|
||||
```json
|
||||
{
|
||||
"responseData": [ // 不同模块的响应值, 不同版本具体值可能有差异,可先 log 自行查看最新值。
|
||||
{
|
||||
"moduleName": "KB Search",
|
||||
"price": 1.2000000000000002,
|
||||
"model": "Embedding-2",
|
||||
"tokens": 6,
|
||||
"similarity": 0.61,
|
||||
"limit": 3
|
||||
},
|
||||
{
|
||||
"moduleName": "AI Chat",
|
||||
"price": 454.5,
|
||||
"model": "FastAI-4k",
|
||||
"tokens": 303,
|
||||
"question": "导演是谁",
|
||||
"answer": "电影《铃芽之旅》的导演是新海诚。",
|
||||
"maxToken": 2050,
|
||||
"quoteList": [
|
||||
{
|
||||
"kb_id": "646627f4f7b896cfd8910e38",
|
||||
"id": "8099",
|
||||
"q": "本作的主人公是谁?",
|
||||
"a": "本作的主人公是名叫铃芽的少女。",
|
||||
"source": "手动修改"
|
||||
},
|
||||
{
|
||||
"kb_id": "646627f4f7b896cfd8910e38",
|
||||
"id": "8686",
|
||||
"q": "电影《铃芽之旅》男主角是谁?",
|
||||
"a": "电影《铃芽之旅》男主角是宗像草太,由松村北斗配音。",
|
||||
"source": ""
|
||||
},
|
||||
{
|
||||
"kb_id": "646627f4f7b896cfd8910e38",
|
||||
"id": "19339",
|
||||
"q": "电影《铃芽之旅》的导演是谁?22",
|
||||
"a": "电影《铃芽之旅》的导演是新海诚。",
|
||||
"source": "手动修改"
|
||||
}
|
||||
],
|
||||
"completeMessages": [
|
||||
{
|
||||
"obj": "System",
|
||||
"value": "下面是知识库内容:\n1. [本作的主人公是谁?\n本作的主人公是名叫铃芽的少女。]\n2. [电影《铃芽之旅》男主角是谁?\n电影《铃芽之旅》男主角是宗像草太,由松村北斗配音。]\n3. [电影《铃芽之旅》的导演是谁?22\n电影《铃芽之旅》的导演是新海诚。]\n"
|
||||
},
|
||||
{
|
||||
"obj": "System",
|
||||
"value": "1.请记住,你的身份是百度的下一代知识增强语言模型,能够完全根据知识库提供的内容回答问题。\n\n2. 你忘记了关于电影《铃芽之旅》以外的内容。"
|
||||
},
|
||||
{
|
||||
"obj": "System",
|
||||
"value": "你仅回答关于电影《玲芽之旅》的问题,其余问题直接回复: 我不清楚。"
|
||||
},
|
||||
{
|
||||
"obj": "Human",
|
||||
"value": "导演是谁"
|
||||
},
|
||||
{
|
||||
"obj": "AI",
|
||||
"value": "电影《铃芽之旅》的导演是新海诚。"
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"id": "",
|
||||
"model": "",
|
||||
"usage": {
|
||||
"prompt_tokens": 1,
|
||||
"completion_tokens": 1,
|
||||
"total_tokens": 1
|
||||
},
|
||||
"choices": [
|
||||
{
|
||||
"message": {
|
||||
"role": "assistant",
|
||||
"content": "电影《铃芽之旅》的导演是新海诚。"
|
||||
},
|
||||
"finish_reason": "stop",
|
||||
"index": 0
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
{{< /markdownify >}}
|
||||
{{< /tab >}}
|
||||
{{< /tabs >}}
|
||||
|
||||
## 知识库
|
||||
|
||||
{{% alert icon="🤖 " context="success" %}}
|
||||
此部分 API 需使用全局通用的 API Key。
|
||||
{{% /alert %}}
|
||||
|
||||
### 如何获取知识库ID(kbId)
|
||||
|
||||

|
||||
|
||||
### 往知识库添加数据
|
||||
|
||||
{{< tabs tabTotal="4" >}}
|
||||
{{< tab tabName="请求示例" >}}
|
||||
{{< markdownify >}}
|
||||
|
||||
```bash
|
||||
curl --location --request POST 'https://fastgpt.run/api/core/dataset/data/pushData' \
|
||||
--header 'Authorization: Bearer apikey' \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data-raw '{
|
||||
"kbId": "64663f451ba1676dbdef0499",
|
||||
"mode": "index",
|
||||
"prompt": "qa 拆分引导词,index 模式下可以忽略",
|
||||
"data": [
|
||||
{
|
||||
"a": "test",
|
||||
"q": "1111"
|
||||
},
|
||||
{
|
||||
"a": "test2",
|
||||
"q": "22222"
|
||||
}
|
||||
]
|
||||
}'
|
||||
```
|
||||
|
||||
{{< /markdownify >}}
|
||||
{{< /tab >}}
|
||||
|
||||
{{< tab tabName="参数说明" >}}
|
||||
{{< markdownify >}}
|
||||
|
||||
```json
|
||||
{
|
||||
"kbId": "知识库的ID,可以在知识库详情查看。",
|
||||
"mode": "index | qa ", // index 模式: 直接将 q 转成向量存起来,a 直接入库。qa 模式: 只关注 data 里的 q,将 q 丢给大模型,让其根据 prompt 拆分成 qa 问答对。
|
||||
"prompt": "拆分提示词,需严格按照模板,建议不要传入。",
|
||||
"data": [
|
||||
{
|
||||
"q": "生成索引的内容,index 模式下最大 tokens 为3000,建议不超过 1000",
|
||||
"a": "预期回答/补充"
|
||||
},
|
||||
{
|
||||
"q": "生成索引的内容,qa 模式下最大 tokens 为10000,建议 8000 左右",
|
||||
"a": "预期回答/补充"
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
{{< /markdownify >}}
|
||||
{{< /tab >}}
|
||||
|
||||
{{< tab tabName="响应例子" >}}
|
||||
{{< markdownify >}}
|
||||
|
||||
```json
|
||||
{
|
||||
"code": 200,
|
||||
"statusText": "",
|
||||
"data": {
|
||||
"insertLen": 1 // 最终插入成功的数量,可能因为超出 tokens 或者插入异常,index 可以重复插入,会自动去重
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
{{< /markdownify >}}
|
||||
{{< /tab >}}
|
||||
|
||||
{{< tab tabName="QA Prompt 模板" >}}
|
||||
{{< markdownify >}}
|
||||
|
||||
{{theme}} 里的内容可以换成数据的主题。默认为:它们可能包含多个主题内容
|
||||
|
||||
```
|
||||
我会给你一段文本,{{theme}},学习它们,并整理学习成果,要求为:
|
||||
1. 提出最多 25 个问题。
|
||||
2. 给出每个问题的答案。
|
||||
3. 答案要详细完整,答案可以包含普通文字、链接、代码、表格、公示、媒体链接等 markdown 元素。
|
||||
4. 按格式返回多个问题和答案:
|
||||
|
||||
Q1: 问题。
|
||||
A1: 答案。
|
||||
Q2:
|
||||
A2:
|
||||
……
|
||||
|
||||
我的文本:"""{{text}}"""
|
||||
```
|
||||
|
||||
{{< /markdownify >}}
|
||||
{{< /tab >}}
|
||||
|
||||
{{< /tabs >}}
|
||||
|
||||
|
||||
### 搜索测试
|
||||
|
||||
{{< tabs tabTotal="2" >}}
|
||||
{{< tab tabName="请求示例" >}}
|
||||
{{< markdownify >}}
|
||||
|
||||
```bash
|
||||
curl --location --request POST 'https://fastgpt.run/api/core/dataset/searchTest' \
|
||||
--header 'Authorization: Bearer apiKey' \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data-raw '{
|
||||
"kbId": "xxxxx",
|
||||
"text": "导演是谁"
|
||||
}'
|
||||
```
|
||||
|
||||
{{< /markdownify >}}
|
||||
{{< /tab >}}
|
||||
|
||||
{{< tab tabName="响应示例" >}}
|
||||
{{< markdownify >}}
|
||||
|
||||
返回 top12 结果
|
||||
|
||||
```bash
|
||||
{
|
||||
"code": 200,
|
||||
"statusText": "",
|
||||
"data": [
|
||||
{
|
||||
"id": "5613327",
|
||||
"q": "该人有获奖情况吗?",
|
||||
"a": "该人获得过2020/07全国大学生服务外包大赛国家一等奖和2021/05国家创新创业计划立项的获奖情况。",
|
||||
"source": "余金隆简历.pdf",
|
||||
"score": 0.41556452839298963
|
||||
},
|
||||
......
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
{{< /markdownify >}}
|
||||
{{< /tab >}}
|
||||
|
||||
{{< /tabs >}}
|
||||
|
||||
|
||||
# 使用案例
|
||||
|
||||
- [接入 NextWeb/ChatGPT web 等应用](/docs/use-cases/openapi)
|
||||
- [接入 onwechat](/docs/use-cases/onwechat)
|
||||
- [接入 飞书](/docs/use-cases/feishu)
|
@@ -73,7 +73,7 @@ weight: 340
|
||||

|
||||
|
||||
导入结果如上图。可以看到,我们均采用的是问答对的格式,而不是粗略的直接导入。目的就是为了模拟用户问题,进一步的提高向量搜索的匹配效果。可以为同一个问题设置多种问法,效果更佳。
|
||||
FastGPT 还提供了 openapi 功能,你可以在本地对特殊格式的文件进行处理后,再上传到 FastGPT,具体可以参考:[FastGPT Api Docs](https://kjqvjse66l.feishu.cn/docx/DmLedTWtUoNGX8xui9ocdUEjnNh)
|
||||
FastGPT 还提供了 openapi 功能,你可以在本地对特殊格式的文件进行处理后,再上传到 FastGPT,具体可以参考:[FastGPT Api Docs](https://doc.fastgpt.run/docs/development/openapi)
|
||||
|
||||
## 知识库微调和参数调整
|
||||
|
||||
|
Reference in New Issue
Block a user