Files
FastGPT/document/content/docs/introduction/development/openapi/chat.mdx
Finley Ge 32a3b9216b perf: deploy docs; docker-compose (#5722)
* docs: https://localhost => http://localhost

* chore: docker compose; deploy/dev docs

* chore: quick-start page

* chore: add comment & remove leading space of vector config

* chore: remove redundant install.sh scripts

* chore: adjust milvus and ob, image dyanmic config; readme.md

* chore: update pnpm-lock.yaml
2025-09-29 11:34:11 +08:00

1249 lines
34 KiB
Plaintext
Raw Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

---
title: 对话接口
description: FastGPT OpenAPI 对话接口
---
# 如何获取 AppId
可在应用详情的路径里获取 AppId。
![](/imgs/appid.png)
# 发起对话
- 该接口的 API Key 需使用`应用特定的 key`,否则会报错。
- 有些包调用时,`BaseUrl`需要添加`v1`路径有些不需要如果出现404情况可补充`v1`重试。
{/* * 对话现在有`v1`和`v2`两个接口可以按需使用v2 自 4.9.4 版本新增v1 接口同时不再维护 */}
## 请求简易应用和工作流
`v1`对话接口兼容`GPT`的接口!如果你的项目使用的是标准的`GPT`官方接口,可以直接通过修改`BaseUrl`和 `Authorization`来访问 FastGpt 应用,不过需要注意下面几个规则:
* 传入的`model``temperature`等参数字段均无效,这些字段由编排决定,不会根据 API 参数改变。
* 不会返回实际消耗`Token`值,如果需要,可以设置`detail=true`,并手动计算 `responseData` 里的`tokens`值。
### 请求
<Tabs items={["基础请求示例","图片/文件请求示例","参数说明"]}>
<Tab value="基础请求示例">
```bash
curl --location --request POST 'http://localhost:3000/api/v1/chat/completions' \
--header 'Authorization: Bearer fastgpt-xxxxxx' \
--header 'Content-Type: application/json' \
--data-raw '{
"chatId": "my_chatId",
"stream": false,
"detail": false,
"responseChatItemId": "my_responseChatItemId",
"variables": {
"uid": "asdfadsfasfd2323",
"name": "张三"
},
"messages": [
{
"role": "user",
"content": "导演是谁"
}
]
}'
```
</Tab>
<Tab value="图片/文件请求示例">
- 仅`messages`有部分区别,其他参数一致。
- 目前不支持上传文件,需上传到自己的对象存储中,获取对应的文件链接。
```bash
curl --location --request POST 'http://localhost:3000/api/v1/chat/completions' \
--header 'Authorization: Bearer fastgpt-xxxxxx' \
--header 'Content-Type: application/json' \
--data-raw '{
"chatId": "abcd",
"stream": false,
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "导演是谁"
},
{
"type": "image_url",
"image_url": {
"url": "图片链接"
}
},
{
"type": "file_url",
"name": "文件名",
"url": "文档链接,支持 txt md html word pdf ppt csv excel"
}
]
}
]
}'
```
</Tab>
<Tab value="参数说明">
<div>
- headers.Authorization: Bearer [apikey]
- chatId: string | undefined 。
- 为时(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 IDFastGPT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。
- variables: 模块变量,一个对象,会替换模块中,输入框内容里的`[key]`
</div>
</Tab>
</Tabs>
### 响应
<Tabs items={['detail=false,stream=false 响应','detail=false,stream=true 响应','detail=true,stream=false 响应','detail=true,stream=true 响应','event值']}>
<Tab value="detail=false,stream=false 响应">
```json
{
"id": "adsfasf",
"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
}
]
}
```
</Tab>
<Tab value="detail=false,stream=true 响应">
```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}]}
```
</Tab>
<Tab value="detail=true,stream=false 响应">
```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": "Dataset 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": [
{
"dataset_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"
},
{
"dataset_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": ""
},
{
"dataset_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
}
]
}
```
</Tab>
<Tab value="detail=true,stream=true 响应">
```bash
event: flowNodeStatus
data: {"status":"running","name":"知识库搜索"}
event: flowNodeStatus
data: {"status":"running","name":"AI 对话"}
event: answer
data: {"id":"","object":"","created":0,"model":"","choices":[{"delta":{"content":"电影"},"index":0,"finish_reason":null}]}
event: answer
data: {"id":"","object":"","created":0,"model":"","choices":[{"delta":{"content":"《铃"},"index":0,"finish_reason":null}]}
event: answer
data: {"id":"","object":"","created":0,"model":"","choices":[{"delta":{"content":"芽之旅》"},"index":0,"finish_reason":null}]}
event: answer
data: {"id":"","object":"","created":0,"model":"","choices":[{"delta":{"content":"的导演是新"},"index":0,"finish_reason":null}]}
event: answer
data: {"id":"","object":"","created":0,"model":"","choices":[{"delta":{"content":"海诚。"},"index":0,"finish_reason":null}]}
event: answer
data: {"id":"","object":"","created":0,"model":"","choices":[{"delta":{},"index":0,"finish_reason":"stop"}]}
event: answer
data: [DONE]
event: flowResponses
data: [{"moduleName":"知识库搜索","moduleType":"datasetSearchNode","runningTime":1.78},{"question":"导演是谁","quoteList":[{"id":"654f2e49b64caef1d9431e8b","q":"电影《铃芽之旅》的导演是谁?","a":"电影《铃芽之旅》的导演是新海诚!","indexes":[{"type":"qa","dataId":"3515487","text":"电影《铃芽之旅》的导演是谁?","_id":"654f2e49b64caef1d9431e8c","defaultIndex":true}],"datasetId":"646627f4f7b896cfd8910e38","collectionId":"653279b16cd42ab509e766e8","sourceName":"data (81).csv","sourceId":"64fd3b6423aa1307b65896f6","score":0.8935586214065552},{"id":"6552e14c50f4a2a8e632af11","q":"导演是谁?","a":"电影《铃芽之旅》的导演是新海诚。","indexes":[{"defaultIndex":true,"type":"qa","dataId":"3644565","text":"导演是谁?\n电影《铃芽之旅》的导演是新海诚。","_id":"6552e14dde5cc7ba3954e417"}],"datasetId":"646627f4f7b896cfd8910e38","collectionId":"653279b16cd42ab509e766e8","sourceName":"data (81).csv","sourceId":"64fd3b6423aa1307b65896f6","score":0.8890955448150635},{"id":"654f34a0b64caef1d946337e","q":"本作的主人公是谁?","a":"本作的主人公是名叫铃芽的少女。","indexes":[{"type":"qa","dataId":"3515541","text":"本作的主人公是谁?","_id":"654f34a0b64caef1d946337f","defaultIndex":true}],"datasetId":"646627f4f7b896cfd8910e38","collectionId":"653279b16cd42ab509e766e8","sourceName":"data (81).csv","sourceId":"64fd3b6423aa1307b65896f6","score":0.8738770484924316},{"id":"654f3002b64caef1d944207a","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。","indexes":[{"type":"qa","dataId":"3515538","text":"电影《铃芽之旅》男主角是谁?","_id":"654f3002b64caef1d944207b","defaultIndex":true}],"datasetId":"646627f4f7b896cfd8910e38","collectionId":"653279b16cd42ab509e766e8","sourceName":"data (81).csv","sourceId":"64fd3b6423aa1307b65896f6","score":0.8607980012893677},{"id":"654f2fc8b64caef1d943fd46","q":"电影《铃芽之旅》的编剧是谁?","a":"新海诚是本片的编剧。","indexes":[{"defaultIndex":true,"type":"qa","dataId":"3515550","text":"电影《铃芽之旅》的编剧是谁22","_id":"654f2fc8b64caef1d943fd47"}],"datasetId":"646627f4f7b896cfd8910e38","collectionId":"653279b16cd42ab509e766e8","sourceName":"data (81).csv","sourceId":"64fd3b6423aa1307b65896f6","score":0.8468944430351257}],"moduleName":"AI 对话","moduleType":"chatNode","runningTime":1.86}]
```
</Tab>
<Tab value="event值">
event取值
- answer: 返回给客户端的文本(最终会算作回答)
- fastAnswer: 指定回复返回给客户端的文本(最终会算作回答)
- toolCall: 执行工具
- toolParams: 工具参数
- toolResponse: 工具返回
- flowNodeStatus: 运行到的节点状态
- flowResponses: 节点完整响应
- updateVariables: 更新变量
- error: 报错
</Tab>
</Tabs>
### 交互节点响应
如果工作流中包含交互节点,依然是调用该 API 接口,需要设置`detail=true`,并可以从`event=interactive`的数据中获取交互节点的配置信息。如果是`stream=false`,则可以从 choice 中获取`type=interactive`的元素,获取交互节点的选择信息。
当你调用一个带交互节点的工作流时,如果工作流遇到了交互节点,那么会直接返回,你可以得到下面的信息:
<Tabs items={['用户选择','表单输入']}>
<Tab value="用户选择">
```json
{
"interactive": {
"type": "userSelect",
"params": {
"description": "测试",
"userSelectOptions": [
{
"value": "Confirm",
"key": "option1"
},
{
"value": "Cancel",
"key": "option2"
}
]
}
}
}
```
</Tab>
<Tab value="表单输入">
```json
{
"interactive": {
"type": "userInput",
"params": {
"description": "测试",
"inputForm": [
{
"type": "input",
"key": "测试 1",
"label": "测试 1",
"description": "",
"value": "",
"defaultValue": "",
"valueType": "string",
"required": false,
"list": [
{
"label": "",
"value": ""
}
]
},
{
"type": "numberInput",
"key": "测试 2",
"label": "测试 2",
"description": "",
"value": "",
"defaultValue": "",
"valueType": "number",
"required": false,
"list": [
{
"label": "",
"value": ""
}
]
}
]
}
}
}
```
</Tab>
</Tabs>
### 交互节点继续运行
紧接着上一节,当你接收到交互节点信息后,可以根据这些数据进行 UI 渲染,引导用户输入或选择相关信息。然后需要再次发起对话,来继续工作流。调用的接口与仍是该接口,你需要按以下格式来发起请求:
<Tabs items={['用户选择','表单输入']}>
<Tab value="用户选择">
对于用户选择,你只需要直接传递一个选择的结果给 messages 即可。
```bash
curl --location --request POST 'http://localhost:3000/api/v1/chat/completions' \
--header 'Authorization: Bearer fastgpt-xxx' \
--header 'Content-Type: application/json' \
--data-raw '{
"stream": true,
"detail": true,
"chatId":"22222231",
"messages": [
{
"role": "user",
"content": "Confirm"
}
]
}'
```
</Tab>
<Tab value="表单输入">
表单输入稍微麻烦一点,需要将输入的内容,以对象形式并序列化成字符串,作为`messages`的值。对象的 key 对应表单的 keyvalue 为用户输入的值。务必确保`chatId`是一致的。
```bash
curl --location --request POST 'http://localhost:3000/api/v1/chat/completions' \
--header 'Authorization: Bearer fastgpt-xxxx' \
--header 'Content-Type: application/json' \
--data-raw '{
"stream": true,
"detail": true,
"chatId":"22231",
"messages": [
{
"role": "user",
"content": "{\"测试 1\":\"这是输入框的内容\",\"测试 2\":666}"
}
]
}'
```
</Tab>
</Tabs>
## 请求插件
插件的接口与对话接口一致,仅请求参数略有区别,有以下规定:
- 调用插件类型的应用时,接口默认为`detail`模式。
- 无需传入 `chatId`,因为插件只能运行一轮。
- 无需传入`messages`。
- 通过传递`variables`来代表插件的输入。
- 通过获取`pluginData`来获取插件输出。
### 请求示例
```bash
curl --location --request POST 'http://localhost:3000/api/v1/chat/completions' \
--header 'Authorization: Bearer test-xxxxx' \
--header 'Content-Type: application/json' \
--data-raw '{
"stream": false,
"chatId": "test",
"variables": {
"query":"你好" # 我的插件输入有一个参数,变量名叫 query
}
}'
```
### 响应示例
<Tabs items={['detail=true,stream=false 响应','detail=true,stream=true 响应','输出获取']}>
<Tab value="detail=true,stream=false 响应">
- 插件的输出可以通过查找`responseData`中, `moduleType=pluginOutput`的元素,其`pluginOutput`是插件的输出。
- 流输出,仍可以通过`choices`进行获取。
```json
{
"responseData": [
{
"nodeId": "fdDgXQ6SYn8v",
"moduleName": "AI 对话",
"moduleType": "chatNode",
"totalPoints": 0.685,
"model": "FastAI-3.5",
"tokens": 685,
"query": "你好",
"maxToken": 2000,
"historyPreview": [
{
"obj": "Human",
"value": "你好"
},
{
"obj": "AI",
"value": "你好!有什么可以帮助你的吗?欢迎向我提问。"
}
],
"contextTotalLen": 14,
"runningTime": 1.73
},
{
"nodeId": "pluginOutput",
"moduleName": "插件输出",
"moduleType": "pluginOutput",
"totalPoints": 0,
"pluginOutput": {
"result": "你好!有什么可以帮助你的吗?欢迎向我提问。"
},
"runningTime": 0
}
],
"newVariables": {
"query": "你好"
},
"id": "safsafsa",
"model": "",
"usage": {
"prompt_tokens": 1,
"completion_tokens": 1,
"total_tokens": 1
},
"choices": [
{
"message": {
"role": "assistant",
"content": "你好!有什么可以帮助你的吗?欢迎向我提问。"
},
"finish_reason": "stop",
"index": 0
}
]
}
```
</Tab>
<Tab value="detail=true,stream=true 响应">
- 插件的输出可以通过获取`event=flowResponses`中的字符串,并将其反序列化后得到一个数组。同样的,查找 `moduleType=pluginOutput`的元素,其`pluginOutput`是插件的输出。
- 流输出,仍和对话接口一样获取。
```bash
event: flowNodeStatus
data: {"status":"running","name":"AI 对话"}
event: answer
data: {"id":"","object":"","created":0,"model":"","choices":[{"delta":{"role":"assistant","content":""},"index":0,"finish_reason":null}]}
event: answer
data: {"id":"","object":"","created":0,"model":"","choices":[{"delta":{"role":"assistant","content":"你"},"index":0,"finish_reason":null}]}
event: answer
data: {"id":"","object":"","created":0,"model":"","choices":[{"delta":{"role":"assistant","content":"好"},"index":0,"finish_reason":null}]}
event: answer
data: {"id":"","object":"","created":0,"model":"","choices":[{"delta":{"role":"assistant","content":""},"index":0,"finish_reason":null}]}
event: answer
data: {"id":"","object":"","created":0,"model":"","choices":[{"delta":{"role":"assistant","content":"有"},"index":0,"finish_reason":null}]}
event: answer
data: {"id":"","object":"","created":0,"model":"","choices":[{"delta":{"role":"assistant","content":"什"},"index":0,"finish_reason":null}]}
event: answer
data: {"id":"","object":"","created":0,"model":"","choices":[{"delta":{"role":"assistant","content":"么"},"index":0,"finish_reason":null}]}
event: answer
data: {"id":"","object":"","created":0,"model":"","choices":[{"delta":{"role":"assistant","content":"可以"},"index":0,"finish_reason":null}]}
event: answer
data: {"id":"","object":"","created":0,"model":"","choices":[{"delta":{"role":"assistant","content":"帮"},"index":0,"finish_reason":null}]}
event: answer
data: {"id":"","object":"","created":0,"model":"","choices":[{"delta":{"role":"assistant","content":"助"},"index":0,"finish_reason":null}]}
event: answer
data: {"id":"","object":"","created":0,"model":"","choices":[{"delta":{"role":"assistant","content":"你"},"index":0,"finish_reason":null}]}
event: answer
data: {"id":"","object":"","created":0,"model":"","choices":[{"delta":{"role":"assistant","content":"的"},"index":0,"finish_reason":null}]}
event: answer
data: {"id":"","object":"","created":0,"model":"","choices":[{"delta":{"role":"assistant","content":"吗"},"index":0,"finish_reason":null}]}
event: answer
data: {"id":"","object":"","created":0,"model":"","choices":[{"delta":{"role":"assistant","content":""},"index":0,"finish_reason":null}]}
event: answer
data: {"id":"","object":"","created":0,"model":"","choices":[{"delta":{"role":"assistant","content":""},"index":0,"finish_reason":null}]}
event: answer
data: {"id":"","object":"","created":0,"model":"","choices":[{"delta":{},"index":0,"finish_reason":"stop"}]}
event: answer
data: [DONE]
event: flowResponses
data: [{"nodeId":"fdDgXQ6SYn8v","moduleName":"AI 对话","moduleType":"chatNode","totalPoints":0.033,"model":"FastAI-3.5","tokens":33,"query":"你好","maxToken":2000,"historyPreview":[{"obj":"Human","value":"你好"},{"obj":"AI","value":"你好!有什么可以帮助你的吗?"}],"contextTotalLen":2,"runningTime":1.42},{"nodeId":"pluginOutput","moduleName":"插件输出","moduleType":"pluginOutput","totalPoints":0,"pluginOutput":{"result":"你好!有什么可以帮助你的吗?"},"runningTime":0}]
```
</Tab>
<Tab value="输出获取">
event取值
- answer: 返回给客户端的文本(最终会算作回答)
- fastAnswer: 指定回复返回给客户端的文本(最终会算作回答)
- toolCall: 执行工具
- toolParams: 工具参数
- toolResponse: 工具返回
- flowNodeStatus: 运行到的节点状态
- flowResponses: 节点完整响应
- updateVariables: 更新变量
- error: 报错
</Tab>
</Tabs>
# 对话 CRUD
* 以下接口可使用任意`API Key`调用。
* 4.8.12 以上版本才能使用
**重要字段**
- chatId - 指一个应用下,某一个对话窗口的 ID
- dataId - 指一个对话窗口下,某一个对话记录的 ID
## 历史记录
### 获取某个应用历史记录
<Tabs items={['请求示例','参数说明','响应示例']}>
<Tab value="请求示例">
```bash
curl --location --request POST 'http://localhost:3000/api/core/chat/getHistories' \
--header 'Authorization: Bearer [apikey]' \
--header 'Content-Type: application/json' \
--data-raw '{
"appId": "appId",
"offset": 0,
"pageSize": 20,
"source": "api"
}'
```
</Tab>
<Tab value="参数说明">
<div>
- appId - 应用 Id
- offset - 偏移量,即从第几条数据开始取
- pageSize - 记录数量
- source - 对话源。source=api表示获取通过 API 创建的对话(不会获取到页面上的对话记录)
</div>
</Tab>
<Tab value="响应示例" >
```json
{
"code": 200,
"statusText": "",
"message": "",
"data": {
"list": [
{
"chatId": "usdAP1GbzSGu",
"updateTime": "2024-10-13T03:29:05.779Z",
"appId": "66e29b870b24ce35330c0f08",
"customTitle": "",
"title": "你好",
"top": false
},
{
"chatId": "lC0uTAsyNBlZ",
"updateTime": "2024-10-13T03:22:19.950Z",
"appId": "66e29b870b24ce35330c0f08",
"customTitle": "",
"title": "测试",
"top": false
}
],
"total": 2
}
}
```
</Tab>
</Tabs>
### 修改某个对话的标题
<Tabs items={['请求示例','参数说明','响应示例']}>
<Tab value="请求示例">
```bash
curl --location --request POST 'http://localhost:3000/api/core/chat/updateHistory' \
--header 'Authorization: Bearer [apikey]' \
--header 'Content-Type: application/json' \
--data-raw '{
"appId": "appId",
"chatId": "chatId",
"customTitle": "自定义标题"
}'
```
</Tab>
<Tab value="参数说明" >
<div>
- appId - 应用 Id
- chatId - 历史记录 Id
- customTitle - 自定义对话名
</div>
</Tab>
<Tab value="响应示例" >
```json
{
"code": 200,
"statusText": "",
"message": "",
"data": null
}
```
</Tab>
</Tabs>
### 置顶 / 取消置顶
<Tabs items={['请求示例','参数说明','响应示例']}>
<Tab value="请求示例">
```bash
curl --location --request POST 'http://localhost:3000/api/core/chat/updateHistory' \
--header 'Authorization: Bearer [apikey]' \
--header 'Content-Type: application/json' \
--data-raw '{
"appId": "appId",
"chatId": "chatId",
"top": true
}'
```
</Tab>
<Tab value="参数说明" >
<div>
- appId - 应用Id
- chatId - 历史记录 Id
- top - 是否置顶ture 置顶false 取消置顶
</div>
</Tab>
<Tab value="响应示例" >
```json
{
"code": 200,
"statusText": "",
"message": "",
"data": null
}
```
</Tab>
</Tabs>
### 删除某个历史记录
<Tabs items={['请求示例','参数说明','响应示例']}>
<Tab value="请求示例">
```bash
curl --location --request DELETE 'http://localhost:3000/api/core/chat/delHistory?chatId=[chatId]&appId=[appId]' \
--header 'Authorization: Bearer [apikey]'
```
</Tab>
<Tab value="参数说明" >
<div>
- appId - 应用 Id
- chatId - 历史记录 Id
</div>
</Tab>
<Tab value="响应示例" >
```json
{
"code": 200,
"statusText": "",
"message": "",
"data": null
}
```
</Tab>
</Tabs>
### 清空所有历史记录
仅会情况通过 API Key 创建的对话历史记录,不会清空在线使用、分享链接等其他来源的对话历史记录。
<Tabs items={['请求示例','参数说明','响应示例']}>
<Tab value="请求示例">
```bash
curl --location --request DELETE 'http://localhost:3000/api/core/chat/clearHistories?appId=[appId]' \
--header 'Authorization: Bearer [apikey]'
```
</Tab>
<Tab value="参数说明" >
<div>
- appId - 应用 Id
</div>
</Tab>
<Tab value="响应示例" >
```json
{
"code": 200,
"statusText": "",
"message": "",
"data": null
}
```
</Tab>
</Tabs>
## 对话记录
指的是某个 chatId 下的对话记录操作。
### 获取单个对话初始化信息
<Tabs items={['请求示例','参数说明','响应示例']}>
<Tab value="请求示例">
```bash
curl --location --request GET 'http://localhost:3000/api/core/chat/init?appId=[appId]&chatId=[chatId]' \
--header 'Authorization: Bearer [apikey]'
```
</Tab>
<Tab value="参数说明" >
<div>
- appId - 应用 Id
- chatId - 历史记录 Id
</div>
</Tab>
<Tab value="响应示例">
```json
{
"code": 200,
"statusText": "",
"message": "",
"data": {
"chatId": "sPVOuEohjo3w",
"appId": "66e29b870b24ce35330c0f08",
"variables": {},
"app": {
"chatConfig": {
"questionGuide": true,
"ttsConfig": {
"type": "web"
},
"whisperConfig": {
"open": false,
"autoSend": false,
"autoTTSResponse": false
},
"chatInputGuide": {
"open": false,
"textList": [],
"customUrl": ""
},
"instruction": "",
"variables": [],
"fileSelectConfig": {
"canSelectFile": true,
"canSelectImg": true,
"maxFiles": 10
},
"_id": "66f1139aaab9ddaf1b5c596d",
"welcomeText": ""
},
"chatModels": ["GPT-4o-mini"],
"name": "测试",
"avatar": "/imgs/app/avatar/workflow.svg",
"intro": "",
"type": "advanced",
"pluginInputs": []
}
}
}
```
</Tab>
</Tabs>
### 获取对话记录列表
<Tabs items={['请求示例','参数说明','响应示例']}>
<Tab value="请求示例">
```bash
curl --location --request POST 'http://localhost:3000/api/core/chat/getPaginationRecords' \
--header 'Authorization: Bearer [apikey]' \
--header 'Content-Type: application/json' \
--data-raw '{
"appId": "appId",
"chatId": "chatId",
"offset": 0,
"pageSize": 10,
"loadCustomFeedbacks": true
}'
```
</Tab>
<Tab value="参数说明" >
<div>
- appId - 应用 Id
- chatId - 历史记录 Id
- offset - 偏移量
- pageSize - 记录数量
- loadCustomFeedbacks - 是否读取自定义反馈(可选)
</div>
</Tab>
<Tab value="响应示例" >
```json
{
"code": 200,
"statusText": "",
"message": "",
"data": {
"list": [
{
"_id": "670b84e6796057dda04b0fd2",
"dataId": "jzqdV4Ap1u004rhd2WW8yGLn",
"obj": "Human",
"value": [
{
"type": "text",
"text": {
"content": "你好"
}
}
],
"customFeedbacks": []
},
{
"_id": "670b84e6796057dda04b0fd3",
"dataId": "x9KQWcK9MApGdDQH7z7bocw1",
"obj": "AI",
"value": [
{
"type": "text",
"text": {
"content": "你好!有什么我可以帮助你的吗?"
}
}
],
"customFeedbacks": [],
"llmModuleAccount": 1,
"totalQuoteList": [],
"totalRunningTime": 2.42,
"historyPreviewLength": 2
}
],
"total": 2
}
}
```
</Tab>
</Tabs>
### 获取单个对话记录运行详情
<Tabs items={['请求示例','参数说明','响应示例']}>
<Tab value="请求示例">
```bash
curl --location --request GET 'http://localhost:3000/api/core/chat/getResData?appId=[appId]&chatId=[chatId]&dataId=[dataId]' \
--header 'Authorization: Bearer [apikey]'
```
</Tab>
<Tab value="参数说明" >
<div>
- appId - 应用 Id
- chatId - 对话 Id
- dataId - 对话记录 Id
</div>
</Tab>
<Tab value="响应示例" >
```json
{
"code": 200,
"statusText": "",
"message": "",
"data": [
{
"id": "mVlxkz8NfyfU",
"nodeId": "448745",
"moduleName": "common:core.module.template.work_start",
"moduleType": "workflowStart",
"runningTime": 0
},
{
"id": "b3FndAdHSobY",
"nodeId": "z04w8JXSYjl3",
"moduleName": "AI 对话",
"moduleType": "chatNode",
"runningTime": 1.22,
"totalPoints": 0.02475,
"model": "GPT-4o-mini",
"tokens": 75,
"query": "测试",
"maxToken": 2000,
"historyPreview": [
{
"obj": "Human",
"value": "你好"
},
{
"obj": "AI",
"value": "你好!有什么我可以帮助你的吗?"
},
{
"obj": "Human",
"value": "测试"
},
{
"obj": "AI",
"value": "测试成功!请问你有什么具体的问题或者需要讨论的话题吗?"
}
],
"contextTotalLen": 4
}
]
}
```
</Tab>
</Tabs>
### 删除对话记录
<Tabs items={['请求示例','参数说明','响应示例']}>
<Tab value="请求示例" >
```bash
curl --location --request DELETE 'http://localhost:3000/api/core/chat/item/delete?contentId=[contentId]&chatId=[chatId]&appId=[appId]' \
--header 'Authorization: Bearer [apikey]'
```
</Tab>
<Tab value="参数说明" >
<div>
- appId - 应用 Id
- chatId - 历史记录 Id
- contentId - 对话记录 Id
</div>
</Tab>
<Tab value="响应示例" >
```json
{
"code": 200,
"statusText": "",
"message": "",
"data": null
}
```
</Tab>
</Tabs>
### 点赞 / 取消点赞
<Tabs items={['请求示例','参数说明','响应示例']}>
<Tab value="请求示例">
```bash
curl --location --request POST 'http://localhost:3000/api/core/chat/feedback/updateUserFeedback' \
--header 'Authorization: Bearer [apikey]' \
--header 'Content-Type: application/json' \
--data-raw '{
"appId": "appId",
"chatId": "chatId",
"dataId": "dataId",
"userGoodFeedback": "yes"
}'
```
</Tab>
<Tab value="参数说明" >
<div>
- appId - 应用 Id
- chatId - 历史记录 Id
- dataId - 对话记录 Id
- userGoodFeedback - 用户点赞时的信息(可选),取消点赞时不填此参数即可
</div>
</Tab>
<Tab value="响应示例" >
```json
{
"code": 200,
"statusText": "",
"message": "",
"data": null
}
```
</Tab>
</Tabs>
### 点踩 / 取消点踩
<Tabs items={['请求示例','参数说明','响应示例']}>
<Tab value="请求示例">
```bash
curl --location --request POST 'http://localhost:3000/api/core/chat/feedback/updateUserFeedback' \
--header 'Authorization: Bearer [apikey]' \
--header 'Content-Type: application/json' \
--data-raw '{
"appId": "appId",
"chatId": "chatId",
"dataId": "dataId",
"userBadFeedback": "yes"
}'
```
</Tab>
<Tab value="参数说明" >
<div>
- appId - 应用 Id
- chatId - 历史记录 Id
- dataId - 对话记录 Id
- userBadFeedback - 用户点踩时的信息(可选),取消点踩时不填此参数即可
</div>
</Tab>
<Tab value="响应示例" >
```json
{
"code": 200,
"statusText": "",
"message": "",
"data": null
}
```
</Tab>
</Tabs>
## 猜你想问
**4.8.16 后新版接口**
新版猜你想问,必须包含 appId 和 chatId 的参数才可以进行使用。会自动根据 chatId 去拉取最近 6 轮对话记录作为上下文来引导回答。
<Tabs items={['请求示例','参数说明','响应示例']}>
<Tab value="请求示例">
```bash
curl --location --request POST 'http://localhost:3000/api/core/ai/agent/v2/createQuestionGuide' \
--header 'Authorization: Bearer [apikey]' \
--header 'Content-Type: application/json' \
--data-raw '{
"appId": "appId",
"chatId": "chatId",
"questionGuide": {
"open": true,
"model": "GPT-4o-mini",
"customPrompt": "你是一个智能助手,请根据用户的问题生成猜你想问。"
}
}'
```
</Tab>
<Tab value="参数说明" >
| 参数名 | 类型 | 必填 | 说明 |
| ------------- | ------ | ---- | ---------------------------------------------------------- |
| appId | string | ✅ | 应用 Id |
| chatId | string | ✅ | 对话 Id |
| questionGuide | object | | 自定义配置,不传的话,则会根据 appId取最新发布版本的配置 |
```ts
type CreateQuestionGuideParams = OutLinkChatAuthProps & {
appId: string;
chatId: string;
questionGuide?: {
open: boolean;
model?: string;
customPrompt?: string;
};
};
```
</Tab>
<Tab value="响应示例" >
```json
{
"code": 200,
"statusText": "",
"message": "",
"data": ["你对AI有什么看法", "想了解AI的应用吗", "你希望AI能做什么"]
}
```
</Tab>
</Tabs>