---
title: '知识库接口'
description: 'FastGPT OpenAPI 知识库接口'
icon: 'dataset'
draft: false
toc: true
weight: 853
---

| 如何获取知识库ID（datasetId） | 如何获取文件集合ID（collection_id） |
| --------------------- | --------------------- |
| ![](/imgs/getDatasetId.jpg) | ![](/imgs/getfile_id.png) |



## 创建训练订单

**请求示例**

```bash
curl --location --request POST 'https://api.fastgpt.in/api/support/wallet/bill/createTrainingBill' \
--header 'Authorization: Bearer {{apikey}}' \
--header 'Content-Type: application/json' \
--data-raw '{
    "name": "可选，自定义订单名称，例如：文档训练-fastgpt.docx"
}'
```

**响应结果**

data 为 billId，可用于添加知识库数据时进行账单聚合。

```json
{
  "code": 200,
  "statusText": "",
  "message": "",
  "data": "65112ab717c32018f4156361"
}
```

## 知识库添加数据

{{< tabs tabTotal="4" >}}
{{< tab tabName="请求示例" >}}
{{< markdownify >}}

```bash
curl --location --request POST 'https://api.fastgpt.in/api/core/dataset/data/pushData' \
--header 'Authorization: Bearer apikey' \
--header 'Content-Type: application/json' \
--data-raw '{
    "collectionId": "64663f451ba1676dbdef0499",
    "trainingMode": "chunk",
    "prompt": "可选。qa 拆分引导词，chunk 模式下忽略",
    "billId": "可选。如果有这个值，本次的数据会被聚合到一个订单中，这个值可以重复使用。可以参考 [创建训练订单] 获取该值。",
    "data": [
        {
            "q": "你是谁？",
            "a": "我是FastGPT助手"
        },
        {
            "q": "你会什么？",
            "a": "我什么都会",
            "indexes": [{
                "type":"custom",
                "text":"你好"
            }]
        }
    ]
}'
```

{{< /markdownify >}}
{{< /tab >}}

{{< tab tabName="参数说明" >}}
{{< markdownify >}}

需要先了解 FastGPT 的多路索引概念：

在 FastGPT 中，你可以为一组数据创建多个索引，如果不指定索引，则系统会自动取对应的 chunk 作为索引。例如前面的请求示例中：

`q：你是谁？a:我是FastGPT助手` 它的`indexes`属性为空，意味着不自定义索引，而是使用默认的索引（你是谁？\n我是FastGPT助手）。

在第二组数据中`q:你会什么？a:我什么都会`指定了一个`你好`的索引，因此这组数据的索引为`你好`。

```json
{
    "collectionId": "文件集合的ID，参考上面的第二张图",
    "mode": "chunk | qa ", //  chunk 模式: 可自定义索引。qa 模型：无法自定义索引，会自动取 data 中的 q 作为数据，让模型自动生成问答对和索引。
    "prompt": "QA 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",
            "indexes": "自定义索引",
        },
        {
            "q": "xxx",
            "a": "xxxx"
        }
    ],
    
}
```

{{< /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
        "overToken": [], // 超出 token 的
       
        "repeat": [], // 重复的数量
        "error": [] // 其他错误
    }
}
```

{{< /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="3" >}}
{{< tab tabName="请求示例" >}}
{{< markdownify >}}

```bash
curl --location --request POST 'https://api.fastgpt.in/api/core/dataset/searchTest' \
--header 'Authorization: Bearer fastgpt-xxxxx' \
--header 'Content-Type: application/json' \
--data-raw '{
    "datasetId": "知识库的ID",
    "text": "导演是谁",
    "limit": 5000,
    "similarity": 0,
    "searchMode": "embedding",
    "usingReRank": false
}'
```

{{< /markdownify >}}
{{< /tab >}}

{{< tab tabName="参数说明" >}}
{{< markdownify >}}

- datasetId - 知识库ID
- text - 需要测试的文本
- limit - 最大 tokens 数量
- similarity - 最低相关度（0~1，可选）
- searchMode - 搜索模式：embedding | fullTextRecall | mixedRecall
- usingReRank - 使用重排

{{< /markdownify >}}
{{< /tab >}}

{{< tab tabName="响应示例" >}}
{{< markdownify >}}

返回 top k 结果， limit 为最大 Tokens 数量，最多 20000 tokens。

```bash
{
  "code": 200,
  "statusText": "",
  "data": [
    {
        "id": "65599c54a5c814fb803363cb",
        "q": "你是谁",
        "a": "我是FastGPT助手",
        "datasetId": "6554684f7f9ed18a39a4d15c",
        "collectionId": "6556cd795e4b663e770bb66d",
        "sourceName": "GBT 15104-2021 装饰单板贴面人造板.pdf",
        "sourceId": "6556cd775e4b663e770bb65c",
        "score": 0.8050316572189331
    },
    ......
  ]
}
```

{{< /markdownify >}}
{{< /tab >}}

{{< /tabs >}}


# 更多接口

目前未整理，简陋导出：

## POST 知识库搜索测试

POST /core/dataset/searchTest

> Body Parameters

```json
{
  "datasetId": "656c2ccff7f114064daa72f6",
  "text": "导演是谁",
  "limit": 1500,
  "searchMode": "embedding",
  "usingReRank": true,
  "similarity": 0.5
}
```

### Params

|Name|Location|Type|Required|Description|
|---|---|---|---|---|
|Authorization|header|string| no |none|
|body|body|object| no |none|
|» datasetId|body|string| yes |none|
|» text|body|string| yes |none|
|» limit|body|integer| no |none|
|» searchMode|body|[search mode](#schemasearch%20mode)| yes |none|
|» usingReRank|body|boolean| no |none|
|» similarity|body|[similary](#schemasimilary)| no |none|

> Response Examples

> 成功

```json
{
  "code": 200,
  "statusText": "",
  "message": "",
  "data": {
    "list": [
      {
        "id": "65962b23f5fac58e46330dfd",
        "q": "# 快速了解 FastGPT\nFastGPT 的能力与优势\n\nFastGPT 是一个基于 LLM 大语言模型的知识库问答系统，提供开箱即用的数据处理、模型调用等能力。同时可以通过 Flow 可视化进行工作流编排，从而实现复杂的问答场景！\n\n🤖\n\nFastGPT 在线使用：[https://fastgpt.in](https://fastgpt.in)\n\n|     |     |\n| --- | --- |\n| ![](https://doc.fastgpt.in/imgs/intro1.png) | ![](https://doc.fastgpt.in/imgs/intro2.png) |\n| ![](https://doc.fastgpt.in/imgs/intro3.png) | ![](https://doc.fastgpt.in/imgs/intro4.png) |\n\n",
        "a": "",
        "chunkIndex": 0,
        "datasetId": "6593e137231a2be9c5603ba7",
        "collectionId": "65962b2089642fd209da3b03",
        "sourceName": "https://doc.fastgpt.in/docs/intro/",
        "sourceId": "https://doc.fastgpt.in/docs/intro/",
        "score": [
          {
            "type": "embedding",
            "value": 0.8036568760871887,
            "index": 20
          },
          {
            "type": "fullText",
            "value": 1.168349443855932,
            "index": 2
          },
          {
            "type": "reRank",
            "value": 0.9870296135626316,
            "index": 0
          },
          {
            "type": "rrf",
            "value": 0.04366449476962486,
            "index": 0
          }
        ]
      },
      {
        "id": "65962b24f5fac58e46330dff",
        "q": "# 快速了解 FastGPT\n## FastGPT 能力\n### 2. 简单易用的可视化界面\nFastGPT 采用直观的可视化界面设计，为各种应用场景提供了丰富实用的功能。通过简洁易懂的操作步骤，可以轻松完成 AI 客服的创建和训练流程。\n\n![](https://doc.fastgpt.in/imgs/ability5.png)\n\n",
        "a": "",
        "chunkIndex": 2,
        "datasetId": "6593e137231a2be9c5603ba7",
        "collectionId": "65962b2089642fd209da3b03",
        "sourceName": "https://doc.fastgpt.in/docs/intro/",
        "sourceId": "https://doc.fastgpt.in/docs/intro/",
        "score": [
          {
            "type": "embedding",
            "value": 0.8152669668197632,
            "index": 3
          },
          {
            "type": "fullText",
            "value": 1.0511363636363635,
            "index": 8
          },
          {
            "type": "reRank",
            "value": 0.9287972729281414,
            "index": 14
          },
          {
            "type": "rrf",
            "value": 0.04265696347031964,
            "index": 1
          }
        ]
      },
      {
        "id": "65962b25f5fac58e46330e00",
        "q": "# 快速了解 FastGPT\n## FastGPT 能力\n### 3. 自动数据预处理\n提供手动输入、直接分段、LLM 自动处理和 CSV 等多种数据导入途径，其中“直接分段”支持通过 PDF、WORD、Markdown 和 CSV 文档内容作为上下文。FastGPT 会自动对文本数据进行预处理、向量化和 QA 分割，节省手动训练时间，提升效能。\n\n![](https://doc.fastgpt.in/imgs/ability2.png)\n\n",
        "a": "",
        "chunkIndex": 3,
        "datasetId": "6593e137231a2be9c5603ba7",
        "collectionId": "65962b2089642fd209da3b03",
        "sourceName": "https://doc.fastgpt.in/docs/intro/",
        "sourceId": "https://doc.fastgpt.in/docs/intro/",
        "score": [
          {
            "type": "embedding",
            "value": 0.8158369064331055,
            "index": 2
          },
          {
            "type": "fullText",
            "value": 1.014030612244898,
            "index": 20
          },
          {
            "type": "reRank",
            "value": 0.9064876908461501,
            "index": 17
          },
          {
            "type": "rrf",
            "value": 0.04045823457588163,
            "index": 2
          }
        ]
      },
      {
        "id": "65a7e1e8fc13bdf20fd46d41",
        "q": "# 快速了解 FastGPT\n## FastGPT 能力\n### 5. 强大的 API 集成\nFastGPT 对外的 API 接口对齐了 OpenAI 官方接口，可以直接接入现有的 GPT 应用，也可以轻松集成到企业微信、公众号、飞书等平台。\n\n![](https://doc.fastgpt.in/imgs/ability4.png)",
        "a": "",
        "chunkIndex": 66,
        "datasetId": "6593e137231a2be9c5603ba7",
        "collectionId": "65a7e1d4fc13bdf20fd46abe",
        "sourceName": "dataset - 2024-01-04T151625.388.csv",
        "sourceId": "65a7e1d2fc13bdf20fd46abc",
        "score": [
          {
            "type": "embedding",
            "value": 0.803692102432251,
            "index": 18
          },
          {
            "type": "fullText",
            "value": 1.0511363636363635,
            "index": 7
          },
          {
            "type": "reRank",
            "value": 0.9177460552422909,
            "index": 15
          },
          {
            "type": "rrf",
            "value": 0.03970501147383226,
            "index": 3
          }
        ]
      },
      {
        "id": "65a7be319d96e21823f69c9b",
        "q": "FastGPT Flow 的工作流设计方案提供了哪些操作？",
        "a": "FastGPT Flow 的工作流设计方案提供了数据预处理、各类 AI 应用设置、调试测试及结果反馈等操作。",
        "chunkIndex": 0,
        "datasetId": "6593e137231a2be9c5603ba7",
        "collectionId": "65a7be059d96e21823f69af5",
        "sourceName": "FastGPT软著.pdf",
        "sourceId": "65a7be059d96e21823f69ae8",
        "score": [
          {
            "type": "embedding",
            "value": 0.8283981680870056,
            "index": 0
          },
          {
            "type": "reRank",
            "value": 0.9620363047907355,
            "index": 4
          },
          {
            "type": "rrf",
            "value": 0.03177805800756621,
            "index": 4
          }
        ]
      },
      {
        "id": "65a7be389d96e21823f69d58",
        "q": "FastGPT Flow 的实验室预约示例中使用了哪些参数？",
        "a": "FastGPT Flow 的实验室预约示例中使用了姓名、时间和实验室名称等参数。",
        "chunkIndex": 0,
        "datasetId": "6593e137231a2be9c5603ba7",
        "collectionId": "65a7be059d96e21823f69af5",
        "sourceName": "FastGPT软著.pdf",
        "sourceId": "65a7be059d96e21823f69ae8",
        "score": [
          {
            "type": "embedding",
            "value": 0.8143455386161804,
            "index": 9
          },
          {
            "type": "reRank",
            "value": 0.9806919138043485,
            "index": 1
          },
          {
            "type": "rrf",
            "value": 0.0304147465437788,
            "index": 5
          }
        ]
      },
      {
        "id": "65a7be309d96e21823f69c78",
        "q": "FastGPT Flow 是什么？",
        "a": "FastGPT Flow 是一款基于大型语言模型的知识库问答系统，通过引入 Flow 可视化工作流编排技术，提供了一个即插即用的解决方案。",
        "chunkIndex": 0,
        "datasetId": "6593e137231a2be9c5603ba7",
        "collectionId": "65a7be059d96e21823f69af5",
        "sourceName": "FastGPT软著.pdf",
        "sourceId": "65a7be059d96e21823f69ae8",
        "score": [
          {
            "type": "embedding",
            "value": 0.8115077018737793,
            "index": 11
          },
          {
            "type": "reRank",
            "value": 0.9686195704870232,
            "index": 3
          },
          {
            "type": "rrf",
            "value": 0.029513888888888888,
            "index": 6
          }
        ]
      },
      {
        "id": "65a7be389d96e21823f69d5e",
        "q": "FastGPT Flow 的实验室预约示例中的代码实现了哪些功能？",
        "a": "FastGPT Flow 的实验室预约示例中的代码实现了预约实验室、修改预约、查询预约和取消预约等功能。",
        "chunkIndex": 0,
        "datasetId": "6593e137231a2be9c5603ba7",
        "collectionId": "65a7be059d96e21823f69af5",
        "sourceName": "FastGPT软著.pdf",
        "sourceId": "65a7be059d96e21823f69ae8",
        "score": [
          {
            "type": "embedding",
            "value": 0.8166953921318054,
            "index": 1
          },
          {
            "type": "reRank",
            "value": 0.8350804533361768,
            "index": 20
          },
          {
            "type": "rrf",
            "value": 0.028474711270410194,
            "index": 8
          }
        ]
      },
      {
        "id": "65a7be389d96e21823f69d4f",
        "q": "FastGPT Flow 的联网搜索示例中使用了哪些参数？",
        "a": "FastGPT Flow 的联网搜索示例中使用了搜索关键词、Google 搜索的 API 密钥和自定义搜索引擎 ID。",
        "chunkIndex": 0,
        "datasetId": "6593e137231a2be9c5603ba7",
        "collectionId": "65a7be059d96e21823f69af5",
        "sourceName": "FastGPT软著.pdf",
        "sourceId": "65a7be059d96e21823f69ae8",
        "score": [
          {
            "type": "embedding",
            "value": 0.8025297522544861,
            "index": 21
          },
          {
            "type": "reRank",
            "value": 0.9730876959261983,
            "index": 2
          },
          {
            "type": "rrf",
            "value": 0.028068137824235385,
            "index": 10
          }
        ]
      },
      {
        "id": "65a7e1e8fc13bdf20fd46d55",
        "datasetId": "6593e137231a2be9c5603ba7",
        "collectionId": "65a7e1d4fc13bdf20fd46abe",
        "sourceName": "dataset - 2024-01-04T151625.388.csv",
        "sourceId": "65a7e1d2fc13bdf20fd46abc",
        "q": "# 快速了解 FastGPT\n## FastGPT 特点\n1.  **项目开源**\n    \n    FastGPT 遵循附加条件 Apache License 2.0 开源协议，你可以 [Fork](https://github.com/labring/FastGPT/fork) 之后进行二次开发和发布。FastGPT 社区版将保留核心功能，商业版仅在社区版基础上使用 API 的形式进行扩展，不影响学习使用。\n    \n2.  **独特的 QA 结构**\n    \n    针对客服问答场景设计的 QA 结构，提高在大量数据场景中的问答准确性。\n    \n3.  **可视化工作流**\n    \n    通过 Flow 模块展示了从问题输入到模型输出的完整流程，便于调试和设计复杂流程。\n    \n4.  **无限扩展**\n    \n    基于 API 进行扩展，无需修改 FastGPT 源码，也可快速接入现有的程序中。\n    \n5.  **便于调试**\n    \n    提供搜索测试、引用修改、完整对话预览等多种调试途径。\n    \n6.  **支持多种模型**\n    \n    支持 GPT、Claude、文心一言等多种 LLM 模型，未来也将支持自定义的向量模型。",
        "a": "",
        "chunkIndex": 67,
        "score": [
          {
            "type": "fullText",
            "value": 1.0340073529411764,
            "index": 12
          },
          {
            "type": "reRank",
            "value": 0.9542227274192233,
            "index": 9
          },
          {
            "type": "rrf",
            "value": 0.027272727272727275,
            "index": 11
          }
        ]
      },
      {
        "id": "65a7be319d96e21823f69c8f",
        "q": "FastGPT Flow 的工作流设计中，模块之间如何进行组合和组装？",
        "a": "FastGPT Flow 允许用户在核心工作流模块中进行自由组合和组装，从而衍生出一个新的模块。",
        "chunkIndex": 0,
        "datasetId": "6593e137231a2be9c5603ba7",
        "collectionId": "65a7be059d96e21823f69af5",
        "sourceName": "FastGPT软著.pdf",
        "sourceId": "65a7be059d96e21823f69ae8",
        "score": [
          {
            "type": "embedding",
            "value": 0.8098832368850708,
            "index": 13
          },
          {
            "type": "reRank",
            "value": 0.9478657435317039,
            "index": 12
          },
          {
            "type": "rrf",
            "value": 0.027212143650499815,
            "index": 12
          }
        ]
      },
      {
        "id": "65a7be359d96e21823f69ce0",
        "q": "FastGPT Flow 的模块的输入和输出如何连接？",
        "a": "FastGPT Flow 的模块的输入和输出通过连接点进行连接，连接点的颜色代表了不同的数据类型。",
        "chunkIndex": 0,
        "datasetId": "6593e137231a2be9c5603ba7",
        "collectionId": "65a7be059d96e21823f69af5",
        "sourceName": "FastGPT软著.pdf",
        "sourceId": "65a7be059d96e21823f69ae8",
        "score": [
          {
            "type": "embedding",
            "value": 0.8060981035232544,
            "index": 16
          },
          {
            "type": "reRank",
            "value": 0.9530133603823691,
            "index": 10
          },
          {
            "type": "rrf",
            "value": 0.027071520029266508,
            "index": 13
          }
        ]
      },
      {
        "id": "65a7be319d96e21823f69c98",
        "q": "FastGPT Flow 的工作流设计方案能够满足哪些问答场景？",
        "a": "FastGPT Flow 的工作流设计方案能够满足基本的 AI 知识库问答需求，并适应各种复杂的问答场景，例如联网搜索、数据库操作、数据实时更新、消息通知等。",
        "chunkIndex": 0,
        "datasetId": "6593e137231a2be9c5603ba7",
        "collectionId": "65a7be059d96e21823f69af5",
        "sourceName": "FastGPT软著.pdf",
        "sourceId": "65a7be059d96e21823f69ae8",
        "score": [
          {
            "type": "embedding",
            "value": 0.814436137676239,
            "index": 8
          },
          {
            "type": "reRank",
            "value": 0.8814109034236719,
            "index": 19
          },
          {
            "type": "rrf",
            "value": 0.026992753623188405,
            "index": 16
          }
        ]
      },
      {
        "id": "65a7e058fc13bdf20fd46577",
        "datasetId": "6593e137231a2be9c5603ba7",
        "collectionId": "65a7e01efc13bdf20fd45815",
        "sourceName": "FastGPT软著.pdf",
        "sourceId": "65a7e01dfc13bdf20fd457f3",
        "q": "FastGPT Flow 工作流设计112312 3123213123 232321312 21312 23一、介绍FastGPT 作为一款基于大型语言模型（LLM）的知识库问答系统，旨在为用户提供一个即插即用的解决方案。它集成了数据处理、模型调用等多项功能，通过引入 Flow 可视化工作流编排技术，进一步增强了对复杂问答场景的支持能力。本文将重点介绍 FastGPT Flow工作流的设计方案和应用优势。\nFastGPT Flow 工 作 流 采 用 了 React Flow 框 架 作 为 UI 底 座 ， 结 合 自 研 的 FlowController 实现工作流的运行。FastGPT 使用 Flow 模块为用户呈现了一个直观、可视化的界面，从而简化了 AI 应用工作流程的设计和管理方式。React Flow 的应用使得用户能够以图形化的方式组织和编排工作流，这不仅使得工作流的创建过程更为直观，同时也为用户提供了强大且灵活的工作流编辑器。在 FastGPT Flow 工作流设计中，核心工作流模块包括用户引导、问题输入、知识库检索、AI 文本生成、问题分类、结构化内容提取、指定回复、应用调用和 HTTP 扩展，并允许用户在这类模块中进行自由组合和组装，从而衍生出一个新的模块。",
        "a": "",
        "chunkIndex": 0,
        "score": [
          {
            "type": "fullText",
            "value": 1.0229779411764706,
            "index": 15
          },
          {
            "type": "reRank",
            "value": 0.9577545043363116,
            "index": 8
          },
          {
            "type": "rrf",
            "value": 0.026992753623188405,
            "index": 17
          }
        ]
      }
    ],
    "duration": "2.978s",
    "searchMode": "mixedRecall",
    "limit": 1500,
    "similarity": 0.1,
    "usingReRank": true,
    "usingSimilarityFilter": true
  }
}
```

### Responses

|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|成功|Inline|

### Responses Data Schema

HTTP Status Code **200**

|Name|Type|Required|Restrictions|Title|description|
|---|---|---|---|---|---|
|» code|integer|true|none||none|
|» statusText|string|true|none||none|
|» message|string|true|none||none|
|» data|object|true|none||none|
|»» list|[object]|true|none||none|
|»»» id|string|true|none||none|
|»»» q|string|true|none||none|
|»»» a|string|true|none||none|
|»»» chunkIndex|integer|true|none||none|
|»»» datasetId|string|true|none||none|
|»»» collectionId|string|true|none||none|
|»»» sourceName|string|true|none||none|
|»»» sourceId|string|true|none||none|
|»»» score|[object]|true|none||none|
|»»»» type|string|true|none||none|
|»»»» value|number|true|none||none|
|»»»» index|integer|true|none||none|
|»» duration|string|true|none||none|
|»» searchMode|string|true|none||none|
|»» limit|integer|true|none||none|
|»» similarity|number|true|none||none|
|»» usingReRank|boolean|true|none||none|
|»» usingSimilarityFilter|boolean|true|none||none|

# openapi/知识库/知识库crud

## GET 获取知识库列表

GET /core/dataset/list

### Params

|Name|Location|Type|Required|Description|
|---|---|---|---|---|
|parentId|query|string| no |父级的ID|
|Authorization|header|string| no |none|

> Response Examples

> 200 Response

```json
{}
```

### Responses

|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|成功|Inline|

### Responses Data Schema

## GET 获取知识库详情

GET /core/dataset/detail

### Params

|Name|Location|Type|Required|Description|
|---|---|---|---|---|
|id|query|string| no |知识库id|
|Authorization|header|string| no |none|

> Response Examples

> 200 Response

```json
{}
```

### Responses

|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|成功|Inline|

### Responses Data Schema

# openapi/知识库/集合crud

## POST 获取知识库集合列表

POST /core/dataset/collection/list

> Body Parameters

```json
{
  "pageNum": 1,
  "pageSize": 10,
  "datasetId": "6597ca43e26f2a90a1501414",
  "parentId": null,
  "searchText": "",
  "simple": true
}
```

### Params

|Name|Location|Type|Required|Description|
|---|---|---|---|---|
|Authorization|header|string| no |none|
|body|body|object| no |none|
|» pageNum|body|integer| no |none|
|» pageSize|body|integer| no |none|
|» datasetId|body|string| yes |none|
|» parentId|body|null| no |none|
|» searchText|body|string| no |none|
|» simple|body|boolean| no |none|

> Response Examples

> 200 Response

```json
{}
```

### Responses

|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|成功|Inline|

### Responses Data Schema

## GET 获取集合详情

GET /core/dataset/collection/detail

### Params

|Name|Location|Type|Required|Description|
|---|---|---|---|---|
|id|query|string| no |知识库id|
|Authorization|header|string| no |none|

> Response Examples

> 200 Response

```json
{}
```

### Responses

|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|成功|Inline|

### Responses Data Schema

## PUT 更新集合

PUT /core/dataset/collection/update

> Body Parameters

```json
{
  "id": "6597ce094e10ee661f0891c8",
  "parentId": null,
  "name": "222"
}
```

### Params

|Name|Location|Type|Required|Title|Description|
|---|---|---|---|---|---|
|Authorization|header|string| no ||none|
|body|body|object| no ||none|
|» id|body|string| yes ||none|
|» parentId|body|null| no | 父级的id|none|
|» name|body|string| no | 名称|none|

> Response Examples

> 200 Response

```json
{}
```

### Responses

|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|成功|Inline|

### Responses Data Schema

## POST 创建空集合（文件夹或者一个空集合）

POST /core/dataset/collection/create

> Body Parameters

```json
{
  "datasetId": "6597ca43e26f2a90a1501414",
  "parentId": null,
  "name": "集合名",
  "type": "folder",
  "metadata": {}
}
```

### Params

|Name|Location|Type|Required|Title|Description|
|---|---|---|---|---|---|
|Authorization|header|string| no ||none|
|body|body|object| no ||none|
|» datasetId|body|string| yes ||none|
|» parentId|body|null| no ||none|
|» name|body|string| yes ||none|
|» type|body|[collection type](#schemacollection%20type)| yes ||none|
|» metadata|body|object| no ||none|

> Response Examples

> 200 Response

```json
{}
```

### Responses

|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|成功|Inline|

### Responses Data Schema

## POST 创建文本集合

POST /core/dataset/collection/create/text

> Body Parameters

```json
{
  "text": "xxxxxxxxxxxxxx",
  "datasetId": "6593e137231a2be9c5603ba7",
  "parentId": null,
  "name": "测试",
  "trainingType": "qa",
  "chunkSize": 8000,
  "chunkSplitter": "",
  "qaPrompt": "",
  "metadata": {}
}
```

### Params

|Name|Location|Type|Required|Title|Description|
|---|---|---|---|---|---|
|Authorization|header|string| no ||none|
|body|body|object| no ||none|
|» datasetId|body|string| no ||none|
|» parentId|body|null| no ||none|
|» name|body|string| yes ||none|
|» text|body|string| yes | 原文本|none|
|» trainingType|body|[training type](#schematraining%20type)| yes ||none|
|» chunkSize|body|integer| no | 分块大小|none|
|» chunkSplitter|body|string| no | 自定义最高优先级的分段符号|none|
|» qaPrompt|body|string| no ||none|
|» metadata|body|object| no ||none|

> Response Examples

> 200 Response

```json
{}
```

### Responses

|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|成功|Inline|

### Responses Data Schema

## POST 创建网络链接集合

POST /core/dataset/collection/create/link

> Body Parameters

```json
{
  "link": "https://doc.fastgpt.in/docs/course/quick-start/",
  "datasetId": "6593e137231a2be9c5603ba7",
  "parentId": null,
  "trainingType": "chunk",
  "chunkSize": 512,
  "chunkSplitter": "",
  "qaPrompt": "",
  "metadata": {
    "webPageSelector": ".docs-content"
  }
}
```

### Params

|Name|Location|Type|Required|Title|Description|
|---|---|---|---|---|---|
|Authorization|header|string| no ||none|
|body|body|object| no ||none|
|» datasetId|body|string| yes ||none|
|» parentId|body|null| no ||none|
|» link|body|string| yes ||none|
|» trainingType|body|[training type](#schematraining%20type)| yes ||none|
|» chunkSize|body|integer| no ||none|
|» chunkSplitter|body|string| no ||none|
|» qaPrompt|body|string| no ||none|
|» metadata|body|object| no ||none|
|»» webPageSelector|body|string| no | web选择器|none|

> Response Examples

> 200 Response

```json
{}
```

### Responses

|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|成功|Inline|

### Responses Data Schema

## DELETE 删除一个集合

DELETE /core/dataset/collection/delete

### Params

|Name|Location|Type|Required|Title|Description|
|---|---|---|---|---|---|
|id|query|string| no ||知识库id|
|Authorization|header|string| no ||none|

> Response Examples

> 200 Response

```json
{}
```

### Responses

|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|成功|Inline|

### Responses Data Schema

# openapi/知识库/数据crud

## POST 获取数据列表

POST /core/dataset/data/list

> Body Parameters

```json
{
  "pageNum": 1,
  "pageSize": 10,
  "collectionId": "65a8d2700d70d3de0bf09186",
  "searchText": ""
}
```

### Params

|Name|Location|Type|Required|Title|Description|
|---|---|---|---|---|---|
|Authorization|header|string| no ||none|
|body|body|object| no ||none|
|» pageNum|body|integer| yes ||none|
|» pageSize|body|integer| yes ||none|
|» searchText|body|string| yes ||none|
|» collectionId|body|string| yes ||none|

> Response Examples

> 200 Response

```json
{}
```

### Responses

|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|成功|Inline|

### Responses Data Schema

## GET 获取数据详情

GET /core/dataset/data/detail

### Params

|Name|Location|Type|Required|Title|Description|
|---|---|---|---|---|---|
|id|query|string| yes ||none|
|Authorization|header|string| no ||none|

> Response Examples

> 200 Response

```json
{}
```

### Responses

|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|成功|Inline|

### Responses Data Schema

## DELETE 删除一条数据

DELETE /core/dataset/data/delete

### Params

|Name|Location|Type|Required|Title|Description|
|---|---|---|---|---|---|
|id|query|string| no ||none|
|Authorization|header|string| no ||none|

> Response Examples

> 200 Response

```json
{}
```

### Responses

|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|成功|Inline|

### Responses Data Schema

## PUT 更新数据

PUT /core/dataset/data/update

> Body Parameters

```json
{
  "id": "6597ce094e10ee661f0891c8",
  "parentId": null,
  "name": "222"
}
```

### Params

|Name|Location|Type|Required|Title|Description|
|---|---|---|---|---|---|
|Authorization|header|string| no ||none|
|body|body|object| no ||none|
|» id|body|string| yes ||none|
|» q|body|string| yes ||none|
|» a|body|string| no ||none|
|» indexes|body|[[数据自定义向量](#schema%e6%95%b0%e6%8d%ae%e8%87%aa%e5%ae%9a%e4%b9%89%e5%90%91%e9%87%8f)]| no ||none|

> Response Examples

> 200 Response

```json
{}
```

### Responses

|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|成功|Inline|

### Responses Data Schema

## POST 知识库插入记录（批量插入）

POST /core/dataset/data/pushData

> Body Parameters

```json
{
  "collectionId": "string",
  "data": [
    {
      "a": "string",
      "q": "string",
      "chunkIndex": 1
    }
  ],
  "trainingMode": "string",
  "promot": "string",
  "billId": ""
}
```

### Params

|Name|Location|Type|Required|Title|Description|
|---|---|---|---|---|---|
|Authorization|header|string| no ||none|
|body|body|object| no ||none|
|» collectionId|body|string| yes ||none|
|» data|body|[object]| yes ||none|
|»» a|body|string| no ||none|
|»» q|body|string| no ||none|
|»» chunkIndex|body|integer| no ||none|
|» trainingMode|body|[training type](#schematraining%20type)| no ||none|
|» promot|body|string| no ||none|
|» billId|body|string| no ||none|

> Response Examples

> 200 Response

```json
{}
```

### Responses

|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|[OK](https://tools.ietf.org/html/rfc7231#section-6.3.1)|成功|Inline|

### Responses Data Schema

# Data Schema

<h2 id="tocS_similary">similary</h2>

<a id="schemasimilary"></a>
<a id="schema_similary"></a>
<a id="tocSsimilary"></a>
<a id="tocssimilary"></a>

```json
1

```

### Attribute

|Name|Type|Required|Restrictions|Title|Description|
|---|---|---|---|---|---|
|*anonymous*|integer|false|none||none|

<h2 id="tocS_search mode">search mode</h2>

<a id="schemasearch mode"></a>
<a id="schema_search mode"></a>
<a id="tocSsearch mode"></a>
<a id="tocssearch mode"></a>

```json
"embedding"

```

### Attribute

|Name|Type|Required|Restrictions|Title|Description|
|---|---|---|---|---|---|
|*anonymous*|string|false|none||none|

#### Enum

|Name|Value|
|---|---|
|*anonymous*|embedding|
|*anonymous*|fullTextRecall|
|*anonymous*|mixedRecall|

<h2 id="tocS_training type">training type</h2>

<a id="schematraining type"></a>
<a id="schema_training type"></a>
<a id="tocStraining type"></a>
<a id="tocstraining type"></a>

```json
"chunk"

```

### Attribute

|Name|Type|Required|Restrictions|Title|Description|
|---|---|---|---|---|---|
|*anonymous*|string|false|none||none|

#### Enum

|Name|Value|
|---|---|
|*anonymous*|chunk|
|*anonymous*|qa|

<h2 id="tocS_collection type">collection type</h2>

<a id="schemacollection type"></a>
<a id="schema_collection type"></a>
<a id="tocScollection type"></a>
<a id="tocscollection type"></a>

```json
"folder"

```

### Attribute

|Name|Type|Required|Restrictions|Title|Description|
|---|---|---|---|---|---|
|*anonymous*|string|false|none||none|

#### Enum

|Name|Value|
|---|---|
|*anonymous*|folder|
|*anonymous*|virtual|
|*anonymous*|link|
|*anonymous*|file|

<h2 id="tocS_数据自定义向量">数据自定义向量</h2>

<a id="schema数据自定义向量"></a>
<a id="schema_数据自定义向量"></a>
<a id="tocS数据自定义向量"></a>
<a id="tocs数据自定义向量"></a>

```json
{
  "defaultIndex": true,
  "type": "string",
  "text": "string"
}

```

### Attribute

|Name|Type|Required|Restrictions|Title|Description|
|---|---|---|---|---|---|
|defaultIndex|boolean|false|none||是否为默认|
|type|string|true|none||none|
|text|string|true|none||索引文本|