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* docs(i18n): translate batch 1 * docs(i18n): translate batch 2 * docs(i18n): translate batch 3 (20 files) - openapi/: app, share - faq/: all 8 files - use-cases/: index, external-integration (5 files), app-cases (4 files) Translated using North American style with natural, concise language. Preserved MDX syntax, code blocks, images, and component imports. * docs(i18n): translate protocol docs * docs(i18n): translate introduction docs (part 1) * docs(i18n): translate use-cases docs * docs(i18n): translate introduction docs (part 2 - batch 1) * docs(i18n): translate final 9 files * fix(i18n): fix YAML and MDX syntax errors in translated files - Add quotes to description with colon in submit_application_template.en.mdx - Remove duplicate Chinese content in translate-subtitle-using-gpt.en.mdx - Fix unclosed details tag issue * docs(i18n): translate all meta.json navigation files * fix(i18n): translate Chinese separators in meta.en.json files * translate * translate * i18n --------- Co-authored-by: archer <archer@archerdeMac-mini.local> Co-authored-by: archer <545436317@qq.com>
168 lines
7.1 KiB
Plaintext
168 lines
7.1 KiB
Plaintext
---
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title: Third-Party Knowledge Base Development
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description: How to integrate a third-party knowledge base with FastGPT
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---
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import { Alert } from '@/components/docs/Alert';
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There are many document libraries available online, such as Lark, Yuque, and others. Different FastGPT users may use different document libraries. FastGPT has built-in support for Lark and Yuque, but if you need to integrate other document libraries, follow this guide.
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## Unified API Specification
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To provide a unified interface for different document libraries, FastGPT defines a standard API specification with 4 endpoints. See the [API File Library endpoints](/docs/introduction/guide/knowledge_base/api_dataset).
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All built-in document libraries are extensions of the standard API File Library. Refer to the code in `FastGPT/packages/service/core/dataset/apiDataset/yuqueDataset/api.ts` to build extensions for other document libraries. You need to implement 4 endpoints:
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1. Get file list
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2. Get file content / file link
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3. Get original file preview URL
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4. Get file detail information
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## Building a Third-Party File Library
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For this walkthrough, we'll use adding a Lark Knowledge Dataset (FeishuKnowledgeDataset) as an example.
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### 1. Add Third-Party Document Library Parameters
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First, go to `FastGPT\packages\global\core\dataset\apiDataset.d.ts` in the FastGPT project and add the third-party document library server type. Design the fields based on your needs. For example, the Yuque knowledge base requires `userId` and `token` for authentication.
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```ts
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export type YuqueServer = {
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userId: string;
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token?: string;
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basePath?: string;
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};
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```
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<Alert icon="🤖" context="success">
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If the document library supports a `root directory` selection feature, add a `basePath` field. [See the root directory feature](/docs/introduction/guide/knowledge_base/third_dataset/#adding-the-configuration-form)
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</Alert>
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### 2. Create the Hook File
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Each third-party document library uses a Hook pattern to maintain a set of API endpoints. The Hook contains 5 functions to implement.
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- Create a folder for your document library under `FastGPT\packages\service\core\dataset\apiDataset\`, then create an `api.ts` file inside it
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- In `api.ts`, define the following 5 functions:
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- `listFiles`: Get the file list
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- `getFileContent`: Get file content / file link
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- `getFileDetail`: Get file detail information
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- `getFilePreviewUrl`: Get the original file preview URL
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- `getFileId`: Get the original file's real ID
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### 3. Add the Knowledge Base Type
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In `FastGPT\packages\global\core\dataset\type.d.ts`, import your new knowledge base type.
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### 4. Add Knowledge Base Data Retrieval
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In `FastGPT\packages\global\core\dataset\apiDataset\utils.ts`, add the following content.
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### 5. Add Knowledge Base Invocation Method
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In `FastGPT\packages\service\core\dataset\apiDataset\index.ts`, add the following content.
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## Adding the Frontend
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Add your i18n translations in `FastGPT\packages\web\i18n\zh-CN\dataset.json`, `FastGPT\packages\web\i18n\en\dataset.json`, and `FastGPT\packages\web\i18n\zh-Hant\dataset.json`. Using Chinese translations as an example, you'll generally need the following:
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In `FastGPT\packages\service\support\user/audit\util.ts`, add the following to support i18n translation retrieval.
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<Alert icon="🤖" context="success">
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The i18n translation content is stored in `FastGPT\packages\web\i18n\zh-Hant\account_team.json`, `FastGPT\packages\web\i18n\zh-CN\account_team.json`, and `FastGPT\packages\web\i18n\en\account_team.json`. The field format is `dataset.XXX_dataset`. For example, for the Lark knowledge base, the field value is `dataset.feishu_knowledge_dataset`.
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</Alert>
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Add your knowledge base icons under `FastGPT\packages\web\components\common\Icon\icons\core\dataset\`. You need two icons: `Outline` (monochrome) and `Color` (colored), as shown below.
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In `FastGPT\packages\web\components\common\Icon\constants.ts`, register your icons. The `import` path points to where the icons are stored.
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In `FastGPT\packages\global\core\dataset\constants.ts`, add your knowledge base type to both `DatasetTypeEnum` and `ApiDatasetTypeMap`.
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| --- | --- |
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<Alert icon="🤖" context="success">
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The `courseUrl` field links to the relevant documentation — add it if available.
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Documentation goes in `FastGPT/document/content/docs/introduction/guide/dashboard/workflow/knowledge_base_search_merge.mdx`.
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The `label` value is the knowledge base name you added via i18n translations.
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`icon` and `avatar` are the two icons you added earlier.
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</Alert>
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In `FastGPT\projects\app\src\pages\dataset\list\index.tsx`, add the following. This file handles the menu that appears when clicking the "New" button on the knowledge base list page. Your knowledge base must be added here to be creatable.
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In `FastGPT\projects\app\src\pageComponents\dataset\detail\Info\index.tsx`, add the following. This configuration corresponds to the UI shown below.
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| --- | --- |
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## Adding the Configuration Form
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In `FastGPT\projects\app\src\pageComponents\dataset\ApiDatasetForm.tsx`, add the following. This file handles the field input form when creating a knowledge base.
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| --- | --- | --- |
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The two components added in the code render the root directory selector, corresponding to the `getFileDetail` API method. If your knowledge base doesn't support this, you can omit them.
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```
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{renderBaseUrlSelector()} // Renders the `Base URL` field
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{renderDirectoryModal()} // The `Select Root Directory` modal that appears when clicking `Select` (see image)
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```
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| --- | --- |
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If the knowledge base needs root directory support, also add the following in the `ApiDatasetForm` file.
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### 1. Parse the Knowledge Base Type
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Parse your knowledge base type from `apiDatasetServer`, as shown:
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### 2. Add Root Directory Selection Logic and `parentId` Assignment
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Add root directory selection logic to ensure the user has filled in all required fields for the API methods, such as the Token.
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### 3. Add Field Validation and Assignment Logic
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Verify that all required fields are present before calling the API, and assign the root directory value to the corresponding field after selection.
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## Tips
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After creating the knowledge base, we recommend running a full test of all knowledge base features to check for issues. If you encounter problems that aren't covered in this documentation, it's likely that some configuration was missed. Do a global search for `YuqueServer` and `yuqueServer` to verify that your type has been added everywhere it's needed.
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