Actions37
- 对话 Actions
- 知识库 Actions
- 集合 Actions
- 数据 Actions
- 应用 Actions
集合 → 创建一个纯文本集合
AI-generatedSummary
Creates a FastGPT dataset collection from pasted plain text by POSTing to /core/dataset/collection/create/text; FastGPT splits the text into chunks or QA pairs for training.
Inputs
- datasetParentId — Parent folder ID (copied from the FastGPT browser URL). Sent as parentId. It also filters the knowledge base dropdown.
- datasetKnowledgeId — Target knowledge base, picked from a list loaded from /core/dataset/list. Sent as datasetId. The properties don't mark it required, but a collection needs a target dataset.
- datasetName (required) — Collection name. The field's own hint suggests using the file name.
- datasetTrainingType (required) — Either 'chunk' (split by text length, the default) or 'qa' (QA splitting). Sent as trainingType.
- datasetChunkSize — Chunk length, default 3000. Shown only when the training type is chunk. Sent as chunkSize.
- datasetChunkSplitter — Custom highest-priority split delimiter. Shown only when the training type is chunk. Sent as chunkSplitter.
- datasetQAPrompt — Custom prompt for QA splitting. Shown only when the training type is qa. Sent as qaPrompt.
- datasetText — The raw text content of the collection. Sent as text.
- requestOptions — Node-level options: batching (items per batch, interval), ignore SSL issues, proxy, and timeout (default 10000 ms).
Output shape
The FastGPT API JSON response for the create-text call, returned as the item's output.
A non-2xx HTTP status, or a body
codeother than 200, raises a node error carrying the API message. The exact response fields aren't defined in the node, so the API result is passed through as-is. Requires the fastGPTApi credential, which supplies the base URL.
Examples
Example 1: Import a text snippet as a chunked collection in a knowledge base
Select the knowledge base, set the name to 'FAQ', set the training type to chunk, leave Chunk length at 3000, and map the text into 原文本.
Example 2: Generate QA pairs from meeting notes
Set the training type to QA拆分, optionally fill the QA custom prompt, and set 原文本 to {{$json.notes}}.