What this category of data looks like
Party building learning data for state-owned enterprises and public sector entities in coal-electric integration comes primarily from branch meeting records, monthly learning ledgers within the internal party building management system, and party building guidance documents issued by the superior party committee. Data is updated in bulk every quarter. Each individual document includes four core structures: learning topic, original text content, supporting thought questions, and assessment key points. Fields include document ID, affiliated branch, release date, and knowledge point tags. The length of a single document typically ranges from 1000 to 5000 characters.
What constraints these characteristics impose on forms and interactions
The knowledge point tags and branch grouping attributes of party building documents require forms to support interaction logic for filtering by tags and grouping by branches. The quarterly bulk update rhythm requires the form’s knowledge base synchronization configuration to support scheduled trigger rules. The relatively long length of individual documents requires the formInput component to support long text input. It also requires limiting the character limit for a single submission to avoid overload when embedding into the knowledge base. Branch-based classification management requires the userSelect component to preset options for corresponding groups, to match the hierarchical management needs of party building learning.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the core paragraph length of coal-electric integration party building documents |
| Overlap Length | 100–200 characters | Prevents party building knowledge points from being truncated during chunking |
formInput | 5000 character limit | Adapts to the long text question requirements of party building learning Q&A |
userSelect | Preset grouping options by branch | Matches the hierarchical management logic of party building learning |
| Single Search Limit | Top 6–8 results | Covers the core knowledge points of a single party building document |
| Maximum Context | 3500–4500 tokens | Controls context load during interactions |
Three common errors
- Phenomenon: The same question returns no matching results on the first submission, but returns valid content on the second submission. Cause: The knowledge base recall logic is not triggered on the first submission, and the form is not bound to the correct recall trigger rule.
- Phenomenon: A context overflow error is displayed during interaction. Cause: No reasonable maximum context value is set, exceeding the token limit supported by the model.
- Phenomenon: The classify module does not run as preset after the workflow is published. Cause: The model selection configuration is not saved before publishing, resulting in lost parameters during deployment.
How to confirm the configuration is complete
- Submit a test question related to party building, and check whether the form normally receives input and triggers knowledge base recall.
- Select a branch option from the
userSelectcomponent, and confirm that the interaction interface only displays party building content from the corresponding group. - Adjust the input text length to the preset limit, and check whether the form normally restricts input length and displays a prompt.
- After publishing the workflow, check whether the workflow modules run normally according to the preset configuration.
The values provided are common starting points and should be evaluated against the user’s own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.