Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data for this category comes from regulatory and rule repositories, higher-level law databases, departmental policy document repositories, and administrative normative document filing systems. Update frequency is irregular. Daily processed documents include draft normative documents, drafting explanations, legality review opinions, summary of solicited comments, and filing reports. Document fields include issuing entity, article content, drafting basis, review opinion number, submitting unit, and others. Single document length varies widely, from hundreds to tens of thousands of characters.
What constraints these characteristics impose on the "workflow orchestration" link
Dispersed data sources require workflow configuration of multi-source recall nodes to cover different data sources such as regulatory and rule repositories and higher-level law databases. Wide variation in document lengths requires support for custom chunking parameters to adapt to policy documents of different lengths. Irregular updates require workflows to trigger data synchronization on demand, without fixed periodic pulling. Review requires comparing articles against higher-level laws one by one. Loop check nodes must be set to cover all article content. Metadata such as the total number of articles in the document must be passed via variables variables. Review results must be associated with drafting explanations and filing reports. Workflows must retain field mapping relationships to ensure accurate associated data.
How to set the configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Multi-source data source configuration | Associate three data sources: regulatory and rule repositories, higher-level law databases, and departmental policy document repositories. Single search upper limit: top 5 entries | This category needs to cover core policy basis. The top 5 entries can cover major associated content and avoid redundant data interfering with review |
| Chunk length | 800–1200 characters | Normative document articles are mostly hundreds to thousands of characters. This range ensures complete single-chunk content and coherent context |
| Loop check node binding | Bind the "total number of articles" variable in variables | Ensure the number of loops matches the number of articles to be reviewed, covering all review content |
| Node jump configuration | Specify the ID of the next processing node via nodeId | Automatically route to the corresponding link based on review results, such as entering permission review after comparison is complete |
| Maximum context window | 8000 token | Need to fully carry draft and higher-level law comparison content to avoid loss of key basis |
| Node timeout period | 600 seconds | The review and comparison link takes a long time. 600 seconds ensures completion of multi-source recall and comparison |
| Data synchronization trigger | Manually trigger on demand | Data updates for this category are irregular. Fixed periodic synchronization wastes resources |
Three common mistakes
- Phenomenon: After enabling the image upload node, the returned image link uses an IP address and port format. Cause: No domain name mapping rule is configured. The workflow calls the local service address by default and does not replace it with the official domain name.
- Phenomenon: Workflow runtime errors include
$schemafield exception prompts. Cause: The workflow metadata structure is not configured correctly, causing the field format to not meet FastGPT's verification rules. - Phenomenon: The loop check node does not execute as expected, only reviewing part of the articles. Cause: The total number of articles variable in
variablesis not bound correctly. The loop trigger condition does not cover all review entries.
How to confirm the configuration is complete
- Check the multi-source data source configuration. Confirm that all required policy and regulatory databases are associated. Verify that the data source list matches business requirements.
- Run a test on a small draft normative document. Check the integrity of the chunked content. Confirm that the chunking parameters adapt to the document length.
- Trigger the loop check node. Verify that the number of loops matches the total number of articles in the document. Confirm that the
variablesvariable binding is correct. - Test the data synchronization function. Confirm that data source updates can be triggered on demand, with no fixed period restrictions.
The values listed here are common starting points. Adjust them based on your own sample data.
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.