What this category of data looks like
Data sources for this category include regulatory and rule databases, higher-level law databases, departmental policy document databases, and administrative normative document filing systems. Update frequency is irregular. Daily processed documents include normative document drafts, drafting explanations, legality review opinions, solicited opinion summaries, and filing reports. Each document is typically several thousand to tens of thousands of characters. Core fields cover article content, formulation authority basis, solicited opinion feedback, review conclusions, and more. There are no fixed universal units. Documents are only categorized and identified by their affiliated department and release level.
What constraints these characteristics impose on the "multi-turn dialogue and prompt engineering" link
Decentralized data sources require multi-turn dialogue to gradually guide users to clarify the higher-level law hierarchy and departmental authority corresponding to the current review document, to avoid cross-database retrieval deviations. Long documents with scattered fields require limiting the effective length of dialogue context, to prevent redundant information from interfering with review logic. Irregular updates require prompts to regularly link newly added regulatory data, to ensure timeliness of the referenced basis. The core review goal focuses on formulation authority and procedural legality. Multi-turn dialogue must strictly anchor the abstract rule attribute of normative documents. It must avoid introducing judgment logic related to individual administrative law enforcement cases, to ensure accuracy of the review direction.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context Length | 8000–12000 characters | Matches the typical length of a single normative document draft, avoids exceeding model processing limits |
| Single Retrieval Result Limit | Top 6 entries | Corresponds to the number of core higher-level laws and departmental regulations that need to be compared for review, avoids information overload |
messages Context Truncation Threshold | Retain the most recent 10 turns of dialogue | Filters out irrelevant early small talk, focuses on the current review process |
variables Preset Values | Fixed as "Regulatory Document Legality Review" and "Party Building Learning Scenario" | Locks the business boundary of the dialogue, prevents deviation from review goals |
chatId Binding Rule | Bind by department and document number | Distinguishes context for different review tasks, avoids information confusion across departments and documents |
| Question Optimization Switch | Enabled | Helps users sort out ambiguous points in review, aligns with core requirements of legality review |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Mistakes
- Phenomenon: A 422 status code is returned when calling the AI dialogue module. Cause: The context format of the
messagesparameter is not configured correctly, causing the passed dialogue content to not comply with interface verification rules. - Phenomenon: Retrieval results miss articles related to formulation authority after long documents are chunked. Cause: Title hierarchy is used as the only delimiter, and context association of authority basis fields is not retained, causing key information to be lost during chunking.
- Phenomenon: The new conversation initialization pop-up cannot be hidden after private deployment. Cause: The compiled front-end static files are modified directly, without modifying the conversation initialization component code in the source code directory, and the deployment is not recompiled to take effect.
How to Confirm Configuration is Complete
- Upload a single normative document draft, check whether the content recalled by the dialogue context covers core review dimensions such as formulation authority and article legality.
- Initiate a multi-turn review dialogue, check whether the preset
variablesparameters lock the current business scenario, and no content deviating from the review goal is introduced. - Test the retrieval effect after chunking long documents, confirm that chunking does not sever key connections between authority basis and related articles.
- Call the AI dialogue interface, confirm that the returned status code meets expectations, and there are no format verification errors.
The values provided are common starting points and should be measured against the reader'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.