What the Data for This Category Looks Like
The core data for this category comes from the randomized inspection and public disclosure supervision platform, enterprise credit information public disclosure system, industry supervision system, and law enforcement officer directory. Daily generated documents include inspection plans, inspection target lists, inspection item lists, on-site inspection records, rectification notices, and inspection result public disclosures. The data update frequency is monthly. In core business processes, maintaining the inspection item and target database, matching law enforcement officers to generate tasks, entering inspection results and publishing disclosures usually take the most time. Common error consequences include missed publication of inspection results, repeated disturbances to enterprises, and non-compliant selection of supervision targets.
Constraints Imposed by Data Characteristics on Multi-turn Dialogue and Prompt Engineering
Monthly updated data requires multi-turn dialogue context to filter expired information, and avoid referencing non-current valid content that exceeds the update cycle. Fixed document structures and clear fields require prompts to specify extraction of specific business fields such as inspection target lists and law enforcement officer information, and avoid generating irrelevant content. Matching between law enforcement officers and inspection targets requires multi-step confirmation in dialogue, to prevent missing associated information in a single conversation. Inspection result public disclosures must strictly match already entered inspection records. Prompts must limit output scope to existing supervision data, and prevent generating unpublished content. Inspection item lists vary across different supervision departments. Prompts must adapt to field requirements of corresponding departments, and avoid matching errors caused by generalized configurations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Context Window Length | 8000–12000 token | Matches the single-turn dialogue information volume of monthly updated data, and avoids exceeding processing limits |
| Historical Dialogue Call Parameter | Only retain the most recent 3 turns in messages | Filters expired data, and complies with monthly update frequency constraints |
| Variable Injection Configuration | Bind current inspection plan ID in variables | Accurately locates supervision business data for the corresponding batch |
| Context Recall Scope | Recall session history bound by chatId | Ensures dialogue context belongs to the same law enforcement inspection task |
| Single Match Limit | Top 5 entries | Matches the single-match quantity for the law enforcement officer directory, and avoids information overload |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Misconfigurations
- Phenomenon: Configuration limits returns to only knowledge base content, but the model still generates external knowledge. Cause: The prompt does not explicitly bind the
variablesparameter for the current law enforcement task, causing the model to fail to accurately match the supervision data scope. - Phenomenon: Markdown table output is truncated, with
...[hide X chardisplayed at the end. Cause: The context window length is set too small, which cannot accommodate complete case file review content, and is automatically truncated after exceeding processing limits. - Phenomenon: Multi-turn dialogue history is pulled by default, leading to increased inference time. Cause: The retention rounds for the
messagesparameter are not configured, and all historical dialogue content is called by default, which does not comply with the timeliness requirements of monthly updated data.
How to Verify Successful Configuration
- Submit a test inspection plan ID, and check whether the content returned by the model only includes supervision data for the bound batch.
- Input test content containing multi-turn law enforcement dialogue, and check that the model only retains the specified number of historical information rounds.
- Generate test case file content containing long tables, and check that the output is complete and not truncated.
- View session logs, and confirm that the session history bound by
chatIddoes not include irrelevant data from cross-tasks.
All listed configuration values are common starting points, and should be evaluated against the samples used in the respective deployments.
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.