What This Type of Data Entails
Typical service subjects include emergency management departments, industry competent departments, safety management departments of production and business units, and park management committees. Data is sourced from work safety supervision systems, enterprise hazard self-inspection systems, law enforcement inspection records, and accident statistics systems, with a daily update frequency. Daily documents include hazard investigation checklists, hazard governance ledgers, rectification notices, review records, and major hazard suspension and supervision orders. Most content is a mix of structured tables and unstructured text. Fields include hazard number, investigation time, rectification responsible person, rectification deadline, review status, and similar items, with no additional custom units.
Constraints Imposed on Citation Sources and Traceability
The daily data update feature requires traceability to support real-time association with the latest supervision and self-inspection data, and avoid citing outdated hazard records. The mixed structured and unstructured document format requires traceability functions to adapt to both table fields and free text citation markers. Data from multiple systems requires clear marking of the system each citation belongs to, to verify the authority of the basis. Key fields such as rectification deadline and review status require precise positioning of specific entries in the corresponding documents; citing the entire document cannot meet the business requirements of precise verification. Fluctuations in daily data volume require traceability links to have flexible context length adaptation capabilities, and avoid missing key hazard entries.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
quoteMaxToken | 8000–12000 token | Adapts to full citation needs for long documents such as hazard governance ledgers and rectification notices, avoids truncating key fields like rectification deadlines and review records |
responseData | Include source document ID and affiliated system | Meets the need to quickly locate corresponding law enforcement inspection records or enterprise self-inspection data during traceability |
detail | Enabled | Supports returning original context of citation fragments, facilitates verification of matching basis for major accident hazard judgment standards |
| Single search limit | Top 100 entries | Covers the volume of newly added investigation checklists and rectification records per day, avoids missing key hazard entries |
| Maximum context | Calibrated based on actual testing | Adapts to the total length of merged data from multiple systems, avoids exceeding the large model's context window limit |
Three Common Misconfiguration Issues
- Uploading a CSV-formatted hazard investigation checklist results in garbled text, and citation sources cannot be parsed correctly. The cause is that the CSV encoding is not set to UTF-8, causing the system to fail to recognize Chinese field content.
- Setting
quoteMaxTokenabove 3000 tokens results in the large model not receiving context content. The cause is exceeding FastGPT's built-in total context length limit, and the system automatically truncates citation data. - Configuring citation sources but not displaying associated document information. The cause is not enabling the source document ID return for the
responseDataparameter, resulting in failure to match corresponding entries in the knowledge base.
How to Verify Successful Configuration
- Upload a test rectification notice document, check if the knowledge base parsing result includes the document ID and affiliated system fields.
- Submit a policy basis Q&A request, verify if the returned result includes the original context of citation fragments.
- Adjust the
quoteMaxTokenparameter to 10000 tokens, submit a query containing multiple hazard records, confirm that the context is fully transmitted. - Verify that when querying mixed data from multiple sources, citation entries from different system sources are correctly marked.
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.