What this data category looks like
This data category originates from work safety supervision systems, enterprise hidden danger self-inspection systems, law enforcement inspection records, and accident statistics systems. Updates occur daily. Daily documents include hidden danger inspection lists, hidden danger management ledgers, rectification notices, recheck records, and major hidden danger listing and supervision orders. Individual documents contain fields such as hidden danger ID, inspection time, hidden danger level, rectification responsible person, rectification deadline, recheck result, and cancellation status. Some documents include on-site photo or video attachments. Most data combines structured tables and unstructured text.
What constraints do these characteristics impose on model access and configuration
Daily updated mixed-format data requires the model access link to support batch import and incremental synchronization, preventing data omission. The mixed structure of multiple document types requires the configuration link to adapt to parsing rules for different text and attachment types. The accuracy of key fields such as hidden danger level and rectification deadline directly impacts the subsequent governance loop, so strict field mapping must be retained during model calls. The need to process on-site photo attachments requires enabling visual-related parameters in the configuration. Additionally, the requirement for multi-document associated queries means the context window must accommodate the length of associated data to avoid truncating key information.
How to set the configuration
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context association needs of multiple hidden danger ledgers and associated documents, avoiding truncation of key rectification information |
temperature | 0.1–0.3 | Ensures consistency of key content such as hidden danger level judgment and rectification deadline matching, reducing random deviations |
quoteMaxToken | 2000 characters | Retains complete quoted content of core documents including hidden danger inspection lists and rectification notices, ensuring traceability of report generation basis |
AIPROXY_API_ENDPOINT | Fill in the corresponding address according to the deployment environment | Adapts to locally built or publicly deployed FastGPT v4.17.0 environments, ensuring smooth model call links |
AIPROXY_API_TOKEN | Fill in the exclusive key generated by the platform | Ensures valid identity verification for model calls, preventing unauthorized external access |
| Visual Support | Enabled | Adapts to the parsing needs of on-site photo attachments, supporting textual description of on-site hidden danger situations |
Three Common Configuration Mistakes
- Phenomenon: Generation results cannot be accessed via external channels after release, while local testing functions normally. Cause: Access permissions for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, or interface parameters changed after a version upgrade. - Phenomenon: The rectification deadline field is missing in the generated ledger summary. Cause:
maxContextis set too small, resulting in truncation of associated rectification notice documents, so key fields are not read by the model. - Phenomenon: Hidden danger level judgment results fluctuate randomly. Cause:
temperatureis set too high, leading to meaningless deviations in the model's hidden danger level judgment.
How to confirm the configuration is complete
- Check if
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENon the model provider configuration page match the deployment environment, to verify the basic call link. - Upload a single sample of a hidden danger inspection list and rectification notice, trigger ledger summary generation, and confirm that key fields are fully retained.
- After enabling visual support, upload a hidden danger record with on-site photos, and verify that the model can generate descriptive content including the on-site situation.
- Switch between different release channels of FastGPT v4.17.0, and test whether call responses meet expectations.
Note: The values provided here are common starting points, and should be evaluated 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.