What this category of data looks like
Data sources for this category include work safety supervision systems, enterprise self-inspection systems for hazards, law enforcement inspection records, and accident statistics systems. The update frequency is daily. Daily documents include hazard inspection checklists, hazard management ledgers, rectification notices, review records, and major hazard suspension and supervision orders. Fields cover hazard level, rectification deadline, responsible entity, review results, and more. Data covers four typical entities: emergency management departments, industry competent departments, production and business unit safety management departments, and park management committees. Single document length varies widely, ranging from single inspection items to multi-page rectification ledgers.
What constraints these characteristics impose on model access and configuration
The multi-source nature of the data requires model access configuration to support associated references across multiple documents, preventing inspection scope limits from a single data source. The daily update frequency requires configuration to adapt to fast loading of incremental data, preventing new hazard information from being excluded from analysis due to insufficient context window. The diverse document types and clear field requirements mean configuration must reserve sufficient context space to accommodate content in different formats, while ensuring the model can accurately match field information. Additionally, the rigor of hazard inspection requires configuration to limit the randomness of model generation, avoiding deviation of judgment results from actual standards due to improper parameter settings.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Safety production-related documents include multi-page inspection checklists and rectification ledgers, requiring space for multi-round Q&A and content from multiple referenced documents |
maxTokens | 4000–6000 token | Single rounds require generating hazard rectification suggestions, review record summaries, and other content. Sufficient length prevents key information from being truncated |
quoteMaxToken | 2000–3000 token | Specific hazard inspection checklist items and rectification notice content must be referenced. Limiting reference length ensures compact context |
temperature | 0.1–0.3 | Hazard judgment standards and rectification requirements must be strictly matched. Lower temperature values reduce randomness in generated results |
toolChoice | auto | Must automatically select whether to call the hazard judgment standard knowledge base or internal ledger data based on user queries, adapting to different scenario needs |
AIPROXY_API_TOKEN | Fill in according to the actual deployed model service | Used to verify the legitimacy of model access and ensure the security of data transmission |
Three common mistakes
- Phenomenon: Model generated content is truncated, and logs show the
context length exceedederror. Cause: The configuredmaxContextis less than the maximum context limit of the deployed model, making it impossible to load all hazard ledger and inspection checklist data. - Phenomenon: The workflow only outputs the result of the final conversation model, and the results of intermediate hazard inspection steps are not correctly passed. Cause: The context transfer rules of the workflow are not configured, and only the result display of the final step is enabled, failing to meet the requirement of displaying only the final conclusion after step-by-step processing.
- Phenomenon: Authentication failure occurs when calling the model, and the service cannot be accessed normally. Cause: The
AIPROXY_API_TOKENconfiguration is incorrect, orAIPROXY_API_ENDPOINTpoints to a non-compliant service address, preventing identity verification from being completed.
How to confirm the configuration is complete
- Upload a single hazard inspection checklist document, initiate a targeted query, and check whether the model can accurately extract hazard items and generate compliant judgment results, confirming that the generated content is not truncated.
- Initiate a query associated with multiple documents, such as asking a question combining a rectification notice and a review record, verifying that the model can correctly associate the content of the two documents and confirming that context transfer works properly.
- Configure a step-by-step workflow, test that after execution only the final rectification suggestion or review conclusion is displayed, confirming that the results of intermediate steps are not publicly displayed.
- Check the model call logs in FastGPT v4.17.0, confirming that the actual effective values of parameters such as
maxContextandmaxTokensmatch the configured content.
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.