What this category of data looks like
Data for this category comes from safety production supervision systems, enterprise self-inspection hazard systems, 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. Data fields cover hazard levels, rectification responsible entities, rectification deadlines, review results, and more. There is no unified fixed format, and the length of individual documents varies widely, ranging from review records of a few hundred characters to management ledgers of several thousand characters.
What constraints do these characteristics impose on the model access and configuration link
Multi-source, high-frequency updated data requires configuration that supports incremental synchronization and multi-source data fusion. This prevents inspection accuracy from being impacted by data lag. Large differences in document format and length require model access configuration to adapt to context inputs of varying lengths. This avoids truncation of critical inspection and rectification information. Fields include clear business attributes. Configuration must guide the model to identify specific business fields, ensuring outputs align with business specifications. Business errors carry severe consequences. Configuration must prioritize output accuracy, avoiding vague or incorrect inspection guidance.
How to set the configuration
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context capacity requirements of long documents such as hazard management ledgers and suspension and supervision orders, preventing critical inspection information from being truncated |
maxTokens | 2000–3000 token | Meets the output length requirements of documents such as rectification notices and review records, ensuring complete return of rectification and review details |
temperature | 0.1–0.3 | Reduces output randomness, ensuring the accuracy of hazard inspection and rectification guidance, and complying with high business compliance requirements |
toolChoice | auto | Automatically triggers knowledge base retrieval tools to match the latest hazard inspection checklist and management ledger data |
AIPROXY_API_ENDPOINT | Configure according to the official access address of the selected model service provider | Binds valid model call endpoints to ensure the stability and compliance of model calls |
AIPROXY_API_TOKEN | Exclusive call key | Restricts unauthorized access, ensuring permission security for model calls |
Three common configuration errors
- After deploying a reranking model, testing passes, but every retrieval result shows a rerank status mark of false, or no sorted results after reranking are displayed. The cause is that the trigger condition for the reranking model is not configured, or the knowledge base retrieval content does not meet the call length requirement of the reranking model.
- When the knowledge base retrieval content exceeds 8000 characters, the reranking model is not called, and the retrieval results are not sorted and optimized. The cause is that the
maxContextconfiguration is not adjusted to adapt to long-text retrieval, causing the reranking trigger logic to fail. - Significant differences exist between online conversation and API call return results. When API enables stream as false and detail as true, the returned content is missing or has abnormal formatting. The cause is that the
maxTokensandtemperatureparameters are not configured uniformly, or complete context parameters are not passed during API calls.
How to confirm successful configuration
- Call the FastGPT v4.17.0 API test interface, check whether the return result contains reranking-related fields, and confirm that the reranking function is triggered normally.
- Upload the longest single hazard management ledger document, verify that the configuration allows complete output of the document content without truncation.
- Verify the
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTconfigurations, confirm that there are no connection errors or permission error reports. - Compare the return results of online conversations and API calls, confirm that the parameter configurations are consistent, and eliminate result differences.
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.