What the data for this category looks like
Data for this category comes from EHS departments and maintenance teams in chemical, metallurgical and other manufacturing enterprises. It typically includes risk identification records, hot work permits, confined space work permits, hoisting work permits, environmental monitoring data, hazardous waste ledgers and accident rectification reports. Data sources include industry regulatory standards, online environmental monitoring equipment, gas detectors, personnel qualification files, equipment isolation lists and regulatory platforms. Updates trigger when work processes start, run in real time, follow monitoring requirements, or occur during regular inspections. Document structures mix structured forms and unstructured text. Fields include business details such as operation time, risk level, monitoring values and qualification numbers.
Constraints on model access and configuration
Multi-source heterogeneous data sources require model access to adapt to different document and device data formats. This prevents format parsing failures. Frequently updated work and monitoring data require context length configuration to cover multiple related documents. This avoids missing key traceability information. Mixed structured and unstructured document structures require model input parameters to support both text processing and field extraction. High-risk business scenarios require stable confidence levels in model outputs. Limit the randomness of generated results to avoid analysis bias caused by improper parameter settings.
How to set configurations
These configuration items are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Cover multiple documents such as work permits, monitoring records and rectification reports linked to a single batch of operations, to ensure complete traceability information |
temperature | 0.1–0.3 | Safety and environmental protection scenarios need stable analysis results. Low temperature values reduce the randomness of generated content and improve conclusion rigor |
quoteMaxToken | 2000–3000 characters | Match the standard length of single documents such as work permits and hazardous waste ledgers, to avoid truncating key business fields |
AIPROXY_API_ENDPOINT | Fill in according to the deployed proxy service address | Enable access to local or third-party models, to support multi-source data processing needs |
AIPROXY_API_TOKEN | Fill in according to the key allocated by the proxy service | Complete authentication verification for model access, to ensure valid call requests |
| Workflow/API tool selection | auto | Automatically adapt to multi-source data processing workflows, no manual tool type specification required, improving configuration flexibility |
Three common configuration mistakes
- Phenomenon: Model calls return 401 or 403 status codes, and analysis results cannot be generated normally. Cause:
AIPROXY_API_TOKENorAIPROXY_API_ENDPOINTis not filled in correctly, leading to authentication failure or unreachable proxy address. - Phenomenon: The number of job-related documents returned by retrieval is insufficient, and cannot cover the complete quality traceability chain. Cause:
maxContextis set too small, not loading enough associated documents, leading to limited recall range. - Phenomenon: Risk identification analysis results have multiple random deviations. Cause:
temperatureis set too high, and the randomness of model-generated content exceeds the rigor requirements for safety scenarios.
How to confirm correct configuration
- Upload a single hot work permit document, trigger model risk identification analysis, and check whether the returned results include risk points and protective measures at the work site.
- Call the model test interface, input the configured
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, and confirm that a 200 status code is returned. - Adjust the
temperatureparameter to 0.2, run the same hazardous waste ledger analysis task twice, and check whether the difference between the two results meets expectations. - Import multiple different types of work documents, confirm that the model can correctly extract core business fields such as operation time and risk level.
The values provided are common starting points and should be evaluated against the reader's own samples.