What data looks like for this category
Data sources for this category include regulatory standards, online environmental monitoring equipment, gas detectors, personnel qualification files, equipment isolation lists, and supervision platforms. Update frequencies cover post-operation completion, real-time monitoring intervals, and regular inspection cycles. Daily documents include risk identification records, work permits for hot work, confined space work, hoisting and other operations, environmental monitoring records, hazardous waste ledgers, emission monitoring data, and accident hazard rectification reports. Fields cover operation types, detection values, qualification validity periods, isolation status, and other attributes. Units include ppm, cubic meters, dates and other professional measurement standards.
Constraints on model access and configuration
Data sources for this category are scattered and diverse. Multi-data source access adaptation must be supported. Update rhythms vary significantly across different data types. A dynamic refresh mechanism must be configured to match the needs of real-time monitoring and regular archiving. Document structures and field complexity are high. Models must recognize professional terms and units, while maintaining sufficient context capacity to cover multi-ledger summarization requirements. Data-related safety and compliance requirements are strict. Permission configuration for model calls must strictly match the access control rules of the business scenario.
How to set configurations
These configurations apply to FastGPT v4.17.0:
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | The content length of a single work permit or monitoring report is usually in the thousands of tokens, and multi-document summarization requires accommodating multiple data sets |
maxTokens | 2000–3000 token | The generated summary report must cover all key points of the ledgers to avoid truncation of critical information |
temperature | 0.1–0.3 | Ledger summarization requires accuracy, avoid generating irrelevant content, and maintain output consistency |
quoteMaxToken | 500–800 token | Extract key quoted content from a single document to avoid redundant information interfering with summarization results |
AIPROXY_API_ENDPOINT | Fill in a valid address according to the deployment environment | Adapt to cross-domain access requirements for multi-data source access |
AIPROXY_API_TOKEN | Security token generated by the deployer | Ensure the security of access permissions for model calls |
Three common mistakes
- Symptom: Environment variable configuration errors appear during workflow debugging, with a prompt that the model cannot be connected. Cause: Valid values for
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENwere not filled correctly, or a spelling error occurred (such as writing localhost as localhos). - Symptom: The model output language does not match the preset prompt, and only returns Chinese. Cause: The target language parameter was not specified in the model access configuration, the associated knowledge base language setting was not configured correctly, or the default language of the local model was not adjusted.
- Symptom: Some ledger data is missing from the generated summary report. Cause: The
maxContextvalue is too small to accommodate all accessed document content, resulting in partial data being truncated.
How to confirm successful configuration
- Check the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfiguration items on the model access page, and confirm that the values match the deployment environment. - Upload a single typical work permit document, trigger a model call, and verify that the output content fully covers the core information of the document.
- Adjust the
temperatureparameter and run another test, observe changes in output consistency to confirm the parameter takes effect. - Connect multiple ledgers of different types, and check that the summary report covers all key fields of the accessed documents.
The values provided are common starting points and should be measured against the reader's own samples.