What data for this category looks like
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 include after each job is completed, real-time updates per monitoring cycle, and verification during regular inspections. Daily documents cover risk identification records, work tickets for hot work, confined space work, lifting work and other operations, monitoring ledgers, hazardous waste management lists, emission monitoring reports, and accident and hidden danger rectification reports. Data fields include operation type, detection parameters, qualification number, isolation status, rectification and closure status, and other items.
What Constraints These Characteristics Impose on Model Access and Configuration
Data sources for this category are scattered, update cycles vary greatly, and document structures are diverse. This creates multiple constraints for model access and configuration. Multi-source data requires adaptation to different access formats, and processing rules for static documents and real-time data streams must be distinguished in configuration. Different update frequencies require corresponding context refresh mechanisms to avoid using expired data. Document structures such as work tickets and rectification reports vary widely, so different content extraction logic must be adapted. For high-risk operation data, the context length must be strictly controlled to avoid redundancy while ensuring sensitive data is not leaked. Therefore, the values of maxContext and maxTokens must strictly match the data scale. In addition, this scenario has high accuracy requirements, so tool calling rules must be properly configured to ensure the model can accurately call compliant documents and equipment data.
How to Set Configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 10000–15000 token | This category needs to cover multiple documents for the full workflow of a single operation, to avoid information loss caused by context overflow |
maxTokens | 4000–6000 token | Inspection and maintenance Q&A and risk analysis content need to be generated, balancing output length and response speed |
| Workflow/API tool selection | auto | Allow the model to independently determine whether to call dedicated tools such as equipment isolation lists and operation specifications, to match scenario requirements |
temperature | 0.2–0.4 | This scenario requires strict adherence to specifications and data facts, reducing output randomness |
AIPROXY_API_ENDPOINT | Fill in the dedicated interface address according to the deployment environment | Adapt to enterprise internal network and data security requirements, avoid exposing sensitive data over the public network |
AIPROXY_API_TOKEN | Bind the enterprise-specific authentication key | Ensure the security of data calls and prevent unauthorized access |
Three Common Configuration Mistakes
- Phenomenon: After configuring
AIPROXY_API_TOKENor a third-party model key, the interface prompts that the model stream response is empty. Reason: The interface format of the corresponding model is not matched, or the key permission does not cover the model call scope required for this category. - Phenomenon: After configuring
toolChoiceasauto, the model does not call tools related to equipment isolation lists or work tickets. Reason: Dedicated documents for this category have not been uploaded to the knowledge base, ormaxContextis set too low, causing data required for tool calls to not be retrieved. - Phenomenon: After updating the FastGPT version, the originally configured
AIPROXY_API_ENDPOINTbecomes invalid. Reason: The proxy service version was not updated synchronously, causing changes to the interface protocol or address.
How to Verify a Successful Configuration
- Initiate a test query that includes work tickets and equipment parameters, check whether the model output includes risk identification or operation specification content exclusive to this category.
- Check the model call log, confirm that the tool call switch is enabled, and the called data source is the dedicated document uploaded for this category.
- Verify the validity of the key and interface address, confirm there are no error prompts for unauthorized access or format errors.
- After adjusting the
temperatureparameter, check the consistency of the output content from two consecutive attempts, confirm it meets the rigor required for the scenario.
The values provided are common starting points and should be measured against the reader's own samples.