What the data for this category looks like
Data sources for this category include regulatory standards, online environmental monitoring equipment, gas detectors, personnel qualification documents, equipment isolation lists, and supervision platforms. Daily documents cover risk identification records, work permits for hot work, confined space work, lifting operations and other types, environmental monitoring records, hazardous waste ledgers, emission monitoring data, and accident and hidden danger rectification reports. Data update frequencies are triggered each time work is conducted, real-time or updated per monitoring cycle, and regular inspections. Document fields include work time, gas concentration value (ppm), guardian qualification number, hazardous waste category, emission limit values and other items, with dedicated unit identifiers.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering link
Data sources are scattered and have diverse formats. Multi-turn dialogue must associate context from different sources to complete full-process audits. Update frequencies are real-time or triggered per single work operation. Prompts must support dynamic loading of the latest monitoring data and work permit information. Document fields have dedicated units. Prompts must explicitly require audit results that match the corresponding units. Audit veto power is high. Multi-turn dialogue must gradually confirm key risk points and qualification information to avoid omissions that lead to safety incidents.
How to set up configurations
These configurations are adapted for FastGPT v4.17.0:
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to work permit number | Ensure each audit conversation is associated with a unique work scenario, avoiding context confusion |
messages | Retain the first 20 conversation records | Adapt to the multi-step confirmation workflow of work audits, covering risk identification, qualification verification and other links |
variables | Inject gas monitoring thresholds for the current work | Dynamic parameters based on real-time monitoring data, ensuring audit results meet on-site requirements |
| Maximum context length | 800–1200 characters | Adapt to the length of single documents such as work permits and monitoring records, avoiding truncation of critical information |
| Conversation timeout | 600 seconds | Cover the multi-turn confirmation duration of complex work audits, avoiding mid-session interruptions |
| Question optimization | Calibrate based on actual measurements | Adapt to professional terminology used in EHS audits, improving the precision of questions |
Three common mistakes
- Conversation logs cannot export all records. The symptom is that calling the export interface returns an empty array. The cause is failure to bind the
chatIdparameter and specify the session scope. - Uploaded images cannot be correctly interpreted. The symptom is that after uploading work permit or monitoring images, the model returns no relevant content. The cause is failure to configure the pre-prompt for visual recognition and link the on-site audit image recognition requirements.
- Conversation context is lost. The symptom is that even after increasing the context length, the model cannot recall previously confirmed qualification information. The cause is failure to limit the injection scope of
variables, causing irrelevant search results to overwrite work-related context.
How to confirm configurations are set correctly
- Initiate a simulated work audit conversation, check whether
chatIdis bound to the work permit number, and confirm that the session is associated with a unique work scenario. - Upload a work permit or monitoring image, initiate a targeted question, and check whether the model can identify the work type and parameter information in the image.
- Adjust the context length, then initiate multi-round qualification verification questions, and check whether the model can recall previously confirmed personnel qualification information.
- Call the conversation export interface, verify that all conversation records for the corresponding
chatIdcan be retrieved.
The values provided are common starting points and should be measured against the reader's own samples.