What this category's data looks like
This category serves EHS departments and production inspection and maintenance teams of manufacturing enterprises in chemical, metallurgical, machinery, light industry and other industries. Daily core documents include risk identification lists, operation tickets for hot work, confined space work, lifting work and other tasks, environmental monitoring records, hazardous waste ledgers, emission monitoring data, and accident and hidden danger rectification reports. Data sources cover regulatory standards, online environmental monitoring equipment, gas detectors, operator qualification files, equipment isolation lists and supervision platforms. Data update cycles include updating operation tickets after each operation is completed, synchronizing online monitoring data in real time, and updating qualification and ledger information at fixed intervals. Document fields include operation type, operation time, guardian qualification, pollutant emission value, isolation measure list and others. Some documents are scanned copies or unstructured text.
Constraints imposed on multi-turn dialogue and prompt engineering
Data sources for this category are scattered and include multiple types such as real-time monitoring data and operation process documents. Multi-turn dialogue must coherently link context across the entire operation process, such as full-link information from operation ticket application to hidden danger rectification. Data update frequencies vary widely: real-time monitoring data must be called immediately during dialogue, while historical ledger data must be stably associated. This means dialogue context cannot expand without limit. Most documents are unstructured or semi-structured, so prompt engineering must clearly specify field extraction rules to avoid ambiguous parsing. Additionally, this category involves safety and environmental protection responsibilities, so dialogue output must strictly match regulatory and operational requirements. Prompt engineering must include verification logic to ensure the rigor of traceability results. The multi-step nature of operation processes requires multi-turn dialogue to gradually supplement information, such as first querying operation ticket information, then associating monitoring data to verify compliance.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Session history retention count | Previous 6–10 entries | Matches the length of key information for the entire operation process, avoids context overload |
| Context window size | 8000–12000 token | Adapts to the total information volume of multi-turn operation-related dialogues, covers the complete process of a single operation |
chatId binding rule | Bind the operation number for the same session | Ensures multi-turn dialogue shares context and associates all documents for the same operation |
messages parameter configuration | Concatenate historical messages in dialogue order | Ensures the correct message order for multi-turn dialogue and retains interaction logic |
variables binding scope | Associate operation tickets, monitoring records and qualification files for the current operation | Allows prompt engineering to call real-time and historical data |
| Prompt template loading rule | Load the corresponding risk identification list by operation type | Matches compliance verification requirements for different operations |
Three common configuration errors
- Setting the session history retention count to 6 prevents the model from associating results from the previous operation ticket query. The cause is failure to bind the same session via the
chatIdparameter, resulting in independent context generated for each request and inability to retain historical dialogue information. - Extracted information appears garbled from scanned operation tickets or non-standard format monitoring records. The cause is failure to perform text standardization preprocessing on unstructured documents and directly passing them to prompt parsing, leading to encoding or format recognition errors.
- No like feedback is displayed after calling the dialogue interface. The cause is failure to configure a custom interaction interface in FastGPT v4.17.0; the like function is not integrated by default, and corresponding interaction logic must be implemented independently.
How to verify successful configuration
- Initiate two consecutive operation-related queries, confirm that the model can associate the operation number and related information from the previous query.
- Pass an unstructured operation ticket document, verify that the prompt engineering can accurately extract specified field information without garbled characters or missing content.
- Check whether the
variablesparameter is bound to real-time monitoring data and qualification information for the current operation, ensuring that corresponding content can be called during dialogue. - Verify that after setting the context window size, the total token count of multi-turn dialogue does not exceed the preset limit, avoiding information truncation.
The values provided are common starting points and should be evaluated against the reader's own samples.