What Data for This Category Looks Like
General equipment data primarily comes from four sources: manufacturer factory certificates, publicly available compliance reports from industry associations, equipment operation and maintenance logs, and third-party quality inspection reports. Factory data consists of static structured documents, including fixed fields such as equipment model, serial number, and rated power. Operation and maintenance logs are updated according to equipment operation cycles, with fields including cumulative operating hours, maintenance cycles, and fault records. Quality inspection reports are updated in batches, including compliance inspection items and compliance status. Most data units use international standard units, such as kW for power and hours for duration.
Constraints for Multi-turn Dialogue and Prompt Engineering
Overlapping fields and unit differences in multi-source general equipment data require continuous tracking of core equipment identifiers during multi-turn dialogue to avoid confusion. The update cycles differ between static factory data and dynamic operation and maintenance data, so prompts must clearly specify data priority to prevent the model from using outdated information. Contextual redundancy caused by long document structures requires limiting the context window during multi-turn dialogue to avoid the model failing to focus on core fields. The need for classification and organization of multiple fields requires prompts to predefine output formats to reduce manual organization costs.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContextWindow | 8000–12000 characters | General equipment due diligence reports often include multiple operation and maintenance logs and compliance documents. Excessively long context will cause the model to confuse field ownership |
systemPromptTemplate | "Please organize all provided general equipment data into three categories: [Basic Equipment Information], [Operation and Maintenance Records], and [Compliance Inspection]. Label corresponding fields and units under each category. In case of conflicts across multi-source data, prioritize operation and maintenance logs from the past three months" | General equipment data has many fields and unit differences. A fixed prompt can unify output formats and avoid confusion between information from different data sources |
retrieveTopK | Top 6 entries | General equipment compliance inspection reports and operation and maintenance log entries are numerous. Retrieving too many will increase contextual redundancy |
enableContextMemo | Enabled | Multi-turn dialogue requires tracking core identifiers such as equipment serial numbers and models. Contextual memory avoids repeated questions |
responseStreamMode | Disabled | General equipment due diligence reports require integration of results from multiple modules. Streaming output will result in messy intermediate results that do not meet report output requirements |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Errors
- Phenomenon: AI dialogue-generated content in the workflow is displayed directly in the dialog box without processing via a text splicing component. Cause: The
responseStreamModeparameter was not disabled, causing model-generated fragments to be pushed directly to the front end without triggering subsequent splicing modules according to workflow processes. - Phenomenon: The model confuses serial numbers and rated power data of different devices during multi-turn dialogue. Cause: The
maxContextWindowwas not set to limit context length, or tracking rules for core identifiers were not specified in the prompt, resulting in loss of key matching information after context overflow. - Phenomenon: The AI dialogue cannot correctly associate general equipment data retrieved from the knowledge base. Cause: Retrieval results were not organized into the standard format of "device ID + field name + field value + unit", preventing the model from matching information corresponding to the correct device.
How to Verify Correct Configuration
- Submit a test dataset containing multiple general equipment operation and maintenance logs and compliance reports, run the workflow, and check whether the final output organizes fields and units according to preset categories.
- Enable workflow debug mode, view the input parameters of the AI dialogue node, and confirm that the
maxContextWindowvalue matches the total character count of the current test dataset. - Trigger multi-turn dialogue, ask for the rated power and operating duration of different devices consecutively, and check whether the model can correctly distinguish the corresponding data of different devices.
- Pass knowledge base retrieval results organized into the standard format to the AI dialogue, and check whether the model can correctly associate information corresponding to the correct device.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.