What This Category of Data Looks Like
Ledger data for power generation equipment primarily comes from the equipment operation and maintenance management system, manufacturing execution system, and spare parts management system. Real-time operating data updates every 15 minutes. Maintenance records are uploaded immediately upon completion. Monthly summary ledgers are updated by the 5th of each month.
Single-device ledgers contain structured fields and unstructured notes. Fields include equipment code, manufacturer, commissioning date, cumulative operating duration, single maintenance duration, and spare part replacement batch. Corresponding units are unit, hour, hour, and batch respectively. Batch summary ledger documents combine single-device ledgers from multiple devices. Individual document lengths range from hundreds to thousands of characters, with some including handwritten notes about equipment operation anomalies.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering by These Data Characteristics
Data sources are scattered across multiple systems, so multi-turn dialogue must gradually guide users to clarify filter conditions to avoid context overload from full data requests. Fields include clear units and unique device identifiers, so prompts must strictly specify field extraction rules to prevent the LLM from mixing up parameters across devices. The update rhythm differs between real-time and historical data, so multi-turn dialogue must first confirm the user’s desired time range, otherwise returned content will lack timeliness. Batch documents vary in length, so prompts must clearly specify summary granularity, while balancing structured field and unstructured note extraction to avoid missing key operation and maintenance information. Additionally, minor field differences exist across unit types, so prompts must pre-specify field priority per unit type to improve summary accuracy.
Reference only; verify against the target FastGPT release and deployment documentation.
How to Configure
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Predefine three variables: equipment code, time range, and summary granularity. Equipment code value range is a specified range. Time range options: Last 7 Days / Last 30 Days. Summary granularity options: Single Device / By Unit Type | The filter dimensions for power generation equipment ledgers are fixed. Predefined variables reduce user input errors and improve filtering accuracy |
messages | Retain the first 20 turns of historical conversation context. Single message length must not exceed 2000 characters | Batch ledger documents are lengthy. Overly long context will cause the LLM to mix up filter conditions. Limiting single message length optimizes context purity |
| Max Context Token Limit | 8000–12000 token | A single-device ledger document averages approximately 1500 characters. A batch summary of 10 devices totals approximately 15000 characters, which aligns with this token range, calibrated via actual testing |
| Single Search Limit | Top 8 results | Power generation equipment ledgers are grouped by unit type, with a maximum of 8 devices per group. Excessive search results will cause context overload |
| Question Optimization | Enabled | Vague user input such as "recent maintenance records" can be optimized to a clear "maintenance records from the last 30 days", improving extraction accuracy |
| Chunk Length | 1000–1500 characters | The average length of a single-device ledger is approximately 1200 characters. This chunk length ensures complete extraction of core information for a single device and prevents field truncation |
Three Common Misconfigurations
- Phenomenon: A connection error is thrown when calling the model, and recovery fails even after enabling a proxy. Cause: The proxy address or proxy port is not correctly configured in FastGPT's model settings, preventing a stable connection to the model service.
- Phenomenon: Equipment structure drawing images uploaded to the knowledge base cannot be displayed in conversation results. Cause: The prompt does not explicitly require the LLM to reference image annotation information, or image extraction trigger rules are not configured.
- Phenomenon: Unable to send equipment images for questioning in conversations, or unable to accurately extract keyword variables such as cumulative operating hours and spare part replacement batches from ledgers. Cause: FastGPT's image upload configuration is not enabled, or the prompt does not explicitly specify the field names and corresponding formats to extract, causing the LLM to mix up the meaning of different fields.
How to Verify Successful Configuration
- Initiate a test conversation including equipment code and time range, verify that the returned ledger data matches the filter conditions set in the predefined
variables, and confirm that the context corresponding to thechatIdmeets the configuration requirements. - View the model call log to confirm that the number of retained context turns for the
messagesparameter aligns with the configuration, with no redundant messages. - Upload a single-device ledger document to test whether the question optimization function converts vague expressions into clear filter conditions.
- Check the maximum context value in the configuration to ensure that the token count per conversation does not exceed the configured limit, with no context overflow errors.
- Test the image upload function to confirm that equipment images can be sent in conversations and that ledger query results based on the images are returned.
The values provided here are common starting points and should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.