What this category of data includes
Ledger data for power generation groups comes from unit operation systems, fuel management systems, safety supervision systems, and financial accounting systems. Data is updated daily or per shift. Each standalone ledger document is organized around a single generating unit as the core unit. It includes fields such as unit number, operating hours, power generation, coal consumption rate, maintenance duration, and number of inspections. The corresponding units for these fields are hours, megawatts (MW), tons per hour (t/h), hours, and times, respectively. Each ledger contains full operation records for a single cycle. The text length of each individual record is usually several hundred characters.
Constraints imposed by these characteristics on multi-turn dialogue and prompt engineering
Power generation group ledger data comes from multiple dispersed, independent business systems. Multi-turn dialogue must gradually align field definitions across different systems to avoid parameter confusion. Data is updated per shift or daily. Dialogue context must limit the valid historical range to prevent loading expired data and harming query accuracy. Professional fields have dedicated units. Prompt engineering must clearly define the standard meanings of fields to prevent the AI from generating summary results that do not meet industry standards. Individual ledger entries include multi-dimensional operation indicators. Multi-turn dialogue must gradually guide users to clarify query scopes to avoid response anomalies caused by excessive context load.
How to configure settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
messages | Retain the most recent 20 conversation messages | Multi-turn interactions for power generation group ledger queries typically do not exceed 20 turns. This setting balances context completeness and token usage, and adapts to the context processing logic of FastGPT v4.17.0. |
variables | Bind two variables: unit number and statistical cycle | Power generation group ledgers are categorized by unit and cycle. Variables can quickly narrow query scopes, reduce invalid data recall, and improve summary efficiency. |
chatId | Generate independent chatId based on unit + cycle | Ledger data for different generating units is mutually independent. Independent chatIds can isolate contexts, avoid cross-unit data confusion, and ensure query accuracy. |
| Maximum context length | 8000–12000 tokens | The text length of a single power generation group ledger report is usually large. This range covers complete summary and analysis needs, and is calibrated based on actual testing. |
| Query optimization | Enabled | Queries from power generation group users often include colloquial expressions. Optimization converts natural language into precise field query instructions, improving recall matching accuracy. |
| Single recall entry count | Top 10 matching entries | The number of ledger entries for a single power generation group cycle is limited. 10 entries cover typical summary needs, and avoid response quality issues caused by excessive context load. |
Three common configuration errors
- Symptom: An error occurs during debug preview, with a prompt that the specified field cannot be matched. Cause: The exclusive field definitions for power generation group ledgers are not clearly stated in the prompt, causing the AI to fail to recognize professional terms such as coal consumption rate and number of inspections.
- Symptom: The maximum response token count for some queries automatically changes to 200, resulting in overly short reply content. Cause: The valid range of dialogue context is not limited. Excessive historical messages occupy most of the token quota, leaving insufficient quota to generate a complete report.
- Symptom: Newly entered ledger data cannot be added to the knowledge base during dialogue. Cause: Variables are not configured to associate with the storage path of new ledger files, or the automatic synchronization update switch for the knowledge base is not enabled, resulting in new data not being properly indexed.
How to confirm correct configuration
- Initiate a multi-turn query that includes a specific unit number and statistical cycle, and verify that the summary data returned by the AI only includes ledger content for the specified unit.
- Adjust the length of dialogue history, and observe whether the response token count stays within the preset range without abnormal reductions.
- Enter a new ledger file, initiate an associated query, and verify that the AI can call the newly entered data to generate a report that meets requirements.
- Test a colloquial query, and verify that the AI can accurately convert it into a corresponding professional field query instruction without ambiguity.
The values provided 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.