What this category of data looks like
Power grid equipment investment research data primarily comes from official operation and maintenance manuals of equipment manufacturers, electric power industry research reports, financial reports of equipment manufacturers, equipment standard specifications of power grid enterprises, and station operation logs. Update cycles adjust with the launch of new equipment models, quarterly industry research report releases, and manufacturer financial report disclosures.
Document structures include structured parameter tables (with fields such as model, rated voltage, rated current, production capacity data, etc.), fault troubleshooting procedures, industry analysis templates, and material coding lists. Fields and units follow electric power industry and financial investment research standards: voltage uses kV as the unit, current uses A as the unit, equipment models adopt fixed coding formats, and revenue data is counted in ten thousand yuan.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Structured parameters and fixed coding formats require multi-turn dialogue to strictly match field rules, to avoid errors in parameter units or model numbers. Long-text industry analysis and fault procedures must retain context to support continuous investment research analysis, and cannot truncate key steps. Non-real-time updated data sources require prompts to clearly mark the reference time range of knowledge base content, to avoid introducing outdated information. Multi-dimensional associated data (such as production capacity corresponding to models, revenue corresponding to financial reports) requires multi-turn dialogue to gradually focus, to avoid irrelevant information interfering with investment research judgments.
How to set configurations
| Configuration Key | Recommended Value | Rationale |
|---|---|---|
maxContext | 9000–11000 characters | Power grid equipment investment research documents include long-text industry analysis, financial report data, and structured parameter tables. Sufficient context must be retained to support multi-round verification |
Recall count | Top 7 entries | Equipment data has many associated fields. A sufficient number of recalled entries is needed to cover dimensions such as models, parameters, revenue, and operation records |
Similarity threshold | 0.78–0.82 | Equipment model coding rules are strict. Low-relevance general power documents must be filtered to improve the accuracy of investment research data |
conversationHistoryMaxLength | Keep the most recent 2 rounds of dialogue | Investment research scenarios mostly focus on a single device or a single type of industry analysis. Excessively long history will interfere with context understanding of current queries |
timeout | 65 seconds | Structured parameter extraction and multi-dimensional data verification processes take a long time. This avoids request interruptions mid-process |
rerankTopN | Top 3 entries | Perform secondary screening on recalled entries to focus on the most relevant industry research reports and equipment standard documents |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: After the workflow is executed, the returned result contains the dialogue text of all series-connected AI modules, not just the output of the last module. Cause: No context truncation rule is configured in the workflow node. All historical dialogue content is carried by default.
- Phenomenon: When launching an investment research dialogue for the first time, the first response delay exceeds 3 seconds. Subsequent dialogue response speeds return to normal. Cause: Session context caching is not enabled. The first request needs to reload all associated knowledge base entries and model context.
- Phenomenon: During multi-turn dialogue, the extracted equipment parameter units are incorrect. For example, rated voltage is marked as "V" instead of "kV". Cause: The prompt template does not clearly specify the standard unit rules for power grid equipment parameters, leading to deviations when the model matches fields.
How to confirm correct configuration
- Launch a parameter query dialogue for a single power grid equipment item. Check that the returned result only includes context related to the current query, with no historical redundant content.
- Record the response delay after launching an investment research dialogue for the first time. Compare the delay with subsequent dialogues to confirm that the initial delay meets expectations.
- Enter a query containing device model and parameters. Check that the parameter units in the returned result conform to the standard format of power grid equipment.
- Test a multi-module series scenario in the workflow. Confirm that the final output only retains the processing result of the last AI module.
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.