What the data for this category looks like
Power industry investment research data sources include real-time time-series data from grid dispatching, generator set operation logs, parameter documents from power equipment manufacturers, industry policy documents, and professional investment research reports. Update frequencies cover minute-level (grid load, voltage and current), monthly/quarterly (research report updates), and irregular (policy releases). Document structures include structured time-series tables (with fields such as timestamp, active power MW, reactive power Mvar), semi-structured research reports (with chapters and chart annotations), and unstructured policy text. Field and unit specifications follow industry standards, for example, installed capacity uses MW as the unit, and load deviation rate uses percentage values.
What constraints do these characteristics impose on the "multi-turn dialogue and prompt engineering" link?
The multi-dimensional time-series characteristics of power data require multi-turn dialogue to retain key context from previous queries, such as equipment numbers and time ranges, to prevent the model from confusing data from different cycles or devices. The minute-level updates of real-time data require prompts to clearly specify the data time interval for each call, to avoid returning outdated information. The existence of long research report documents requires limiting the size of the single-turn context window to prevent token overflow that interrupts the dialogue. The coexistence of multiple types of units requires clear unified unit specifications in prompts, to prevent the model from outputting results with mixed units.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | The length of a single power industry research report typically ranges from 5000 to 8000 characters. Combined with the need to retain key context from previous queries such as equipment numbers and time ranges for multi-turn dialogue, this prevents context overflow |
streamResponse | Enabled | Power industry investment research dialogues often require returning long-text analysis results. Streaming output reduces front-end waiting perception and aligns with the user's needs for real-time progress viewing |
contextRecallCount | Top 6–8 entries | Power data includes multi-dimensional time-series indicators. Too many recalled entries increases token consumption, while too few fails to cover complete investment research analysis dimensions |
similarityThreshold | 0.75–0.85 | Semantic similarity between power equipment parameters and policy clauses is relatively high. A threshold that is too low introduces irrelevant data, while a threshold that is too high misses relevant historical load data |
promptTemplate | "Combine the provided operating data of power equipment [equipment number] for the [time range] and the clauses of the [policy document] to answer the following question: {query}" | Clearly constrain the specific power data dimensions associated with the context, preventing the model from confusing data from different devices or time cycles |
apiTimeout | 120 seconds | Multi-turn power industry dialogues often need to call multiple data sources to generate analysis reports. A longer timeout period prevents incomplete analysis from being interrupted |
streamChunkInterval | 1000–2000 milliseconds | Adapts to the long-text output rhythm of power industry investment research dialogues, adjusting the default return interval to improve front-end display smoothness |
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 the user's own samples before finalizing the configuration.
Three common mistakes
- Symptom: When streaming output is returned, embedded links to power equipment parameters cannot open in a new page, and only overwrite the current page. Cause: The callback logic for streaming output is not configured with new window jump rules, and only default in-page jump is used.
- Symptom: After calling the dialogue API in version v4.8.10, the content of the dialogue log details does not match the actual response content, and the log still displays the first response content after multiple conversations. Cause: Versioned storage of dialogue context is not enabled, and the log interface reuses cached data from the first request.
- Symptom: The streaming output return interval is fixed at 4 seconds and cannot be adjusted to 1-2 seconds. Cause: The default configuration of the
streamChunkIntervalparameter is not modified, and the default interval is locked to 4 seconds by the platform's underlying logic.
How to confirm the configuration is complete
- Users can initiate a multi-turn dialogue that includes multiple sets of power equipment numbers and time ranges, and check whether the context fully retains key parameters from previous queries, with no context loss or confusion occurring.
- Users can enable the streaming output function to initiate a test, observe the segment interval of returned data, and confirm that the return speed can be modified by adjusting the
streamChunkIntervalparameter. - Users can view the dialogue log details, confirm that the returned content of each dialogue matches the actual output, and no cache reuse occurs.
- Users can enter a query that includes professional power units such as MW, kVar, and confirm that the model's returned results uniformly use the specified units, with no mixed unit issues occurring.
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.