Multi-turn Dialogue and Prompt Engineering for Thermal

What the data for this category looks like

Thermal industry data primarily comes from real-time monitoring terminals at thermal substations, pipeline SCADA systems, weather stations, and user heat consumption ledgers. Update cycles cover three categories: minute-level real-time operating parameters, daily energy consumption statistics, and monthly business reports. Individual documents are mostly structured tables or time-series CSV files. They include fields such as point number, collection time, supply and return water temperature, pipeline pressure, instantaneous flow, and cumulative heat consumption. Units include multiple measurement standards such as ℃, MPa, m³/h, GJ.

Constraints imposed by these characteristics on multi-turn dialogue and prompt engineering

The multi-time-series layered characteristics of thermal data require multi-turn dialogue to retain timestamp associations in context. This prevents cross-period data from being mixed. The structure with multiple fields and units requires prompt engineering to clearly specify field priorities and measurement rules. This stops the model from mixing parameters with different units. High-frequency real-time collected data requires the dialogue link to prioritize recalling monitoring data from the latest time period. This avoids using outdated historical data. The fixed field format of structured documents requires prompt engineering to limit the output structure of recalled content. This ensures returned parameters correspond one-to-one with business fields.

How to Configure Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	Previous 12–18 rounds of dialogue context	Thermal investment research questions mostly focus on time-series changes of a single point or area. Excessive context will interfere with the association logic of core parameters
`Recall count`	Previous 6–10 entries	Thermal data has dense fields. A limited recall volume prevents the model from confusing monitoring parameters of different points
`Similarity threshold`	0.78–0.82	Structured thermal data has clear matching characteristics. This interval filters irrelevant data while retaining valid matching results
`Citation Content Template`	`[{point_id}] {collection_time}: Supply Temp {supply_temp}℃, Return Temp {return_temp}℃, Pressure {pressure}MPa`	Clarifies the display format of structured fields, making it easier for the model to associate point and time information in dialogue
`Citation Template Prompt`	Please answer based on the provided thermal monitoring data, prioritize using the latest collected parameters	Strengthens the model's priority judgment of real-time data, adapting to the timeliness requirements of thermal industry investment research
`stream`	Select based on scenario, must match business logic during API calls	Adapts to output format requirements of different dialogue scenarios, conforming to parameter combination specifications for actual calls

The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, data volume and business rules. Each scenario requires individual analysis. It is recommended to test on your own samples before finalizing values.

Three Common Mistakes

Phenomenon: After configuring the Citation Template Prompt prompt, recalled content does not display in the expected format. Cause: The Citation Template Prompt is mistakenly mixed with global system prompts, and its sole purpose of formatting knowledge base recalled snippet content is not clarified.
Phenomenon: When calling the API with stream: false and detail: true, the returned result lacks the point number field of thermal data. Cause: The target thermal knowledge base is not bound in the knowledgeBaseIds parameter of the API request, so knowledge base recall is not triggered.
Phenomenon: In the open-source version v4.9.14, concurrent calls return a 503 Service Unavailable status code. Cause: The default concurrency limit of v4.9.14 is adapted for general scenarios. Structured data recall for thermal knowledge bases consumes more computing resources, exceeding the default threshold.

How to Confirm Configuration is Complete

Initiate a single-turn dialogue test, input a question that includes a specific point number and time, check whether the returned result includes the corresponding thermal parameters of the point.
View the dialogue log, confirm that the context rounds retained by the maxContext parameter fall within the preset range, and no irrelevant historical conversations cause interference.
Call the API interface and pass the detail: true parameter, check whether the returned sourceDocuments field includes matching thermal knowledge base documents.
Gradually increase the number of concurrent calls, observe whether a 503 error occurs, and use this to determine the concurrency limit adapted for the current thermal knowledge base.

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.