What the Data for This Category Looks Like
Construction machinery research report data mainly comes from public industry reports, original equipment manufacturer public materials, and third-party industry survey datasets. Update cadence includes monthly operational data, quarterly sales reports, and annual industry white papers. New product parameters are updated in real time upon release.
Document structures typically include industry overviews, segmented category data, product performance parameters, market share analysis, and policy impact content. Fields and units are as follows: sales in units, power in kilowatts, operating radius in meters, revenue in 100 million yuan, and product unit price in 10,000 yuan.
Constraints for Multi-turn Dialogue and Prompt Engineering
Construction machinery research reports are generally long, with individual reports reaching tens of thousands of characters. Multi-turn dialogue requires limiting the context window size to prevent model context overflow and loss of key information.
There are many segmented category fields and strict requirements for unit consistency. Prompts must clearly specify the corresponding relationship between different parameters to avoid the model confusing units for excavator bucket capacity and loader rated power.
Data update frequencies vary: monthly operational data and annual white papers follow different update cadences. Multi-turn dialogue needs to regularly trigger knowledge base recall to ensure the model uses the latest dataset.
Significant differences exist across research report structures. Prompts must clearly specify a fixed format for extracting fields to match the conventional typesetting logic of industry documents.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Single construction machinery research report can reach tens of thousands of characters, this range retains sufficient multi-turn dialogue context and avoids overflow |
recallCount | Top 8–12 entries | Construction machinery research reports include multiple types of segmented parameters, appropriate recall can cover key data such as sales, power, operating radius |
rerankTopN | Top 5–7 entries | Filter redundant recall results, focus on industry data highly relevant to the current query, reduce model analysis interference |
promptTemplate | Organize answers by research report field categories, clearly mark parameter corresponding units | Construction machinery has diverse parameter units, unified format avoids model confusion of parameter definitions across different categories |
stream | false | Multi-turn dialogue requires complete return of analysis conclusions and citation sources, non-streaming ensures uninterrupted content output |
apiDetailLevel | full | Requires returning complete knowledge base citation entries and corresponding fields, facilitating subsequent verification of data sources |
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 specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Phenomenon: Confusing the roles of
promptTemplateandreferenceTemplateduring configuration, resulting in returned results not correctly associating knowledge base recall content. Cause: Failing to clarify thatpromptTemplateis used to define the overall answer rules for the model, whilereferenceTemplateis used to format raw data returned by the knowledge base. - Phenomenon: Significant differences exist between online dialogue and API call return results. Citation source fields are still missing when the API enables
stream=falseanddetail=true. Cause: The API request did not correctly pass thedetailparameter configuration, or the prompt did not require the model to label citation sources. - Phenomenon: After deploying version v4.9.14, concurrent multi-turn dialogue requests trigger timeout errors. Cause: The
maxConcurrentparameter was not adjusted based on server computing power configuration, and the default concurrency threshold is lower than actual business requirements.
How to Confirm Proper Configuration
- Initiate a single-turn test query, verify that the returned result includes clear parameter units and classifications, and check whether it matches the conventional format of the construction machinery industry.
- Initiate multiple consecutive queries, check that no context window overflow error occurs, and that the model can continue analysis logic based on historical dialogue content.
- Call the API interface, check that the returned result includes knowledge base citation information corresponding to the
detailfield, which is consistent with the online dialogue result. - Adjust the number of concurrent requests, verify that the server does not trigger timeout errors, and confirm that the concurrency threshold adapts to the actual load of the current business.
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.