What the data for this category looks like
General equipment research report data sources include securities firm machinery sector research reports, public materials from general equipment industry associations, public disclosure documents of domestic and overseas listed companies, and industrial operation monitoring databases. Update frequency varies by data type: securities firm reports are released irregularly alongside industry developments, industry association reports are updated quarterly, and listed company announcements are released immediately based on operating milestones. Document structure includes industry overview, segmented product parameter tables, market supply and demand data, and enterprise competition landscape sections. Core fields include equipment model, rated power, monthly shipment volume, and unit production cost, with corresponding units of kW, units, and yuan/unit.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
General equipment research reports contain scattered cross-source data, structured parameters, and diverse units. This creates multiple constraints for multi-turn dialogue and prompt configuration. Cross-source data correlation requires retaining contextual entity associations to support linked follow-up questions across research reports, announcements, and association data. Structured parameters require clear prompt rules to distinguish qualitative analysis and quantitative parameter extraction, preventing confusion between parameters and analysis content. Diverse units require prompts to explicitly mark units to avoid ambiguous responses. Non-real-time updated data requires dynamic recall logic to ensure the latest industry and enterprise data is retrieved.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Single general equipment research report is typically 5000-8000 characters long; multi-turn dialogue needs to retain context for at least two follow-up questions |
recall_count | Top 6–8 results | Structured data from general equipment research reports is scattered across different documents; too many recalled results cause context overload, too few miss critical parameters |
similarity_threshold | 0.72–0.78 | Parameter queries for general equipment require high matching accuracy to avoid irrelevant industry analysis documents being included |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Large research report PDFs contain numerous tables, leading to long parsing times |
prompt_template | Answer solely based on the provided general equipment research report data, clearly label the source document type for all data, and attach units for quantitative parameters | Distinguish cross-source data, avoid fabricated content, and clarify unit requirements |
reranked_return_count | Top 3–4 results | Retain the most relevant research report content after reranking to reduce context interference |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to conduct testing with relevant samples before finalizing settings.
Three Common Mistakes
- Symptom: LaTeX-formatted parameter formulas render correctly in debug preview, but only display raw LaTeX code after publishing. Cause: Markdown rendering compatibility configuration for the publishing environment is not enabled, or the prompt does not specify LaTeX rendering rules.
- Symptom: AI dialogue node results inserted in the Workflow are automatically appended to the final output. Cause: The "Output to Context" switch for this node is not turned off, causing intermediate results to be included in the final dialogue flow.
- Symptom: After deploying using the deployment tool, AI dialogue remains in a loading state with no response for a long time. Cause: Insufficient memory quota is configured, or the knowledge base parsing process fails to start normally, leading to context recall timeout.
How to Confirm Configuration is Correct
- Initiate two or more linked follow-up questions. For example, first query the rated power of a specific equipment model, then follow up with the monthly shipment volume of the corresponding enterprise, to verify that the AI can correctly correlate relevant data for both entities.
- Enter a parameter query containing LaTeX format, verify the rendering result in the publishing environment, and adjust the rendering configuration until the expected result is achieved.
- Trigger a Workflow run, check that the final output only contains results from the target node, and confirm that the output switch for intermediate nodes is turned off.
- View the knowledge base parsing logs, confirm that the parsing time of research report documents does not exceed the configured timeout threshold, to verify that the parameter configuration takes effect.
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.