What this category’s data looks like
Industrial park yield rate data primarily comes from park operation management systems, public monitoring ledgers from local industrial park management committees, and monthly operating reports of settled enterprises. Data is updated on a monthly cycle, aligned with the cycles of rent settlement and operating cost accounting. A single data document includes fields such as unique park identifier, list of settled entities, leased area per entity, monthly actual rent collected, public area operation and maintenance costs, property costs, and current distributable revenue amount. Leased area is measured in square meters. Actual rent collected is measured in yuan. Operation and maintenance costs are measured in yuan. Distributable revenue amount is measured in yuan.
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering
The monthly update cycle of industrial park data requires that multi-turn dialogue must limit query time ranges to settled natural months. Real-time daily revenue queries are not supported. Data fields are numerous and have complex interrelationships. Prompts must clearly specify filtering conditions such as park identifier and tenant scope. This prevents the model from mixing data from different parks or tenants. The multi-tenant detailed document structure requires that dialogue context retain previously specified park and dimension information. This supports follow-up questions about individual tenant revenue proportions, cost breakdowns, and other detailed queries. Data sources include internal ledgers and external monitoring data. Prompts must restrict the model to only use annotated data within the knowledge base. External unverified information must not be included.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Industrial park data includes multi-tenant details. Single-turn dialogue context must accommodate 3+ follow-up questions about parks, tenants, and revenue dimensions to avoid context overflow |
recall_top_k | Top 6 entries | A single park data document contains multiple tenant fields. Sufficient associated documents must be recalled to cover all monthly data for the specified park, preventing information gaps |
prompt_template | Extract corresponding revenue data from the knowledge base according to the user-specified park identifier and time range, split and summarize distributable revenue by tenant dimension, only use annotated data within the knowledge base, do not add external information | Industrial park data has numerous fields and complex relationships. The model's information source and output format must be clearly restricted |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Park data documents may contain large numbers of tenant details. Parsing takes longer, so the timeout period must be extended to avoid parsing failures |
enable_multi_round | Enabled | Users need to ask follow-up questions about different tenants and cost items for the same park. Multi-turn dialogue can retain contextually associated information |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Each situation requires specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Symptom: In the published dialogue window, LaTeX-formatted revenue calculation formulas only display raw code, but work correctly in the debug preview. Cause: The LaTeX rendering rule was not specified in the
prompt_template, or the knowledge base Markdown rendering switch was not enabled. - Symptom: The model mixes revenue data from different parks during multi-turn dialogue, and the output does not match the specified park. Cause: The prompt did not enforce binding to the user-provided unique park identifier, causing the model to call irrelevant documents.
- Symptom: After inserting an AI dialogue node into a workflow, intermediate results are appended to the final output. Cause: The "Output to context" switch for this node was not turned off, or the node's output filtering rules were not configured.
How to confirm configurations are correctly set
- Enter the debug preview interface, enter a revenue query question for the specified park, and verify that the output only includes annotated data within the knowledge base, with no external supplementary information.
- Initiate 3+ consecutive follow-up questions, such as first querying overall revenue, then asking about the revenue proportion of a single tenant, and verify that the model retains contextually associated information.
- Upload a test park data document, wait for parsing to complete, and verify that the parsed fields match the preset structure.
- After publishing the application, access it using an external device, and verify that LaTeX-formatted content renders correctly, with no raw code displayed.
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.