What the Data for This Category Looks Like
Residential development yield-related data is primarily sourced from internal cost ledgers, sales filing systems, financing ledgers, and local housing authority project disclosure information for individual residential development projects. This data supports the material requirements for daily yield rate market reports. Data is updated monthly. Core documents are organized per single project, and include fields such as project unique identifier, land acquisition date, total construction area, salable construction area, cumulative contracted collection amount, cumulative development investment, current period financing cost, and current salable unit filing average price. Amount fields use RMB yuan as the unit, area fields use square meters, and date fields follow the YYYY-MM format.
Constraints for Multi-turn Dialogue and Prompt Engineering
Data is aggregated per single project and sourced from multiple channels. Multi-turn dialogue must retrieve the target project's unique identifier during the first interaction, otherwise accurate corresponding data cannot be retrieved. Data is updated monthly, so dialogue flows must prompt users to specify the query month range to avoid mixing cross-cycle data. Fields cover multiple dimensions including area, amount, cost, and collection, so prompts must clearly limit the scope of field calls to prevent the model from generating irrelevant data. Single project data has strong interrelatedness, so multi-turn dialogue context must retain information about the current project and query cycle to avoid repeated queries for basic information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Single project data includes multiple field types, multi-turn dialogue requires sufficient context to avoid repeated queries for basic project information |
recallTopK | Top 6–8 entries | Residential development yield data covers multiple scattered fields including cost, collection, financing, etc. A sufficient number of knowledge base fragments must be recalled to cover complete data dimensions |
similarityThreshold | 0.75–0.85 | Low-correlation general data must be filtered, only recall knowledge base content strongly related to the current project and monthly cycle |
system_prompt | Fixed requirement to first confirm project ID and query month, then call corresponding fields to generate content, only use residential development project data within the knowledge base | Residential development data is aggregated per single project and monthly cycle, mandatory model adherence to fixed interaction processes ensures data accuracy |
workflow_output_filter | Only retain output from the final AI node | Multi-node workflows must avoid displaying intermediate dialogue results, aligning with final broadcast content requirements |
apiResponseIncludeRef | Enabled | Knowledge base identifiers of referenced content must be included in returned results to enable data source tracing |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require individual analysis, and testing on local samples is recommended before finalizing settings.
Three Common Misconfigurations
- Phenomenon: The workflow displays all AI node dialogue results after running. Cause: The
workflow_output_filterconfiguration is not enabled, or not set to only retain output from the final AI node. - Phenomenon: No referenced knowledge base identifier is returned when calling the dialogue interface. Cause: The
apiResponseIncludeRefconfiguration is not enabled, or not correctly associated with the current dialogue node. - Phenomenon: When connecting to external dialogue channels, the output contains a large number of punctuation marks such as #, *. Cause: Markdown formatted content output by the model has not been adapted and filtered for the target channel, resulting in unparseable format symbols.
How to Verify Successful Configuration
- Initiate a query for single project monthly yield rate, check that the dialogue context retains project ID and query cycle information, with no repeated queries for basic information.
- Call the dialogue interface, verify that the returned results include the required reference information as specified in the configuration, confirming the configuration has taken effect.
- After configuring workflow output filtering, trigger the workflow to run, check that the displayed results only include output from the final AI node.
- Send a query request to an external dialogue channel, verify that the output contains no unadapted format symbols, confirming channel adaptation configuration has taken 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.