What the Data for This Category Looks Like
Data sources for commercial real estate marketing content include investment management systems, footfall statistics platforms, merchant lease contract repositories, and offline event registration systems. Basic project information is updated monthly, footfall and merchant revenue data is updated daily, and event-related data is updated in real time. Data documents contain structured fields and unstructured attachments: structured fields include project ID, building area (square meters), number of settled merchants, average daily footfall (person-times), and event schedules. Unstructured attachments include investment brochures, event planning documents, and merchant qualification files. The text length of individual event planning documents varies widely, with a maximum of several thousand characters.
What Constraints Do These Characteristics Impose on Workflow Orchestration
Dispersed data sources require workflow orchestration to configure multi-source data pull nodes, adapting to authentication rules and return formats of different systems. Differences in update frequencies require workflows to set differentiated trigger cycles: daily scheduled triggers for basic project information, and real-time triggers for event data. The wide range of unstructured attachment text lengths requires configuring text processing nodes to adapt to long text splitting. Marketing content needs to associate real-time footfall and merchant revenue data, so the field mapping link must strictly match units and data types to avoid unit errors or data misalignment during content generation.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing durations for commercial real estate attachments such as investment brochures and event planning documents typically fall between 2 and 5 minutes. 300 seconds covers normal parsing requirements |
Chunk size | 1000–1500 characters | Effective information length per segment of commercial real estate marketing-related documents mostly falls within this range, adapting to context windows for AI generation |
Scheduled trigger interval | Daily 09:00 | Commercial real estate basic project information updates daily. Triggering in the early morning allows access to the latest data from the previous day |
Recall count | Top 3 entries | Commercial real estate marketing content requires precise matching of core project information. Excessive recall leads to redundant context |
Similarity threshold | 0.75–0.85 | Used to filter knowledge base content matching marketing themes. This range balances precision and coverage |
Field Mapping Validation Toggle | Enabled | Commercial real estate data includes fields with units such as building area (square meters) and average daily footfall (person-times). Validation prevents data misalignment |
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. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: The workflow throws a
Cannot convert undefined or null to objecterror during runtime, and logs for the corresponding node show empty output fields. Cause: No null fallback logic is configured for multi-source data pull nodes. No default values are filled when partner system APIs return empty data, leading to type conversion errors in subsequent splicing links. - Phenomenon: AI dialogue node generation content is displayed directly on the frontend page without output through a specified text splicing link. Cause: No text splicing component is added to the workflow, and no frontend output filter parameters are configured, leading to direct exposure of AI-generated content.
- Phenomenon: Knowledge base retrieval results passed to the AI dialogue node cannot correctly associate referenced content. Cause: Retrieval results are not passed in standard format, so AI cannot identify referenced knowledge base snippet information.
How to Confirm Proper Configuration
- Trigger the workflow once, check log outputs for each node, and confirm that multi-source data pull nodes return fields containing expected project information and corresponding units.
- Run the text segmentation node, and confirm that segmented text lengths match the preset segmentation parameter range.
- Submit a test request, and confirm that AI dialogue node generated content associates correct knowledge base retrieval results and real-time data fields.
- Call the workflow information retrieval API interface, and confirm that the returned workflow name matches the currently configured name.
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.