What data for this category looks like
Commercial real estate data sources include official property filing systems, internal operation ledgers, business district linkage data, and third-party industry research. Update rhythms vary significantly by data type: lease contracts update dynamically with renewals and terminations, operation logs sync daily, and passenger flow and industry data update weekly. Documents primarily use structured tables paired with unstructured descriptions, covering project location, hardware parameters, contract terms, operation records, and more. Most fields have clear units, such as square meters, yuan/square meter·month, visits/quarter, and others.
What constraints do these characteristics impose on knowledge base retrieval and recall?
The high proportion of structured fields with clear units in commercial real estate data requires retrieval and recall to support precise field matching and unit verification to avoid semantic confusion. Large differences in update rhythms across data sources require incremental indexes to be configured with different synchronization cycles based on data source type, to prevent data lag or redundancy. Long documents such as project feasibility studies and long-term lease contracts coexist with short texts such as single operation records, requiring chunking strategies adapted to different document lengths to avoid truncating critical clauses or including redundant fragments. High demand for cross-source data association requires recall results to support associating information from different sources by project ID.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Commercial real estate documents include long contract terms and short operation records. This range balances context completeness and retrieval accuracy |
recall_top_k | Top 8–12 results | Commercial real estate investment research requires balancing basic project information and multi-dimensional operation data. Too many results increase context pressure |
similarity_threshold | 0.72–0.80 | Commercial real estate data includes clear fields and units. A higher threshold prevents unrelated property projects from being included in results |
incremental_sync_interval | Lease contracts every 30 minutes, operation logs daily | Matches the actual update rhythms of different data sources, preventing data lag or redundant synchronization |
parse_file_timeout_seconds | 600 seconds | Parsing large feasibility reports or bulk lease contracts requires extended time, preventing mid-parsing interruptions |
enable_field_match | Enabled | Adapts to the structured field characteristics of commercial real estate, improving the precision of retrieval results |
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. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common misconfigurations
- Phenomenon: No matching results are returned when calling the knowledge base retrieval API. Cause: Precise matching for commercial real estate structured fields is not enabled. Relying solely on semantic recall cannot match clear project numbers or rent units.
- Phenomenon: Crashes occur in the chat interface or knowledge base management interface, with
504 Gateway Timeouterrors in logs. Cause: Theparse_file_timeout_secondsparameter is not adjusted. Parsing large feasibility reports or bulk lease contracts times out without proper handling, leading to abnormal service processes. - Phenomenon: Retrieval results do not combine other associated property operation data from the context. Cause: Cross-source data association rules are not configured. Only single-document content is recalled, and associated information is not merged by project ID.
How to confirm the configuration is correct
- Submit a mixed document of commercial real estate lease contracts and operation logs, check that chunking results adapt to document type lengths, with no critical clause truncation.
- Call the retrieval interface with a query term that includes clear units, check that returned results include matching structured fields and unit information.
- View incremental synchronization logs, confirm that different data sources complete synchronization according to preset cycles, with no data lag or duplicate synchronization.
- Trigger a large document parsing task, check that the service completes parsing within the preset timeout period, with no process crashes.
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.