What Data for This Category Looks Like
Commercial real estate investment research data sources include public district planning documents from government housing and urban-rural development departments, annual/quarterly operational reports from commercial operators, district research reports from third-party consulting firms, on-site survey project ledgers, and real-time foot traffic monitoring API interfaces. Update frequencies vary significantly: government documents are updated irregularly, operator reports are updated quarterly/annually, on-site survey data is updated monthly, and real-time API data is updated minute-by-minute. Document structures include structured fielded reports, unstructured long research reports, and standardized API response messages. Fields and units follow unified standards, such as "leasable area (square meters)", "average monthly rent (yuan/square meter/day)", and "district catchment population (ten thousand people)".
Constraints on Citation Sources and Provenance Workflow
The multi-source nature, varied update frequencies, and structured characteristics of commercial real estate investment research data impose three core constraints on citation provenance. First, structured report fields and units follow highly standardized rules. Accurately match field names and release cycles to avoid confusing same-name data with different cycles. Second, unstructured research reports are lengthy. Support for paragraph-level provenance is needed to ensure citations can be traced to specific analysis sections or data paragraphs. Third, real-time API data has strong timeliness. Record call timestamps and interface identifiers to ensure provenance information reflects data acquisition time, preventing use of outdated real-time data. Additionally, multi-source cross-validation investment research requirements mandate that provenance information clearly marks data authority levels.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
| Segment Length | 800–1200 characters | Structured report paragraphs and research report sections for commercial real estate typically range from 800 to 1200 characters. Proper segmentation allows precise location of cited content |
| Recall TopK | 10–15 entries | Commercial real estate investment research data spans multiple districts and projects. Recallsufficient candidate sources to cover core data, while avoiding redundancy |
| Similarity Threshold | 0.75–0.85 | Commercial real estate data has a high degree of field standardization. A threshold that is too low introduces irrelevant same-name segment data. A threshold that is too high may miss valid data from the same project with different cycles |
| Citation Provenance Granularity | Paragraph-level | Commercial real estate reports and operational reports require precision down to specific paragraphs or table rows. Document-level provenance does not meet requirements |
| Data Source Priority | Government public data > operator annual reports > third-party consulting reports > real-time API data | Matches core requirements of investment research scenarios for data authority and timeliness |
| Number of Citations Displayed | 5–8 entries | Investment research reports require display of core citation sources. Excessive entries disrupt reading experience |
The parameter values provided on this page are general recommendations used to establish a starting point 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
- Scenario: Setting Recall TopK to over 100 results in a large number of non-core citations. Reason: Commercial real estate data often contains same-name segments, such as "vacancy rate" fields for different projects. Excessive recall introduces irrelevant data sources, exceeding the reasonable range for provenance display.
- Scenario: Setting Similarity Threshold to 1 causes some associated structured report data to not be recalled. Reason: Structured commercial real estate data fields may have minor format differences, such as "square meters" vs "㎡". An overly high threshold filters valid data with semantic matching but format differences.
- Scenario: Attempting to cite real-time foot traffic data returned by an HTTP interface results in a system prompt "Unable to trace this data source". Reason: No provenance identifiers for HTTP data sources, such as interface address or call timestamp, are configured in the knowledge base. Specific citation source information cannot be recorded.
How to Verify Proper Configuration
- Upload a commercial real estate quarterly operational report. Trigger knowledge base recall. Check if citation provenance displays specific table rows or page numbers/positions of paragraphs.
- Initiate a query including "2024 Q3 vacancy rate for a specific district". Check if cited data sources in the response include correct release time and source type.
- Adjust Similarity Threshold to 0.7. Verify that recalled data sources cover core investment research data with no obvious irrelevant content.
- Configure an HTTP data source and initiate a real-time data query. Confirm that the response displays interface address and call timestamp as provenance information.
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.