What the Data for This Category Looks Like
Data sources for commercial real estate intelligent due diligence reports include public ownership data from real estate registration centers, operation logs submitted by commercial operators, and public reports from third-party business district monitoring institutions. Update rhythms vary across sources: ownership data is updated quarterly, lease data is updated monthly, and surrounding supporting data is updated every two weeks.
A single due diligence report has a fixed structure: basic project information, ownership document number, 12-month lease details, quotation ranges for similar nearby projects, and property maintenance records. Fields include clearly unitized data such as building area (square meters), monthly rent (yuan), and available lease area (square meters). A single document can be dozens of pages long.
Constraints Imposed on Knowledge Base Retrieval and Recall
Data for commercial real estate due diligence reports comes from multiple dispersed sources. These include structured registration fields, semi-structured lease logs, and unstructured operation descriptions. This requires retrieval systems to support semantic matching rules for multiple document types.
Update frequencies differ widely across data sources. Lease data has strong timeliness and must be prioritized for recall to avoid using expired information. Specialized fields have clear units. Retrieval processes must match unit-related keywords to prevent semantic ambiguity.
Single documents are lengthy. When using segmented retrieval, context associations between fields must be preserved. This avoids splitting critical combinations of ownership numbers and project information.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Recall Count | Top 10-15 | Core information for commercial real estate due diligence reports is spread across multiple documents. 10-15 results cover the main ownership, lease, and supporting data while avoiding redundant outputs |
Similarity Threshold | 0.72-0.80 | Semantic matching for commercial real estate specialized fields such as building area and rent requires high precision. A threshold that is too low will introduce irrelevant data from non-similar projects |
Segment Length | 800-1200 characters | Commercial real estate due diligence reports include long paragraphs of financial calculations and ownership descriptions. Segments that are too long will lose context, while segments that are too short will split critical field combinations |
Retrieval Scope | Filter by document tags | Commercial real estate due diligence reports can be tagged by project type such as office buildings, shopping centers. Specifying a scope excludes documents from non-target categories |
Pinyin Matching Toggle | Disabled | Specialized terms for commercial real estate such as project names and ownership numbers are mostly in Chinese. Pinyin matching will introduce distracting data from homophones with different meanings |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Commercial real estate due diligence reports are often lengthy, with single documents reaching up to 50 pages. The default parsing duration is insufficient. Extend the timeout to complete full parsing |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- A large volume of non-target project entries with similar pronunciations appear in retrieval results. For example, searching for "XX Plaza" returns content related to "XX Guangchao". This occurs when the
Pinyin Matching Toggleis enabled without filtering for commercial real estate specialized terms. - Retrieval results include documents from non-target categories. For example, searching for office building due diligence data returns content about shopping centers. This occurs when tag filtering rules for the
Retrieval Scopeare not configured, and knowledge base documents are not categorized by project type. - In version V4.13.0, when importing a backup-exported knowledge base CSV file into a platform of the same version, the original multi-folder classification is lost, and all documents are merged into a single list. This occurs when only a single CSV file is imported, without using the complete multi-file backup import process.
How to Confirm Proper Configuration
- Enter commercial real estate specialized keywords such as project building area and rent per unit area, and verify that retrieval results only include documents from the target category with no irrelevant distractions.
- Review parsing logs to confirm that long documents are not abnormally truncated, and that segmented content retains complete field combinations.
- Test data sources with different update frequencies to confirm that timely lease data is prioritized for recall.
- Check plugin loading status to confirm that there are no syntax errors in database connections, and that plugins can trigger retrieval normally.
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.