What Data for This Category Looks Like
Data related to commercial real estate yield is sourced from property ERP systems, lease management ledgers, and local real estate filing platforms. Data is organized per individual commercial property project, with operational summary information from the previous natural day updated daily. Each data entry includes fields such as unique project identifier, total leaseable area (square meters), total actual rent received (CNY), total operating costs (CNY), number of leased units, total number of units, and report date. All field values are structured numerical or standardized text, with no nested complex formats.
Constraints Imposed by These Characteristics on Deployment and Upgrade
Since data is sourced from multiple heterogeneous systems including property ERP and lease ledgers, configure multi-source data synchronization links during deployment, and ensure compatibility with different systems' interface formats and permission verification rules. Set scheduled task scheduling precise to the hour level to meet the daily update frequency requirement, avoiding data delays or duplicate synchronization. Preset standardized field mapping rules during deployment, as each data entry includes multiple numerical fields, to ensure consistent dimensions for yield calculations. For upgrades, ensure compatibility with new business type and business district association fields, and reserve expansion space for vector indexes to avoid retrieval delays as data volume grows. Adjust sharding strategies for batch ingestion of structured data to adapt to the single submission size of single-project data.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
SYNC_DATA_INTERVAL | 3600 seconds | Matches the daily update rhythm of commercial real estate daily reports. Hourly synchronization covers data delay requirements and avoids frequent requests to source systems |
PARSE_FILE_TIMEOUT_SECONDS | 1200 seconds | Adapts to parsing time of multi-source heterogeneous data, prevents task interruptions during batch data synchronization |
VECTOR_INSERT_BATCH_SIZE | 50–100 items per batch | Adapts to the scale of commercial real estate batch data, prevents database connection timeouts caused by overly large single submissions |
EMBEDDING_BATCH_SIZE | 16–32 items | Reduces the single request load of embedding models, adapts to the feature of multiple data fields in commercial real estate data |
RECALL_TOP_K | Top 8–12 items | Balances retrieval accuracy and computing costs, matches the number of association dimensions for commercial real estate projects |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to the single-file size of commercial real estate operational reports, prevents parsing failures for large files |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Errors
- Phenomenon: Disk read/write rate remains consistently high after container startup, with a large amount of disk IO occupancy generated in a short time. This issue is particularly noticeable when incremental synchronization is not enabled in FastGPT v4.9.3. Cause: No reasonable value is configured for
SYNC_DATA_INTERVAL, or the incremental synchronization logic is not enabled, resulting in repeated synchronization of full data. - Phenomenon: Vector database fails to start, and a PostgreSQL connection error prompt appears in the logs. Cause: A vector database deployment configuration file containing PostgreSQL dependencies is used incorrectly, and it is not replaced with a pure vector database configuration adapted to the current environment.
- Phenomenon: Yield calculation results are empty or have abnormal values. Cause: Standardized field mapping rules are not preset, resulting in inconsistent field names from different source systems, making it impossible to correctly extract core calculation fields such as rent and costs.
How to Confirm Proper Configuration
- Check the running logs of data synchronization tasks to confirm that the synchronization cycle matches the preset
SYNC_DATA_INTERVALvalue, and only incremental data is synchronized. - Perform a batch ingestion test for single-project data, observe the response status of the vector database, and confirm there are no connection timeout or sharding abnormal error messages.
- Manually import a standard commercial real estate operational report, and verify whether the calculated parameters after field mapping match the preset rules.
- Adjust the test value of
PARSE_FILE_TIMEOUT_SECONDSto verify whether the timeout logic can normally trigger task retries.
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.