What the Data for This Category Looks Like
The data for property management intelligent due diligence reports mainly comes from property project archives filed by housing and construction authorities, daily operation and maintenance ledgers of property enterprises, regular inspection reports of public facilities, and owner complaint registration records. There are two data update cycles: daily operation and maintenance data is updated daily, while project archive data is synchronized quarterly or annually. A single due diligence document usually includes fields such as basic project information, property type, building area, facility and equipment list, maintenance cycle records, and arrears ledgers. Field units mostly use industry-standard metrics such as square meters, person-times, and yuan.
What Constraints Do These Characteristics Impose on Knowledge Base Retrieval and Recall
Multi-source heterogeneous data structures require retrieval to support both structured field matching and unstructured semantic recall, preventing key project information from being missed by single recall logic. Differences in update cycles create different timeliness weights for incremental operation and maintenance data and archived baseline data. Priority must be differentiated for the two data types during the recall phase. Multi-field document structures can cause ambiguous field values, so field-level filtering rules must be added to narrow the retrieval scope. Time-series maintenance records must support recall sorted by timestamp, to ensure the latest operation and maintenance data is returned.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall count | Top 10-15 | Property management due diligence documents contain multi-dimensional fields. Too many recall results increase context redundancy, while too few fail to cover all critical information |
similarity threshold | 0.75-0.85 | In mixed scenarios of structured field matching and semantic recall, this threshold balances precision and recall rate, preventing irrelevant operation and maintenance records from being recalled |
chunk length | 800-1200 characters | A single due diligence document includes associated field content. This chunk length preserves contextual logic and avoids losing field association information after splitting |
incremental sync interval | Once daily | Daily operation and maintenance data is updated daily. This interval ensures the latest owner complaints and maintenance records are added to the knowledge base in a timely manner |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Due diligence documents for large property projects have large data volumes. This timeout duration allows complete document parsing and field extraction to complete |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test against your own samples before finalizing settings.
Three Common Configuration Mistakes
- Symptom: After restarting the Docker container, the knowledge base list and workflow configuration are empty, but the API interface returns normal results. Cause: Local storage volumes were not mounted, so knowledge base metadata and document cache inside the container are lost when the container is destroyed.
- Symptom: Retrieval results include fixed preset system response fragments. Cause: The default knowledge base Q&A pre-prompt configuration was not disabled, causing the prompt to be mixed into the recall context.
- Symptom: Knowledge base retrieval response times out, returning status code
504 Gateway Timeout. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted, and large document parsing timeout caused retrieval link blockage.
How to Verify Successful Configuration
- Upload a test property management due diligence document, check if the parsed fields fully match the preset structure, and confirm that the
chunk lengthandincremental sync intervalconfigurations are active. - Enter a structured search query such as "XX project 2024 maintenance records", verify that the field filtering rules for recall results are active, and confirm that the
similarity thresholdvalue meets business requirements. - Simulate a daily incremental update scenario, upload a new operation and maintenance ledger document, check if the knowledge base automatically synchronizes the latest content, and confirm that the incremental sync configuration is correct.
- Initiate a batch retrieval test, check if the response time meets business expectations, and confirm that the
PARSE_FILE_TIMEOUT_SECONDSconfiguration adapts to the document volume.
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.