Conversation Logs and Auditing for Property Management

What the data for this category looks like

Property management research knowledge base data comes primarily from property project operation ledgers, public area inspection records, owner repair work orders, industry regulatory announcement documents, and property bidding archives. Update cycles vary significantly: repair work orders and real-time inspection records are updated in real time. Monthly operation reports are synced daily. Industry regulatory documents are pushed irregularly. Each document typically includes fields such as project unique identifier, region code, event type, processor ID, timestamp, and disposal result. Some documents include structured numerical values, such as repair response duration (unit: hours) and inspection frequency (unit: times/month). Also included are PDF-format property qualification documents and Excel-format operation reports.

What constraints these characteristics impose on conversation logs and auditing

Property management research data is multi-source, decentralized, and divided by project. This requires conversation logs to link project unique identifiers and user operation identifiers, to ensure audits can be traced by project dimension. Differences in data update frequencies require audit systems to support two modes: real-time log retention and batch historical data auditing. Fields include structured numerical values with units, so logs must retain original fields and units fully, to avoid information distortion from secondary conversion. Additionally, property management research involves owner privacy and industry regulatory requirements. The audit link must retain complete large model call chains, to ensure immutability and traceability.

How to set configurations

Configuration Item	Recommended Value	Rationale
`LOG_STORAGE_RETENTION_DAYS`	`180 days`	Matches the legal retention period for compliance audits in the property management industry, covering annual inspection requirements
`CONTEXT_HISTORY_MAX_TOKENS`	`8000–12000 characters`	Property management research conversations often contain long-text operation ledgers and work order records, requiring complete context retention for tracing
`EMBEDDING_BATCH_THREADS`	`2–4`	Property knowledge base vectorization primarily targets structured ledgers and long documents. Excessively high threads will trigger embedding rate limit errors. Reducing threads can alleviate throttling pressure
`AUDIT_FILTER_BY_PROJECT_ID`	`Enabled`	Property management data is divided by project, requiring audit logs to be filtered by project dimension to match the segmented requirements of research scenarios
`LOG_RECORD_INCLUDE_FIELDS`	`["user_id", "project_id", "event_type", "timestamp"]`	Property management research audits require linking operating subjects, project scope, event types, and operation times to ensure complete traceability
`WORKFLOW_GPT_ERROR_LOG_ENABLE`	`Enabled`	Property management research workflows often call large models to process compliance documents and work orders, requiring retained call error logs for troubleshooting

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 case-by-case analysis. It is recommended to test on your own samples before finalizing settings.

Three common mistakes

Phenomenon: The Human field is null in API call response previews. Cause: The user_id or human_identifier configuration in LOG_RECORD_INCLUDE_FIELDS is not enabled, causing the conversation initiator identifier to not be written to logs.
Phenomenon: 429 rate limit errors appear for vectorization tasks. Cause: The value of EMBEDDING_BATCH_THREADS is too high, exceeding the platform's rate limit threshold, and no rate limit parameters for batch requests are configured.
Phenomenon: Errors occur when calling gpt-4o-mini in a workflow, but no corresponding logs are retained. Cause: The WORKFLOW_GPT_ERROR_LOG_ENABLE configuration is not enabled, and global error log collection is not enabled. For version v4.9.0, the default status of the workflow log switch must additionally be confirmed.

How to confirm configurations are set correctly

A test conversation including a project ID and user identifier can be initiated. The log preview interface is then checked to confirm that the corresponding fields have been correctly recorded.
Batch structured documents can be submitted for vectorization. The task queue status is observed to confirm that the number of threads does not exceed the preset threshold, with no rate limit errors.
A workflow call can be triggered, and an error scenario simulated. The system log panel is then checked to confirm that the corresponding error information has been retained.
The audit configuration panel can be accessed, and AUDIT_FILTER_BY_PROJECT_ID confirmed as enabled, with audit logs filterable by project dimension.

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.