What Data for This Category Looks Like
Residential development investment research data sources include public land transaction information, housing and urban-rural development department filing documents, project construction logs, commercial housing pre-sale permit announcements, and industry special research reports. Data updates follow project milestones or official announcement cycles. Individual document lengths vary widely, ranging from a few pages of transfer announcements to dozens of pages of full-cycle feasibility study reports. Document fields include project location, floor area ratio, floor area, land acquisition cost, construction milestones, filing average price, and others. Units involve square meters, ten thousand yuan, floor area ratio (dimensionless), construction period (months), and others. Some documents contain nested tables and multiple version attachments.
Constraints on Conversation Logs and Auditing
Large differences in document length cause significant fluctuations in conversation log storage usage. Limit log shard size after single-document parsing. Multiple version attachments and nested tables increase the number of context-related log entries. Support tracing multi-version data by project ID during audits. Record original data units and field names in logs to avoid unit confusion during audits. Mark data update timestamps in each log entry to ensure traceable data versions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
LOG_RETENTION_DAYS | 90 days | Covers the standard audit cycle from land acquisition to project launch for residential development projects, meeting audit retention requirements |
MAX_CONTEXT_TOKENS | 16384 tokens | Adapts to context recall requirements for dozens of pages of feasibility study reports, preventing log truncation after long text parsing |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Reserves sufficient time for text splitting and log generation for multi-page documents, preventing log loss caused by parsing timeouts |
RECALL_TOP_N | Top 8 entries | Balances recall accuracy and log redundancy, covering query scenarios for core residential development investment research indicators |
AUDIT_LOG_AUTO_SYNC | Enabled | Automatically syncs all conversation interactions to the audit module, avoiding missing key records from manual exports |
SIMILARITY_THRESHOLD | 0.75–0.85 | Filters low-correlation recall results, reduces invalid log entries, and improves audit efficiency |
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 against your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: After Docker deployment, accessing the address spins and fails, with no clear error in system logs. Cause:
LOG_FILE_MAX_SIZEis not configured to limit log file size, log overflow causes service port blocking. - Symptom: After configuring a third-party robot, the third-party side shows message sending successful, but no corresponding interaction record appears in FastGPT conversation logs. Cause:
AUDIT_LOG_AUTO_SYNCconfiguration is not enabled, so interaction logs are not synced to the audit module. - Symptom: A 500 error is returned when calling the associated model, and the system logs do not capture specific error information. Cause:
LOG_LEVELis not set toDEBUG, only INFO-level logs are recorded, so exception details of underlying model calls cannot be captured.
How to Confirm Configuration is Correct
- Check the
LOG_LEVELandLOG_RETENTION_DAYSitems on the system configuration page, confirm their values match the current business audit requirements. - Initiate a conversation involving long document parsing, check whether the conversation logs contain complete text splitting and recall entries, with no truncation or loss.
- Trigger a model call, compare the FastGPT configured model parameters with the actual call parameters recorded in the logs, confirm consistency.
- Check the log list in the audit module, confirm that recent interaction records have been automatically synced, with no missing entries.
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.