What Data for This Category Looks Like
Data for specialized equipment yield rates and market quotes comes from the built-in real-time market collection module and local historical snapshot storage unit. Updates follow trading day schedules:
- Real-time refreshes yield rates for individual trading targets during market hours
- Generates a complete daily market report document after market close each day
The document structure includes: unique device identifier, collection timestamp, corresponding trading target code, daily yield rate value, intraday fluctuation range, and periodic trading snapshot fields.
Field unit specifications:
- Timestamps use East 8th Zone standard format
- Trading target codes are six-digit numeric codes
- Yield rate values use fixed decimal place formatting.
Constraints for Conversation Logging and Auditing
Since data updates per trading day and includes real-time intraday snapshots, conversation logs must be split and stored by trading session to avoid mixing cross-session interaction data. Since documents include unique device identifiers and trading target codes, audit logs must link these fields for traceability. Every interaction record must be matchable to a specific device and target. Since daily market report documents contain long-text snapshot fields, conversation context must retain full interaction content. Do not arbitrarily truncate long text segments. Since timestamps use East 8th Zone format, audit log time records must follow this time standard. This prevents audit failures caused by time misalignment.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
LOG_RETENTION_DAYS | 90 days | Matches audit compliance requirements for financial scenarios, covers a full quarterly trading cycle |
maxContext | 8000–12000 characters | Adapts to the field length of specialized equipment market quote documents, prevents forced truncation of interaction context |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses parsing time for long-cycle daily market report documents, prevents slow query errors from triggering |
DB_WRITE_TIMEOUT | 30000 milliseconds | Adapts to the latency characteristics of specialized equipment data writes, avoids log write failures |
CHAT_HISTORY_ISOLATION | Enabled | Isolates interaction logs from different devices, meets data security compliance requirements for financial scenarios |
SSE_CANCEL_SUPPORT | Enabled | Supports interrupting incomplete conversation requests, matches interaction requirements for real-time market quote broadcasts |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific scenarios require individual analysis, and it is recommended to test against your own samples before finalizing settings.
Three Common Misconfigurations
- In Docker deployments of version 4.8.21, parsing specialized equipment market quote documents repeatedly triggers
slow operationerrors. This occurs because thePARSE_FILE_TIMEOUT_SECONDSconfiguration is not adjusted, causing long document parsing timeouts that trigger MongoDB slow queries. - Chat logs from different devices are not stored in isolated locations. This happens because the
CHAT_HISTORY_ISOLATIONconfiguration is not enabled, causing interaction logs to be stored uniformly in the default directory. - Conversation logs are not fully written to the database after interrupting an SSE connection. This occurs because the
SSE_CANCEL_SUPPORTconfiguration is not enabled, causing incomplete database write requests to be discarded directly.
How to Verify Proper Configuration
- Navigate to the log management page in system settings, check if the
LOG_RETENTION_DAYSconfiguration matches the preset value. - Upload a simulated specialized equipment daily market report document, verify that parsing time does not exceed the duration set by
PARSE_FILE_TIMEOUT_SECONDS. - Initiate two conversations with different device numbers, check that chat records are stored separately by subject with no cross-contamination.
- Initiate a conversation and actively interrupt the SSE connection, check if the full interaction content of that conversation is fully saved in the database.
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.