What data for this category looks like
Data for multi-financial yield and market quotes comes from institutional internal transaction accounting systems, third-party market data service interfaces, and regulatory-mandated disclosure platforms. Update cadence differs between exchange-traded products and over-the-counter (OTC) products. Exchange-traded products generate daily reports per natural day. OTC products update on agreed weekly or monthly cycles.
Documents use structured table format, with fields including product unique identifier, accounting date, unit net value, cumulative net value, and interval yield-related indicators. The unit for unit net value and cumulative net value is yuan. Yield indicators only show the indicator name, with no specific numerical values.
What constraints do these characteristics impose on dialogue logging and auditing
Daily high-frequency updated market daily reports generate large volumes of structured log data. Logs must be archived by accounting date to prevent single log file size from growing too large and impairing retrieval. Data calls from multiple sources require recording interface identifiers, call parameters, and returned fields in logs to ensure traceability of data authenticity during audits. Field-level metadata for structured documents must be synchronized to logs to facilitate verification of consistency between net value and yield indicators referenced in conversations and original files. Differing update cycles across products require the logging system to support filtering audit scope by product type to avoid mixing data across cycles.
How to configure the system
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
LOG_RETENTION_DAYS | 90 days | Meets the audit retention period required by financial industry regulations to ensure compliance |
PARSE_CHUNK_SIZE | 800–1200 characters | Adapts to the length of single structured rows in multi-financial market daily reports, preventing truncation of critical indicators |
MAX_CONTEXT_TOKENS | 16000–32000 | Supports retaining both historical audit logs and current market data simultaneously, avoiding context overflow |
LOG_RECORD_DETAIL_LEVEL | Full fields | Meets audit traceability requirements, fully recording parameters and return results of data calls |
UPLOAD_FILE_MAX_SIZE | 500 MB | Adapts to the typical size of daily batch market daily report files, preventing upload interruptions |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Reserves sufficient time for parsing large batch daily report files, preventing incomplete logs caused by premature timeout |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis, and testing on relevant samples prior to finalization is recommended.
Three common configuration errors
- Symptom: After the application is called, the conversation detail page only displays top-level interaction text, with no detailed logs of internal data calls. Cause: The full field recording configuration for
LOG_RECORD_DETAIL_LEVELis not enabled, only retaining surface-level interaction content between users and assistants. - Symptom: After importing a multi-financial market daily report file, the system remains in indexing state for an extended period, and background logs prompt that the document length exceeds the limit. Cause:
PARSE_CHUNK_SIZEis not adjusted to the segmented range adapted to market daily reports, causing single-segment text to be too long and triggering truncation errors. - Symptom: After connecting the configured MongoDB database, no conversation history records can be queried. Cause: The database connection parameters for log storage are not correctly configured, or
LOG_RETENTION_DAYSis set to 0, causing automatic cleanup of conversation logs.
How to verify the configuration is correct
- Log in to the FastGPT backend log management page, check whether there are conversation log entries archived by accounting date, and confirm that the archiving rules match the product update cycle.
- Upload a test multi-financial market daily report file, check the parsing progress and background logs, and confirm that the configurations of
PARSE_CHUNK_SIZEandUPLOAD_FILE_MAX_SIZEdo not trigger abnormal errors. - Initiate a market query conversation, enter the conversation detail page, check whether it contains complete records of data call interfaces, parameters and returned fields, and verify that the log recording level configuration is correct.
- Connect the configured MongoDB database, query the conversation log collection, confirm that records for the corresponding session exist, and verify that the storage configuration is effective.
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.