What data for this category looks like
Multi-financial investment research data mainly comes from public industry research reports, listed company periodic and interim announcements, private equity product net value databases, regulatory policy documents, and third-party industry datasets. Data update rhythms vary widely: industry research reports update weekly or daily, listed company announcements are disclosed in real time, and regulatory documents are updated as policies are released. Single-document length spans a large range, from hundreds of words of regulatory notices to tens of thousands of words of in-depth research reports. Document fields include product code, disclosure date, core financial indicators, investment ratings, and more. Some datasets come with standardized numerical units such as annualized rate of return and management scale.
What constraints do these characteristics impose on the conversation logging and auditing workflow
Large differences in single-document length create significant variation in the length of conversation logs generated after dialogue context splicing. Adapt sharding storage rules for logs of different lengths to avoid a single log exceeding storage limits. Uneven data update frequency requires that the latest research reports and announcements referenced in conversations be linked to accurate timestamps. The auditing link must verify the match between the release time of referenced data in logs and the actual disclosure time, to ensure the timeliness of investment research basis. In scenarios with mixed references to multi-source data, logs must fully record data source identifiers and field information to facilitate backtracking of the original basis for investment research conclusions. Coexistence of standardized numerical fields and non-standardized text requires simultaneous verification of numerical unit consistency and text content compliance during auditing, to meet regulatory requirements for traceable investment research processes.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
LOG_STORAGE_RETENTION_DAYS | 30–90 days | Multi-financial investment research auditing cycles typically cover 1 to 3 months, meeting compliance traceability requirements |
MAX_SESSION_CONTEXT_LENGTH | 8000–16000 characters | Adapt to log storage for conversations involving long research reports and announcements, prevent session content from being truncated |
ONEAPI_CALL_LOG_ENABLE | Enabled | Synchronously record requests, responses, and error information for large model calls via OneAPI, facilitate troubleshooting of abnormalities in investment research conversation generation. This parameter was officially launched in version v4.9.7 |
LOG_CLEANUP_MAX_RECORDS | 100000 records | Control the total log storage volume of a single database instance to avoid excessive database load. Adjust based on actual storage capacity |
PG_CONNECTION_TIMEOUT | 60 seconds | Prevent log loss caused by PostgreSQL connection timeouts during log storage, adapt to batch write scenarios for multi-source data |
AUDIT_DATA_SOURCE_VERIFY | Enabled | Automatically verify the consistency between investment research data sources, timestamps referenced in conversation logs and original data sources, meet auditing and compliance requirements |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material formats, data volumes, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Symptom: The aiproxypg container exits abnormally when starting with docker-compose, with logs showing connection timeouts or port occupation. Cause: The `PGCONNECTION_TIMEOUT` parameter is not configured correctly, or the default port of the PostgreSQL database is not reserved, resulting in abnormal log storage links.
- Symptom: After conversation log storage exceeds the preset upper limit, new session logs cannot be written, or old logs are accidentally deleted. Cause: The
LOG_STORAGE_RETENTION_DAYSparameter is not adjusted based on the investment research auditing cycle, or a reasonable threshold forLOG_CLEANUP_MAX_RECORDSis not set. - Symptom: After a large model call via OneAPI fails, error details cannot be found in the conversation logs. Cause: The
ONEAPI_CALL_LOG_ENABLEparameter is not enabled, resulting in failure to synchronously store call link logs.
How to confirm configuration is successful
- Access the system log management interface, randomly select investment research conversation logs from the past 7 days, confirm that each log contains a session ID, initiation time, referenced data source identifier, and complete interaction content, with no missing auditing fields.
- Run the docker-compose up -d command, check the container running status, and confirm that the aiproxy_pg container has no abnormal exit logs, and the database connection link is normal.
- Initiate a conversation that references public research reports, trigger a large model call, and check the OneAPI call log module to confirm that request parameters, return results, and error information are fully recorded.
- Manually trigger a log cleanup task, confirm that old logs that meet the preset threshold are archived or deleted according to rules, and verify that the cleanup configuration takes effect.
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.