What the data for this category looks like
Data sources for securities research knowledge bases include public announcements of listed companies, brokerage research reports, industry association reports, and real-time market data APIs. Update schedules follow different cycles: real-time push for listed company announcements, periodic updates for brokerage research reports, and monthly or quarterly updates for industry research reports. Most document structures combine structured tables (financial data, market indicators) and unstructured analytical text. Fields include publishing entity, publish time, target securities code, investment rating, target price, and core logic. Units involved include yuan, percentage, price-to-earnings ratio multiples, and others.
Constraints on Conversation Logging and Auditing
The real-time nature, structured characteristics, and compliance requirements of securities research data create multiple constraints for the conversation logging and auditing process. High-frequency updates of real-time announcements generate large volumes of incremental indexing logs. Configure reasonable sampling and storage thresholds to avoid overloading storage resources. Interactions involving sensitive structured fields such as financial data, target price, and investment rating require retaining the correspondence between original fields and AI-generated content in logs. This meets the traceability requirements of compliance audits. For the chunking and retrieval logic of long-text research reports, logs must record chunking nodes, sources of retrieved segments, and matching scores. This allows backtracking of knowledge call links.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Individual securities research reports often exceed 5000 characters. Sufficient context must be retained to link core research report logic and conversation content |
PARSE_FILE_TIMEOUT_SECONDS | 1000 seconds | Parsing long individual research reports or batch announcements takes significant time. This setting must accommodate the actual processing duration of long documents |
Recall count | Top 10–15 entries | Securities research requires covering multi-dimensional data sources. Too few retrieved entries fail to cover complete analytical logic, while too many increase logging storage and auditing overhead |
Similarity threshold | 0.75–0.85 | Securities data is highly specialized. Low-matching irrelevant documents must be filtered out to avoid invalid retrieval records being included in audit logs |
LOG_RETENTION_DAYS | 180–365 days | Meets the requirements of financial industry compliance audits for interaction log retention periods |
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates upload requirements for large research report packages, historical announcements, and other bulk research datasets |
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 individual analysis. It is recommended to test on local test samples before finalizing settings.
Three Common Configuration Errors
- Phenomenon: After importing batch research reports or announcement packages, indexing tasks remain stuck. The console returns a
413 Request Entity Too Largeerror, or logs display the prompt "content exceeds length limit". Cause: Document chunking parameters or themaxContextconfiguration was not adjusted. The length of individual text segments exceeds the system default threshold, which interrupts the parsing process. - Phenomenon: The application's conversation details page only displays surface-level interaction content between users and AI. It does not show internal execution links such as knowledge retrieval and prompt splicing. Cause: The
ENABLE_DETAILED_LOGconfiguration item is not enabled. The system does not collect detailed audit logs. - Phenomenon: After logging into the bound MongoDB database, no conversation history or audit log records are found in the preset collection. Cause: The
LOG_MONGODB_URIparameter is not configured, or the collection name does not comply with the system default rules. Logs are not correctly written to the target storage location.
How to Confirm Configuration is Complete
- Upload a single long-text research report. Check whether the parsing task completes within the preset timeout period, and no content truncation errors occur.
- Initiate a conversation covering multi-dimensional research logic. Enter the conversation details page, and confirm that internal link information such as knowledge retrieval segments and prompt splicing processes can be viewed.
- Log into the bound MongoDB database. Check whether interaction records and audit fields for this conversation exist in the corresponding log collection.
- Adjust the similarity threshold and initiate multi-round conversations. Check whether the matching degree of retrieval results conforms to the expected threshold range.
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.