What This Type of Data Looks Like
Joint-stock bank investment research data sources include internal in-house investment research reports, macroeconomic monitoring data, regulatory policy documents, public announcements of listed companies, peer industry research results, and more. Data updates follow different schedules. Regulatory documents are updated immediately upon release. Listed company announcements are synced with their disclosure schedule. Internal investment research documents are updated on demand. Macroeconomic data is updated daily or weekly. This data includes three document structure categories. Structured financial data tables include fields such as net profit attributable to shareholders, ROE, with attached numerical units. Unstructured research reports are primarily long-form text with chapter hierarchies. Semi-structured regulatory correspondence includes fixed fields such as document number, issuing authority, effective date, and more.
What Constraints Do These Characteristics Impose on Model Access and Configuration?
The multi-structure nature of investment research data requires model access and configuration to support both structured field parsing and long-text semantic understanding. Structured financial data with multiple fields and units requires configured field mapping rules. This avoids unit confusion or missing fields in extraction results. Set reasonable thresholds for chunking long-text research reports. This prevents semantic fragmentation that harms retrieval performance. Align vector database sync frequencies with update schedules for high-timeliness announcements and regulatory data. This avoids retrieving expired content. Investment research scenarios have strict requirements for data caliber consistency. Configure parameters to filter data sources with non-standard calibers.
How to Configure Parameters
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Aligns with the content length of joint-stock bank investment research long-text reports, ensuring a single context window covers complete core arguments |
vectorStoreRefreshInterval | Hourly | Matches the update frequency of macroeconomic data and listed company announcements to sync the latest investment research materials in a timely manner |
parseChunkSize | 1500 characters | A reasonable chunking threshold that balances the integrity of structured data fields and semantic coherence of unstructured text |
similarityThreshold | 0.75–0.85 | Balances retrieval accuracy and coverage for investment research scenarios, avoiding omission of key research report content |
UPLOAD_FILE_MAX_SIZE | 500 MB | Supports uploading single large research report collections or batches of regulatory documents, adapting to bulk import requirements for investment research documents |
parseStructuredFieldMapping | Auto-match by field name | Simplifies structured extraction configuration for financial data and regulatory documents, reducing manual mapping workload |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. Testing on internal samples is recommended before finalizing settings.
Three Common Misconfigurations
- Phenomenon: A 400 Bad Request error is returned when calling a third-party visual model. Cause: The
apiAuthKeyparameter is not configured, or the key does not have permission to call the corresponding model for investment research scenarios. - Phenomenon: Extraction results for financial fields in investment research documents are empty or have incorrect units. Cause: The
parseStructuredFieldMappingconfiguration is not enabled, and no field mapping rules for financial data are specified. - Phenomenon: Knowledge base retrieval results include expired regulatory policy documents. Cause: The
filterExpiredDocsparameter is not configured, and no automatic filtering is performed by associating the document's effective date field.
How to Verify Successful Configuration
- A structured financial data table and long-text research report are uploaded. Parsed chunk length and field extraction results are verified to confirm alignment with
parseChunkSizeand field mapping rules. - A vector database sync task is triggered. Sync duration and update frequency are verified to confirm compliance with the
vectorStoreRefreshIntervalsetting. - An investment research-related query is initiated. The similarity and quantity of retrieval results are checked, and
similarityThresholdand retrieval count parameters are adjusted to meet business requirements. - A third-party model interface is called. The format and content of returned results are verified to confirm the correctness of
apiAuthKeyand model access configuration.
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.