What this category of data looks like
Large state-owned bank investment research data comes primarily from internal industry research reports, publicly available regulatory documents from the central bank and banking and insurance regulatory authority, periodic reports of listed companies, macroeconomic statistical data, and publicly disclosed information from peer institutions. Documents include both structured fields and unstructured text. Structured fields often cover items such as credit balances and interbank offered rates, with units including 100 million yuan and percentage. Regulatory documents have standard document numbers and clause hierarchies. Research reports include abstracts, core viewpoints, and data support modules. Update frequency varies by source. Regulatory documents update in real time upon release. Internal research reports update weekly or monthly.
What constraints these characteristics impose on model access and configuration
The mixed structure and multi-source nature of large state-owned bank investment research data creates multiple constraints for model access configuration. First, structured fields and unstructured text coexist. Configure both structured data parsing rules and unstructured text retrieval strategies to avoid single-mode failure to cover all data types. Second, update cycles vary widely across data sources. Set differentiated incremental sync trigger conditions for each data source to avoid excessive resource usage from full syncs. Third, data fields include specialized units. Configure unit recognition and verification logic for the model to ensure unit consistency in output results. Fourth, some data requires strict access permission controls. Integrate a permission verification module during the access link to restrict unauthorized access.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Investment research documents are mostly long texts containing multi-page research reports and structured data, requiring complete context coverage to support professional analysis |
chunkSize | 1500–2000 characters | Investment research documents contain a large number of professional terms and structured tables. The segment length adapts to the content structure to avoid damaging the integrity of professional content |
recallTopK | Top 8–12 entries | Investment research data is highly professional, requiring retrieval of a sufficient number of relevant documents to support analysis while avoiding interference from redundant information |
similarityThreshold | 0.75–0.85 | There are many professional terms in investment research data, requiring strict screening of highly matched content to ensure retrieved content is highly relevant to business needs |
INCREMENTAL_SYNC_INTERVAL | Regulatory data sources: 300 seconds, research report data sources: 86400 seconds | Matches the update cycles of different data sources. Regulatory documents update frequently, while internal research reports update daily, reducing invalid sync overhead |
ENABLE_STRUCTURED_PARSE | Enabled | Investment research data contains a large number of structured fields. Enabling this allows accurate extraction of key business data such as credit balances and interest rates |
The parameter values provided on this page are common recommended starting points for configurations. Actual values are affected by material form, data volume, and business rules. Address specific issues on a case-by-case basis, and test on your own samples before finalizing.
Three common mistakes
- Phenomenon: Only thought process text is returned after model invocation, with no final business results. Cause: The model's output format parameters are not correctly configured, and the requirement to generate structured business conclusions is not specified, leaving only the output from the thought process.
- Phenomenon: The iframe-embedded conversation interface displays English content. Cause: The interface language parameter is not specified in the access configuration, and the English localization configuration is loaded by default.
- Phenomenon: The model output includes
thinktags, but the thought process is not hidden from display. Cause: The model's thought process switch is not correctly enabled, or the configured tag parsing rules do not match the actual output format of the model.
How to confirm the configuration is complete
- Initiate a single test call, verify the content completeness and unit consistency of the returned results, and confirm that the output meets business expectations.
- Check the running logs of synchronization tasks to verify that the synchronization trigger intervals of different data sources match the configured requirements.
- Test the parsing effect of structured documents to confirm that the extracted fields are consistent with the structure and units of the original data.
- Check the configuration parameters of the embedded interface to confirm that the interface language matches business requirements, with no abnormal display.
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.