What this category of data looks like
Data for multi-financial research reports comes from niche track reports published by securities research institutes and licensed financial data service providers. Updates are completed 1 to 2 hours after publication by securities firms. Most documents are in PDF format, with structures including title, publishing institution, publish date, core business data tables, risk warnings, investment ratings and other modules. Fields include standardized rating text, target price (unit: CNY), revenue forecast (unit: CNY 100 million), leverage ratio, non-performing ratio and other professional indicators. Some research reports contain embedded formulas and charts.
What constraints do these characteristics impose on model access and configuration workflows
The multi-source nature of multi-financial research reports requires configuring multiple data authentication rules to adapt to API protocols of different financial data platforms. Embedded tables and professional formulas require adjusting chunking logic to avoid breaking data association. Standardized financial fields and units require configuring field mapping rules to ensure the model correctly identifies the unit and meaning of indicators. High-frequency updated report content requires configuring timed sync cycle parameters to ensure the timeliness of recalled data. Long text content may exceed the context window of general models, requiring adjustment of context truncation priority to prioritize retaining core ratings and data sections.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Multi-financial research reports contain embedded tables and professional formulas. This chunk length preserves complete semantics of table rows and formulas, avoiding splitting that breaks data association |
parseTableEnable | Enabled | Core report data is presented in table form. Enabling table parsing preserves structured data and improves the accuracy of model retrieval and Q&A |
recallTopK | Top 8–12 entries | Multi-financial research reports have high core information density. Too many recalled entries exceed the model context window, while too few fail to cover multi-dimensional analysis requirements |
similarityThreshold | 0.75–0.85 | Financial research reports have high professional term similarity. This threshold filters low-relevance content while retaining precise matching results for niche segments |
syncInterval | 3600 seconds | The release cycle of securities research reports is measured in hours. This sync interval ensures the timeliness of recalled data |
toolCallStrategy | Triggered call | Research report retrieval requires precise matching of professional terms and data. Triggered calls avoid invalid tool calls and improve response efficiency |
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 case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common misconfigurations
- Phenomenon: After configuring tool call related parameters, the LLM does not trigger MCP calls, only returns general Q&A results. Reason: The tool call strategy is not adapted to the professional scenario of research report retrieval, and the general trigger rule cannot identify professional trigger words in research report scenarios.
- Phenomenon: Embedded table data is lost or formatted incorrectly in synced knowledge base reports. Reason: The
parseTableEnableconfiguration is not enabled. The default chunking logic splits the table structure and destroys the integrity of structured data. - Phenomenon: A
413 Request Entity Too Largeerror is returned when calling the model. Reason: The uploaded research report PDF file size exceeds the default limit ofUPLOAD_FILE_MAX_SIZE, and the value of this configuration item is not adjusted.
How to confirm configurations are correctly set
- Access the configuration interface of the corresponding knowledge base, check the current values of
parseTableEnable,chunkSize,syncIntervaland other configuration items to confirm they match the preset configuration rules. - Upload a multi-financial research report file containing embedded tables, perform a manual sync operation, and check whether there is a prompt for successful table parsing in the sync log. No error messages indicate that the parsing configuration is effective.
- Initiate a test query containing professional financial terms, observe whether the LLM output includes core data from the research report, or whether a tool call action is triggered.
- View the model call log records to confirm that the context length of each call does not exceed the upper limit supported by the current model, and there are no truncation or overflow errors.
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.