What the data for this category looks like
Data for IT service investment research knowledge bases comes primarily from public industry research reports, listed companies’ periodic financial reports, industry chain research meeting minutes, regulatory policy documents, and third-party industry databases. Update rhythms vary significantly across sources: research reports are updated weekly or daily, financial reports quarterly or annually, and policies are released alongside regulatory developments.
Documents include structured financial fields such as revenue and price-to-earnings ratio, semi-structured research report sections, and unstructured research content. Fields include enterprise code, report date, investment rating, target price, and more. Units include RMB yuan, percentage, multiple, and others.
What constraints these characteristics impose on model access and configuration
Fixed formats for structured financial fields require configuring field mapping rules during model access to avoid disorganized parsed data. Data sources with multiple update rhythms need incremental sync trigger logic configured to adapt to different source update cycles. Long documents such as 10,000-word research reports require segment parameter configuration to fit the model’s context window limits. When multiple types of data sources are accessed together, differentiated parsing templates must be configured to distinguish structured fields from unstructured content, while also being compatible with the interface format requirements of different data sources.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | Adapt to segment processing of 10,000-word investment research documents, prevent model context overflow |
chunkSize | 1000–1500 characters | Balance single-segment information density and model parsing efficiency, fit the paragraph structure of investment research documents |
retrievalTopK | Top 8–12 entries | Cover multi-dimensional investment research data, avoid missing key logic due to insufficient recall |
similarityThreshold | 0.75–0.85 | Filter low-correlation industry data, retain content highly matched to investment research themes |
requestLogEnable | Enabled | Facilitate troubleshooting of parameter exceptions during model calls, meet log troubleshooting requirements |
apiTimeout | 300 seconds | Adapt to time requirements for investment research document parsing and model calls, prevent long tasks from timing out and being interrupted |
The parameter values provided on this page are common recommended starting points for configuration work. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: After model invocation, full logs of request parameters cannot be viewed, making it impossible to locate parameter configuration errors. Cause: The
requestLogEnableconfiguration item is not enabled, and the request log recording function is not activated. - Symptom: When calling WeChat Official Account access configuration, the token field entry cannot be found. Cause: The authentication parameter module in the cloud space configuration page was not searched, and the configuration paths of general API and Official Account access were confused.
- Symptom: Parameter errors are continuously reported when calling multimodal models. Cause: The required field format for multimodal input is not configured correctly, or the specified model version parameter is not passed.
How to confirm configurations are correctly set
- Perform a single small-document parsing test, check if the parsed fields match the data source, and confirm that the segment and field mapping configurations take effect.
- Initiate a model invocation request, check if the console logs contain complete request parameters and return results, and confirm that the log configuration takes effect.
- Simulate data source synchronization tasks with different update frequencies, check if the incremental sync logic triggers according to the preset cycle, and confirm that the update configuration takes effect.
- Call the interface of the specified model version, check if the return result format matches expectations, and confirm that the model version and parameter configurations are correct.
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.