What the Data for This Category Looks Like
Securities due diligence data sources include periodic reports and temporary announcements of listed companies publicly disclosed by the Shanghai and Shenzhen Stock Exchanges, regulatory documents released by the China Securities Regulatory Commission (CSRC) and local securities bureaus, and publicly available statistical data from industry associations. Update schedules follow fixed quarterly and annual cycles for periodic reports. Temporary announcements are pushed in real time alongside major events. Regulatory documents are released alongside regulatory actions. Document structures combine structured reports and unstructured text. Structured parts include financial metrics such as operating revenue and attributable net profit. Unstructured parts cover business progress and risk warnings. Field units mostly use financial measurement units such as yuan, ten thousand yuan, and ten thousand shares. Some fields are enumeration types, such as regulatory inquiry types and audit opinion types.
Constraints Imposed on Model Access and Configuration
The multi-source mixed nature, highly varied update schedules, wide range of document lengths, and fixed measurement rules for fields of securities due diligence data impose multiple constraints on model access and configuration. Multi-source data requires differentiated parsing rules to distinguish processing logic for structured financial reports and unstructured announcement text. Real-time updated temporary announcements need short-cycle incremental sync trigger parameters to avoid resource waste from full data pulls. Long periodic reports require adjustments to context window and segmentation parameters to prevent content truncation. Fields with fixed units need extraction verification rules to ensure output metrics match disclosed units. Data source compliance verification logic must also be added to prevent unauthorized data access.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8192–16384 tokens | Single periodic securities report can reach hundreds of thousands of characters in text length. This range adapts to long context processing needs and avoids truncation of core content |
PARSE_STRUCTURED_TABLE | Enabled | Securities due diligence data contains large volumes of structured financial reports. Enabling this option accurately extracts fields and their corresponding units, avoiding errors from unstructured parsing |
RECALL_INCREMENTAL_INTERVAL | 5–15 minutes | Temporary announcements update in real time with major events. Short intervals ensure the latest announcements are recalled promptly, meeting due diligence timeliness requirements |
FIELD_VALIDATION_RULES | Match disclosed units and enumeration value ranges | Securities disclosure data follows fixed measurement rules and enumeration types. Verification prevents extraction errors for metrics and units |
UPLOAD_FILE_MAX_SIZE | 200 MB | Single annual report PDF files typically do not exceed 200 MB, adapting to size limits of mainstream disclosure documents |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing long reports takes significant time. Setting a reasonable timeout prevents task interruptions |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom:
Unexpected end of JSON inputerror occurs when calling the model. Cause: The model interface request header or request body format is not configured correctly, resulting in incomplete returned response content that cannot be parsed normally. - Symptom: When using an open-source large model to process long-text due diligence reports, the output is irrelevant to the query. Cause: The context window parameters adapted for long text are not adjusted, so the model cannot obtain complete input context and cannot associate context information to generate accurate responses.
- Symptom: After uploading a QA-format knowledge base file, the large model hits the query but outputs unexpected additional content. Cause: The output format constraint parameters for the knowledge base are not configured, causing the model to generate undefined additional content that deviates from preset output rules.
How to Verify Proper Configuration
- Upload a standard listed company annual report, check that the parsed structured fields are complete and have correct units, and verify that the configured field verification rules take effect.
- Initiate an incremental sync task for temporary announcements, confirm that the task trigger interval matches the preset parameters, and that the latest announcements are recalled normally.
- Call the model interface to test long-text input, check that the context window adapts to the input length with no content truncation.
- Simulate a QA-format knowledge base query, confirm that the model output only contains preset content with no extra redundant information.
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.