What the data for this category looks like
Professional chain industry research report data is mainly sourced from brokerage retail industry research reports, public annual reports of chain brands, and monthly operation monitoring reports from industry associations. Update cycles are divided into quarterly regular research reports, monthly operation data updates, and temporary event announcements. The document structure includes four parts: core indicator module, regional store distribution, supply chain cost breakdown, and revenue breakdown. Fields include total number of stores, per-square-meter monthly revenue, monthly revenue per single store, and regional revenue share, with units being count, yuan per square meter per month, ten thousand yuan, and percentage respectively.
Constraints imposed on model access and configuration
The data characteristics of professional chain research reports create multiple constraints for the model access and configuration process. Multi-source mixed data access scenarios require configuring cross-source data format verification rules to avoid field conflicts between public research reports and operation data. Coexisting data sources with different update cycles require adjusting the recall data time range parameters to distinguish recall cycles between regular research reports and real-time operation data. Fixed document structures paired with specialized segmented fields require configuring field mapping templates to ensure the model can recognize category-specific indicators such as per-square-meter monthly revenue and store expansion rate. Refined regional operation data increases the length of individual documents, requiring adaptation of longer context processing parameters.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
RECALL_TOP_K | Top 6-8 entries | Professional chain research reports include segmented operation indicators. Too many recall entries will cause context redundancy, while too few will fail to cover core business data |
PARSE_FILE_TIMEOUT_SECONDS | 120-180 seconds | Individual research reports often include multi-page refined tables and regional distribution data, with longer parsing time than general documents |
UPLOAD_FILE_MAX_SIZE | 50 MB | Professional chain research reports may include attachments such as store maps and supply chain reports, requiring an increased single-file upload limit |
SIMILARITY_THRESHOLD | 0.75-0.85 | Semantic similarity for specialized segmented indicators must be higher than general text to avoid recalling irrelevant general retail research reports |
AIProxy_LOCAL_MODEL_ADDR | Fill in according to the locally deployed API address | Adapt to access requirements for locally deployed large models, requiring specification of the correct service port and path |
FIELD_MAPPING_RULE | Preset retail chain field template | Quickly align category-specific fields such as total number of stores, per-square-meter monthly revenue, and single store revenue to reduce manual configuration costs |
The parameter values provided on this page are common recommendations used as a starting point for configuration. 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 configuration mistakes
- Symptom: Connection error occurs when accessing a locally deployed large model, with logs showing
404 Not Found. Cause: The API address forAIProxy_LOCAL_MODEL_ADDRis not filled correctly, with the local service port or path parameters omitted. - Symptom: The model test has no response, and the interface shows a timeout status. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and the long text parsing time of professional chain research reports exceeds the default threshold. - Symptom: Recall results include a large number of general retail research reports and fail to match category-specific indicators for professional chains. Cause:
SIMILARITY_THRESHOLDis set too low, failing to filter irrelevant documents with insufficient semantic similarity.
How to confirm successful configuration
- Enter the model access test page, upload a single segment of a professional chain research report, and verify whether the parsed fields match the preset retail chain field template.
- Initiate a simulated retrieval request, and check whether the number of returned recall entries matches the configured
RECALL_TOP_Kvalue. - Call the published API interface to verify whether the returned results include detailed information about model responses and associated documents.
- View system logs to confirm there are no data format verification errors or connection timeout warnings.
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.