What the Data for This Category Looks Like
Data sources for this category include public quotation systems from domestic textile fabric wholesale markets, point-of-sale ledgers from branded home textile brands, and ex-factory price reports from upstream raw material suppliers. Update cadence: raw material data updates daily, terminal sales data syncs weekly, and daily market summary documents are generated per calendar day. Documents use a structured table format, with fields including category identifier, specification parameters, pricing benchmark, transaction base, and latest transaction price. Field units are physical pricing units such as yuan/square meter, yuan/kilogram, piece, with no additional converted units.
What Constraints Do These Characteristics Impose on Model Access and Configuration?
This category's data characteristics impose three constraints on model access and configuration.
- Adaptation to multi-source heterogeneous data: This category has both structured wholesale market quotation sheets and unstructured sales ledgers. Configure rules that support multi-format data source access to prevent data reading failures.
- Trigger constraints from differentiated update cadences: Raw material data updates daily, and terminal sales data syncs weekly. Configure independent scheduled trigger parameters for different data sources to avoid calling datasets that have not completed updating.
- Diversity requirements for fields and units: Naming of category specification parameters varies, and physical pricing units include multiple forms. Configure field mapping and unit normalization rules to ensure the field information read by the model corresponds accurately.
How to Set the Configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
dataSourceType | ["structured_table", "spreadsheet"] | This category's data includes structured quotation sheets and sales ledgers; supporting multi-type data source access covers all data sources |
fieldMappingRule | Map in the hierarchy of "category identifier → specification parameters → pricing benchmark" | Naming of this category's fields varies; hierarchical mapping ensures the field information read by the model corresponds accurately |
scheduleTriggerCron | 0 8 * * * | Daily reports for this category are completed before 7 AM daily; triggering 1 hour in advance ensures complete same-day data is read |
unitNormalizationSwitch | Enabled | This category has multiple physical pricing units; enabling this switch unifies the pricing units read by the model to avoid calculation errors |
dataMergeTimeout | 300 seconds | This category's multi-source data merge requires handling cross-platform API requests; a 300-second timeout covers most merge scenarios |
toolCallModel | Lightweight model that supports function calls | This scenario frequently calls field mapping and unit normalization tools; a lightweight function call model can improve response speed |
The parameter values provided on this page are all common recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis; it is recommended to test on your own samples before finalizing.
Three Common Misconfiguration Mistakes
- Symptom: Empty results returned during tool calls or specified tools not triggered. Cause: The
toolCallModelparameter is not configured correctly; a basic model that does not support function calls is selected, which cannot recognize tool call instructions. - Symptom: Disorganized numerical values or unit mismatches during field reading. Cause:
unitNormalizationSwitchis not enabled, andfieldMappingRuleis not configured, causing the model to confuse pricing units and field meanings across different categories. - Symptom: Unupdated data returned after a scheduled task triggers. Cause: The trigger time configured in
scheduleTriggerCronis earlier than the completion time of the daily report generation, causing the model to read incomplete temporary data.
How to Verify Successful Configuration
- Manually trigger a data access process and check whether the read fields match the configured mapping rules.
- View the scheduled task execution logs to confirm that the trigger timing matches the completion time of the daily report for the current day.
- Submit test data containing different pricing units to confirm that the unit normalization function works correctly.
- Initiate a tool call request to confirm that the model can correctly trigger the preset field processing tools.
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.