What the data for this category looks like
Financial market and yield data for the apparel and home textile sector comes from A-share textile and apparel sector public financial report APIs, domestic textile raw material trading public data sources, and brand retail monitoring databases. There are two update cycles: raw material price data is updated daily, and daily monitoring data derived from listed companies' revenue is updated daily. Documents are grouped by sub-category. Each data entry includes a category identifier, transaction/sales price fields, statistical cycle, and unit profit fields. Fields include category name, unit price (yuan/unit), weekly sales volume (units), unit profit (yuan). Units are yuan, units, meters, and days. No percentage-based statistical values are included.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Data is split into daily raw material data and daily/quarterly derived yield data. Multi-turn dialogue must clearly distinguish between data type and statistical cycle. Prompts must require the model to verify that the current dialogue's statistical cycle matches the data type before each response. Fields include prices, sales volumes, and profits with different units. Prompts must require the model to annotate corresponding units in responses to avoid confusion. There are many sub-categories. Multi-turn dialogue must bind the currently focused category context to prevent cross-category query errors. Data source field names vary. Prompts must configure unified field mapping rules to reduce model understanding costs.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | First 1000–1200 characters | Apparel and home textile financial data has rich fields. Excessively long context causes the model to lose core category and cycle information. This range covers complete cycle data for a single category. |
retrieval_top_k | Top 5–7 entries | There are many entries for market and yield data. Too many retrieval results distract the model. This range covers key daily data for core categories. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Apparel and home textile data requires integration of multi-source API content. 300 seconds covers the time required for multi-source data pulling and format organization. |
system_prompt | Fixed to include category, cycle, and unit verification rules | There are many sub-categories of apparel and home textiles. This forces the model to verify that the current dialogue's category and statistical cycle match before responding, avoiding cross-category confusion. |
similarity_threshold | 0.75 | Distinguishes market data for different categories. 0.75 accurately matches the sub-category corresponding to user queries, avoiding data misalignment. |
The parameter values provided on this page are general recommendations used as starting points for configuration. Actual values are affected by material form, 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 Mistakes
- Phenomenon: The top title of the embedded mini-program dialogue page is modified by users and cannot be restored. Cause: The
page_titleparameter was not locked in the configuration, allowing front-end custom logic to override the system preset title. - Phenomenon: The AI response exceeds the information range limited by the prompt, and additionally mentions fields not listed in the rules. Cause: The system prompt did not explicitly bind the exclusive field verification for the apparel and home textile category, causing the model to generalize beyond preset limits.
- Phenomenon: The dialogue context loses the currently focused category information, causing subsequent questions to fail to associate with previous market data. Cause: The
maxContextparameter was set too short, or the prompt did not enforce context retention rules for the current category.
How to Confirm Proper Configuration
- Initiate a query that specifies a target apparel and home textile category and statistical cycle, then verify that the AI response accurately matches the corresponding data fields and units.
- Attempt to modify the top title of the dialogue page via front-end interaction, then confirm that the operation cannot override the system preset title configuration.
- Initiate consecutive cross-category queries, then verify that the AI can correctly distinguish market data for different categories without field or unit confusion.
- Trigger a data pull timeout scenario, then confirm that the system returns error messages that comply with the configured timeout rules, with no abnormal interruptions.
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.