What the Data for This Category Looks Like
Footwear investment research data primarily comes from brand official supply chain documents, industry association category monitoring reports, e-commerce platform sales details, customs import and export trade data, and fabric supplier raw material quotation sheets. Data update cycles vary: supply chain cost data is updated monthly, e-commerce sales data is updated daily, and industry reports are released quarterly. Document structures mostly combine structured tables and long text, including fields such as SKU style numbers, material compositions, weight per pair, factory prices, and competitive product benchmarking parameters. Style number formats typically use brand abbreviations + quarter + serial number, and units uniformly use category-specific units such as pairs, grams, and yuan per square meter.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
The fixed format of SKU style numbers and multi-category characteristics require multi-turn dialogue to accurately track the currently discussed SKU identifier to avoid context drift. Differences in update frequencies across data sources require prompts to clearly distinguish between real-time sales data and historical document retrieval timing to prevent the output of outdated information. The presence of structured fields and specialized units requires prompts to enforce unified output formats to avoid unit confusion or missing fields. The increasing proportion of long text documents requires multi-turn context windows to adapt to long text splicing needs, preventing key information from being truncated.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Footwear investment research documents often contain long text supply chain breakdowns and competitive product benchmarking content. This range covers valid information for most single documents and avoids context truncation |
prompt_template | Fixed prefix + current SKU style number + user's original question | Footwear has a large number of SKUs. Anchoring the style number ensures that each round of dialogue accurately binds the target category to the context, avoiding confusion between parameters of different style numbers |
recall_top_k | Top 6–8 entries | Footwear investment research data has rich dimensions. Too many retrieved entries will dilute valid information. This quantity balances comprehensiveness and accuracy |
temperature | 0.3–0.5 | Investment research scenarios require precise output. This range reduces the probability of generating fictional SKU data or incorrect parameters |
multi_turn_history_strategy | Store historical context grouped by SKU | Footwear investment research often involves multi-turn questions centered on a single SKU. Grouped storage avoids context confusion across different SKUs |
global_variable_sync_mode | Dialogue-level temporary storage | SKU variables for a single dialogue in footwear investment research are only valid within the current session. This mode prevents variable conflicts across sessions |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfiguration Issues
- Issue: After modifying a global variable during multi-turn dialogue, subsequent questions do not read the updated value. Cause:
global_variable_sync_modeis not configured as dialogue-level temporary storage. The variable is only bound to the fixed initial value during the first request and is not synchronized with dialogue updates. - Issue: Unable to obtain the original content of each round of user questions, only integrated context fragments are available. Cause: The user's original question field is not retained in
prompt_template, or the single-round question extraction switch formulti_turn_history_strategyis not enabled, resulting in loss of the original question identifier in the context. - Issue: Dialogue call duration exceeds 600 seconds, and only 1 model call group is displayed. Cause: The length of
maxContextis not limited, causing long document context splicing to trigger long text processing timeouts. Context grouping for multi-turn dialogue is not split, resulting in excessive load on a single request.
How to Verify Proper Configuration
- Initiate three consecutive questions centered on a single SKU, and verify that each round of answers anchors the current SKU style number without context drift.
- View the model call log to confirm that the original content of each round of questions is correctly extracted and not overwritten or truncated by the context.
- Adjust the
temperatureparameter and verify that the rigor of the answers changes as expected, with no fictional SKU parameters or unit errors. - Test the global variable update operation to confirm that modified variables can be correctly read and applied by subsequent steps in multi-turn dialogue.
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.