What the data for this category looks like
Footwear financial report data primarily comes from periodic reports and temporary announcements on securities exchange disclosure platforms and listed companies’ investor relations sections. Update schedules follow regulatory requirements: quarterly reports are disclosed within 10 business days after the quarter ends, annual reports are disclosed within 4 months after the fiscal year ends, and temporary announcements are released in real time alongside business events. Most documents are in PDF format, and include consolidated financial statements and business analysis chapters. Core fields include category-specific revenue amounts, store count, total inventory, and unit production cost. Amounts are denominated in RMB, store count is measured in locations, and inventory is measured in pairs.
What constraints these characteristics impose on multi-turn dialogue and prompting
Footwear financial reports have high volumes of segmented category data, frequent temporary announcement updates, and scattered document structures. These create multiple constraints for multi-turn dialogue and prompting. Multi-turn dialogue must retain category tracking in context to avoid mixing data dimensions when users ask about different footwear segments. Real-time updates to temporary announcements require prompts to specify prioritizing disclosure documents from the last 7 days, to ensure data timeliness. Financial statements and business analysis chapters are scattered across documents, so prompts must guide models to first locate fields for the corresponding business segment, to prevent mixing cross-category data. Field units are fixed. Multi-turn dialogue must automatically match the unit type the user queries, with no extra explanation needed.
How to Set Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Footwear financial reports have multiple segmented data dimensions. Multi-turn dialogue must retain context information such as category and time to prevent data loss from context overflow |
recall_top_k | Top 6–8 results | Footwear financial reports include operating data across multiple segmented categories. A sufficient number of recalled segments is needed to cover different business segments |
similarity_threshold | 0.72–0.78 | Names of footwear segmented categories have high similarity. A threshold that is too low will recall irrelevant category data, while a threshold that is too high will miss valid related content |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Complete footwear financial report PDFs include multi-page segmented category business analyses, which take longer to parse. This avoids parsing failures due to timeout |
UPLOAD_FILE_MAX_SIZE | 50 MB | Annual footwear financial report PDF files have large file sizes. Allowing a larger upload size covers complete financial report content |
reorder_top_n | Top 3–4 results | Relevant segments from footwear financial reports are easily disrupted by category keywords. Reordering improves the display priority of highly relevant segments |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Issue: A
504 Gateway Timeouterror appears after starting a multi-turn dialogue, and the interface shows parsing failure. Cause: ThePARSE_FILE_TIMEOUT_SECONDSconfiguration was not adjusted. Multi-page segmented category data in footwear financial reports takes longer to parse, exceeding the default timeout limit. - Issue: Calling the conversation history interface only returns the most recent dialogue turn, and the complete category follow-up chain cannot be viewed. Cause: The unique session identifier parameter was not included in the request. Multi-turn dialogue for footwear financial reports requires binding a session ID to track context. Omitting this parameter causes conversation data to be truncated.
- Issue: When asking about children's footwear revenue during a multi-turn dialogue, adult footwear data is returned, resulting in category confusion. Cause: A text understanding model was not specifically selected. General models have insufficient differentiation for footwear segmented category keywords, and the prompt did not explicitly specify binding the footwear financial report knowledge base.
How to Confirm Configurations Are Correctly Set
- Upload a complete annual footwear financial report PDF, check if the parsed segments include exclusive fields such as category-specific revenue and store count, to confirm the parsing configuration is effective.
- Initiate two consecutive dialogues: first ask about athletic footwear revenue, then ask about children's footwear revenue for the same period. Check if the context retains the "same period" time dimension, to confirm the context configuration is effective.
- Call the conversation history interface, pass in the session ID, check if the complete multi-turn dialogue content is returned, to confirm the session tracking parameter configuration is correct.
- Adjust the similarity threshold value, test recall results for different footwear segmented category keywords, to confirm the recall range meets business requirements.
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.