What This Category’s Data Looks Like
Investment research data for the apparel and home textile category comes primarily from industry association public reports, brand supply chain ledgers, in-store POS sales data, fabric supplier quotation sheets, and customs import and export declarations. Update frequencies vary widely: terminal retail data is updated daily, industry monthly reports are updated weekly, and supply chain fulfillment data is synchronized in real time. Most documents take the form of structured tables and semi-structured reports, with fields including style number, fabric composition, gram weight (g/㎡), tag price (CNY per item), inventory turnover days, and customs declaration number. Some long documents include quarterly full-category sales breakdowns and regional market share details.
Constraints Imposed on Context and Token Management
The multi-structured fields, high update frequency, and long-document characteristics of this category create multiple constraints for context and token management. First, single SKU details or supply chain reports have high field density. If recall is not split by field dimension, single recalled content will occupy too many tokens, crowding out space for other valid investment research information. Second, real-time retail and fulfillment data has a high update frequency. If the context window does not have a reasonable expiration cleanup mechanism, old data will occupy token resources, preventing the latest terminal sales data from being recalled. Third, long quarterly full-category reports require retaining cross-field association logic during parsing, avoiding damage to data relevance after splitting. Otherwise, token allocation will be inefficient and recall accuracy will decline.
Configuration Settings
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the field density of structured reports and long-text reports in the apparel and home textile category, avoids token overflow caused by overly long single chunks, and retains cross-field association logic |
similarityThreshold | 0.5–0.6 | This category has a large number of SKUs and high field similarity. A threshold that is too low will recall irrelevant SKU data, while a threshold that is too high will fail to cover accurate niche category information |
recallTopK | Top 8–12 results | Balances token usage and information coverage for single-round investment research conversations, avoids context overload caused by excessive recall |
tokenLimitPerRequest | 12000–14000 characters | Matches the output token limit of most large models, avoids truncation errors, and reserves sufficient space for context splicing |
PARSE_CHUNK_OVERLAP | 100–150 characters | Retains cross-chunk field association information in long documents, avoids losing the binding relationship between SKUs and corresponding fabric costs |
UPLOAD_FILE_MAX_SIZE | 500 MB | Adapts to the file size of quarterly industry reports and large supply chain ledgers, avoids upload timeouts |
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 individual analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Setting
tokenLimitPerRequestto 16384 characters results in truncation and an "exceeded reply limit" error when output reaches 12288 characters. The cause is that the actual available tokens of the large model include additional overhead from context splicing, and no sufficient reply buffer space is reserved. - The knowledge base fails to recall target SKU data, even when
similarityThresholdis set to 0.4. The cause is that metadata annotation is not performed based on the category’s structured fields, making it impossible to match precise retrieval fields such as style number and fabric composition during recall. - Ultra-long investment research tasks time out. The cause is that a reasonable duration is not set for
PARSE_FILE_TIMEOUT_SECONDS, or the incremental synchronization mechanism is not enabled, resulting in timeout during one-time parsing of full quarterly reports.
How to Verify Correct Configuration
- Upload a quarterly industry report, check the parsed chunked content, confirm that there is a 100–150 character overlap between adjacent chunks, and that single chunks do not lose the association between SKUs and corresponding fields.
- Initiate a query comparing fabric costs across multiple brands, check that the number of recall results falls within the 8–12 range, and that the token usage of each single piece of content does not exceed 1/10 of the total context limit.
- Upload the largest-volume supply chain ledger, confirm that the upload proceeds normally with no timeout errors, and that the number of parsed chunks meets expectations.
- Manually adjust
similarityThresholdto 0.5, retrieve SKU data for the target style number, and confirm that the corresponding fabric composition and price information can be recalled.
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.