What the Data for This Category Looks Like
Research reports for apparel and home textile categories come from securities firm research institute industry reports, public data from textile and apparel industry associations, quarterly financial reports and new product launch announcements from brand owners. Update schedules include monthly industry dynamic updates, quarterly core operating data updates, and real-time updates for some channel and raw material price data. Document structures include industry prosperity overviews, sales and inventory data for segmented categories, raw material price trends, policy and channel analysis, with clearly layered content. Fields include revenue growth rate, in-store sales per square meter, raw material unit price, inventory turnover days, and other metrics. Different indicators use specific units such as yuan per square meter, yuan per ton, days, and others.
Constraints on Knowledge Base Retrieval and Recall
The characteristics of apparel and home textile research reports impose multiple constraints on the retrieval and recall process. High-frequency updated dynamic data requires configuring an incremental synchronization mechanism to prevent recalling outdated content. Multiple fields with specific units require enabling field-level semantic matching rules to avoid recalling invalid data with mismatched units. The layered structure and large number of professional terms require enabling layered recall and term enhancement modules to prioritize content from corresponding segmented categories and improve matching accuracy. The relatively long content of individual research reports requires adjusting chunking strategies to avoid breaking contextual associations during splitting, while controlling single-chunk length to prevent context overflow.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Core information of a single segment of apparel and home textile research reports falls within this range, avoiding breaking the association between professional terms and context during splitting |
recall_top_k | Top 6–8 results | This category of research reports has many segmented dimensions. Too many recalled results will introduce irrelevant content, while too few will fail to cover core information |
similarity_threshold | 0.72–0.78 | A high threshold is required for professional term matching to avoid recalling research report content from non-corresponding categories |
parse_file_timeout | 300 seconds | Individual research reports may contain multiple pages of charts and tables, leading to long parsing time, avoiding mid-process timeout interruptions |
enable_field_match | Enabled | Research reports include segmented indicators with units, and field matching can improve recall accuracy |
incremental_sync_interval | Every 12 hours | Balances the timeliness of industry data and resource consumption, adapting to the update rhythm of dynamic information |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: Incomplete output content during queries, with core information truncated. Cause: The
chunk_sizeparameter is set too small, breaking the association between professional terms and context when splitting research reports, resulting in recalled fragments failing to fully cover the core content required for the query. - Phenomenon: File upload failure, with the interface displaying upload timeout or returning a 413 error code. Cause: The
UPLOAD_FILE_MAX_SIZEparameter is not adjusted to a range suitable for research report file sizes, or the uploaded file contains encrypted content that cannot be parsed. - Phenomenon: The AI generates fabricated answers even when no matching content exists in the knowledge base. Cause: The
stop_when_no_matchparameter is not enabled, or thesimilarity_thresholdis set too low, resulting in recalled irrelevant low-similarity content being used as generation basis.
How to Confirm Proper Configuration
- A standard apparel and home textile research report is uploaded. Parsed chunking results are reviewed, confirmation is made that chunking does not break the contextual association of core terms, and verification is done that the
chunk_sizevalue matches the actual document length. - A query targeting a segmented category is submitted. The number and similarity of recalled results are reviewed, and confirmation is made that the values of
recall_top_kandsimilarity_thresholdmatch business requirements. - A research report exceeding the default size is uploaded. Successful upload is confirmed, and verification is done that the
UPLOAD_FILE_MAX_SIZEconfiguration takes effect. - A query for which no matching content exists in the knowledge base is submitted. Confirmation is made that the AI does not generate fabricated answers, and verification is done of the enabled status of the
stop_when_no_matchparameter.
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.