What This Category of Data Looks Like
Investment research data for textile manufacturing comes primarily from production workshop ledgers, fabric/yarn test reports, supply chain order systems, industry association capacity statistics, and equipment operation logs. Data update rhythms fall into three categories: production batch data updates daily, monthly capacity reports update per calendar month, and industry trend reports release quarterly. Most documents use structured fields, including batch number, yarn count, gram weight, pass rate, equipment model, order number, and more. Units include meters, kilograms, counts, and others. Some long documents contain continuous sequential records of equipment operation.
What Constraints These Characteristics Impose on Knowledge Base Retrieval and Recall
A high proportion of structured fields with clear core fields requires retrieval to support both keyword matching and precise numerical field matching, preventing irrelevant data from single-keyword recall. Frequent production data updates require the retrieval system to support incremental index synchronization, avoiding data lag that harms investment research accuracy. Strong correlation between single-batch data requires recall results to link corresponding order, equipment, and other context information, avoiding the return of only isolated text fragments. Long sequential log documents have considerable length, so segment retrieval must retain field association relationships, preventing core business information loss after splitting.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall_count | Top 10-15 entries | Textile manufacturing investment research data has a large number of entries; excessive recall results increase screening costs for investment research personnel |
similarity_threshold | 0.72-0.85 | Structured field matching requires high precision to avoid recalling low-relevance batch data |
rerank_return_count | Top 3-5 entries | Investment research decisions rely on precise core data; a small number of reranked results improves information acquisition efficiency |
segment_length | 800-1200 characters | Adapts to the valid single-segment information length of textile manufacturing test reports and equipment logs, avoiding damage to business logic from improper splitting |
incremental_sync_cycle | Every 4 hours | Matches the daily update rhythm of production data to ensure data timeliness |
field_weight_configuration | Batch number:1.5, yarn count:1.2, pass rate:1.0 | Prioritizes matching core business fields focused on by investment research, improving retrieval accuracy |
The parameter values provided on this page are common recommended 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 samples specific to the target deployment before finalizing settings.
Three Common Misconfigurations
- Symptom: Retrieval results are empty or return empty citations after enabling
rerank. Cause: The lower limit ofsimilarity_thresholdis not configured, or the threshold is set too high, resulting in no valid data after the original recall results are filtered by reranking. - Symptom: Duplicate batch data is returned during multi-index retrieval. Cause: The index deduplication function is not enabled, or batch number is not added to index metadata as a unique identifier, leading to the same data being recalled repeatedly by multiple indexes.
- Symptom: Incremental index synchronization task times out and fails. Cause:
PARSE_FILE_TIMEOUT_SECONDSis set too short, not matching the parsing time required for long textile manufacturing documents such as monthly capacity summary sheets.
How to Confirm Correct Configuration
- Batch test retrievals are executed using commonly used investment research query terms. Returned results are checked to confirm inclusion of core business fields and alignment with the configured recall count requirement.
- Index update logs are reviewed to confirm that incremental synchronization tasks execute automatically per the configured cycle, with no frequent timeouts or failure records.
similarity_thresholdis adjusted and retrieval results are compared, to confirm that valid results remain available after reranking, avoiding retrieval abnormalities caused by unreasonable threshold settings.- Cross-index associated retrieval is tested, to verify that batch data from different data sources can be recalled simultaneously and displayed with associated context.
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.