What the data for this category looks like
Chemical fiber category data sources cover production-side factory quality inspection reports, industry association supply and demand weekly reports, customs import and export ledgers, and spot quotes from futures exchanges. Data update cycles include daily (spot prices, batch quality inspections), monthly (industry supply and demand data), and quarterly (import and export statistics). Documents are primarily structured forms, weekly reports, and ledgers. Core fields include batch number, fineness, breaking strength, elongation at break, raw material ratio, and origin. Exclusive units include denier (D), decitex (dtex), centinewton per decitex (cN/dtex). Some documents include detailed proportions of multi-component raw materials.
Constraints for knowledge base retrieval and recall
Multi-source, multi-cycle data requires the retrieval process to support filtering by update time to avoid introducing expired data. Exclusive fields and units require the retrieval system to support unit normalization and precise field matching. Otherwise, recall failure may occur due to mismatched fineness units. The large number of chemical fiber sub-categories requires retrieval to support filtering rules by category, to prevent irrelevant data from other chemical fiber categories such as polyester and nylon from being recalled. The mixed structure of long and short documents requires retrieval segmentation strategies adapted to different document lengths, to avoid core fields being truncated or context fragmentation. Time-sensitive spot and batch data requires recall results to prioritize the latest updated content, to ensure the accuracy of due diligence reports.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Number of recalled entries | Top 10-15 entries | Chemical fiber due diligence requires covering multi-dimensional associated data including quality inspection, supply and demand, and price. Too many entries will exceed the LLM context window, while too few will fail to cover all necessary information |
| Similarity threshold | 0.75-0.85 | Chemical fiber data includes exclusive units and sub-category fields. A threshold that is too low will introduce irrelevant category documents, while a threshold that is too high will miss sub-category batch data of the same category |
| Chunk length | 800-1200 characters | Single-segment core data from chemical fiber quality inspection reports is approximately 300-500 characters, while industry weekly report paragraphs are approximately 1000 characters. This length preserves field integrity while avoiding context fragmentation |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Batch processing of fiber quality inspection ledgers requires long parsing time. Sufficient time must be reserved to avoid parsing task interruptions |
| Knowledge base cache expiration time | 24 hours | Chemical fiber spot prices and supply and demand data have high update frequencies. A 24-hour cache balances query speed and data timeliness |
maxContext | 8000-12000 characters | Due diligence reports require integrating multi-source data. This window can cover the content of at least 3 complete industry weekly reports and 5 batch quality inspection reports |
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 specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Errors
- Phenomenon: The due diligence report generated by the LLM contains content not referenced from the knowledge base, or outputs general textile information unrelated to the current retrieval. Cause: The similarity threshold configuration is not set, or the threshold is set too high, resulting in insufficient valid recalled data, causing the LLM to fall back to general training data.
- Phenomenon: Knowledge base query response time is long, and background logs show repeated file parsing tasks. Cause: The knowledge base cache function is not enabled, or the knowledge base cache expiration time setting is unreasonable, resulting in frequent queries repeatedly triggering the file parsing process.
- Phenomenon: The model output language does not match the configured prompt language. For example, outputting Chinese content when using an English prompt and an English knowledge base. Cause: The LLM output language parameter is not configured, or the locally deployed LLM does not correctly load the corresponding language tokenizer model, resulting in ignoring preset output requirements.
How to Verify Correct Configuration
- Retrieve data for a specific chemical fiber batch number, and confirm that recalled results only include associated data for that batch, with no irrelevant content from other categories.
- Check the knowledge base background cache monitoring panel, confirm that cache hit records exist, and the cache update cycle matches the actual data update frequency.
- Submit a retrieval task with preset English output requirements, and confirm that the model output language matches the preset requirements when using English knowledge base content.
- Run three consecutive identical queries for chemical fiber supply and demand data, and confirm that the response time of the second and third queries is significantly shortened, verifying that the cache is effective.
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.