What This Category’s Data Looks Like
Due diligence data for textile manufacturing enterprises is a core basis for financial institutions to carry out investment and financing in the textile and apparel sector, as well as supply chain financial services. Data sources include production ledgers, quality inspection reports, supply chain cooperation agreements, and industry process specification documents. Update rhythms vary by data type: production ledgers are generated per batch, quality inspection reports are updated after each batch’s inspection is completed, and supply chain agreements are adjusted when cooperation terms change. Document structures include dedicated fields: batch number, yarn count, gram weight, roll length, raw material ratio, and inspection results. Units include meters, kilograms, counts, rolls, and others. Supported document formats include structured Excel tables, PDF quality inspection reports, Word process specifications, and text contracts.
Constraints on Knowledge Base Retrieval and Recall
Mixed multi-source document formats require unified parsing of structured and unstructured data to prevent incorrect splitting of professional parameters. Differentiated update rhythms require adaptation to different synchronization cycles; a uniform full update frequency cannot be used. Dedicated fields and professional terminology require retrieval models to have industry semantic understanding capabilities. General matching is prone to misjudgment of terms. The coexistence of long and short documents requires a chunking strategy that balances information integrity and retrieval accuracy. This avoids excessive truncation of long documents or overly fine splitting of short documents.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall_top_k | Top 8-12 results | Due diligence data for textile manufacturing is often scattered across multi-dimensional fragments such as process, supply chain, and quality inspection. A sufficient number of candidate results must be recalled to cover core query requirements |
similarity_threshold | 0.72-0.78 | Balances semantic matching accuracy and recall rate for professional terms, avoiding missed detection of content related to specific process parameters |
chunk_size | 800-1200 characters | Balances the segmentation integrity of long document process specifications and the information concentration of short document production ledgers, adapting to the length differences of documents in this category |
incremental_sync_cron | 0 /6 | Performs incremental synchronization every 6 hours, adapting to the batch-based update rhythm of production data |
rerank_top_k | Top 3-5 results | Reranks recalled candidate fragments to prioritize matching core professional parameters required for due diligence, such as yarn count, roll length, etc. |
The parameter values provided on this page are common starting points for configuration setup. Actual values are influenced by material form, data volume, and business rules. Each scenario requires targeted analysis. Testing using local samples is recommended before finalizing values.
Three Common Configuration Errors
- Phenomenon: Target batch raw material ratio data does not appear in retrieval results. Cause:
recall_top_kis set to fewer than 5 results, failing to cover professional parameters scattered across different document fragments. - Phenomenon: Long document process specifications are truncated, with key parameters missing. Cause:
chunk_sizeis set too small, splitting long process descriptions into too many short fragments, leading to loss of core information through splitting. - Phenomenon: Incremental synchronization tasks do not execute on schedule, and old batch data in the knowledge base is not updated. Cause:
incremental_sync_cronis set to daily execution, failing to adapt to the high-frequency batch update requirements of textile manufacturing.
How to Confirm Configuration Is Correct
A single textile manufacturing quality inspection report and batch production ledgers may be uploaded, and the parsed text is reviewed to confirm retention of dedicated fields including batch number, yarn count, and gram weight. Queries containing professional terms are submitted, and the number of recalled results is verified to match the configured recall_top_k value. Execution logs for scheduled synchronization tasks are reviewed to confirm incremental updates trigger normally per the preset cycle. The chunked retrieval effect of long documents is tested to confirm core process parameters are not excessively truncated.
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.