What the data for this category looks like
Textile manufacturing financial report and industry data mainly comes from public periodic reports of domestic and overseas stock exchanges, and monthly production and sales statistics documents released by industry associations. Data update rhythm follows the disclosure rules of regulatory authorities. Annual reports are released within four months after the end of the year, semi-annual reports are released within two months after the end of the first half of the year, and monthly production and sales data is updated monthly. Document structure includes three modules: core financial indicators, core production data, and supply chain related data. Fields involve revenue, unit production cost, inventory turnover days, yarn count, fabric gram weight, capacity utilization rate, etc. Units include RMB yuan, ton, meter, square meter, etc.
What constraints do these characteristics impose on the "knowledge base retrieval and recall" link
The textile manufacturing category has high density of specialized terminology, including yarn count, fabric gram weight and other domain-specific words. This requires the retrieval logic to accurately match professional expressions, to avoid irrelevant content being mixed in due to vague recall. Different types of data have different update frequencies. Monthly production and sales data need high-frequency synchronization, while annual financial report data is updated quarterly. This requires the knowledge base to be managed in partitions according to data update cycles, to ensure the timeliness of retrieval results. Documents are long with many field dimensions, and context association must be retained during segmentation. Otherwise, the complete logic of indicators such as unit consumption and capacity utilization rate will be damaged. In addition, some data includes unit information, and retrieval results must retain units to ensure the accuracy of subsequent analysis.
How to set the configuration
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
chunkSize | 800–1200 characters | Long paragraphs of production data exist in textile manufacturing financial reports. Appropriately lengthening segmentation can retain the integrity of professional logic such as unit consumption and capacity |
similarityThreshold | 0.75–0.85 | Specialized terminology in textile manufacturing has high recognition. A higher threshold can filter irrelevant general financial content and avoid recalling redundant information |
recallTopK | Top 8 entries | Financial report data has many fields. A sufficient recall volume is needed to cover indicators of different dimensions, while avoiding excessive content exceeding the model's context limit |
rerankTopK | Top 3 entries | The most relevant core data must be retained. Reranking can ensure that retrieval results focus on the specific financial report chapter of the user's query |
knowledgeBaseSyncCycle | Daily synchronization | Monthly production and sales data are updated monthly. Daily synchronization can ensure that the latest industry data is included in the knowledge base in a timely manner, while avoiding excessive synchronization frequency occupying resources |
parseChunkOverlap | 100–150 characters | Context association of specialized terms must be retained after segmentation, to avoid missing definitions of terms such as unit consumption and count after splitting |
The parameter values provided on this page are all conventional recommendations used to determine the starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Phenomenon: Mathematical formulas embedded in the knowledge base (such as gross profit margin calculation formulas, unit consumption calculation formulas) cannot be displayed normally, only garbled characters or text fragments are shown. Cause: Exclusive configuration for formula parsing is not enabled, and the default segmentation logic does not retain the LaTeX syntax structure of the formula, resulting in lost format during parsing.
- Phenomenon: After calling the knowledge base with a locally deployed model, the number and content of results returned by the same query vary each time. Cause: The recall similarity threshold is not fixed, or the fixed random seed for reranking is not enabled, resulting in deviations in the candidate set recalled each time.
- Phenomenon: When calling the knowledge base, relevant answers for corresponding fields cannot be extracted from the full financial report data table, or the specified column cannot be used as a retrieval index. Cause: Field-level retrieval mapping is not configured, and when importing full table data directly, the columns to be indexed are not specified, resulting in the retrieval scope covering irrelevant fields.
How to confirm the configuration is complete
- Upload a segment of a publicly available textile manufacturing financial report, use the knowledge base preview function to view the segmented content, and confirm that the structure of specialized terms and formulas is complete.
- Initiate a query containing specialized terms, check whether the number of recalled results matches the configured
recallTopKvalue, and the reranked results focus on the target indicator. - Verify whether the results returned by the same query initiated at different times are consistent, and confirm that the recall logic does not introduce random deviations.
- Check the knowledge base synchronization log to confirm that the latest industry data has been successfully synchronized, and the update cycle meets the configuration requirements.
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.