What the Data for This Category Looks Like
Textile manufacturing investment research data sources include upstream raw material spot ledgers, factory production work orders, quality inspection reports, industry capacity briefs, and customs import and export declaration data. Update frequencies vary. Raw material quotes are updated daily. Production work orders and quality inspection reports are updated in real time or after each shift alongside production processes. Industry briefs and import and export data are updated weekly or monthly.
Document types include structured tables such as raw material unit prices and capacity data, semi-structured quality inspection reports including batch numbers and pass rates, and long-form industry research reports. Unique fields include yarn count, grey fabric width, and loom speed, with corresponding units of count, centimeter, and rotations per minute.
What Constraints Do These Characteristics Impose on Model Access and Configuration
Data sources with different update frequencies require support for custom synchronization cycle configuration to avoid redundant data pulling or data lag. Multiple document types require the model to support structured entity extraction. Exclusive vocabulary lists must be configured for specific terms such as yarn count and width to prevent entity recognition errors.
Cross-data source association analysis requirements mean the retrieval module must recall multiple types of data including raw materials, production, and import and export data at the same time. It must also adapt to the length characteristics of different documents. In addition, units and naming rules for textile-specific fields must be standardized during model calls to ensure consistency of retrieval results.
How to Set the Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Most textile manufacturing work orders and quality inspection reports are short paragraphs. Long industry research reports need to retain complete process descriptions after segmentation. This range balances context coherence and retrieval accuracy |
recallCount | Top 8–12 results | Textile investment research requires reference to multiple data sources such as raw material quotes, capacity data, and import and export declarations. 8-12 results can cover core associated information |
similarityThreshold | 0.72–0.78 | There are many textile-specific terms such as yarn count and width. A threshold that is too low may recall irrelevant textile data, while a threshold that is too high will fail to cover cross-data source association logic |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Large textile capacity research reports have large file sizes, requiring sufficient time for text parsing and entity extraction |
requestLogEnable | Enabled | Textile investment research data has strict field verification requirements. Enabling logs can troubleshoot parameter transfer errors during model calls, such as abnormal unit conversion |
maxContext | 12000–15000 characters | Textile investment research requires integrating context from multiple data sources. This range supports complete process and market association analysis |
The parameter values provided on this page are common starting points for configuration setup. 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 values.
Three Common Mistakes
- Phenomenon: Request parameter logs cannot be printed when calling third-party large models, with no valid data available for troubleshooting field transfer errors. Cause: The
requestLogEnableconfiguration is not enabled, or the log output of the third-party API is not bound to the FastGPT call chain. - Phenomenon: Ambiguous content appears in retrieval results, such as "this width" that cannot be matched to a specific grey fabric batch. Cause: Pre-retrieval reference resolution and question expansion functions are not enabled, and the textile industry-specific entity vocabulary list is not loaded, leading to failed entity association.
- Phenomenon: An analysis timeout error is displayed in the interface after uploading a textile capacity research report larger than 100MB. Cause: The
PARSE_FILE_TIMEOUT_SECONDSconfiguration value is set too short, failing to adapt to the parsing and entity extraction duration of large files.
How to Confirm the Configuration is Correct
- Run a model call test that includes textile-specific terms. Check whether the log contains complete request parameters and return results to confirm the log function works properly.
- Enter a query that involves association across multiple data sources, such as "Import duties and current quotes for 32-count pure cotton yarn". Verify whether the retrieval result recalls data from the corresponding category.
- Upload a small textile quality inspection report. Check whether the parsed text segments meet expectations, with no truncation or loss of key fields such as batch numbers and pass rates.
- Adjust the similarity threshold. Compare retrieval result relevance across different thresholds to confirm the threshold configuration adapts to the characteristics of the current data source.
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.