What the data for this category looks like
Textile manufacturing industry research report data comes from multiple sources: China National Textile and Apparel Council monthly monitoring reports, A-share listed companies’ periodic reports, General Administration of Customs import and export statistics, raw material price data from commodity trading platforms, and industry tracking reports organized internally by financial institutions. Update frequencies vary across sources. Macro industry reports are updated monthly. Listed companies’ annual and quarterly reports are updated quarterly or annually. Raw material price data is updated daily. Documents typically include industry operation overviews, production and sales data by category (cotton spinning, chemical fiber, dyeing and finishing, etc.), price trends, upstream and downstream industrial chain analysis, and relevant policy interpretations. Fields include production volume (units: 10,000 meters, tons), price (units: yuan/ton, yuan/meter), year-on-year and month-on-year growth rates, and policy document numbers.
Constraints on vector models and indexing
Research report text lengths vary widely. Short documents like policy interpretations run hundreds of words, while in-depth industrial chain analysis can span thousands of words. This requires adapting to vector model token limits and properly setting chunking parameters. Data includes numerous numerical fields with professional units and segmented product category labels. Vector models must accurately recognize these semantic dimensions to avoid confusion across product categories. Update frequencies differ significantly across data sources. This requires configuring differentiated index refresh strategies to balance data timeliness and computing resource usage. Excel source data includes multi-header fields. This requires correctly associating headers with data content to avoid losing statistical dimensions during vector matching.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the chunking logic of FastGPT 4.9.12 and the 1024 token limit of text-embedding-ada-002 (approximately 800 characters after conversion). Covers the text length requirements of both short policy documents and long industrial chain analysis in textile manufacturing research reports, and balances chunk granularity and context integrity |
chunk_overlap | 100–150 characters | Retaining overlapping parts prevents professional terms from being truncated, ensures semantic coherence of industrial chain data, and supports cross-block association of segmented product category labels |
recall_top_k | Top 8–12 results | There are many segmented product categories in textile manufacturing. Too many recall results introduce irrelevant category data, while too few fail to cover all relevant research reports. This range balances recall accuracy and coverage |
index_refresh_interval | Every 12 hours | Adapts to the mixed update rhythm of monthly industry reports and daily price data, and balances data timeliness and computing resource consumption |
similarity_threshold | 0.72–0.78 | Professional terms in textile manufacturing have high semantic similarity. A threshold that is too low introduces irrelevant research reports, while a threshold that is too high misses relevant segmented category data. This range matches the matching accuracy required for business needs |
parse_excel_header_mode | Retain headers as chunk prefixes | Excel data for textile manufacturing includes fields with units. Retaining headers allows vector models to clearly identify the statistical dimensions of data, improving the matching accuracy of numerical fields |
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 specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Phenomenon: Vector generation fails or core professional terms are lost after chunking when uploading textile manufacturing industrial chain research reports longer than 3000 words. Cause: The
chunk_sizeparameter was not adjusted to adapt to long texts, and the default 500-character chunking setting was used directly. Excess content was forcibly truncated, andchunk_overlapwas not enabled to retain context association. - Phenomenon: Irrelevant non-textile industry research report data appears in recall results, or matching results for segmented product categories such as chemical fiber are missing. Cause: A reasonable range for
similarity_thresholdwas not set, or indexes were not partitioned by product category, leading to general recall covering irrelevant domains. - Phenomenon: After importing Excel source data, the vector matching effect of numerical fields is poor, and header information is not correctly associated. Cause:
parse_excel_header_modewas not enabled to retain headers, so vector models cannot identify the statistical dimensions corresponding to numerical values, such as the raw material type linked to "yuan/ton".
How to verify correct configuration
- Upload the longest single textile manufacturing research report, check the chunking preview interface, and confirm that no core professional terms or unit fields are truncated in any chunk.
- Enter a segmented product category query, such as "viscose staple fiber monthly production volume", verify the matching degree between the source document of the recall results and the query, and adjust
similarity_thresholdto a range that meets business requirements. - Check the index refresh log to confirm that data sources with different update frequencies have completed synchronization according to the configured
index_refresh_interval. - Import test Excel data, check whether the chunked text includes the corresponding header information, and confirm that the
parse_excel_header_modeconfiguration takes effect.
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.