What This Category’s Data Looks Like
Apparel and home textile investment research data primarily comes from industry association monthly supply and demand reports, brand quarterly financial reports, supply chain raw material quotation platforms, e-commerce sales monitoring data, and patent office home textile technical documents. Data update frequencies vary: raw material quotations are updated daily, industry reports are updated monthly, and financial reports and patent documents are released quarterly or annually. Most documents use structured tables, with fields including raw material name, yarn count, gram weight, unit selling price, inventory turnover days, brand identifier, and more. Units include g/㎡, yuan/meter, yuan/item, and other detailed specifications.
What Constraints Do These Characteristics Impose on Knowledge Base Retrieval and Recall
Multi-source data with inconsistent update frequencies requires the retrieval module to support filtering documents by update time, to avoid recalling outdated supply chain data or non-current brand financial reports. Documents with a high proportion of structured fields require retrieval to support both semantic matching and exact field matching. For example, for a query like "40-count pure cotton fabric", both semantic relevance and the yarn count field must be matched. Disparate data sources need to use tag classification to limit the retrieval scope, preventing investment research data from other categories from being included, and ensuring recall results focus on apparel and home textile specific segments.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Recall Count | top 10-15 results | Most apparel and home textile investment research documents focus on specific segments. Too many results increase screening costs for researchers, while too few will miss relevant data for detailed specifications. |
Similarity Threshold | 0.72-0.85 | This category has a large number of standardized parameters such as yarn count and gram weight. A threshold that is too low will introduce irrelevant raw material data, while a threshold that is too high will miss valid documents for different specifications of the same category. |
Chunk Length | 800-1200 characters | Technical documents and supply chain tables for apparel and home textiles are mostly structured paragraphs. Too long chunks will lose the logical connection between parameters, while too short chunks will destroy context coherence. |
Document Filter Rules | Filter by document tag + last 30 days update time | Data sources for this category are dispersed. It is necessary to limit the document tags for the corresponding category, while prioritizing recall of recently updated valid data. |
Reranked Return Count | top 5-8 results | Researchers need to quickly locate core data. Retaining the most relevant results after reranking meets retrieval needs. |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Some bulk raw material quotation sheets and e-commerce monitoring reports have large file sizes. This configuration prevents out-of-memory error triggers. |
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 case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Issue: Retrieval results include documents from non-apparel and home textile categories, and the retrieval scope cannot be restricted. Cause: The
Document Filter Rulesconfiguration is not set, and the corresponding category's document tags or classifications are not bound. - Issue: The
worker terminated due to reaching memory limiterror is triggered when creating a knowledge base in FastGPT 4.13.2. Cause: The uploaded document size exceeds theUPLOAD_FILE_MAX_SIZEconfiguration value, or the number of bulk uploaded documents exceeds the memory threshold. - Issue: The variable reference in the knowledge base search module returns a null value, and retrieval results do not meet investment research query needs. Cause: The user's investment research query content is not correctly mapped to the retrieval variable, and the bound knowledge base ID or target tag is not specified.
How to Confirm Configuration Is Correct
- Run a test query targeting specific apparel and home textile parameters, and check if the document tags of returned results match the configured filter rules.
- Upload a single large apparel and home textile document, check if an out-of-memory error is triggered, and confirm the upload size configuration is active.
- After configuring variable references, replace different query keywords and verify if retrieval results adjust along with the variable changes.
- View retrieval logs, and check if the recall count and similarity threshold match the configured values.
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.