What this category of data looks like
Knowledge base data for textile manufacturing mainly comes from internal process specifications, fabric composition test reports, marketing script libraries, exhibition promotional materials, and customer custom requirement documents. This data is often used by financial institutions to build wealth management or insurance marketing content targeting textile manufacturing enterprises. The update rhythm is flexible, adjusted with new product launches, fabric trend adjustments, or changes in custom orders, with no fixed cycle. Document structures vary significantly, including dozens of pages of long process descriptions, parameter cards for individual fabric styles, and short marketing script snippets. Fields and units have industry-specific characteristics: fabric weight is measured in gsm, yarn count in S, plus metadata fields such as certification numbers, minimum order quantities, and applicable scenarios.
What constraints do these characteristics impose on the knowledge base retrieval and recall link?
The coexistence of long process documents and short script snippets requires the retrieval system to balance long-context matching and precise short-keyword recall. Avoid overly long segments that break professional term integrity, or overly short segments that cause semantic fragmentation. Industry-specific units and fields require the retrieval model to retain unit recognition capabilities. Prevent matching deviations caused by unit conversion or field loss, which would reduce the accuracy of financial marketing content. Flexible update rhythms require the system to support incremental updates and dynamic metadata filtering. This prevents expired fabric standards or invalid marketing content from being recalled, which could mislead financial clients’ decision-making. Multi-dimensional metadata tags require the retrieval process to support quick filtering by scenario and category, narrowing the recall scope and improving the targeting of financial marketing content.
How to set the configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Most textile manufacturing process documents are long-form technical descriptions. Overly long segments will lose contextual relevance, while overly short segments will damage the integrity of professional terms |
similarity_threshold | 0.75–0.85 | Matching between marketing scripts and fabric parameters requires high precision to avoid recalling irrelevant process content or materials from non-target categories |
recall_top_k | Top 6 results | Textile manufacturing marketing content often combines fabric parameters and scenario descriptions. A small number of precise recalls ensures result relevance and readability |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large process manuals or high-definition exhibition materials takes a long time. This setting prevents parsing processes from being interrupted by timeouts |
enable_label_filter | Enabled | Textile manufacturing knowledge bases are stored with tags based on fabric category and marketing scenario. Enabling this allows quick filtering of irrelevant content, meeting the precise targeting needs of financial marketing |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Some exhibition materials include high-definition fabric sample images and long documents. Large file upload support is required to cover all business data |
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 targeted analysis. Testing on self-provided samples is recommended before finalizing settings.
Three Common Mistakes
- Phenomenon: A large number of custom fabric marketing consultation contents are omitted from retrieval results. Cause: No dedicated question classification for textile manufacturing-specific scenarios was created, and only general classification rules were used, leading to insufficient matching accuracy.
- Phenomenon: Expired fabric composition standard documents are included in retrieval results. Cause: The
metadata_filterparameter was not configured to filter invalid metadata fields, and expired knowledge base entries were not cleaned up regularly. - Phenomenon: Text information cannot be extracted after uploading fabric sample images in the local deployment version. Cause: The local image understanding model was not enabled during deployment, causing the parsing process to fail.
How to Confirm the Configuration is Correct
- Upload one typical textile manufacturing document, such as a fabric process manual, and check the parsed segment results to confirm that the segment length meets the configured requirements.
- Initiate a retrieval request containing industry-specific terms, such as "60S combed cotton" or "180gsm", and verify that the similarity and number of recalled results meet expectations.
- Check the knowledge base metadata configuration to confirm that the
metadata_filterparameter has been configured with filterable field types, such as certification status and update time. - Call the
api/v1/chat/completionsinterface, carry thecollection_tagsfield to initiate a request, and confirm that only knowledge base content of the corresponding tags is recalled, meeting the targeted delivery requirements of financial marketing.
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.