What the data for this category looks like
Data comes primarily from production system fabric composition, weight, and process parameter ledgers, e-commerce platform product detail pages and marketing material libraries, and offline exhibition customer communication records. Ledger data is updated after each production batch. Marketing materials are updated in real time when launched. Document structures include structured tables such as fabric specification sheets, long-text process descriptions, and product descriptions with images. Fields include fabric weight (unit g/㎡), yarn count (unit S), order quantity (unit pieces), material release time (format YYYY-MM-DD), and customer age range (unit years).
Constraints imposed on model integration and configuration
Structured fabric parameter tables include fields with units. Configured model parsing tools must accurately associate units and numerical values to avoid parsing errors. Long-text process descriptions and product descriptions have significant length. Configured context windows must adapt to long-text input to avoid truncating critical information. Real-time updated marketing materials require model integration API response latency to meet real-time marketing content generation requirements. Multi-format materials including text, tables, and images require enabling multi-modal adaptation parameters to support parsing of different types of marketing content.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | Textile manufacturing marketing content often includes long-text process descriptions and product details. This range covers complete material context |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Fabric test reports and long process documents take longer to parse. This setting avoids interrupting the parsing process due to timeout |
rerankTopN | Top 6–10 results | Recall results for textile manufacturing marketing materials need to balance accuracy and material diversity, avoiding excessive redundant content |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Supports uploading batch fabric sample images, large process documents and other marketing materials |
enableMultiModal | Enabled | Marketing content includes multi-modal materials such as fabric sample images and process diagrams. Model support for image parsing is required |
modelApiKeyAuth | Enable key verification | Prevents unauthorized calls to model interfaces and ensures the security of marketing material generation |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Model calls return
401 unauthorizederrors. The cause is failure to correctly configure the local model's API key, or failure to bind valid key verification parameters in FastGPT. - The model cannot accurately parse the unit information of fabric weight and yarn count after integration. The cause is failure to enable the adaptive configuration for parameter unit recognition, leading to the model confusing the association between numerical values and units.
- Timeout interruptions occur during marketing material generation. The cause is that the set
PARSE_FILE_TIMEOUT_SECONDSvalue is too short, which cannot cover the parsing duration of long process documents.
How to confirm the configuration is complete
- Upload a structured table document containing fabric weight and yarn count, and check whether the parsed fields of the model completely match the original data.
- Call the model to generate a piece of fabric marketing copy, and check whether the returned result includes correct units and parameter information.
- View the FastGPT model integration logs to confirm there are no
401 unauthorizedor timeout-related errors. - Test uploading batch large process documents, and confirm that the system does not trigger upload size limit prompts.
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.