What the Data for This Category Looks Like
Data primarily comes from internal brand ERP systems, partner e-commerce platform product libraries, and offline store POS data. Update frequency aligns with new product launch cycles. Higher frequency is used during concentrated new product launches. Monthly updates apply to regular styles. Inventory data is synchronized in real time. The document structure combines structured tables and individual product detail pages. Core fields include style number, SKU code, upper material, sole material, multi-unit size chart (CN/EU/US), tagged price, actual selling price, inventory quantity, main image and detail image links, and preset marketing copy fields.
What Constraints Do These Characteristics Impose on Tool Calling and Plugins?
Footwear data characteristics such as multi-unit size charts, multi-image links, and real-time inventory impose multiple constraints on tool calling and plugins. Multi-unit size charts require plugins to include built-in unit conversion logic, ensuring consistent size information across channels. Multi-angle detail images require tools to support batch pulling and format verification, to meet image calling needs when generating marketing content. Real-time inventory data requires tools to use short-interval polling parameters, preventing outdated inventory from appearing in marketing materials. The uniqueness of footwear SKU codes requires precise field matching during tool calls, avoiding content confusion between different color versions of the same style. Custom field mapping support is also needed to adapt to product data formats from different sources.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Footwear product details include multiple high-definition images and structured tables, with higher parsing time than general documents |
UPLOAD_FILE_MAX_SIZE | 800 MB | Footwear marketing materials include multiple sets of high-definition product images and detail documents, requiring allowance for larger file uploads |
multimodal_image_format | JPEG/PNG/WebP | Multimodal models have good parsing compatibility with these three formats, adapting to mainstream footwear detail image formats |
plugin_api_request_interval | 1000 milliseconds | Footwear inventory data requires real-time synchronization, requiring a balance between interface rate limiting and information timeliness |
max_context | 8000–12000 characters | Generating footwear marketing content requires associating detailed parameters of multiple SKUs, and a longer context ensures complete parameter invocation |
DOC_PARSE_SPLIT_LENGTH | 1000–1500 characters | Structured paragraphs in footwear product details are relatively long, requiring reasonable segmentation to avoid model understanding deviations |
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 validation against local samples is recommended before finalizing.
Three Common Mistakes
- Scenario: A 400 Bad Request error is returned when calling a multimodal tool to generate footwear marketing materials, with a prompt indicating unsupported image format. Cause: The
multimodal_image_formatparameter is not specified as a compliant format, and HEIC or RAW format footwear real-shot images are passed in. - Scenario: After upgrading to version v4.8.13, passing a footwear product detail link from an e-commerce platform, the document parsing tool only returns empty content without extracting product parameters. Cause: This version adds anti-crawling verification, and the
HTTP_REQUEST_HEADERSparameter is not configured to carry a valid User-Agent identifier, resulting in the request being blocked. - Scenario: When using ollama to access a vector model, FastGPT calls return a 403 Forbidden error, but the curl test works normally. Cause: Plugin identity verification parameters are not configured, and FastGPT's request does not pass the identity verification of the target interface.
How to Confirm the Configuration Is Correct
- A high-definition main footwear image is uploaded, a multimodal tool is called for testing, and the returned parsing results are checked to confirm whether they include core parameters such as shoe material and size, verifying that the format parameter configuration is correct.
- A footwear product detail link from an e-commerce platform is passed, the document parsing tool is run, and the extracted fields are checked to confirm whether they include information such as style number, inventory, and selling price, verifying that the timeout and segmentation parameters are properly configured.
- After configuring the plugin API request interval, batch calls to the footwear inventory query interface are simulated, and the returned inventory data is checked to confirm whether it is synchronized with the brand's ERP system, verifying that the rate limiting parameter settings are compliant.
- A private LLM model is called to generate a footwear marketing copy, multiple tests are conducted, and whether occasional empty responses occur is checked, verifying that the retry parameter configuration is effective.
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.