Data format and sources for this category
Intelligent due diligence data for home goods primarily comes from SKU ledgers in brand ERP systems, public reports from third-party quality inspection agencies, and product detail page data from e-commerce platforms. Update frequencies vary across sources: SKU basic information is updated quarterly, quality inspection reports are updated with each production batch, and sales data is synced daily.
Each due diligence document is a structured package for a single SKU, with four modules: basic attributes, production compliance, supply chain, and market performance. Fields include SKU name, material parameters, production qualification number, lead time, compliance test item results, and more. Parameters have clear physical units, such as millimeters for sheet thickness, grams for fabric weight, and days for lead time.
Constraints imposed on model access and configuration
The multi-channel sources, segmented parameter structure, and differentiated update rhythms of home goods due diligence data impose core constraints on model access and configuration. First, each SKU due diligence package contains multi-module long text. Configuration of model access parameters adapted for long context is required to avoid truncation of critical compliance parameters and detailed material data. Second, the data includes both structured ledgers and unstructured quality inspection reports. Configuration of permission isolation and mixed recall rules for multiple knowledge bases is required to ensure accurate invocation of different data source types. Third, parameters have clear physical units. Configuration of entity extraction unit verification rules is required to avoid model output errors with mixed units. Additionally, the differing update rhythms of different data sources require configuration of scheduled trigger logic for incremental embedding to ensure data timeliness.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | The single SKU due diligence package contains multi-module content, and complete parameters and compliance descriptions must be retained to avoid truncation of critical information |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Quality inspection reports for home goods usually contain multi-page test data and high-definition images, resulting in longer parsing times |
embedding_model | text-embedding-3-large | Supports multilingual vector generation, adapting to multilingual recall needs for cross-border home goods due diligence |
chunk_size | 1000–1500 characters | Home goods parameters are detailed, and complete parameter groups and contextual associations must be retained to avoid splitting that disrupts parameter logic |
similarity_threshold | 0.75–0.85 | Filters low-relevance non-home goods documents while retaining high-relevance recall results for detailed materials and compliance items |
UPLOAD_FILE_MAX_SIZE | 200 MB | Supports uploading home goods quality inspection report documents containing high-definition test images and multi-page data |
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 specific analysis, and it is recommended to test on your own samples before finalizing.
Three common configuration errors
- Phenomenon: The model returns complete intermediate thinking steps and Prompt template content. Cause: The "retain thinking process" switch is not turned off in the model configuration, and no clear output format constraints are set.
- Phenomenon: Only some documents are re-embedded during batch updates of knowledge base embedding vectors. Cause: No scheduled trigger rules based on document update time are configured, or re-embedding document screening conditions are not set correctly.
- Phenomenon: A
429 Too Many Requestserror code appears when calling the model interface. Cause: No load balancing rules for multiple model channels are configured, and the concurrency request limit for a single channel is not set, resulting in overload of a single channel.
How to verify correct configuration
- Upload a single home goods SKU due diligence document, check that the model returned context contains complete parameters and compliance descriptions with no truncation.
- Run a batch re-embedding task, check that all updated documents have completed vector updates with no omissions.
- Call the model interface, check that the returned results only contain due diligence conclusions with no intermediate thinking steps or Prompt template content.
- Simulate multi-channel request calls, check that load balancing rules allocate requests to different model channels as configured, with no single channel overload.
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.