What the data for this category looks like
Data sources for personal care product smart due diligence reports include brand owners’ public compliance filing documents, quality inspection reports from raw material suppliers, and compliance disclosure documents from industry associations. Updates align with new raw material filings and quarterly spot check results, with no fixed schedule. Single documents are mostly 10-30 page PDFs or structured tables, containing fields such as raw material name, test item number, test value, test method standard number, production batch number, and traceable warehouse code. Most test value units are milligrams per kilogram, grams per liter, and kilograms per cubic meter.
What constraints these characteristics impose on model access and configuration
Data sources are scattered and have heterogeneous formats, requiring support for parsing and adapting multi-source files, with high requirements for field extraction accuracy. Updates have no fixed schedule, so automatic incremental synchronization rules must be configured to avoid missing the latest filing data. Single document lengths vary widely, so reasonable segmentation thresholds must be set to prevent core field loss caused by long text truncation. Diverse field units require unit normalization rules to ensure data consistency. Multiple SKUs for the same batch of products are common, so batch parsing parameter configuration must be supported.
How to Configure
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Personal care due diligence reports can be up to 30 pages per document, with long parsing times. 600 seconds covers the full parsing workflow. |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Single multi-SKU due diligence reports may include multiple attachments. 1000 MB accommodates the total file volume for standard batch uploads. |
maxContext | 800–1200 characters | Core fields of personal care reports are mostly concentrated on a single page. 800–1200 characters fully covers the context of single-page test data, preventing cross-page field loss. |
chunk_overlap | 150 characters | Cross-page raw material traceability information requires retained context association. 150 characters of overlap ensures field continuity. |
Recall Count | Top 8 entries | Core compliance fields in personal care due diligence reports do not exceed 8. Recalling the top 8 entries accurately matches target fields. |
Similarity Threshold | 0.85 | There is little variation in wording for personal care compliance fields. A threshold of 0.85 filters irrelevant documents while retaining valid matching results. |
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. Analyze specific issues on a case-by-case basis, and it is recommended to test on your own samples before finalizing.
Three Common Configuration Mistakes
- Symptom: Model calls return the error
429 Current group upstream load is saturated, please try again later. Cause: No rate limiting rules for batch requests are configured. Uploading multiple large personal care due diligence reports simultaneously causes the interface call frequency to exceed limits. - Symptom: Parsed fields are missing or misaligned. Cause: No reasonable
chunk_overlapparameter is set. Cross-page raw material traceability information is truncated, preventing the model from associating complete fields. - Symptom: Local Ollama model access fails after connection. Cause: The model port is not configured as an internal network address accessible to FastGPT, or correct access permission configuration is not added in the model settings.
How to Verify Successful Configuration
- Upload a standard personal care due diligence report PDF, check if parsed fields fully cover core items, and verify that field units comply with preset normalization rules.
- Initiate a batch upload of 3-5 similar reports, check that interface calls have no errors, and confirm that rate limiting configuration is effective.
- After connecting a local Ollama model, initiate a simple field extraction test, check that model return results match actual data in the document.
- View the parsing success rate metric in the configuration panel, confirm that the single-parsing success rate meets the qualifying standard corresponding to business requirements.
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.