What the data for this category looks like
The data for personal care product smart due diligence reports comes primarily from brand filing documents, third-party quality inspection reports, public product detail pages on e-commerce platforms, and compliance regulatory disclosure information. Updates are triggered by new product launches, ingredient formula adjustments, or changes to compliance requirements, with no fixed schedule. Each report document includes fields such as filing number, ingredient list, applicable skin type notes, production batch number, shelf life, and compliance statements. Most ingredient entries use qualitative descriptions; some list net content units in grams or milliliters. Batch information uses a combination of letters and numbers.
What constraints these characteristics impose on vector models and indexing
The high proportion of qualitative descriptions in ingredient lists requires adjusting text chunking granularity to avoid semantic fragmentation in long texts. The lack of a fixed update cycle requires configuring incremental index synchronization logic, triggering full or incremental updates only when data changes. Structured fields such as filing numbers and production batch numbers must be separately indexed as metadata, stored separately from unstructured ingredient descriptions and efficacy explanations, to improve retrieval accuracy. Compliance statements have high requirements for semantic accuracy, so a vector model adapted for long texts must be selected to avoid information loss from short text truncation.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Personal care reports contain many long texts such as ingredient descriptions and compliance statements. This range preserves complete semantic units and avoids excessive splitting |
incremental_sync_trigger | Triggered by changes in data hash values | Personal care data has no fixed update cycle. Hash verification can accurately identify content changes and reduce invalid index updates |
structured_metadata_fields | filing number, production batch number, net content | These fields are fixed structured information. Separate indexing improves precise retrieval efficiency |
vector_model_choice | Domestic vector model adapted for long texts | Personal care report text lengths vary widely. Long-text adapted models preserve complete semantics |
recall_top_k | Top 8–12 results | Personal care due diligence requires covering multiple dimensions including ingredients, compliance, and user feedback. An appropriate number of recalls ensures comprehensive information |
similarity_threshold | 0.75–0.85 | Ingredient descriptions in personal care reports have high similarity. This threshold filters irrelevant results and retains accurately matched content |
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
- Symptom: A 401 unauthorized status code is returned when calling the vector model. Cause: The API access key or permission scope for the domestic vector model was not configured correctly.
- Symptom: Duplicate entries remain in the knowledge base index after merging. Cause: The
enable_deduplicationparameter was not enabled, or the filing number was not specified as the unique deduplication identifier. - Symptom: Calls to a vector model deployed via Ollama are rejected by the platform. Cause: The request header does not carry correct authentication information, or the port configuration does not match platform requirements.
How to confirm configurations are set correctly
- Navigate to the index management page, view incremental sync trigger records, and confirm that updates are only executed when the hash of personal care report data changes.
- Upload a single test report, and check whether structured metadata such as filing numbers and production batch numbers are correctly extracted in retrieval results.
- Submit two test reports with identical content but different filing numbers, and verify that the deduplication logic only retains entries matching the unique identifier.
- Submit a retrieval request, and check whether the similarity matching of returned results conforms to the preset threshold rules.
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.