What Data for This Category Looks Like
Data sources for cosmetic intelligent due diligence reports include brand-side filing materials, ingredient safety test reports, e-commerce platform product detail pages, and regulatory agency public information. The update rhythm adjusts with new SKU launches, ingredient compliance changes, or regulatory requirements, with no fixed cycle. Document structures include structured tables (ingredient lists, test indicator tables), unstructured text (efficacy claims, filing notes), and some compliance document scans. Fields include ingredient name, CAS Registry Number, risk level, test indicators and corresponding legal units, filing number, production batch number, and others. Some fields must match the format and units specified by regulatory rules.
Constraints Imposed on Vector Models and Indexing
Mixed multi-type data requires vector models to support multimodal encoding to process text, table, and scan data simultaneously. Structured fields and non-semantic test indicators require indexes to support both vector semantic retrieval and structured precise filtering, to avoid field confusion caused by relying solely on vector retrieval. The non-fixed update schedule requires indexes to support incremental updates to reduce resource overhead from full reindexing. Ingredient-related content is the core of due diligence, so vector encoding quality for ingredient text and tables must be prioritized to avoid breaking the contextual relevance of ingredient descriptions due to overly long segments.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | Tongyi Multimodal Embedding Model or similar multimodal-supported models | Cosmetic due diligence data includes text, ingredient tables, and compliance document scans; multimodal models can encode multiple data types simultaneously |
chunk_size | 800–1200 characters | Paragraphs in cosmetic ingredient test reports are relatively long. Overly long segments will lose contextual relevance, while overly short segments will damage the integrity of ingredient descriptions |
index_strategy | Hybrid index (vector index + structured field index) | Due diligence data includes structured test indicators and unstructured text; hybrid indexes can support both precise matching and semantic retrieval |
retrieval_top_k | Top 10–15 results | Cosmetic due diligence reports need to cover multiple ingredients and test items. Too many recalled results will increase context length, while too few will miss key information |
enable_incremental_index | Enabled | New SKUs and compliance updates will import data in batches; incremental indexing can reduce duplicate indexing overhead |
filter_field_list | Filing number, ingredient name, risk level | Due diligence scenarios often require precise filtering of results by filing number or ingredient |
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 case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: An "unsupported input type" error is thrown when calling the embedding model, or only text vectors are generated and compliance document scans cannot be processed. Cause: Multimodal support is not enabled in the embedding model configuration, or a text-only embedding model is connected to multimodal data.
- Phenomenon: A "dimension mismatch" error occurs when configuring a hybrid index, and retrieval result accuracy declines. Cause: Embedding models and index models with different vector dimensions are mixed, and vector dimension configurations are not unified.
- Phenomenon: Retrieval results do not include information related to the specified ingredient, and the number of recalled results is 0. Cause: Ingredient names are not configured as filter fields, or the similarity threshold is set too high, filtering out relevant semantic matching results.
How to Confirm Proper Configuration
- Upload a single cosmetic compliance document scan, check that the embedding task status is successful, and that the generated vector dimensions match the dimensions configured for the
embedding_model. - Perform batch incremental updates, check that the index backend only displays index tasks for newly added data, with no full reindexing logs.
- Enter ingredient name keywords for retrieval, check that results include both semantically matched text content and structured field-matched ingredient information.
- View the index configuration page, confirm that
filter_field_listhas added core fields such as filing number and ingredient name, and that theretrieval_top_kvalue meets scene 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.