Vector Models and Indexing for Cosmetics Investment Research

What the data for this category looks like

Cosmetics investment research data comes primarily from brand public ingredient filing documents, third-party testing agency ingredient analysis reports, e-commerce product detail pages, and industry compliance disclosures. Updates trigger irregularly alongside new product launches, ingredient compliance adjustments, and regulatory policy changes. Document structures include structured parameter tables, long-form ingredient educational content, and aggregated user reviews. Fields cover ingredient names, concentration values, efficacy descriptions, and production batch numbers. Common units are percentage, milligrams per milliliter, and ppm.

What Constraints These Characteristics Impose on Vector Models and Indexing

Structured parameter tables require vector models to support precise field-level embedding. This prevents loss of critical matching information such as concentration and compliance status due to semantic fragmentation. Long-form educational content needs reasonable segmentation rules. These rules avoid truncating associations between ingredients and their efficacy descriptions. Irregular update cycles require indexes to support incremental synchronization. This reduces resource overhead from full index rebuilds. Concentration fields with units require embedding processes to retain unit information. This avoids matching errors caused by missing units. Colloquial user review content requires indexes to balance semantic fuzzy matching and invalid noise filtering.

How to Configure Settings

Configuration Item	Recommended Value	Rationale
`embedding_model`	`text-embedding-3-large`	Adapts to embedding accuracy for long text and structured fields, covering multiple types of investment research content such as ingredient concentrations and efficacy descriptions
`chunk_size`	`800–1200 characters`	Prevents truncation of associations between ingredients and their efficacy descriptions, ensuring semantic integrity of long-form cosmetic educational content
`chunk_overlap`	`100–150 characters`	Retains semantic connections between adjacent segments, preventing ingredient information from being split by segmentation
`retrieve_top_k`	`Top 8–12 results`	Balances retrieval recall scope and response speed, adapting to the aggregation needs of multi-dimensional information in investment research scenarios
`similarity_threshold`	`0.72–0.85`	Filters low-match irrelevant content, retaining retrieval results highly relevant to target ingredients and efficacy
`enable_incremental_index`	`Enabled`	Adapts to irregular data synchronization needs for new cosmetics and compliance updates, reducing resource consumption from full index rebuilds
`parse_image_index`	`Calibrated based on actual testing`	Adapts to ingredient annotation extraction from product detail page images in version v4.9.0, requires confirmation of version permission configuration

The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.

Three Common Errors

Symptom: After local deployment of version v4.9.0, no image indexing model option appears when creating a new knowledge base. This prevents extraction of ingredient information from product images. Cause: This function requires manual enabling of the corresponding permission module in local deployment versions, or the access key for the corresponding model has not been configured.
Symptom: Retrieval results include a large number of low-match non-target ingredient contents. The number of recalled results far exceeds the configured value. Cause: The similarity_threshold parameter is not set, or the threshold is set too low. This fails to filter retrieval results with semantic deviations.
Symptom: After uploading an ingredient parameter table, precise matching of ingredients with specified concentrations fails during retrieval. Cause: The chunk_size setting is too small. This truncates the association fields between concentration values and ingredient names, leading to loss of critical matching information during embedding.

How to Verify Configurations Are Correct

Upload a single cosmetics ingredient filing document. Trigger the vector embedding and indexing process. Check if the number of segments shown in the indexing task log matches the document structure.
Input the target ingredient name and concentration value as retrieval terms. Verify that the retrieval results include matching field information. Adjust the similarity_threshold to a range that meets business requirements.
Upload a new compliance announcement document. Verify that the index completes incremental updates automatically without triggering a full rebuild process.
Check the status of each configuration item in the knowledge base settings interface. Confirm that the status matches the preset values. Verify that the permission configuration for the parse_image_index function is correct.

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.