What the data for this category looks like
Cosmetics investment research data primarily comes from official brand filing documents, ingredient test reports, e-commerce platform user reviews, industry compliance announcements, and new product launch information. Most filing documents use PDF format. They contain ingredient lists, compliance statements, and efficacy claim fields. Ingredient lists label units such as CAS numbers and concentration percentages. E-commerce reviews consist of structured text paired with purchase scenarios and star ratings. New product launches and compliance updates trigger irregularly in line with brand release cycles. Industry announcements update in concentrated batches each quarter.
Constraints these characteristics impose on context and token handling
The multi-source nature of cosmetics investment research data creates a need for context splicing. Long texts from individual filing documents may consume large amounts of tokens. They must be split before integration. Precise field matching for ingredient lists requires context to retain complete ingredient concentration and CAS number information. This avoids information loss from truncation. High-volume incremental e-commerce reviews increase pressure on context recall count limits. If recall volume is not restricted, it easily exceeds the model’s token threshold. Irregular new product updates also require context windows to flexibly adapt to newly added compliance fields and efficacy claim content. This prevents context from becoming outdated.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
Max knowledge base citations | Top 8–12 entries | Cosmetics investment research data includes long-text filing documents and large volumes of reviews. Restricting recall counts controls context token usage, and avoids single requests exceeding model thresholds |
Max Response Tokens | 2000–4000 characters | Cosmetics investment research responses require ingredient analysis, compliance explanations, and efficacy comparisons. Sufficient tokens are reserved for complete output, to adapt to long-text response needs |
Chunk size | 1000–1500 characters | Adapts to long-text splitting of individual filing documents. Ensures each segment retains complete ingredient and compliance fields, and avoids key information being truncated |
Similarity threshold | 0.75–0.85 | Filters low-relevance reviews and non-core filing content. Reduces invalid token usage, and improves context matching accuracy |
Rerank result count | Top 3–5 entries | Rearranges recalled candidate content to return core information. Compresses context length, while retaining key ingredient and efficacy data |
ai proxy api | Fill in Custom Model API Address and Key | Supports access to locally deployed large models. Adapts to cosmetics investment research requirements for ingredient data privacy, and avoids token restrictions from third-party interfaces |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Each scenario requires specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Symptom: Ingredient analysis in model outputs has missing or incorrect concentration values. Cause: No reasonable
Chunk sizeis set, so long PDF filing documents are truncated, and ingredient concentration fields are not fully included in the context. - Symptom: Token overflow errors occur when calling the knowledge base, with a 413 status code. Cause:
Max knowledge base citationsis not restricted, so large volumes of e-commerce reviews and filing documents are recalled simultaneously, exceeding the context token threshold. - Symptom: Custom model access fails after configuration, with an "invalid key" error returned. Cause: The interface address and key for
ai proxy apiare not filled correctly, or the model’s token format requirements are not matched, leading to failed context request transmission.
How to confirm proper configuration
- Upload a brand filing PDF file, check if the parsed text retains complete ingredient concentration and CAS number fields, and confirm no truncation occurs.
- Submit an investment research query, check if the number of context references returned matches the preset
Max knowledge base citations, with no extra redundant content. - Test custom model access, submit a query that includes multi-source data, confirm the model can return complete ingredient analysis and compliance explanations without token errors.
- Check the knowledge base image upload records, confirm returned image links are fully accessible addresses with no truncation.
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.