What the data for this category looks like
Data sources for cosmetics research reports include public industry research institute reports, official brand compliance announcements, public data from third-party compliance testing institutions, and e-commerce platform transaction data. Update frequencies vary:
- New product-related reports are updated in real time alongside product launches
- Quarterly industry reports are updated on a quarterly cycle
- E-commerce sales data is updated daily
Single documents follow a standard structure: product category classification, core ingredient list, compliance testing conclusions, sales channel performance, and user feedback keywords.
Available fields include: product SKU code, price range, ingredient annotation items, test qualification status, and user mention frequency. Corresponding units are: code, yuan, item, boolean value, and count.
What constraints these characteristics impose on multi-turn conversation and prompt engineering
Dispersed data sources require prompts for multi-turn conversations to explicitly define data source scope. This prevents mixing information from different sources.
Varying update frequencies require prompts to include time-bound restrictions. This stops outdated data from being returned.
Multi-dimensional information in document structures requires multi-turn conversations to support layered follow-up questions. For example, extending a category query to detailed information about specific ingredients or prices.
Fields with multiple unit types require prompts to clearly match unit rules. This prevents incorrect numerical units in outputs.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Cosmetics research reports have long segmented lengths per document. Sufficient context retains multi-turn conversation history and avoids losing category restrictions from prior queries |
recall_top_k | Top 8–12 results | Cosmetics research reports contain multi-dimensional data. Too many recalled results cause information overload. Too few fail to cover required dimensions such as ingredients and sales |
similarity_threshold | 0.75–0.85 | Keywords in cosmetics research reports, such as ingredient names and brand names, have high recognizability. This threshold filters low-relevance non-target report content |
parse_chunk_size | 1000–1500 characters | Cosmetics research reports include long paragraphs of ingredient analysis and sales data. This chunk size preserves complete information units and avoids breaking the coherence of ingredient descriptions |
conversation_max_rounds | 10–15 turns | Cosmetics research report queries typically include multi-round follow-up questions. This turn count covers most users' complete query workflows |
re_rank_top_n | Top 3–5 results | Reranking optimizes the relevance of recalled results. Limiting returned results reduces LLM processing load and speeds up multi-turn conversation responses |
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. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: Parsed research report text in multi-turn conversations is too long, triggering a
413 Request Entity Too Largeerror. Cause: TheUPLOAD_FILE_MAX_SIZEandmaxContextconfigurations are not adjusted to match each other, causing single-round input or total context length to exceed system limits. - Symptom: After connecting multiple cosmetics research report knowledge bases, conversation results only return content from some categories. Cause: No reasonable range is set for
similarity_thresholdandrecall_top_k, causing highly relevant results to be overwritten by low-relevance content, or insufficient recalled results. - Symptom: In international deployment environments, the knowledge base works correctly in preview but throws errors during formal conversations. Cause: No cross-origin access whitelist rules are configured, or the
PARSE_FILE_TIMEOUT_SECONDSparameter value for international deployments differs from domestic deployments, causing research report parsing timeouts.
How to Confirm Configurations Are Set Correctly
- Manually input a long text segment from a cosmetics research report, start a multi-turn conversation, and confirm that category and ingredient information from prior queries is correctly linked to subsequent responses.
- Adjust the number of recalled results and similarity threshold, run a query with multi-dimensional requirements, and verify that the coverage of returned results matches expectations.
- Upload cosmetics research report documents from different sources, test that parsed chunks preserve complete information units with no content breaks.
- Switch to the formal deployment environment, run a query identical to the one used in the preview phase, and verify that returned results match preview results.
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.