What the Data for This Cosmetics Category Looks Like
Data sources for cosmetics profit margin and market trend data include official daily sales briefings published by brands, real-time sales monitoring data from e-commerce platforms, and public datasets from third-party monitoring institutions in the beauty industry. Update frequency is daily, covering full-channel sales and profit-related data from the previous calendar day. Most documents are structured tables containing fields such as brand name, SKU unique identifier, listing period, channel category, revenue amount, profit margin ratio, and benchmark brand peer comparison data. Revenue amount is denominated in Chinese yuan. Profit margin ratio is recorded as a decimal value, and percentage notation is not used.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
The multi-SKU nature of the cosmetics category requires multi-turn dialogue to track brand and SKU filtering conditions specified by users. Prompts must explicitly require aligned field dimensions in outputs. The daily updated data characteristic requires configuring scheduled tasks to synchronize data sources. Multi-turn dialogue must validate the time range requested by users to avoid calling expired data. The large number of structured fields in documents requires prompts to specify field mapping rules to prevent field misalignment during parsing. Additionally, the need for benchmark brand comparisons requires multi-turn dialogue to retain previous round comparison context, and prompts must explicitly require aligned formats for comparison items in outputs.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | Previous 15 rounds of dialogue context | The cosmetics category has a large number of SKUs, so multi-turn dialogue must track brand and SKU filtering conditions. Excessive context will interfere with field matching and logical judgment. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Cosmetics revenue daily reports may contain bulk data for dozens of SKUs, resulting in long parsing times. This setting must accommodate the parsing duration of bulk files. |
UPLOAD_FILE_MAX_SIZE | 200 MB | Daily reports for bulk SKUs may include detailed data across multiple channels, so support for large-capacity file uploads is required. |
Recall count | Top 8 entries | The number of benchmark cosmetics brands is relatively concentrated. Excessively retrieved knowledge base content will overload the prompt and reduce field matching accuracy. |
Similarity threshold | 0.75–0.85 | Cosmetics SKU names have high similarity, so this setting must balance precise matching and coverage of similar SKUs. |
Chunk size | 800–1200 characters | Cosmetics revenue data has many structured fields. Segmentation must retain complete SKU data blocks to avoid parsing errors caused by field splitting. |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: A 503 error is displayed in the frontend when uploading a cosmetics revenue daily report document, but backend logs show the file upload was successful. Cause: The
UPLOAD_FILE_MAX_SIZEconfiguration does not accommodate the volume of bulk SKU documents. The backend triggers a timeout threshold during file parsing, returning a 503 status code even though the upload interface has completed file reception. - Symptom: When using a strict question-and-answer template to initiate a query, the system retrieves cosmetics SKU image addresses from the knowledge base and prompts that no answer was found. Cause: The
Enabled图片OCR解析parameter is not configured. The system only recognizes image links and cannot extract revenue data fields embedded in images, so it cannot match the field requirements of the strict question-and-answer template. - Symptom: When asking about the profit margins of different brands of cosmetics in sequence during multi-turn dialogue, the system repeatedly requests brand information. Cause: The
maxContextconfiguration round count is too low, and the previous round's brand filtering conditions are not retained, causing a break in multi-turn dialogue context.
How to Verify Proper Configuration
- Upload a single cosmetics revenue daily report document containing 10 or more SKUs, check the frontend upload status and backend parsing logs to confirm no 503 errors occur.
- Create test data containing SKU packaging images, initiate a query using a strict question-and-answer template, and check whether the system can correctly extract field information from the images and return results.
- Initiate 2 or more concurrent cosmetics profit margin query requests, check whether they can be processed normally in parallel with no request blocking.
- Initiate multi-turn dialogue, ask about the profit margins of different brands and SKUs in sequence, and check whether the system can retain the previous round's filtering conditions without repeatedly requesting basic information.
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.