What the data for this category looks like
The market and profit margin data for personal care products is sourced primarily from public e-commerce platform sales APIs, brand distributor inventory and sales datasets, and public industry sales monitoring documents. Data is updated daily to support daily report broadcasting requirements. Each individual data entry includes SKU identifier, full product name, core ingredient tags, same-day terminal selling price, price difference from the previous day, same-day units sold, cumulative sales amount, and remaining inventory quantity. Field units include Chinese yuan, units, and count; no percentage-based units are used.
What constraints these characteristics impose on multi-turn dialogue and prompting
The daily updated data characteristic requires that multi-turn dialogue must limit the time range of the context to avoid referencing expired historical data. The structure with multiple fields and SKU identifiers requires that prompts must explicitly specify the SKU and target field to prevent confusion over market values for different products. The multi-source data characteristic requires that prompts must explicitly specify the preferred data source type to avoid conflicting data from mixed channels. Additionally, multi-SKU query scenarios require that the dialogue flow must support filtering by user-specified categories or SKUs to reduce interference from irrelevant data.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 800–1200 characters | Personal care product data has many fields; overly long contexts risk confusing SKUs and values. This range preserves valid conversation history while avoiding redundancy |
ragTopK | Top 6 results | The volume of data per personal care SKU is moderate; retrieving 6 results covers market data for mainstream competing products and core SKUs, avoiding interference from excessive irrelevant information |
similarityThreshold | 0.72–0.78 | SKU names for personal care products have high similarity; this threshold filters low-relevance recall results while retaining valid data for niche product categories |
responseMode | Non-streaming output | Daily market reports require complete results to be spliced and returned uniformly; streaming output causes chaotic intermediate fragments. The AI must finish generating before the component splices and returns the final result |
contextCacheTTL | 300 seconds | The valid cycle of daily updated market data is 1 day; setting a 300-second cache reduces repeated retrieval while avoiding use of expired data |
promptTemplate | Generate query instructions by specifying SKU + target fields | There are many personal care product SKUs; explicitly specifying the SKU and field allows the AI to accurately locate required data, avoiding generalized queries |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require on-site analysis; it is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Phenomenon: The conversation interface displays fragmented AI-generated content in real time, without splicing and returning the complete result as expected. Cause: The
responseModeis not configured for non-streaming output, causing the dialogue flow to push real-time generated intermediate content directly. - Phenomenon: The second round of AI dialogue cannot read the output results from the first round, resulting in an error that the input field is empty. Cause: Context variable binding configuration is not enabled, and the output content from the first round of dialogue is not passed as an input parameter to the second round of dialogue flow.
- Phenomenon: Annotated content in conversation logs cannot be called by subsequent dialogues, causing context association to fail. Cause: Conversation log field extraction configuration is not enabled, so annotated content is not included in the context window.
How to confirm correct configuration
- A query containing multiple specified SKUs can be initiated to verify that the number of results returned by the AI matches the configured
ragTopKvalue; the threshold may be adjusted to align with actual requirements. - The multi-turn dialogue flow may be tested: after submitting a first-round query, a second-round query that relies on the prior round’s results is initiated, confirming the second round can correctly read and use the first round’s output content.
- The annotated fields in the conversation log may be viewed to confirm that the annotated content has been included in the context window and can be called by subsequent dialogue flows.
- After configuring non-streaming output, a query may be initiated to confirm that the interface only displays the complete result after the AI finishes generating, with no intermediate fragment output.
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.