What the Data for This Category Looks Like
Data sources include national regulatory department filed product information, brand official test reports, and third-party quality inspection public data. Updates occur irregularly, aligned with new product launches and compliance requirement adjustments. Single document structure includes product name, filing identifier, ingredient list (with ingredient identifiers and content value items), production entity information, usage specifications, expiration date identifier, and compliance statement. Fields include string-type filing numbers, numeric content parameters, time-type expiration dates, and units such as milligrams per gram and months.
Constraints on Multi-turn Dialogue and Prompt Engineering
Scattered, complex structured data creates core constraints for multi-turn dialogue. Explicitly specify the priority of three data sources (filing, quality inspection, e-commerce) in prompts to avoid mixing information from different sources. The multi-field structure of ingredient lists requires guiding users to clarify their focused ingredient dimension during multi-turn dialogue. This prevents context overflow that causes the model to mix ingredient parameters of different products. For time-type expiration date fields, configure context association logic. This ensures subsequent user queries about shelf life match the product information in the current session. Fixed fields for compliance statements require explicitly stating in prompts that only compliant content from filing documents may be referenced. Unfiled statements must not be generated.
Configuration Settings
This configuration is based on FastGPT V4.9.1.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Single personal care product documents range from 1000 to 3000 characters in length. Multi-turn dialogue requires retaining 3 to 4 rounds of context. This interval covers product data and conversation history |
recallTopK | Top 6 entries | Personal care product data is scattered across three sources: filing, quality inspection, and e-commerce. Recalling 6 entries covers core information while avoiding redundancy |
similarityThreshold | 0.72–0.85 | Semantic matching precision requirements for ingredient and compliance fields are high. This interval filters low-relevance recall results |
memoryWindowSize | 4 rounds | Personal care due diligence focuses on ingredients, shelf life, and compliance. 4 rounds of context cover the complete user query chain |
fileParseChunkSize | 1200 characters | Ingredient lists in personal care documents are lengthy. 1200-character chunking preserves ingredient information integrity and avoids split breaks |
apiChatHistoryScope | Isolated by user | In multi-user scenarios, this configuration ensures different users can only view their own conversation and product query history |
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 case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Debug preview functions normally, but formal deployment fails to recall personal care product data. The symptom is that formal environment API calls return no matching documents, while the preview interface works correctly. The cause is that the formal environment is not bound to the corresponding knowledge base, or the knowledge base permission configuration does not grant access to the formally used API key.
- Ingredient information of different personal care products is mixed during multi-turn dialogue. The symptom is that the model associates ingredients from product A with queries for product B. The cause is that
memoryWindowSizeis set too small, failing to retain the product identifier context of the current session, leading to context overflow and confusion. - API calls for chat data only return page-side conversations. The symptom is that API response results do not include conversation records created via the API. The cause is that
apiChatHistoryScopeis not configured as user-isolated, or thechatIdparameter is not specified to filter the target session.
How to Verify Correct Configuration
- Access the application configuration page, confirm core parameters including
maxContextandrecallTopKmatch preset configurations and the recommended values in the table. - Create a test session, submit ingredient queries for different personal care products, and confirm model output only draws from the bound knowledge base, with no irrelevant information present.
- Switch between test user identities to initiate conversations, and confirm only the active user can view their own conversation records, with no cross-user access allowed.
- Call the API interface with a specified
chatId, and confirm returned results only include chat data from the corresponding session, with no cross-session content included.
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.