What the Data for This Category Looks Like
Data for white goods primarily comes from official brand product manuals, e-commerce platform parameter pages, national energy efficiency label databases, after-sales operation and maintenance documents, and installment policy documents from financial cooperation institutions. Updates follow new product launch cycles, energy efficiency standard adjustments, and financial policy changes, with no fixed high-frequency update schedule. Each document mostly consists of structured parameter tables paired with explanatory text. Fields include product model, rated power, volume, energy efficiency rating, installation dimensions, installment rates, and more. Power is measured in watts, volume in liters, and dimensions in millimeters. Some documents include marketing adaptation scripts and financial cooperation rule templates.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
The coexistence of structured parameters and unstructured explanatory text requires multi-turn dialogue to distinguish three scenarios: precise parameter queries, marketing script generation, and financial rule adaptation. This prevents mismatched output of parameter values, units, and financial rates. The non-fixed update rhythm requires the dialogue system to support periodic full refresh of the knowledge base, avoiding calls to expired energy efficiency ratings or financial installment policies. Clear unit requirements for fields require prompt engineering to enforce unit validation rules. This ensures output values such as power and volume are paired with correct units, while matching the precision of financial installment rates. Differences across document sources require the dialogue system to filter recalled content by document type, preventing after-sales fault content from being mixed into marketing and financial adaptation workflows.
How to Set Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Covers context information for 3 or more rounds of marketing conversations, preventing the model from confusing multiple sets of product parameters and financial rules |
recall_top_k | 8–12 entries | Covers multi-dimensional needs for white goods parameters and financial installment rules, while controlling the proportion of redundant information |
similarity_threshold | 0.72–0.78 | Filters low-relevance after-sales documents, retaining only content strongly matched with marketing themes and financial rules |
parse_chunk_size | 600–800 characters | Preserves semantic integrity of product parameter blocks and explanatory text, avoiding splitting that breaks the association between parameters and financial rules |
var_persist_mode | Session-level persistence | Retains temporary variables specified by users such as product model, budget, and installment number, preventing conversation resets |
knowledge_base_filter | Filter by document tags | Only recalls documents categorized as "marketing scripts", "product parameters", and "financial rules", excluding interference from after-sales content |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis, and testing on own samples is recommended before finalizing settings.
Three Common Mistakes
- Phenomenon: When new attachments are uploaded, the system automatically loads all previously uploaded documents for parsing. Cause: The "parse only currently uploaded files" configuration item is not enabled, and the full historical document pool is called by default.
- Phenomenon: A format error is triggered when the
knowledge_base_idvariable is passed. Cause: The correct data type for the variable is not configured as required by the system, or the variable value is not passed correctly in the conversation chain. - Phenomenon: Some conversations do not recall knowledge base content and directly generate generic responses. Cause: The "must call knowledge base" configuration switch is not enabled, or the
similarity_thresholdis set too high, causing no matching content to trigger recall.
How to Verify Proper Configuration
- A single white goods product document is uploaded. Parsing is triggered, and the parsing record is checked to confirm only the current document is processed.
- A conversation including product parameter and financial installment queries is initiated. The output fields, units, and financial rules are verified to match the knowledge base documents.
- A preset knowledge base identifier variable is passed to initiate a conversation. The knowledge base content called by the system is confirmed to meet expectations.
- A multi-turn marketing conversation is initiated. Temporary variables specified by the user are verified to be used normally in subsequent interactions.
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.