What the data for this category looks like
Data is sourced from dealer financing ledgers on home appliance supply chain finance platforms, daily micro-credit reporting systems for home appliance-related small businesses from banks, and internal payment settlement records of brand owners. Data is updated per calendar day, with aggregation and verification of the prior day’s data completed each early morning. Each data entry is stored as a structured table, containing seven core fields: enterprise entity name, small home appliance SKU code, daily financing quota, single financing amount (unit: ten thousand yuan), repayment term, dealer affiliated region, and financing purpose. No nested sub-documents are included.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The structured storage and daily update characteristics of small home appliance financing daily reports impose clear constraints on multi-turn dialogue and prompt configuration. First, structured fields include precise filter options such as SKU code and region. Multi-turn dialogue must support users to gradually supplement filter conditions to avoid irrelevant results returned by fuzzy retrieval. Second, data is updated per calendar day. Prompts must explicitly specify that the large model only uses the latest daily data loaded in the current conversation, and prohibit calling historical financing records. The amount unit in the fields is ten thousand yuan. Prompts must uniformly mark the unit to prevent the large model from making unit conversion errors. Multi-turn dialogue context must retain the financing cycle range from the user’s first question, to avoid loss of key filter conditions during subsequent follow-up questions.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall count | top 8 entries | Small home appliance financing daily reports have a large number of daily entries. 8 entries cover most users’ filter needs and avoid excessive redundant information interfering with the large model |
similarity threshold | 0.72–0.78 | Small home appliance SKU codes are unique. This threshold filters out inaccurately matched financing records while retaining reasonable fuzzy retrieval space |
max_tokens | 1200–1500 characters | Each small home appliance financing record is approximately 100 characters. 1200 characters can accommodate around 10 recalled results, meeting the context length requirements for multi-turn dialogue |
temperature | 0.1–0.3 | Financing data requires strict accuracy. A lower temperature reduces random generation by the large model, ensuring responses align with recalled data |
reranked return count | top 5 entries | Users typically only focus on the top 5 most relevant financing records during multi-turn dialogue. Excessive entries increase conversational burden |
maxContext | 3 turns of dialogue | Multi-turn follow-up questions for small home appliance financing daily reports usually do not exceed 3 turns. Retaining 3 turns of context avoids context overflow while preserving key filter conditions |
The parameter values provided on this page are all conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to conduct actual tests on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: Multi-turn dialogue returns empty results from knowledge base retrieval, but retrieval works normally when tested in the knowledge base backend. Cause: A reasonable range for the
similarity thresholdis not configured, or therecall countis set too low, causing the current conversation context to interfere with the keyword weight of retrieval matching. - Symptom: A
404 Invalid URL (POST /api/chat/completions)error is returned during dialogue. Cause: The calling address of the large model interface is not configured correctly, or interface permissions are not enabled, causing conversation requests to fail to reach the large model service normally. - Symptom: A word count limit is set in the prompt, but the large model’s generated reply exceeds the limited word count. Cause: The effective scope of the word count limit is not clearly marked in the prompt, or the prompt verification logic of the large model is not enabled, causing the prompt to not be executed correctly.
How to Verify Proper Configuration
- Access the knowledge base backend, enter common user retrieval keywords for small home appliance financing daily reports, check whether the number and fields of retrieval results meet expectations, and adjust
recall countandsimilarity thresholdto the range matching required needs. - Initiate a multi-turn dialogue, enter filter conditions and follow-up questions in sequence, check whether the context is correctly retained, and that no key filter conditions are lost.
- Configure a test conversation, enter a prompt with a word count limit, check whether the large model’s generated reply meets the limited requirements, and adjust
max_tokensandtemperatureparameters to match the scenario. - Check interface configuration items, confirm that the calling address and permissions of the
chat/completionsinterface are correctly set, to avoid call failures.
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.