What the data for this category looks like
Data sources for white goods financing daily reports include supply chain finance systems, bank credit ledgers, and financing filing data from home appliance industry associations. Updates follow a daily T+1 schedule, with full financing records from the previous day updated each day. Documents are stored in structured CSV or JSON formats. Core fields included in a single document are: financing_entity (financing subject, e.g., regional home appliance dealers, contract manufacturers), amount (financing amount, unit: ten thousand yuan), tenor (financing term, unit: days), channel (financing channel, e.g., state-owned banks, supply chain finance platforms), purpose (financing purpose, e.g., raw material procurement, offline store restocking), record_date (financing record date).
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Dispersed data sources require multi-turn dialogue to first guide users to clarify data source scope. This prevents inclusion of non-filing financing data. The daily T+1 update schedule requires prompts to strictly limit usage to uploaded daily and historical T+1 daily report data. Unupdated real-time information must be excluded. Fields have clear unit requirements. Amount uses ten thousand yuan, term uses days. Multi-turn dialogue must verify that user input units match these requirements. This avoids unit confusion. Financing purposes mostly center on restocking and raw material procurement for home appliance supply chains. Prompts must link to this category’s specific purposes, and filter irrelevant general financing records.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | Previous 9 turns of dialogue history | Multi-turn dialogue for white goods financing daily reports mostly focuses on multiple financing records from a single dealer. 9 turns is sufficient to cover context associations, and avoids excessive context leading to overflow |
similarityThreshold | 0.72 | Core fields of white goods financing (amount, term, purpose) have high distinctiveness. A threshold of 0.72 filters out financing data from non-home appliance categories, while retaining accurately matched entries |
fileParseChunkSize | 900–1100 characters | The standard length of individual white goods financing daily report documents falls within this range. After segmentation, all fields of a single financing record can be fully retained |
recallTopK | Top 7 entries | Valid entries in daily white goods financing daily reports typically range from 5 to 8. Recalling 7 entries covers all valid data and avoids omissions |
promptTemplate | Only use uploaded white goods financing daily report data. When answering, unify the amount unit to ten thousand yuan and term unit to days, and only extract financing records related to white goods | Clearly define data sources and output formats, avoid mixing in financing information from other categories, and unify output units |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test against local samples before finalizing.
Three Common Mistakes
- Symptom: After executing a dialogue deletion operation, associated log records are synced and deleted, with no corresponding entries in the
conversation_logdatabase table. Cause: Thepreserve_log_after_deleteconfiguration item is not enabled. The default logic clears associated logs when a dialogue is deleted. - Symptom: When referencing multi-turn dialogue history in prompts, financing data from non-white goods categories appears, and results include irrelevant industry financing records. Cause: The prompt does not limit the data source to white goods financing daily reports, and does not specify filtering rules.
- Symptom: When calling the prompt content acquisition interface, application configurations from other accounts are returned, resulting in permission leaks. Cause: The
account_idandapp_idparameters are not included in the interface request, and account permission verification is not performed.
How to Verify Configurations Are Set Correctly
- Upload one test white goods financing daily report document, initiate a query that includes the amount and term fields, and verify that returned result units match preset requirements.
- Initiate consecutive multi-turn dialogues, test whether context from previous N turns is correctly called, and verify that dialogue history context association logic functions as expected.
- Execute a dialogue deletion operation, check whether log records are retained, and verify the actual effect of the
preserve_log_after_deleteconfiguration. - Call the prompt acquisition interface for the specified account and application, verify that returned content only includes the current application’s configurations, with no cross-account data.
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.