What this category of data looks like
Automated equipment financing daily report data draws from industrial and commercial procurement record announcements, industry bidding platforms, and bank corporate financing ledgers. The system completes a full update every midnight. Each document includes these fields: equipment model, purchasing entity, financing amount, financing term, disbursement date, supplier qualification level, and others. Financing amount is measured in ten thousand RMB. Disbursement date uses the ISO 8601 standard date format. The equipment model field must follow industry-general equipment coding rules. Some documents include attached technical parameter files for the equipment.
Constraints for multi-turn dialogue and prompt engineering
Daily full data updates require limiting multi-turn dialogue context to the most recent 3 days of financing data. This avoids interference from cross-cycle redundant information. Standardized field rules require prompts to clearly specify extraction formats for each field. For example, disbursement date must return the ISO 8601 format. Financing amount must include the unit. In multi-turn dialogue, avoid repeating questions about already collected equipment model and purchasing entity information. Also, check if the financing term unit aligns with industry conventions (month/year). This prevents generation of results that do not fit business logic. Unstructured supplier qualification level fields must be supplemented gradually during multi-turn interactions. This ensures complete information.
How to set configuration values
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | Last 8 rounds of dialogue + most recent 3 days of financing dataset | Automated equipment financing daily report data updates daily. The most recent 3 days of data covers the current business analysis cycle. Excessive context exceeds model token limits and causes content truncation |
recallTopK | Top 6 recall results | Core fields of automated equipment financing daily reports focus on three categories: equipment, amount, and date. Too many recall results add prompt redundancy and slow model response |
similarityThreshold | 0.75–0.85 | Matching accuracy for automated equipment models and financing information is high. A low threshold introduces irrelevant data. A high threshold misses valid matching items |
multiRoundMaxTurn | 5 rounds of interaction | Information supplementation for financing daily reports focuses on qualification and term fields. 5 rounds of interaction cover complete information collection. It avoids model confusion from overly long dialogues |
responseFormat | JSON format, including three core fields: equipment model, financing amount, disbursement date | Most downstream processing of financing daily reports uses structured data imports. A fixed format simplifies subsequent splicing and calling workflows |
The parameter values provided on this page are all conventional recommendations used to determine the starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common errors
- Phenomenon: The output of the AI dialogue node in the workflow is displayed directly on the front-end page, without being processed by the text splicing component before output. Reason: The "hide intermediate node output" configuration item of the workflow is not enabled, and the AI response is not set to a structured output format.
- Phenomenon: After knowledge base retrieval results are passed to the AI dialogue node, the model cannot correctly extract equipment model and financing amount fields from the financing daily report. Reason: The prompt is not written according to the actual field names in the knowledge base documents, so the model cannot match valid information in the retrieval results.
- Phenomenon: The second AI node in a multi-turn dialogue cannot access the financing daily report analysis result generated by the first node. Reason: No variable mapping is configured in the workflow, and the output of the first AI node is not used as the context input parameter for the second node.
How to confirm configuration is complete
- Start workflow testing. Check the intermediate node logs. Confirm the AI dialogue node output is in structured format and is not directly displayed on the front-end page.
- Import a test document for the automated equipment financing daily report. Launch a multi-turn dialogue. Verify the model correctly extracts specified fields and matches business logic.
- Configure two series-connected AI dialogue nodes. Launch a test. Confirm the second node can correctly access the output content of the first node as input.
- Test the multimodal input scenario. Upload images of equipment models or technical parameter drawings. Confirm the model can associate relevant data from the financing daily report.
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.