What the Data for This Category Looks Like
Financial leasing research report data is sourced from public reports released by domestic financial leasing industry associations, annual information disclosure documents of licensed leasing companies, industry operation briefs issued by regulatory authorities, and special analysis documents from professional consulting institutions. Public disclosure documents are updated quarterly, special research reports are released on demand, and industry briefs are updated monthly. Documents typically include project overview, transaction structure, financial forecasting, risk warning, and industry benchmarking modules. Core fields include financing amount, lease term, repayment cycle, lease asset category, and lessee industry, with corresponding units being ten thousand yuan, month, repayment times, general category names, and large categories of the National Economic Industry Classification.
Constraints for Multi-turn Dialogue and Prompt Engineering
Scattered data sources with inconsistent update cycles require clarification of required data time range and release channel type during multi-turn dialogue to avoid retrieving outdated or mismatched content. Core field classification logic differs from general industry research reports. For example, lessee industry must align with large categories of the National Economic Industry Classification. Define field definitions in initial prompts to reduce subsequent corrections. Single research report financial forecasting modules contain lengthy content. Limit total retrieved context length to avoid exceeding model processing limits. Some special research reports only cover specific lease asset types. Guide users to confirm their target lease asset category at the start of dialogue to narrow retrieval scope.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
recall count | Top 8–12 entries | Financial leasing research reports have lengthy core content per document. Too many recalled entries will exceed the model's context limit, while too few will fail to cover required project or industry information |
similarity threshold | 0.75–0.85 | Financial leasing research reports contain a large number of professional terms and specific business logic. It is necessary to ensure the semantic matching degree between retrieved content and queries, to avoid introducing irrelevant general industry content |
maxContext | 6000–8000 characters | The financial forecasting and transaction structure modules of a single research report have lengthy content. It is necessary to balance context length and model processing capability to avoid truncating key information |
promptTemplate | Custom template containing the constraint "Answer only based on the provided research report content. If no matching content is found, state that no corresponding information was retrieved" | Clearly limit the model to only use retrieved research report data to generate responses, avoid calling external irrelevant knowledge, and ensure response accuracy |
globalVariable | Preset fixed variables such as "lessee industry classification rules" and "research report update cycle" | Uniformly transmit industry-wide general rules, reduce repeated prompt content in multi-turn dialogue, and lower configuration complexity |
segment length | 1000–1500 characters | Professional paragraphs of financial leasing research reports are lengthy. The segment length adapts to the integrity of professional terms, avoiding splitting that disrupts business logic |
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 specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: Global variables fail to replace into prompts as expected after calling the AI dialogue node. Cause: The
{{变量名}}syntax is not used correctly in the prompt template, or global variable names contain special characters leading to parsing failure. - Symptom: The
chat:LLM_model_response_emptyerror appears after multiple calls, with empty returned content. Cause: Matching degree between retrieved research report content and queries is too low, or the model receives insufficient valid context to generate a valid response. - Symptom: Garbled half-width quotation marks appear at the end of AI output, followed by automatic correction to standard quotation marks. Cause: Incorrect handling of trailing punctuation during document segmentation causes the model to receive incomplete punctuation context, generating abnormally formatted content.
How to Verify Proper Configuration
- Initiate a single-round professional query, verify whether retrieved research report content includes query keywords, and adjust corresponding configuration items until retrieved content matches business requirements.
- Input a test query containing global variables, check whether the prompt template correctly replaces variable content, and confirm that variable transfer logic operates normally.
- Trigger multiple dialogue calls, observe whether the
chat:LLM_model_response_emptyerror occurs, and troubleshoot rationality of context length and retrieval rule configurations. - Export a single segment of retrieved research report content, check whether segmented paragraphs retain complete professional terms and punctuation, and verify segmentation configuration effectiveness.
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.