What the data for this category looks like
Data sources for financial leasing intelligent due diligence reports include internal business ledgers, industrial and commercial public platforms, third-party credit reporting agencies, and leased asset registration systems. Basic subject information is synchronized and updated quarterly. Rent repayment and leased asset operation status are updated monthly. Initial certification documents during the project approval stage are archived once. Document structures include structured project ledgers and unstructured ownership certification documents. Fields cover leased asset original value, rent amount, lease term, lessee credit rating, and more. Corresponding units are yuan, yuan, month, and rating level respectively.
Constraints for model integration and configuration
Format differences across multi-source heterogeneous data require configuring unified mapping rules for multi-source data, to avoid field parsing deviations. Monthly updated rent data and quarterly updated subject information require matching the model’s context refresh interval to the data update rhythm, to avoid using expired information. The high proportion of unstructured ownership certification documents requires configuring segmentation parameters adapted for long text parsing, to avoid truncating key ownership clauses. Some fields must strictly match industry standard units. This requires the model’s output to include standardized unit verification logic.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | Financial leasing due diligence reports contain long text spliced from multi-source data, requiring a sufficient context window to retain complete project information |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Unstructured ownership documents such as lease contracts are usually lengthy, requiring sufficient parsing time to be reserved |
RECALL_TOP_N | Top 8–12 entries | Financial leasing due diligence requires associating multi-dimensional project data, and an appropriate number of recalled entries can cover core associated information |
SIMILARITY_THRESHOLD | 0.75–0.85 | Low-relevance redundant data must be filtered, while retaining lease-related information across different dimensions |
MODEL_AVATAR_URL | HTTPS-format static image link, in PNG or JPG format | The platform requires avatar links to support the HTTPS protocol, and common image formats are compatible with front-end rendering |
AUDIO_TRANSCRIBE_MODEL | Locally deployed open-source speech recognition model | Avoid reliance on third-party accounts, adapting to usage scenarios without public accounts |
The parameter values provided on this page are all conventional recommendations used as a 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 conduct tests on your own samples before finalizing the configuration.
Three common mistakes
- Phenomenon: The model avatar fails to display normally on the front-end interface, or a loading failure prompt appears. Cause: The avatar link does not use the HTTPS format, or the image format does not meet the platform's compatibility requirements.
- Phenomenon: The speech recognition task returns an empty result or error code 400. Cause: A speech recognition model dependent on third-party accounts is selected, and no local open-source alternative is configured.
- Phenomenon: The unit of fields in the due diligence report output by the model is inconsistent, such as marking the leased asset original value as "yuan" while mixing non-standard units. Cause: No prompt for field unit verification is configured, or the
SIMILARITY_THRESHOLDvalue does not meet the configuration requirements.
How to confirm the configuration is complete
- Upload a standard financial leasing due diligence document, check whether the parsed structured fields are complete, and whether the units of corresponding fields comply with industry specifications.
- Initiate a model invocation test, verify whether the context window of the returned result covers the core content of the uploaded document without truncation.
- View the display effect of the model avatar on the front-end interface, confirm that the link is valid and the format is compatible.
- Test the speech recognition function, upload a speech file related to a leasing project, and check whether transcribed text can be generated normally.
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.