What the data for this use case looks like
Data sources for urban commercial bank intelligent due diligence reports include the bank's internal credit ledgers, audited financial statements submitted by customers, enterprise operation data from third-party credit reporting agencies, and industry submission data from local regulatory authorities. Update frequencies vary by source: internal credit ledgers update on a T+1 basis, customer financial statements update semi-annually or annually, and regulatory submission data updates monthly.
Document structure consists primarily of structured fields paired with PDF-format attachment documents. Structured fields include unified social credit code, registered capital, establishment date, credit amount, revenue, and more. Units are mostly ten thousand yuan and date format. Attachment documents are mostly audit reports or credit reports, with a single document length of 100 to 500 pages.
What constraints do these characteristics impose on model access and configuration?
The above data characteristics impose clear constraints on the model access and configuration process. Multi-source heterogeneous data sources require model access to support a hybrid mode of structured field parsing and unstructured PDF text extraction, with corresponding data format adaptation parameters to be configured. Differences in update frequencies across data sources require configuring incremental sync and full sync switching parameters to avoid repeated data pulls or missed updates. Fixed units and format requirements for structured fields require the model to support field type verification and unit standardization to prevent unit errors in parsing results. The presence of long attachment documents requires configuring reasonable segment length and context window parameters to avoid parsing timeouts or context overflow.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Adapts to the total length of structured fields and attachment fragments of urban commercial bank due diligence reports, avoiding context overflow |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Matches the parsing time of long PDF audit reports, preventing parsing tasks from being interrupted mid-process |
RECALL_TOP_K | Top 10–15 entries | Covers core credit and operation field dimensions of urban commercial bank due diligence reports, balancing recall rate and retrieval efficiency |
SIMILARITY_THRESHOLD | 0.72–0.78 | Adapts to the high-precision matching requirements of structured fields, avoiding false recall of irrelevant data |
Rerank result count | Top 5 entries | Focuses on core analysis indicators of due diligence reports, reducing interference from non-essential information on model inference |
SYNC_INCREMENTAL | Enabled | Adapts to the high-frequency updates of internal credit ledgers, reducing resource usage from full sync |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis, and testing on relevant samples is recommended before finalizing settings.
Three common configuration errors
- Phenomenon: After configuring
Rerank result count, theis_rerankfield in the question answering session return result is false. Cause: Reranking is only enabled in the knowledge base settings, and the trigger node for the reranking model is not bound in the workflow, so the reranking logic is not actually executed. - Phenomenon: A
404 Not Founderror is returned when calling an external MCP plugin in a workflow. Cause: The interface address and permission parameters of the corresponding MCP plugin are not registered in the platform plugin management page, so the workflow cannot find the plugin service. - Phenomenon: The front end displays "Current browser does not support voice input" when calling the voice recognition function. Cause: The request is not forwarded through the platform backend voice service node, and the browser's native voice recognition API is called directly, without adapting to the network and permission restrictions of the internal office environment.
How to confirm the configuration is complete
- View the knowledge base parsing log, check that the extracted fields match the preset field list of the urban commercial bank due diligence report, and adjust parsing-related parameters until the extraction accuracy meets requirements.
- Run a test workflow, call the configured MCP plugin and voice service, check that the returned status code and field content meet expectations, and adjust interface parameters until the call is successful.
- Initiate a simulated question answering request, check that the returned result includes the output fields of the reranking model, and adjust reranking-related parameters until the result meets analysis needs.
- View the running records of the scheduled sync task, check that the data update interval matches the actual update rhythm of the data source, and adjust sync parameters until resource usage is reasonable.
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.