What This Category of Data Looks Like
Rural commercial bank research report data primarily comes from internal credit approval archives, county-level economic monitoring ledgers, local banking and insurance regulatory submission documents, and regional agricultural subject survey records. Update cycles include monthly fixed regulatory compliance reports, weekly dynamic regional industry surveys, and one-off dedicated credit research reports triggered on an ad-hoc basis. Most documents use a structured header plus semi-structured body, containing report subject, report date, coverage area, credit subject name, and core data fields. Common fields include non-performing loan ratio (unit: %), credit approval amount (unit: ten thousand yuan), and agricultural loan proportion (unit: %). Some documents include Excel attachments of quarterly operating data.
What Constraints Do These Characteristics Impose on Workflow Orchestration?
Rural commercial bank research reports have multiple traits that create constraints for workflow orchestration: multi-source dispersion, uneven update cadence, semi-structured format, and attached Excel files. Configure cross-data-source access nodes to support mixed calls of internal document libraries and API interfaces for multi-source data. Pair mixed update cycles with composite trigger rules combining scheduled scheduling and manual triggering to meet processing needs for fixed-cycle reports and one-off dedicated research reports. Integrate both structured field extraction and non-natural language text parsing nodes into the workflow to handle semi-structured documents and Excel attachments. Configure unit normalization rules to unify measurement standards for credit amount and proportion fields across different reports. Add entity recognition nodes to anchor regional and operating subject keywords to support precise matching of county-level and agricultural subjects.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Rural commercial bank research reports include multi-page documents and Excel attachments. Reserve sufficient parsing time to avoid process interruption due to timeout. |
maxRecall | Top 10 results | Rural commercial bank research reports cover narrow regions and concentrated subjects. Too many recall results are unnecessary; priority should be given to accurate matching. |
similarityThreshold | 0.75–0.85 | Research report content has high professionality. Use a high similarity threshold to filter irrelevant content and avoid recalling non-target reports. |
rerankTopN | Top 3 results | Combined with the business scenario of rural commercial banks, only the most relevant research reports are needed for decision-making reference. |
GLOBAL_VAR_PERSIST | Enable session-level persistence | Rural commercial bank research report retrieval often requires reusing variables such as regions and credit subjects across sessions. Retain variable values until the end of the session. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Adapt to the need for batch uploading quarterly operating data attachments by rural commercial banks, to avoid failures when uploading large files. |
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 case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: When re-entering after a session ends, regional and credit subject parameters stored in global variables are empty. Cause: The session-level persistence switch for
GLOBAL_VAR_PERSISTis not enabled. Variables are only temporarily stored during the current session and automatically cleared after the session closes. - Phenomenon: When calling an MCP node, only a single parameter can be passed, and the dual parameters of region and subject required for research reports cannot be obtained. Cause: The parameter collection branch is not configured in the question answering node of the workflow, and multi-dimensional information entered by users is not mapped to multiple input parameters required by the MCP.
- Phenomenon: The workflow returns a correct research report list during debugging, but the number of results returned during front-end testing does not match. Cause: The test dataset used during debugging is a locally filtered sample, while the front-end test calls the full knowledge base. No knowledge base filtering rules are configured in the workflow, leading to inconsistent recall ranges.
How to Verify Correct Configuration
- Enter the parameter configuration page of the workflow, check the
GLOBAL_VAR_PERSISTswitch status, and confirm it matches the business requirements. - Upload a copy of internal rural commercial bank research report documents, trigger workflow parsing, and check if the parsed fields include the preset core fields with no missing or incorrect content.
- Simulate a user query containing regions and credit subjects, trigger a full-process test, and verify that the number of returned research reports matches the configured recall and reranking parameters.
- Call the MCP node, pass multiple preset parameters, check if the node returns results that meet expectations, and confirm that multi-parameter transmission works 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.