What this category of data looks like
Financial report data for urban commercial banks mainly comes from officially disclosed annual reports, quarterly regulatory submission reports, and internal operational ledgers. There are three update cycles: annual reports are updated once per year, quarterly regulatory reports are updated every quarter, and internal operational ledgers are updated daily. A single financial report document contains three core modules: asset and liability details, operating profit and loss details, and regulatory indicator ledger. Each module includes dozens of detail fields, most of which are absolute numerical values. Units are primarily RMB yuan or ten thousand yuan, and there is no complex nested sub-document structure.
What constraints do these characteristics impose on workflow orchestration
The need to access multi-source data creates differentiated constraints for data source configuration: separate pull trigger rules must be configured for annual reports, quarterly regulatory reports, and internal operational ledgers to avoid duplicate pulls or missed updates. The large number of detail fields requires precise field mapping rules in the workflow to ensure extracted financial report data fully matches the input fields of subsequent analysis models. Since units are primarily RMB yuan or ten thousand yuan, a unified unit conversion node must be added to the workflow to avoid numerical magnitude deviations during analysis. The absence of complex nested sub-document structures simplifies configuration for the document parsing step, but adaptation to format differences across different data sources is required.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
trigger_rule | {"annual": "0 0 30 4 *", "quarterly": "0 0 15 1,4,7,10 *", "real_time": "*/10 * * * *"} | Matches the update schedule for urban commercial banks: annual reports disclosed before April 30, quarterly reports disclosed within 15 days after the end of each quarter, and internal ledgers synced every 10 minutes |
field_mapping_list | Group by three modules: asset and liability, profit and loss, regulatory ledger, match field names exactly | A large number of financial report detail fields require exact matching to prevent extraction of incorrect data |
unit_convert_factor | 10000 | Most financial report fields are counted in ten thousand yuan; multiply by this coefficient to convert to a unified yuan unit |
parse_chunk_size | 800–1200 characters | Financial report documents have no complex nesting, and this chunk length adapts to parsing and analysis of long-form text |
recall_top_k | Top 10 entries | Single financial report documents have large information volumes; recalling an appropriate number of entries balances analysis accuracy and efficiency |
similarity_threshold | 0.75 | Filters low-relevance financial report fragments to avoid interference from unrelated content on analysis results |
global_var_kb_id | Dynamically assign based on current data source type | Different financial report data sources correspond to independent knowledge bases, and dynamic assignment enables precise calling of the corresponding knowledge base |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common errors
- Symptom: No matching content is returned during knowledge base retrieval after the workflow runs, or the error message
Knowledge base did not find matching documentsis returned. Cause: The dynamic assignment rule forglobal_var_kb_idwas not configured correctly, a fixed knowledge base ID was entered instead, and the ID was not matched according to data source type, resulting in calling a knowledge base not associated with the corresponding financial report data. - Symptom: Workflow parsing node times out, returning the
PARSE_FILE_TIMEOUTerror code. Cause: A reasonableparse_chunk_sizeparameter was not set, and long-form financial report documents were not split, resulting in a single segment of text exceeding the maximum processing limit of the parsing node. - Symptom: Numerical magnitude deviations appear in extracted financial report fields, with some fields using yuan units and others using ten thousand yuan units. Cause: The
unit_convert_factorwas not configured, and statistical units of different data sources were not uniformly converted, resulting in incorrect numerical calculations during analysis.
How to confirm the configuration is correct
- Trigger a manual workflow run, check if the pull results of each data source match expectations, and verify that the number of fields and module classification match.
- View the assignment log of
global_var_kb_idto confirm that the knowledge base ID called during runtime for different data sources matches correctly. - Extract numerical fields from a single financial report document and verify that the results after unit conversion meet the unified magnitude requirements.
- Run the AI conversation module, input preset financial report analysis questions, and check if the returned results are generated based on correct field data.
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.