Industrial park intelligent due diligence reports are core supporting materials for financial institutions conducting park-related financial business. Workflow orchestration is a key link for integrating multi-source data and generating standardized reports.
What the Data for This Use Case Looks Like
Data for industrial park due diligence comes from dispersed sources. These include investment promotion ledgers published by park management committees, land planning information from real estate registration systems, enterprise directories filed with industrial and commercial authorities, and monthly rent reports from park operators.
Update rhythms vary across data types. Settled enterprise and rent data updates monthly. Land planning and industrial policy data updates annually.
Document structures include structured tables and unstructured PDFs. Structured table fields include enterprise name, settlement date, tax per mu, with a unit of ten thousand yuan/mu. Unstructured documents are mostly park master plan manuals and investment promotion policy documents. Some data has inconsistent formatting.
Constraints Imposed by These Characteristics on Workflow Orchestration
Dispersed data sources and inconsistent formats require configuring multi-source data pull nodes, and setting parsing rules separately for structured and unstructured documents.
Differences in update rhythms across data sources require workflows to support triggering nodes on different cycles, to avoid pulling old data unnecessarily.
Park data includes fields with unique units. Workflows need to add field standardization nodes to unify units and data formats.
Large park planning documents have significant file sizes. Workflow file parsing nodes need sufficient timeout tolerance and parallel processing capabilities.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Documents related to industrial park due diligence often include planning drawings and annual operation ledgers, which take longer to parse individually |
chunk_size | 800–1200 characters | Park data includes structured ledgers and unstructured policy texts; segment length adapts to semantic completeness for both document types |
recall_top_k | Top 8–12 entries | Due diligence reports need to cover multiple knowledge base contents including settled enterprises, rent data, and surrounding supporting facilities; balances recall accuracy and efficiency |
similarity_threshold | 0.72–0.85 | Park-related terms have industry-specific definitions; the threshold prevents accidental recall of unrelated general real estate documents |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Files such as park planning drawings and large investment promotion manuals are generally large in size |
workflow_parallel_limit | 3–5 parallel branches | Park data sources are dispersed; parallelly pulling data from multiple systems shortens total processing time |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, 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 Configuration Mistakes
- Phenomenon: When exporting workflow conversation records, only the last 6 questions are returned, and all 88 records cannot be exported. Cause: The
max_historyparameter is not configured, or its value is set too small, so only the most recent interaction context is retained. - Phenomenon: After connecting 3 workflow nodes at the same time, only 1 branch completes execution, and the remaining branches remain suspended. Cause: The
workflow_parallel_limitparameter is not set; the default parallel count is insufficient, causing branch blocking. - Phenomenon: In version v4.9.0, workflow runtime generates gpt-4o-mini call error logs, even though the model is not actively called. Cause: The
model_nameparameter of the model call node is not explicitly configured in the workflow; the default call uses the unauthorized gpt-4o-mini model.
How to Confirm Correct Configuration
- Upload a single 500-page park planning PDF, check the running logs of the parsing node, confirm that the elapsed time does not exceed the set
PARSE_FILE_TIMEOUT_SECONDSvalue. - Trigger the workflow to pull data from 3 data sources in parallel, check the node status panel, confirm that all branches start normally and complete execution.
- Enter a park due diligence query term, check the knowledge base recall results, confirm that the number of recalled entries matches the
recall_top_ksetting. - Run the complete workflow and export conversation records, check whether the number of records covers all interaction links.
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.