What the data for this category looks like
Shipping port due diligence data sources include port operation management systems, customs clearance data platforms, public statistical reports from port authorities, ship agency job application forms, and charter party archives. Update frequencies vary: real-time berthing data updates every 15 minutes, daily throughput reports are generated the next day, and monthly compliance reports are released by the 5th of each month. Document structures include structured tables (berth usage ledger, cargo loading and unloading details) and unstructured text (compliance inspection notes, contract clauses). Fields and units are as follows: berth number (character type, no unit), loading and unloading tonnage (tons), berthing duration (hours), ship draft (meters), and work shifts (times).
What constraints these characteristics impose on workflow orchestration
Dispersed multiple data sources require configuring multiple parallel nodes to access data from different sources, preventing single-node blocking. Varied update frequencies require workflows to support mixed triggering modes, balancing scheduled batch processing of monthly reports and manual triggering for ad-hoc due diligence requests. Complex document structures require configuring segmented parsing and structured data extraction nodes to handle long text and tabular data separately. Diverse field units require configuring standardized mapping steps to unify field formats across different data sources, avoiding unit conversion errors. Fluctuating data volume requires configuring current-limiting and batch parsing nodes to adapt to processing needs for both real-time small-volume data and monthly large-volume data.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Port due diligence reports often include large ship scheduling logs and monthly throughput reports. Single-file parsing takes a long time, and 600 seconds covers the parsing needs of most files |
Segment Length | 800–1200 characters | Port data documents often include long tables and continuous operation records. Excessively long segments will lose context association, while excessively short segments will disrupt field logic. This range balances context integrity and parsing efficiency |
Retrieval Count | Top 8 entries | Port due diligence requires covering three core data types: berths, ships, and cargo. Too many retrieved entries will introduce irrelevant fields, while too few will fail to cover all associated information |
Similarity Threshold | 0.72–0.78 | Port field terms (such as "berth utilization rate" and "ship draft") have fixed industry expressions. This threshold range avoids retrieving unrelated documents while covering associated data |
Workflow Trigger Mode | Scheduled Trigger + Manual Trigger | Port operation data follows fixed update rhythms (daily/monthly), while ad-hoc emergency due diligence requests need to be supported. Mixed mode covers both regular and special scenarios |
Knowledge Base File Filter Rule | Filter .csv, .xlsx, .pdf by file extension | Common data sources for port due diligence are structured tables and compliance reports. Filtering unnecessary files improves parsing efficiency and retrieval accuracy |
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 targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: Fields returned during workflow debugging do not match actual test results from the frontend. Cause: Temporary data sources used during debugging do not sync the production environment's port data mapping rules, leading to deviations in field parsing.
- Phenomenon: The workflow cannot accurately extract berth and cargo data for a specified port. Cause: No screening condition for vector retrieval based on port ID is configured, or the classified index of knowledge base files by port area is not enabled, resulting in retrieval of documents from unrelated port areas.
- Phenomenon: The content returned by the AI chat node does not include the field details extracted from the knowledge base. Cause: The scope of action between prompt configuration and reference content template is confused. The prompt only defines the overall logic of AI responses, while the reference content template is responsible for formatting the original text retrieved from the knowledge base.
How to Confirm Successful Configuration
- Run a manually triggered workflow, verify that the returned due diligence report includes three core fields: port name, berth number, and loading and unloading tonnage, to confirm no deviations in field mapping.
- Adjust the similarity threshold, compare the number of retrieved documents across different threshold values, to confirm the threshold setting matches the term matching accuracy requirements of the current data source.
- Generate a login-free link, input custom variable values and run the workflow, verify that variables are correctly substituted into the prompt and knowledge base retrieval logic.
- View the workflow execution logs, confirm that the execution duration of each node does not exceed the configured timeout period, and there are no error records for failed file parsing.
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.