What the Data for This Category Looks Like
Shipping port investment research data originates from multiple sources. These include port official operation ledgers, route updates released by international shipping associations, customs clearance record data, and ship scheduling system logs.
Three update frequency categories apply. Real-time updates cover berth scheduling and ship berthing/departure information. Daily updates cover container throughput and single-port loading/unloading efficiency data. Monthly updates cover annual operation review and policy interpretation documents.
Three main document structures are used. These are structured time-series data tables, long policy documents, and route-related text materials.
Common fields and units include TEU, berthing duration, route frequency, and port operation fees.
Constraints on Model Access and Configuration
Real-time ship scheduling and berthing/departure information requires controllable model call latency. Set a limit on single-round call timeout duration.
Structured time-series data tables account for a large share of the dataset. Configure vector chunking rules adapted to table structures. This prevents semantic segmentation from breaking business associations between data points.
Frequently updated data requires distinct recall time windows. Configure different context recall parameters for each window.
Long documents such as annual operation reports make up a large portion of the dataset. Adjust segmentation parameters to retain cross-paragraph business logic.
Some professional fields and expressions require domain-specific semantic understanding from the model. Configure corresponding domain fine-tuning parameters during model access.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300-600 seconds | Shipping port investment research documents include long text reports and structured tables. Parsing takes longer than general documents. Reserve sufficient time to complete parsing |
chunkSize | 800-1200 characters | Balance context completeness of long documents and semantic association of professional terms. Avoid segmenting and breaking the coherence of business logic such as routes and berths |
recallCount | Top 6-10 entries | Adapt to the information density requirements of investment research scenarios. Balance the recall range of real-time scheduling data and historical operation data. Do not exceed the context carrying capacity of the model |
MODEL_API_TIMEOUT | 120 seconds | Meet the rapid response requirements of real-time scheduling data. Avoid timeouts interrupting business call processes |
similarityThreshold | 0.75-0.85 | Filter irrelevant content while retaining weakly related professional information in the shipping port domain. Adapt to the semantic matching characteristics of domain terms |
enable_table_parse | Enabled | Structured throughput and route frequency data account for a large share. Enabling this retains original data fields and business association relationships. Improve the accuracy of vector retrieval |
The parameter values provided on this page are all conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Errors
- Phenomenon: After adding any third-party model, the interface returns "Model configuration verification failed". The log displays a
400 Bad Requesterror code. Reason: Failed to fill in the correct API path and request headers as required by the model service provider, or failed to adapt the custom parameter format required by the shipping domain model. - Phenomenon: When configuring multiple models with the same name, the system only recognizes the first configured key. Subsequent models cannot initiate normal calls. Reason: Failed to enable independent identification configuration for multi-model instances, or failed to bind a unique alias and key correspondence for each model.
- Phenomenon: When a workflow node calls a model, it returns recall content from the global knowledge base. It does not only recall the business context of the current node. Reason: Failed to turn off the global context recall switch for the current node, or failed to configure node-specific context range restrictions.
How to Verify Successful Configuration
- Upload a structured table document for shipping ports. Check if the parsed vector chunks retain original fields and data associations.
- Initiate a model call test. Check if the returned results include shipping port domain professional terms and business logic. Adjust the similarity threshold as needed to match the expected recall range.
- Configure two models with the same name but different keys. Initiate separate calls. Confirm that each model's returned results match the configuration of its corresponding key.
- Enter the workflow node configuration page. Confirm that the global context recall switch is turned off. Verify that the node only uses the currently configured context content.
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.