What This Category of Data Looks Like
Port and shipping inquiry data mainly comes from port and shipping government service platforms, vessel scheduling systems, customs approval systems, and historical inquiry archives. Data update frequencies are divided into three categories: real-time (vessel berthing progress, customs clearance status), daily (hot inquiry statistics), and weekly (policy document updates). Each individual inquiry document includes fields such as inquiry subject type, item category, submission time, processing status, and standardized reply content. Item categories cover vessel berthing approval, port fee inquiry, customs clearance procedures, and more. Some inquiries come with attachment materials such as vessel photos and berth drawings.
Constraints Imposed by These Characteristics on Model Connection and Configuration
Port and shipping inquiry data includes long-text approval processes, real-time dynamic information, and visual attachments, which clearly require the model to have sufficient context carrying capacity. Multi-class item categories require precise knowledge base retrieval logic to prevent responses from deviating from official work rules. Real-time updated berthing and customs clearance data require the model's response speed to match business timelines. Frequent updates to policy documents require adjustments to the knowledge base refresh frequency. Some inquiries involve visual material verification, so the model needs to support visual understanding capabilities, further expanding configuration dimensions.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Port and shipping inquiries often contain long-text content such as vessel archives and approval processes, to avoid context truncation |
quoteMaxToken | 2000–3000 token | Sufficient retrieved fragments of port service guidelines and policy documents are needed to support standardized responses |
temperature | 0.1–0.3 | Inquiries require uniform and accurate reply standards, reducing randomness in generated content |
vision | Enabled | Adapt to verification needs for visual inquiries such as vessel photos and berth planning drawings |
AIPROXY_API_ENDPOINT | http://{local model IP}:{port}/v1 | Specify the correct API access address when connecting to localized models such as Xinference |
AIPROXY_API_TOKEN | Access key generated by the model platform | Used to verify identity legitimacy when connecting to localized models |
Three Common Configuration Errors
- Phenomenon: Streamed return results only contain model-generated content, and do not include original text fragments cited from the knowledge base. Cause: The
quoteMaxTokenparameter was not set correctly, resulting in retrieved fragments of port service guidelines and policy documents not being included in the model context. - Phenomenon: Return format errors occur when a visual model processes vessel photo inquiries. Cause: The
visionconfiguration item was not enabled, or the submitted visual material format does not meet the model's requirements. - Phenomenon: Unable to properly respond to requests when connecting to a localized Xinference model. Cause: The
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENparameters were not filled in correctly, or there are spelling errors in the parameter formats.
How to Verify Correct Configuration
A test inquiry about vessel berthing approval can be initiated, and the returned results checked for corresponding knowledge base citation fragments. A vessel appearance photo can be uploaded to initiate a compliance inquiry, and the model's ability to generate relevant reply content verified. After connecting to a localized model, a request can be sent through the test interface, and normal streamed response functionality confirmed. The maxContext parameter can be adjusted to a lower range, a long-text inquiry test initiated, and context truncation-related prompts checked for.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.