What the data for this category looks like
Data for port and shipping internal office and employee services originates from port scheduling systems, crew management platforms, vessel dynamic databases, internal OA systems, and security announcement systems. Structured data includes berth numbers, voyage plans, crew employee IDs, operation durations, and other fields with standard units such as hours, meters, and tons. Unstructured data includes operation process manuals, safety training documents, and temporary scheduling notices.
Data update cycles vary significantly. Vessel dynamics and real-time operation data update every 1 to 5 minutes. Crew scheduling and monthly reports update on fixed daily and monthly cycles. The length of individual documents ranges from several hundred characters for notices to dozens of megabytes for scheduling reports.
Constraints Imposed by Data Characteristics on Multi-turn Dialogue and Prompt Engineering
Multi-turn dialogue and prompt configuration for port and shipping industries must adapt to multiple constraints from data characteristics. The high-frequency updates of real-time operation and vessel dynamic data require that dialogue context effective duration not be overly long, to avoid calling outdated data. For structured fields with standard units, prompts must explicitly require the model to match field units, to prevent mismatches between numerical values and units.
The wide range of unstructured document lengths requires long scheduling reports to be split into chunks before being embedded into context. Multi-turn dialogue must also control the total volume of historical messages loaded, to prevent slowdowns in retrieval and generation stages. Differing update cycles across multiple data sources require prompts to specify data call priorities, giving preference to real-time updated operation data.
How to Set Configuration Values
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Maximum loaded messages count | Top 15 entries | Historical dialogue for multi-source port and shipping data does not need to be overly long, to avoid loading redundant data and slowing down responses |
| Context cache effective duration | 300 seconds | Adapt to the update frequency of real-time operation data, preventing calls to outdated vessel dynamic information |
Storage scope for variables | Global variable pool | Meet storage needs for globally invariant variables such as fixed port berth numbers, preventing single-turn dialogue variables from being cleared |
| Knowledge base single recall limit | Top 8 entries | Balance retrieval efficiency and data coverage, adapting to the multi-dimensional characteristics of port operation data |
| Prompt unit verification switch | Enabled | Match the characteristic of port data fields with standard units, preventing mismatches between numerical values and units |
| Maximum generated token count | 1500–2000 tokens | Adapt to the typical response length of port operation processes and scheduling information, avoiding overly long generated content |
Three Common Configuration Errors
- Symptom: Global default variables are cleared after a single-turn dialogue, making fixed berth numbers unavailable for cross-turn calls. Cause: Variables were not configured for global storage, and only single-turn temporary variable pools were used.
- Symptom: As the number of questions in the same dialogue window increases, response slows down and knowledge base retrieval time exceeds 60 seconds, with normal performance restored after switching to a new window. Cause: Historical context loading count and cache duration were not limited, resulting in excessive redundant data being loaded for each retrieval.
- Symptom: Automatic AI dialogue content generated during tool calls cannot be disabled, and returned results include additional natural language replies. Cause: Prompts did not explicitly specify that only execution results should be returned after tool calls, retaining the default AI reply generation logic.
How to Verify Proper Configuration
- A multi-turn dialogue including globally fixed variables is initiated, and variables are verified to remain retained across subsequent turns without being cleared.
- Multiple related queries are launched consecutively, and response latency and knowledge base retrieval duration are observed to confirm they remain stable.
- A tool call workflow is configured, a tool call request is launched, and returned results are checked to confirm they only include execution data with no additional AI dialogue content.
- A query including unit fields is launched, and replies are checked to confirm they match the standard units of the data with no unit confusion.
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.