What the Data for This Category Looks Like
Shipbuilding ledger data mainly comes from block manufacturing management systems, main engine accessory warehousing systems, welding quality inspection record databases, and sea trial data documents. Data updates follow production batches, with real-time updates during a single batch production cycle, and are archived after the batch is completed. Most document structures use nested structured fields, including dedicated fields such as block number, material specification, welding current parameters, inspector ID, and rectification items. Units include industry standard units such as millimeters, hours, megapascals, and tons. Some ledgers include scanned ship block drawings, and the length of individual documents varies widely.
What Constraints These Characteristics Impose on the Model Integration and Configuration Link
The nested structured fields of shipbuilding ledgers require configured models to support multi-field associated extraction, to avoid missing key production data. Documents with attached drawings require models to have visual parsing capabilities to extract structured parameters from the drawings. Long document content requires adaptation to a larger context window, to ensure the model can fully read information from multiple associated ledgers. Reasonable context grouping is also needed to adapt to the input length limit of the vector model. Real-time updated production data requires tool call response speeds to match the rhythm of production links, to avoid delaying production processes. The presence of dedicated shipbuilding terminology requires the addition of industry-specific prompt engineering during configuration, to prevent the model from generating incorrect term explanations or field mappings.
How to Configure the Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Single shipbuilding ledger documents often reach thousands of characters, and the context content of multiple associated ledgers needs to be accommodated |
maxTokens | 2000–3000 token | A single summary report needs to cover the collation and output of all ledger fields to avoid accidental content truncation |
quoteMaxToken | 1500 token | Sufficient original ledger fragments must be retained as reference basis for the report, which meets the traceability requirements of shipbuilding ledgers |
toolChoice | auto | Automatically call ledger reading tools to complete data summary, adapting to the automation needs of production links |
temperature | 0.2–0.4 | Ledger summary requires maintaining data accuracy, reducing the randomness of generated content |
vision | auto | Some ledgers include scanned ship block drawings, and visual parsing capabilities need to be enabled to extract drawing parameters |
AIPROXY_API_TOKEN | Valid token calibrated through actual testing | Verify the access permission of the proxy service to ensure the security of internal data transmission |
Three Common Configuration Mistakes
- Symptom: Tool calls return empty results or 500 status codes. Cause: The correct internal proxy address for
AIPROXY_API_ENDPOINTis not configured, orAIPROXY_API_TOKENhas expired and become invalid, resulting in failure to normally call large model services. - Symptom: Parameter information from ship drawings is missing after document parsing. Cause: The
visionconfiguration item is not enabled, so the model cannot recognize and extract structured parameters from the drawings. - Symptom: Configured model and application information is lost when restarting the FastGPT service after deployment on a Windows system. Cause: The configuration file is not stored in a non-temporary system directory, or configuration persistent storage is not enabled. The temporary directory is cleared by the system after restart.
How to Verify That the Configuration Is Correct
- Upload a sample shipbuilding block data set that includes both text ledgers and scanned drawings to the test application, and check whether the generated summary report includes all specified structured fields, and that drawing parameter extraction is complete.
- View the access logs of the proxy service, confirm that the call requests for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENreturn a 200 status code, with no permission error messages. - Adjust the
temperatureparameter to 0.5, compare the content of the two generated reports, confirm that the randomness of the generated content changes after the parameter adjustment, and verify that the configuration takes effect. - Restart the FastGPT service, log in to the management backend to check that the previously configured model parameters and application configurations have not changed, and confirm that configuration persistence takes effect.
The values provided are common starting points and should be evaluated 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.