What the data for this category looks like
Data sources for shipbuilding internal office and employee services mainly include production process MP4 videos, CAD/SOLIDWORKS design drawings, equipment maintenance logs, work orders, crew attendance records, and office OA documents. Structured data such as work orders and attendance records include fixed fields: device ID, maintenance time, main engine power (unit: kilowatts), draft depth (unit: meters). Unstructured data such as drawings and videos have large file sizes. Its update cadence adjusts based on project milestones and daily operations: work orders update in real time, maintenance logs are archived weekly, and design drawings only update during project iterations. FastGPT v4.17.0’s toolchain supports bulk import and parsing of this type of data.
Constraints imposed by these characteristics on model integration and configuration
Fixed fields in structured data require models to accurately match business terminology and units to avoid parsing errors. Unstructured video and drawing data require multi-modal processing capabilities. Without these capabilities, content cannot be recognized. Large video files generate long URLs. If the model’s input limits are not adapted, recognition may fail. Differences in update cadences across data sources require model integration to support dynamic calls to the latest data. Balance context length and processing efficiency to avoid timeouts or response delays caused by long documents.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
vision | Enabled | A large number of multi-modal documents such as production process MP4 videos and design drawings exist in shipbuilding internal offices. Multi-modal recognition capabilities must be enabled, and the multi-modal module of FastGPT v4.17.0 supports this scenario. |
maxContext | 8000–12000 token | Internal office documents include long work order records and multi-page design drawings, requiring adaptation to long context processing needs. |
temperature | 0.3–0.6 | There are many professional terms in shipbuilding. Balance answer accuracy and flexibility to avoid excessive divergence. |
maxTokens | 2000–3000 token | Internal employee inquiries mostly cover equipment issues and process queries. Control the length of single-round responses to avoid exceeding interface limits. |
AIPROXY_API_TOKEN | Exclusive key generated by the platform | Ensure identity legitimacy for model calls and prevent unauthorized access. |
toolChoice | auto | Support automatic tool calling based on user needs, adapting to internal office scenarios such as process queries and data retrieval. |
Three common misconfigurations
- Phenomenon: After uploading MP4 video files of ship production processes, the model returns that the video content cannot be recognized, or the generated recognition results contain garbled characters. Cause: The
visionconfiguration item is not enabled, or the API endpoint of the multi-modal model is not correctly configured. The multi-modal module of FastGPT v4.17.0 has specific requirements for long URL processing; non-adaptation will cause recognition failure. - Phenomenon: Calling the model interface returns a format that does not meet the docking requirements of the internal system and cannot be directly used for display. Cause: The response format is not explicitly specified in the model configuration, resulting in returned content that does not match expectations.
- Phenomenon: Unable to add an online retrieval button in the workflow, or unable to switch the current conversation model, and the built-in embedding page is unavailable. Only API access is possible. Cause: Tool calling and model switching functions are not enabled in the workflow configuration, and API authorization and custom front-end docking logic are not configured according to new version requirements.
How to confirm configuration is complete
- Upload MP4 video files of ship production processes, check whether the model can return a structured summary of the video content, to verify that the multi-modal configuration takes effect.
- Initiate equipment maintenance inquiries containing professional terms, check whether the term accuracy and response length of the model's answer meet expectations, to verify that the temperature and context configuration are reasonable.
- Call the API interface to initiate a request, check whether the returned response format meets the docking requirements of the internal system, to verify that the interface key and endpoint configuration are correct.
- Add a tool calling node in the workflow, check whether the model can be switched normally and online retrieval is triggered, to verify that the tool calling configuration takes effect.
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.