Reference only; verify against the target FastGPT release and deployment documentation.
What data for this category looks like
Shipbuilding declaration materials and files mainly come from design documents of shipbuilding projects, classification society inspection reports, internal enterprise production ledgers, and compliance declaration files. Update rhythm follows project construction milestones. New materials are generated in phases including preliminary design, detailed design, and completion inspection. Documents include structured declaration forms, 2D/3D drawing text, qualification certificates, and inspection records. Fields include professional information such as ship type number, construction batch number, material grade, hull dimensions (millimeters), displacement (tons), and inspection date.
Constraints Imposed by These Characteristics on Model Access and Configuration
The combination of professional fields and drawing text requires models to recognize shipbuilding industry terminology and structured information. Configurations must adapt to high-precision vector models and visual parsing capabilities. Batch documents updated per project milestones require configurations that support incremental synchronization and large context loading. This prevents one-time data import from exceeding limits. Enterprise environments deployed on internal networks cannot access public network model services. Internal network model access parameters must be configured. Compliance review requirements for declaration materials demand strict control over model output rigor. This avoids generation of deviant content.
How to Set Configurations
| Configuration Item | Suggested Value | Basis for This Setting |
|---|---|---|
maxContext | 8000–12000 token | Shipbuilding declaration materials include multiple document fragments. Sufficient context is needed to cover core review content |
maxTokens | 4096 token | Controls the length of single model output to avoid exceeding model interface limits |
temperature | 0.1–0.3 | Compliance review requires rigorous output logic. This prevents the model from generating content deviating from facts |
quoteMaxToken | 2000–3000 token | Sufficient original file content must be extracted as review basis. This ensures completeness of matching content |
vision | enabled | Declaration materials include visual documents such as hull structure drawings. Image parsing capability is required |
AIPROXY_API_ENDPOINT | API address of internally deployed model | Enterprise internal networks cannot access public networks. Internal network model access endpoints must be configured |
Three Common Configuration Mistakes
- Phenomenon: Model calls return
errorprompts. Logs show data acquisition exceptions. Cause:AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, or parameters are filled incorrectly. This results in failure to connect to the specified model service. - Phenomenon: Model calls time out. Connection failure status codes are returned. Cause: FastGPT servers are deployed in internal network environments. Internal network model access configurations are not adapted. Public network model access parameters are used incorrectly. This prevents successful request forwarding.
- Phenomenon: Review results fail to match key file fields. Matching accuracy is insufficient. Cause:
temperaturevalue is too high, causing model output to deviate from rigorous review logic. OrmaxContextconfiguration is too small, failing to fully load core content of declaration materials.
How to Confirm Configuration Is Complete
- Test internal network model connectivity. Initiate a test call. Check whether returned results meet expectations. Adjust
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENuntil the call succeeds. - Upload a typical shipbuilding declaration material. Trigger the review process. Check whether the returned context includes complete material content. Adjust
maxContextandquoteMaxTokenuntil core document fragments are covered. - Import a declaration material with hull drawings. Check whether the model can parse professional information in the drawings. Confirm that
visionconfiguration is enabled and the model supports visual parsing. - View model call logs. Confirm that returned content includes chain-of-thought related fields. Check whether the model supports and has chain-of-thought output function enabled.
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.