What the data for this category looks like
Data related to construction machinery business handling is primarily sourced from equipment factory parameter documents, on-site operation and maintenance logs, maintenance work order records, and working condition collection data. Factory parameters are static documents, including fixed information such as equipment model, serial number, and rated parameters. Operation and maintenance logs are real-time data updated at high frequency, recording operating hours, working condition pressure, fault codes, and other content. Maintenance work order records are updated per work order cycle, including replaced parts, maintenance time, and other details. Data fields contain a large number of professional engineering units, such as hours, megapascals (MPa), revolutions per minute (r/min), and others. The length of single documents varies significantly, ranging from parameter tables with hundreds of characters to complete maintenance manuals with tens of thousands of characters.
What constraints do these characteristics bring to the model access and configuration link
The differing update frequencies of static and dynamic data require separate data source synchronization strategies during model access, preventing repeated synchronization of static data or delayed updates of dynamic data. Professional engineering units and multi-field structures require the configured model to support industry term parsing, so a model endpoint optimized for construction machinery scenarios must be selected. The wide range of document lengths, from short parameter tables to lengthy maintenance manuals, requires support for context processing of varying lengths to avoid truncating core information or consuming excessive computing resources.
How to set the configuration
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
AIPROXY_API_ENDPOINT | Fill in the deployment address of the industry-fine-tuned large construction machinery model | Construction machinery business requires compatibility with professional terms. Using scenario-adapted model endpoints can improve the accuracy of process guidance |
AIPROXY_API_TOKEN | Fill in the exclusive call key issued by the corresponding model platform | Ensure identity verification and permission control for interface calls, prevent unauthorized access |
maxContext | 8000–12000 token | Cover the complete equipment maintenance manual and continuous operation log context, avoid truncating core business information |
maxTokens | 2000–3000 token | Control the output length of single-round process guidance, ensure steps are clear and easy to read, avoid redundant content |
quoteMaxToken | 1500–2000 characters | Accurately extract core reference fields such as equipment fault codes and operating parameters, filter interference from irrelevant content |
temperature | 0.3–0.5 | Reduce random output probability, ensure the description of business handling processes is rigorous and accurate, meeting the strict requirements of engineering scenarios |
Reference only; verify against the target FastGPT release and deployment documentation.
Three common mistakes
- Phenomenon: Interface calls return a 401 unauthorized error, and the interface displays the prompt "authentication failed". Cause:
AIPROXY_API_TOKENis not filled correctly, the key does not match the configuredAIPROXY_API_ENDPOINT, or API credentials from other scenarios are mixed. - Phenomenon: After configuring a third-party endpoint, the corresponding model does not appear in the model selection list, and the selection box is empty. Cause: The path configuration of
AIPROXY_API_ENDPOINTis incorrect, or the endpoint does not return valid model metadata, causing the system to fail to pull the available model list. - Phenomenon: The generated process guidance has incorrect professional unit identification, such as misidentifying "MPa" as another unit. Cause: A fine-tuned model adapted to construction machinery scenarios is not selected, or the
temperaturevalue is too high, causing random parsing of professional terms.
How to confirm the configuration is complete
- Enter common construction machinery fault codes, call the model to generate process guidance, and verify whether the standard handling steps for the corresponding equipment are included.
- View the model call log, confirm that the configuration parameters of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatch the actual filled values, with no spelling or format errors. - Check the vector database synchronization records, confirm that data sources such as equipment operation and maintenance logs and maintenance documents have completed synchronization according to the configured update frequency.
- Upload the complete maintenance manual of a single device, test whether the model can correctly extract core parameters and generate coherent business handling guidance.
The values given 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.