This document is written based on the model access and configuration workflow of FastGPT v4.17.0.
What data for this category looks like
Data for this category covers equipment ledgers, spot inspection checklists, maintenance plans, work orders, failure analysis, spare parts lists, and special equipment inspection records. Data sources include EAM/CMMS systems, equipment sensor collection data, manual spot inspection records from operators, historical maintenance archives, spare parts inventory data, and statutory inspection information. Update frequencies cover shifts, daily/weekly/monthly cycles, and sudden failure scenarios. Data fields include equipment number, rated power, spot inspection points, measured parameters, failure codes, spare part models, etc., with clear units and status identifiers.
What constraints these characteristics impose on the "model access and configuration" link
Multi-source heterogeneous data sources require configurations that support parallel recall of multiple documents, to adapt to different formats of spot inspection records, sensor data, and work order archives. High-frequency updated real-time sensor data and sudden failure scenarios require model access configurations to support low-latency responses, to avoid timeouts that disrupt maintenance scheduling. Equipment fields with attached units and specialized codes require the model to accurately identify and associate corresponding business meanings, so parameters need to be adjusted to ensure semantic alignment. Additionally, maintenance windows must comply with production scheduling, so configurations need to limit unnecessary long-context calls to avoid excessive resource usage that impacts concurrency.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to batch concatenation of equipment ledgers, spot inspection records, and work orders, covering the context requirements of multi-source business documents |
maxTokens | 2000 token | Meets the output length required for troubleshooting Q&A, avoiding overly long responses that delay maintenance scheduling |
quoteMaxToken | 1500 token | Limits the reference length of recalled documents, ensuring the model focuses on core business fields and failure information |
temperature | 0.3–0.5 | Ensures the accuracy of business responses, avoiding generated content that does not comply with equipment parameter specifications |
AIPROXY_API_ENDPOINT | Fill in the dedicated interface address according to the deployment environment | Adapts to the enterprise internal network environment, ensuring secure data transmission |
AIPROXY_API_TOKEN | Permission key calibrated through actual testing | Ensures the legality of interface calls, preventing unauthorized access |
Three common mistakes
- Model tests return context over-limit errors or empty results. Cause: The
maxContextparameter was not adjusted according to the multi-source document characteristics of the category, causing the total length of uploaded documents such as equipment ledgers and spot inspection records to exceed the configured limit. - Shared pages have no response after exceeding the set concurrency threshold. Cause: The real-time requirements of the equipment spot inspection scenario were not considered, and
maxTokensand interface response parameters were not reasonably set, leading to resource overload under high concurrency. - Incorrect equipment parameter units or non-standard failure codes appear in responses. Cause: The
temperatureparameter was set too high, causing the model to generate content that does not comply with business field specifications, and failing to accurately match business identifiers from spot inspection checklists and spare parts lists.
How to confirm the configuration is complete
- Import a single equipment ledger and spot inspection checklist, test whether the model can accurately extract core fields such as equipment number and spot inspection cycle, and verify field matching accuracy.
- Initiate a query simulating a sudden failure scenario, check whether the response time meets business scheduling requirements, and confirm that no timeout errors occur.
- Conduct a multi-concurrency test, verify whether the system can stably return results during business peak periods, and adjust parameters to adapt to concurrency requirements.
- If the
visionconfiguration is enabled, upload images of equipment sensor data, and test whether the model can accurately recognize spot inspection values and abnormal identifiers in the images.
The values provided are common starting points and should be measured against the reader's own samples.