What the data for this category looks like
The business data for the special equipment safety supervision scenario covers standardized documents from multiple types of entities, including usage registration certificates, periodic inspection reports, maintenance records, hidden danger rectification notices, accident reports, and operator certificates. Data sources include the special equipment safety supervision system, inspection and testing institution systems, user ledgers, and operator certificate databases. The update frequency is monthly. Data fields include unique device identifiers, inspection cycle nodes, maintenance unit information, operator qualification numbers, hidden danger rectification statuses, and more. Document formats vary across different sources, and core fields require cross-system cross-checking.
What constraints these characteristics impose on the "model access and configuration" link
The format differences of multi-source data require that model access adapts to the parsing rules of different documents to avoid field extraction deviations. The monthly update frequency means there is no need for frequent context window refreshes, so a relatively stable context length can be set. Core business links require cross-system cross-checking of multiple fields, which requires the model to support automatic tool invocation to reduce manual intervention. The core fields of different documents have different weights, so reference range restrictions need to be configured to ensure responses focus on key information required for supervision, and avoid redundant content interfering with process guidance.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context requirements of splicing multiple documents, and avoids truncating core supervision information such as device registration and inspection cycles |
maxTokens | 4000 token | Controls the length of single-round responses, ensuring that business process guidance steps are clear and not verbose |
quoteMaxToken | 1500–2000 token | Limits the length of referenced source documents, focusing on core fields such as device numbers, inspection validity periods, and operator qualifications |
toolChoice | auto | Automatically triggers tool invocation to adapt to high-frequency business operations such as checking qualifications and querying rectification statuses |
temperature | 0.2–0.4 | Reduces response randomness, ensuring that process steps accurately comply with supervision regulations |
AIPROXY_API_ENDPOINT | Fill in the address provided by the deployment service provider | Adapts to access requirements for domestic or overseas large models |
Three common mistakes
- Phenomenon: The model cannot automatically retrieve device inspection cycle or operator qualification data, and the response shows "no relevant information found". Cause:
toolChoiceis not configured as auto, ormaxContextis set too small, truncating key content from inspection reports or qualification certificates. - Phenomenon: A 401 authentication error is returned when accessing overseas large models. Cause:
AIPROXY_API_TOKENis configured incorrectly, or the access key for the corresponding large model is not bound as required. - Phenomenon: The response content is redundant, mixing irrelevant information unrelated to the supervision process. Cause:
temperatureis set too high, orquoteMaxTokendoes not limit the reference range, causing non-core document content to be included in the response.
How to confirm the configuration is correct
- Upload a special equipment periodic inspection report, initiate a query for "extract the inspection validity period of this device", and check whether the model can accurately return the corresponding field content.
- After configuring
toolChoiceas auto, initiate a request for "query the maintenance record of a certain device", and confirm that the model automatically triggers tool invocation and returns the corresponding results. - Enter a query containing keywords from multiple documents, and check whether
maxContextdoes not truncate core information and there is no content missing. - After verifying the
AIPROXY_API_ENDPOINTconfiguration, initiate a large model invocation test to confirm that there are no authentication errors.
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.