What the data for this category looks like
Data sources for this category include EAM/CMMS systems, equipment sensor data, operator inspection records, maintenance work orders, fault analysis reports, spare parts lists, and special equipment inspection records. Data update frequencies cover shift, daily, weekly, monthly cycles, and real-time updates when faults occur. Daily documents include equipment ledgers, inspection forms, maintenance plans, work orders, and similar items. Core fields include equipment numbers, inspection items, fault codes, spare part models, inspection validity periods, and others. Formats vary by document type. Some documents require matching legally required fields related to special equipment inspections.
What constraints these characteristics impose on the model access and configuration step
Multi-source data with inconsistent update frequencies requires model access to adapt to different synchronization rhythms. This prevents generated content from mismatching actual equipment status due to data update delays. Diverse document formats require configuration to retain core field recognition rules for each document. This ensures imported equipment ledgers, inspection forms, work orders, and similar content can be parsed correctly. Highly real-time work orders and fault data require context configuration to support quick association with historical data. It must also adapt to special field processing logic for special equipment inspection records, to ensure compliance of bid documents and quotation preparation.
How to Set Configuration
This document provides configuration instructions based on FastGPT v4.17.0. Specific configuration items and recommendations are as follows:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Equipment inspection and preventive maintenance documents include multiple types of historical data, requiring sufficient context to associate faults with maintenance solutions |
maxTokens | 2000–3000 token | Bid document and quotation preparation requires generating complete sectioned content, to avoid output truncation |
temperature | 0.3–0.5 | Consistency of quotations and inspection standards must be maintained, to avoid excessively deviant generated content |
AIPROXY_API_ENDPOINT | Fill in according to the official access address of the connected model | Adapt to the call path of custom models, supports access to third-party models |
AIPROXY_API_TOKEN | Fill in according to the exclusive key generated by the platform | Complete identity verification for model calls, ensuring interface access security |
Three Common Errors
- Using incorrect or expired keys when configuring
AIPROXY_API_TOKENcauses failed interface calls and no response to queries. - Importing documents such as equipment inspection forms or work orders may trigger a 500 error. This happens when the
maxContextparameter is not set reasonably, and cannot accommodate the full content of the document. - Connecting the DeepSeek model without handling the native
<think>tag in its output results in redundant thinking process text being mixed into generated bid documents.
How to Confirm Configuration Is Complete
- After filling in
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, run a model connectivity test to confirm the interface returns a normal response. - Import a single equipment ledger document, check that core fields such as equipment numbers and inspection items can be parsed normally, with no error prompts.
- Run a test query for inspection plan preparation, confirm that the generated content is consistently associated with the imported equipment historical data.
- Adjust the
temperatureparameter, compare the two generated quotation contents, and confirm the deviation meets expected standards.
The values provided are common starting points and should be measured against the reader's own samples.