What the data for this category looks like
Business processing data for rail transit equipment mainly comes from three types of systems: operation and maintenance repair ledgers, parts requisition work orders, and fault reporting records. Data updates follow two schedules: real-time updates when a work order is submitted, and daily aggregated updates for ledger data. Documents are stored in structured format, including fields such as equipment number (12-character alphanumeric combination), fault type, occurrence time, processing progress, parts model, and requisition quantity. Fields have clear units: for example, the unit for equipment quantity is "unit", and the unit for duration is "hour". Some fields follow fixed encoding rules.
What constraints these characteristics impose on model access and configuration
The structured data and fixed encoding rules for rail transit equipment require model access configurations to adapt to field format verification, to avoid generating guidance content that does not meet business specifications. Real-time updated work order data requires sufficient context capacity to ensure the coherence of multi-round processing workflows. Fixed units and encoding rules further require clear field matching logic during configuration, to reduce incorrect output. Most rail transit equipment business data is deployed on internal networks. Model access must adapt to internal network access environments to ensure data transfer security.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Rail transit equipment business work orders contain multi-round processing records, parts details and other long text. Sufficient context capacity is required to accurately guide workflows |
maxTokens | 2000 token | Business process guidance needs to output clear steps, avoid overly long content that confuses users, and adapt to single-round response latency |
quoteMaxToken | 1500 token | Business document fragments such as work orders and ledgers need to be referenced. Limiting reference length ensures context cleanliness |
toolChoice | auto | Business process processing requires calling tools such as parts query and progress query. Automatic tool selection adapts to different processing scenarios |
temperature | 0.3–0.5 | Business process processing needs to accurately match workflow rules. Reduce randomness to ensure consistency of guidance steps |
AIPROXY_API_ENDPOINT | Exclusive API address of the internal network deployment node | Most rail transit equipment business data is transferred on internal networks. Use internal network endpoints to ensure data security |
AIPROXY_API_TOKEN | Exclusive call token generated during deployment | Verify call permissions and prevent unauthorized access to business data |
Three common configuration mistakes
- Phenomenon: GPU utilization remains below 30% during model calls, and single-core CPU utilization reaches 100%. Cause: GPU acceleration was not specified during model deployment, and only a single CPU thread was used to process long context data, resulting in unbalanced resource allocation.
- Phenomenon: Field values that do not comply with equipment encoding rules appear in business guidance results. Cause: No format verification for structured fields was configured for the model, resulting in generated guidance content that does not meet business specifications.
- Phenomenon: Empty results are returned after tool calls are triggered. Cause:
toolChoiceis set to none, so the tool call logic is not triggered, and real-time business data such as parts inventory and work order progress cannot be obtained.
How to confirm the configuration is effective
In FastGPT v4.17.0, the following executable actions can be used to check whether the configuration takes effect:
- Submit a simulated rail transit equipment maintenance work order, and check whether the processing steps output by the model match fields such as the work order equipment number and fault type.
- View FastGPT call logs to confirm that the GPU/CPU resource utilization of model calls meets expectations, with no abnormal single-core 100% utilization.
- Trigger tool call actions such as parts query and work order progress query, and verify that correct business data is returned.
- Adjust the
temperatureparameter value, and confirm that the randomness changes of the output content meet expectations, with no situations that excessively deviate from business rules.
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.