What the Data for This Category Looks Like
Policy and regulatory basis data for the rail transit equipment sector originates primarily from industry standards, administrative normative documents, and equipment access announcements released by the National Railway Administration and the Ministry of Industry and Information Technology. Data updates are adjusted irregularly in line with the release schedule of new policies. Most individual documents are structured long texts, containing fields such as policy document number, issuing authority, effective date, applicable equipment type, and clause details. The clause details section often includes multiple sections of technical requirements and implementation specifications. Units mostly use metric standards specified by national standards, such as millimeters, kilometers per hour, and tons.
Constraints Imposed on Model Integration and Configuration
The long-text structure of rail transit equipment policy data requires models to support long-context processing capabilities, to avoid truncation caused by overly long single-segment input. The structured fields in policy documents require clear retrieval rules to be configured during setup, so only clause content matching the applicable equipment type is extracted. The irregular update rhythm requires flexible adjustment space for data synchronization to be reserved in the configuration, to support rapid access to new policies. At the same time, professional measurement units in policy clauses require model outputs to strictly follow national standard labeling conventions. Rules for identifying professional terms must be specified in the configuration.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long-text structure of rail transit policy documents, preventing core clauses from being truncated |
maxTokens | 2000–3000 token | Matches the output length of single policy clauses, ensuring complete return of technical requirements |
quoteMaxToken | 1500–2000 characters | Limits the length of retrieved and cited policy text, avoiding context overload |
toolChoice | auto | Adapts to scenarios requiring knowledge base retrieval for policy Q&A, enabling automatic tool selection |
temperature | 0.1–0.3 | Maintains accuracy of policy content, preventing non-compliant interpretations from being generated |
AIPROXY_API_ENDPOINT | Fill in the deployed OneAPI address | Connects to locally deployed large model interfaces, ensuring correct request routing |
Three Common Configuration Mistakes
- When integrating policy data sources via the HTTP API, returning long policy texts are passed to the model in one go, triggering context overflow. No long-text segmentation logic is configured, and content is not split into segment-adapted lengths before being sent sequentially.
- When creating a knowledge base, the locally deployed ChatGLM2 model cannot be selected, and no available model options are displayed in the interface. OneAPI access keys and endpoints are not correctly associated in FastGPT configuration, or the model is not registered with the OneAPI service as required.
- After configuring
toolChoiceto auto, the model does not automatically trigger knowledge base retrieval and returns generic responses. The system prompt does not explicitly specify that policy Q&A must prioritize knowledge base calls, or thetoolChoiceconfiguration does not match the model's capabilities.
How to Verify Successful Configuration
- Upload a rail transit equipment policy document to the knowledge base, run a single Q&A, and check if the returned content includes accurate clauses from the document.
- View the model call logs to confirm that the
maxContextandmaxTokensparameter values match the configured requirements, with no context truncation errors. - Test calls to the locally deployed model interface, confirm that requests can be sent normally and valid responses are returned, with no authentication or routing errors.
- Adjust the
temperatureparameter, check for changes in the rigor of responses, and confirm that the parameter takes effect.
The values listed here 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.