What the Data for This Category Looks Like
Railway operation policy and regulatory reference data is primarily sourced from the official regulatory repositories of the National Railway Administration and China State Railway Group Co., Ltd., as well as internal operation rules issued by each railway bureau group company. Data is synced within seven working days after a new policy is released. Documents follow a fixed structure with five core sections: regulatory number, issuing authority, effective date, scope of application, and full clause text. Fields include regulatory document number, issuing entity, effective time, applicable operation section, clause number, with core units of kilometers, year/month/day, and count of clauses.
Constraints on Model Integration and Configuration
The fixed structure and clear fields of railway operation policy documents require precise matching of field extraction logic during model integration. This prevents misalignment of core information such as regulatory numbers and applicable sections caused by generalized understanding. The scope of application, which includes operation sections and kilometer units, requires retaining entity recognition weights related to geography and mileage during configuration. This improves Q&A accuracy. The document update rhythm requires the knowledge base sync mechanism to support incremental updates, to avoid resource consumption from full re-pulling. The length of full clause texts varies widely; long clauses may exceed model context limits, so reasonable context truncation rules must be set during integration.
How to Configure Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | The longest single railway policy document can reach thousands of tokens, so this setting must cover the context and referenced content for a single round of Q&A |
maxTokens | 2000 token | Controls the length of single-round responses, to avoid output exceeding the policy clause scope required for railway operation Q&A |
quoteMaxToken | 1500 token | Limits the length of a single referenced document, to adapt to the clause splitting needs of railway policy documents |
toolChoice | auto | Allows the model to independently choose whether to call the knowledge base tool, to adapt to scenarios where specific regulatory references are needed for policy Q&A |
temperature | 0.1–0.3 | Reduces response randomness, to ensure accuracy and consistency in policy clause references |
AIPROXY_API_ENDPOINT | Fill in the actual interface address of the privately deployed model | Adapts to the compliance requirements of privately deployed large models commonly used in the railway industry |
AIPROXY_API_TOKEN | Configure according to the authentication rules of the privately deployed model | Ensures the legitimacy of identity for model calls |
Three Common Configuration Mistakes
- Issue: Connection timeout or 401 unauthorized error occurs when calling a privately deployed large model. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, or the interface authentication rules of the privately deployed model do not match the requirements of FastGPT. - Issue: Q&A results do not reference matching policy documents, or referenced content is incomplete. Cause: The
maxContextconfiguration value is too small to load the complete knowledge base context for policy Q&A, or thequoteMaxTokensetting limits the length of referenced documents. - Issue: Q&A results do not include the associated URL of the policy document. Cause: The knowledge base metadata extraction function is not enabled, so the model cannot obtain the URL field of uploaded files and cannot return the corresponding link in the response.
How to Verify a Successful Configuration
- Call the test interface, enter "What is the effective date of the Railway Passenger Transport Regulations?", and check whether the response includes the accurate effective time and corresponding regulatory document number.
- View the model call log to confirm that the configuration values of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatch the deployment environment, with no format errors. - Trigger a knowledge base incremental sync task, and check the sync log to confirm that newly released policy documents have been correctly loaded.
- Adjust the
temperatureparameter to 0.2, call the same test question multiple times, and confirm that the response results have no obvious differences, meeting the accuracy requirements for policy Q&A.
The values listed here are common starting points, and should be evaluated 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.