What this category of data looks like
Data is primarily sourced from daily railway operation records, including train operation logs, ticket settlement reports, safety inspection records, dispatch orders, and passenger injury incident handling documents. Data update rhythms vary by operation scenario. Real-time dispatch documents are updated immediately. Settlement documents are summarized and updated daily. Most documents use structured, templated formats, with fixed fields such as operation date, train number, line section, incident type, and handling result. Common units include train trips, kilometers, passenger counts, and hours.
What constraints these characteristics impose on model access and configuration
Structured, templated document characteristics require model access to support precise identification and association of structured fields. This prevents generated content from deviating from preset formats. Differences in data update rhythms across scenarios require index configuration to support flexible switching between incremental and full updates. This ensures data timeliness. A large number of railway operation-specific professional terms require configuration of domain-adapted parameters during model access. This ensures generated content complies with industry standards. Wide variation in document length requires context parameter configuration to adapt to input texts of different lengths.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the average length of railway operation documents, covering context requirements for most dispatch and inspection documents |
maxTokens | 2000–3000 token | Meets the output length requirement for single document drafting, to avoid truncated generated content |
temperature | 0.3–0.5 | Reduces random generation probability, ensuring accuracy of format and terminology for railway professional documents |
quoteMaxToken | 1500 token | Limits the length of referenced source text, to avoid context overload impacting generation logic |
AIPROXY_API_ENDPOINT | Fill in the dedicated endpoint according to the deployment environment | Adapts to access paths for local or cloud deployments, ensuring model call connectivity |
AIPROXY_API_TOKEN | Use application-specific keys | Complies with permission control requirements for railway operation data, avoiding security risks from universal keys |
Three Common Mistakes
- Phenomenon: Insufficient permission error is returned when calling the model, or specified knowledge base documents cannot be retrieved. Cause: A universal API key is used for calls, and no application-specific key for the corresponding scenario is used, resulting in failed permission verification.
- Phenomenon: After creating an index model, the language model cannot associate source document content during calls. Cause: The association relationship between the index model and language model is not explicitly configured, resulting in a broken call link between the two.
- Phenomenon: In local deployment versions, the image understanding function cannot be enabled to process ticket voucher documents. Cause: The
visionparameter is not enabled in the configuration, and the local deployment package does not preinstall adapted visual model dependencies.
How to Verify Successful Configuration
- Call the test interface with a single railway operation document. Check whether the generated content includes correct fields such as train number and line section. Verify that field matching meets expected standards.
- View the model call log. Confirm that the configuration values of
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTmatch the deployment environment, with no spelling errors. - Test input texts of different lengths. Check that the generated content has no truncation. Confirm that the configuration of
maxContextandmaxTokensadapts to the current input scenario. - Trigger an incremental update task. Check that the index update time matches the data update frequency. Confirm that the timeliness of the index configuration meets requirements.
The values provided 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.