What data for this use case looks like
Data for railway operation inquiries mainly comes from official operation systems, the 12306 platform knowledge base, station on-site announcements, and historical passenger inquiry records. Data is divided into two categories: structured and unstructured. Structured data includes train numbers, origin and destination stations, departure times, ticket prices, refund and change rules, and other standardized fields. Unstructured data includes temporary suspension notices, security check guidelines, special period service descriptions, and similar content. Routine operational data is updated daily. Real-time changes such as delays and temporary added trains are synchronized immediately. The length of single inquiry-related documents ranges from tens to thousands of characters.
What constraints these characteristics impose on model access and configuration
Standardized structured data fields require precise matching of fixed parameters when the model is called, to avoid information deviation caused by fuzzy matching. Real-time updated delay and temporary added train data require configuring a mechanism to quickly pull the latest data during model access, to avoid returning outdated information. Large variations in unstructured document length require limiting the token count of single referenced documents, to prevent exceeding the model's output limit. The context accumulation feature of multi-round passenger inquiries requires limiting the total token count of the context window, to avoid model response delays or errors.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 10000–15000 token | Adapts to the context accumulation needs of multi-round railway operation inquiries, avoids truncating valid historical information such as travel itineraries and train numbers |
maxTokens | 2000–3000 token | Covers the full response requirements for a single inquiry, including rule explanations, specific information, and operation guidelines, without exceeding the model's output limit |
quoteMaxToken | 800–1200 token | Limits the length of single referenced railway official document fragments, retains key rule details while not occupying model thinking and output space |
temperature | 0.3–0.5 | Reduces random generation probability, ensures responses comply with strict railway operation rules, avoids incorrect content such as wrong ticket prices and refund ratios |
AIPROXY_API_ENDPOINT | Proxy endpoint bound to the dedicated network of state-owned enterprises | Meets data security and compliance requirements, ensures the security and controllability of inquiry data transmission |
AIPROXY_API_TOKEN | Access token bound to dedicated permissions | Limits the access scope of model calls, prevents unauthorized requests from triggering operational data leaks |
Three common mistakes
- Phenomenon: When calling the model alone, standardized input parameter fields cannot be extracted, and key information such as train numbers and travel dates is missing from returned results. Cause: Standardized questions for railway inquiries are not encapsulated into a fixed input parameter format in advance, so the model cannot obtain matching basis for structured data.
- Phenomenon: When calling the O1 series model in multiple rounds, a 400 status code is returned, prompting that the context length exceeds the limit. Cause: The
maxContextparameter is not limited, and accumulated multi-round conversation tokens exceed the context upper limit supported by the model. - Phenomenon: When the
temperatureparameter is set higher than 0.6, responses contain content that does not comply with railway operation rules, such as incorrect refund fee standards. Cause: The temperature parameter is not adjusted downward per the strictness requirements of railway inquiries, leading the model to generate randomly inaccurate content.
How to confirm the configuration is correct
- Initiate a test inquiry with a known train number and travel date, verify that the time, ticket price, refund and change rules and other information returned by the model match officially published data.
- View the model call log to confirm that the actual values of configuration parameters such as
maxContextandmaxTokensmatch the preset values. - Initiate continuous multi-round inquiries, check whether context overflow errors or chaotic response logic occur.
- Test an inquiry scenario that triggers tool calls, confirm that the process of the model automatically calling related tools to obtain real-time information operates normally.
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.