Railway Operations Service Inquiry and Reply: Multi-turn Dialogue and Prompt Words

What the Data for This Category Looks Like

Railway operations service inquiry data comes from official railway operation manuals, the 12306 railway ticketing platform’s public knowledge bases, station service logs, and real-time dispatching systems. Teams update basic ticketing rules and boarding process documents quarterly. The system synchronizes real-time data such as delay announcements and remaining ticket information every minute. Teams split documents into major categories by service scenario: ticketing, boarding, refund and exchange, special services, and more. These documents include fields such as train number, travel time, station name, service process, and charging standards, using units like yuan, minute, kilometer, and passenger trips.

Constraints for Multi-turn Dialogue and Prompt Word Workflows

Basic rules update quarterly, requiring teams to synchronize knowledge base content used in multi-turn dialogue regularly to avoid referencing expired information. Real-time data updates every minute, requiring teams to limit the valid duration of conversation context to prevent calling outdated dispatching or ticketing information. Fields include key identifiers such as train number and date, requiring prompt words to guide extraction of specific parameters to ensure accurate matching of knowledge base entries. Teams split documents by service scenario, requiring clear specification of the current service scenario during dialogue to avoid confusion across scenarios.

Configuration Settings

Reference only; verify against the target FastGPT release and deployment documentation.

| Context Valid Duration | 300–600 seconds | Matches the update frequency of real-time railway ticketing and delay information, ensuring that information called during dialogue remains valid |

| variables configuration | Pre-set train number, travel date, and station name variables | Extracts key parameters from user dialogue to accurately match corresponding knowledge base entries |

| messages format specification | Store conversation history in alternating user-assistant order | Ensures correct parsing of multi-turn dialogue context, avoiding confusion between different rounds of interactive content |

| chatId association rule | Bind to a single independent user session | Ensures multi-turn dialogue context only corresponds to the current user, avoiding content confusion across sessions |

| Question optimization switch | Enabled | Helps refine the core demands of vague user inquiries, such as converting general questions into precise queries with specific parameters |

Three Common Misconfigurations

• Dialogue terminates directly after a certain round without completing subsequent interactions. Cause: The maximum context length is set too small, truncating historical dialogue containing key parameters such as train number and date, preventing the system from continuing to match corresponding knowledge base content.
• Frequent dialogue timeout errors occur. Cause: The context valid duration is set too long, requiring the system to load a large amount of expired real-time railway data, or the single-dialogue knowledge base recall upper limit is not restricted, leading to exhaustion of computing resources.
• Unable to set a single dialogue to trigger a plugin prompt only once. Cause: The single plugin trigger rule is not enabled in the session configuration, causing the plugin to be called multiple times in a single round of dialogue, and the corresponding switch cannot be found through the interface.

How to Verify Proper Configuration

• Initiate a continuous multi-turn dialogue containing train number, date, and station, check that the context retains all historical interactive information without truncation.
• Simulate a real-time updated inquiry scenario, initiate a dialogue with an interval exceeding the preset context valid duration, check that the system prompts information status changes or reloads the latest content.
• After configuring the variables parameter, initiate a vague inquiry, check that the system automatically extracts key parameters and matches corresponding knowledge base content.
• After enabling the question optimization switch, initiate a general question, check that the system converts it into a precise query with specific parameters.

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.