What this category of data looks like
Urban public transit policy and regulatory reference data primarily comes from official documents released by local transportation authorities. These include measures for the administration of urban bus passenger transport, fare adjustment plans, operational service standards, and internal operational management compendiums of urban public transit groups. The data update schedule aligns with policy releases, with no fixed cycle. Each policy document is treated as a single file, containing fields such as policy document number, issuing authority, effective date, clause entries, applicable region, and more. Each clause corresponds to specific operational requirements or service standards. Field units include document numbers, year-month-day dates, regional scope, and other relevant units.
Constraints on Multi-turn Dialogues and Prompt Engineering
The characteristics of urban public transit policy data impose clear constraints on multi-turn dialogue and prompt engineering configurations. First, policy documents are lengthy with clear clause hierarchies. Multi-turn dialogues must retain limited historical context to avoid diluting valid policy content with excessive redundant information. Second, policies are localized and time-sensitive. Prompts must require the model to reference specific document numbers and effective dates to ensure authoritative and accurate responses. Third, user question-and-answer scenarios vary widely across use cases. Session history must be strictly isolated to prevent interference between cross-user policy data. Configurations must also adapt to policy differences across cities to avoid mixing cross-regional policy content.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind unique identifiers for user sessions | Isolate policy Q&A history across different users to avoid context confusion |
messages | Retain the first 8 rounds of dialogue content | Urban public transit policy Q&A mostly involves clause verification. Excessive history will consume context quota |
variables | Bind the current city's localized policy dataset | Significant differences exist between public transit policies of different cities. Variables limit the data source scope |
| Maximum context length | 4000–6000 characters | Policy documents are lengthy. Sufficient context must be retained for multi-turn verification |
| Prompt triggering rule | Match based on the policy type associated with the question | Guide the model to prioritize matching detailed policy clauses for bus operations, fares, subsidies, and other categories |
Three Common Misconfigurations
- Calling the API to start a dialogue returns a 404 error, prompting that the model or interface cannot be found. Cause: The embedding model and dialogue model connection paths are not correctly configured, and the model parameters required for urban public transit policy Q&A are not matched.
- Retrieving historical sessions returns Q&A records for all users, and filtering by customUid is not possible. Cause: The
chatIdparameter is not bound in the historical query logic, and session isolation rules are not correctly configured. - Workflows retain think tag content after running, and they can be removed normally during debugging. Cause: The context format for multi-turn dialogues is not adapted in the code running node, and the thinking content identifier generated by the model output is not correctly matched.
How to Verify Successful Configuration
- Initiate a test dialogue, input a question related to a city's public transit fare policy, and verify whether the returned result references the corresponding localized policy document number and effective date.
- Initiate test dialogues for two different users, input similar questions each time, and verify that the historical record query interface only returns session content corresponding to the specified
chatId. - Run a workflow that includes a code node, and verify that no think tag content generated by the model appears in the output results.
- Configure the session context retention rules, initiate consecutive multi-turn questions for testing, and verify that the context does not exceed the preset character or round limit.
The values provided 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.