What the data for this category looks like
The data for this category mainly comes from the flight scheduling system of the airport operation management platform, offline merchant transaction system, member service database (including financial-related records such as points and consumer installments), and regional transportation passenger flow statistics tool. Different systems use different update rhythms: the platform updates flight scheduling information at fixed daily times, syncs merchant transaction logs in real time, and runs daily batch syncs for member financial behavior records. Most documents are in structured JSON or table format, containing fields such as flight number, takeoff and landing time, service area, user tags, consumption preferences, and point balance. Field units include hours, passenger trips, plain character IDs, currency units, etc., with no special custom units.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Flight scheduling data updates daily, so multi-turn dialogue context retention duration must not exceed 24 hours to avoid calling outdated flight takeoff and landing information. The multi-dimensional structure of merchant transaction and member financial data requires prompts to limit the scope of recalled fields, only extracting information such as service area, user tags, and point balance related to the current conversation, to avoid redundant data interfering with dialogue logic. Periodic passenger flow features require multi-turn dialogue to dynamically match the time parameter of the current conversation, giving priority to calling marketing content such as merchant business status and point redemption activities corresponding to the current time period, to ensure that the output content matches the current scenario. At the same time, differences in cross-system data sources require clear priority for data calls in the dialogue process: first use real-time synced transaction log data, then call batch-synced member financial data.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 10 turns of dialogue context | Marketing consultations for airport users usually consist of 3-5 consecutive rounds of questions. 10 turns can cover the complete consultation chain while avoiding redundant data occupying tokens |
contextExpireTime | 24 hours | Flight scheduling data updates daily. Context older than 24 hours may contain outdated flight and merchant business information, so expired sessions need to be cleaned up regularly |
recallCount | Top 3 recalled results | Merchant and service information in airport marketing scenarios are relatively specialized. A small number of recalled results can meet user needs and avoid excessive information interfering with dialogue |
promptTemplate | Prioritize matching corresponding services based on current conversation time period | For passengers in different time slots such as early morning and late night, dynamically adjust the priority of recommended content to improve the targeting of marketing content |
apiWorkflowChatHistory | Only retain dialogue data from the current conversation | Cross-session historical data may contain outdated information. Only retaining the current conversation ensures that each call's context is the latest valid data |
The parameter values provided on this page are common recommended starting points for configuration. The actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Phenomenon: An invalid token error is returned when calling the model, and the oneapi log shows that the token field value is "fastgpt". Cause: The default placeholder text of the platform was mistakenly used when configuring the API key, and it was not replaced with an actual valid key content.
- Phenomenon: When accessing the workflow multiple times with the same conversation ID, the context data of the two calls is inconsistent, resulting in deviations in recommended content. Cause: Two AI model nodes in the workflow are configured with different context retention rules, and the session-level context management strategy is not unified.
- Phenomenon: An interface does not exist error is returned when initiating a workflow creation request via API. Cause: The API permission related to the workflow is not enabled, or the used API version does not cover the workflow module functions.
How to confirm the configuration is complete
- Initiate a simulated user consultation, send 3-5 consecutive related questions, check whether the dialogue context retains complete conversation content and does not include outdated flight or merchant information.
- View the oneapi or model call log, confirm that the incoming API key is the actual configured valid content, and exclude default placeholders.
- Test initiating multiple workflow calls with the same conversation ID, check whether the context data of the two calls is consistent and complies with the configured retention rules.
- Call the workflow API, confirm that the model node call can be triggered normally, and the returned results conform to the preset marketing scenario logic.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.