What the data for this category looks like
Internal office data sources for civil aviation airports include flight scheduling systems, employee attendance management systems, logistics support ticket systems, terminal equipment operation and maintenance logs, and employee appeal records. Update rhythms vary significantly: real-time flight dynamic data syncs every 30 seconds. Employee attendance data updates daily at midnight. Operation and maintenance tickets and appeal records update when events are triggered. The data includes structured fields such as ticket ID, equipment number, flight number, and processing status, as well as unstructured content such as maintenance notes and speech-to-text employee appeals. Field units include hours, times, person-times, and similar metrics.
What constraints these characteristics impose on the model integration and configuration link
Real-time flight data has strong timeliness requirements, so model call response delays must be controlled. This requires limiting the total token count of the context window to avoid excessive inference time. Multi-source mixed documents, including structured tickets and unstructured speech-to-text content, have format differences. Appropriate tokenization and retrieval logic must be configured. Employee appeals are mostly colloquial, so model generation parameters must be adjusted to match natural conversation scenarios. The differing update rhythms of various data sources require differentiated configuration of knowledge base synchronization strategies. This prevents outdated data from interfering with model outputs. Additionally, multi-role scenarios in airport internal office work require configuring model call scopes with corresponding permissions to prevent sensitive data leaks.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the mixed length of multi-source airport documents, balances inference delay and context integrity |
maxTokens | 2000 token | Limits the length of a single model output, meets the conciseness requirements of employee service conversations |
quoteMaxToken | 4000 token | Limits the total token count of retrieved knowledge base content, avoids overloading the model context |
toolChoice | auto | Allows the model to independently determine whether to call tools, supports multiple scenarios such as ticket queries and flight queries |
temperature | 0.6–0.7 | Balances answer accuracy and colloquial adaptation, meets the naturalness requirements of daily employee consultations |
AIPROXY_API_ENDPOINT | Calibrated according to the intranet deployment environment | Adapts to the intranet access specifications of FastGPT v4.17.0, ensures the security of model calls |
Three common configuration mistakes
- The phenomenon is that
chat:LLM_model_response_emptyerrors appear in logs, or conversation nodes return empty content. The cause is thatAIPROXY_API_TOKENorAIPROXY_API_ENDPOINTis not configured correctly, leading to an interruption of the model call link. - The phenomenon is that the same question returns significantly different answers across multiple attempts, and the expected content from the private knowledge base is not matched. The cause is that the value ranges of
maxContextandquoteMaxTokenare not set reasonably, leading to irrelevant or outdated data sources being included during model calls. - The phenomenon is that there is no voice output or recognition failure after the agent enables the voice function. The cause is that correct voice model parameters are not specified in the configuration, and access restrictions for the voice model on the airport intranet are not adapted.
How to confirm the configuration is correct
- Initiate a flight query test, verify that the content returned by the model matches the current flight scheduling data, confirming that the knowledge base retrieval and model call link are functioning properly.
- Trigger a ticket processing workflow test, verify that the parameters of the tool call match the fields of the airport's internal ticket system, confirming that the tool configuration is correct.
- View the model call logs, confirm that the
chat:LLM_model_response_emptyerror no longer appears, confirming that the API configuration is correct. - Enable the voice function to initiate a test conversation, verify that the results of speech recognition and synthesis meet expectations, confirming that the voice model configuration is correct.
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.