Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Follow-up record summary data is derived from post-call transcription texts of government hotlines and state-owned enterprise service hotlines, as well as original communication records from associated tickets. Data updates in real time alongside ticket workflows. Most individual documents range from 300 to 1500 characters in length. Documents always include six core fields: follow-up ID, associated ticket number, communication start and end time, original user request text, handler feedback, and follow-up conclusion. Communication duration is measured in seconds, and request classification uses standardized enumeration items.
Constraints on model access and configuration from these data characteristics
The real-time update nature of follow-up record summaries requires the model call chain to support low-latency responses, to avoid blocking ticket workflow processes. The character span of individual documents requires sufficient context window space in configuration, to prevent truncation of core communication content. The mixed structure of standardized enumeration fields and free text requires the model to support both structured information extraction and unstructured content summarization. Corresponding parameters must be configured to adapt to both task types. Second-level unit communication duration data can be used as an auxiliary reference field during model calls. Parameter configuration must allow passing custom metadata.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 2000–4000 characters | Adapts to the 300–1500 character length of individual follow-up record summaries, to avoid truncating core communication content |
maxTokens | 1000–1500 tokens | Controls the length of summary output, matching the scale of core information in follow-up records |
temperature | 0.2–0.4 | Balances summary accuracy and flexibility, to avoid redundant or off-topic content in free text sections |
AIPROXY_API_ENDPOINT | Fill in based on deployment environment | Adapts to the access address of the proxy service, resolving cross-environment call issues |
AIPROXY_API_TOKEN | Key bound to the proxy service | Ensures compliance of identity verification for model calls, meeting security requirements for government scenarios |
toolChoice | auto | Allows the model to automatically determine whether to call tools to associate ticket information, adapting to multi-task scenarios |
Three common misconfigurations
- A 503 status code is returned on call, with a prompt that no available channels exist for the target model. The cause is failure to configure an access channel for the target model in the proxy service, or a mismatch between the group setting and the default group of FastGPT.
- Model call times out, with a request failure log returned. The cause is failure to properly set the
maxContextparameter, causing the model to exceed the chain timeout threshold when processing overly long follow-up records. - Generated summary content deviates from the core user request of the original text. The cause is an excessively high
temperatureparameter value, causing the model to generate overly divergent content that does not align with the original information in the follow-up record.
How to confirm the configuration is correct
- Manually input a real follow-up record sample to trigger a model call, and check if the returned summary includes the core user request and conclusion from the original text.
- View the model call log to confirm that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfiguration values match the deployment environment, with no formatting errors. - Adjust the
temperatureparameter and compare output results, to confirm that changes to the parameter have the expected impact on summary style. - Batch import follow-up records of varying lengths, to confirm that all model calls complete normally, with no truncation or errors.
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.