What this category of data looks like
This category of data mainly comes from the 12345 citizen hotline platform, contractor business systems, government knowledge bases, return visit evaluation data, and local hotline management specifications. Daily processed documents include demand registration forms, dispatch records, handling responses, return visit records, extension applications, return explanations, and knowledge base entries. Data updates occur daily. Core fields include demand number, demand content, contractor unit, handling status, return visit result, and others. The overall process follows the closed-loop circulation of mass demands.
What These Characteristics Impose on the "Model Access and Configuration" Link
The daily data update feature requires configuring trigger logic for daily incremental synchronization in the model access link, to avoid repeated processing of historical tickets. Differences in data structures across multiple sources require the model mapping link to match field formats of different systems. This ensures core fields such as demand number and contractor unit are accurately mapped. The core business revolves around the demand closed loop. The model must accurately associate related data across dispatch, response, return visit, and other links. It must also support processing logic for abnormal tickets such as order returns and redistribution, extension applications. This prevents ticket overdue or response rejection caused by field identification errors.
How to Set the Configuration
| Configuration Item | Recommended Value Range | Rationale for This Setting |
|---|---|---|
maxContext | 8000–12000 token | Carries the complete context of multi-link tickets, avoiding loss of key related information such as dispatch records and responses due to insufficient context |
maxTokens | 2000–3000 token | Ensures the output of ledger summary and report generation fully covers all ticket entries, avoiding truncation of key statistical content |
temperature | 0.2–0.4 | Ensures format consistency and data accuracy of summary results, reducing the probability of generating irrelevant content |
toolChoice | auto | Lets the model independently determine whether to call a knowledge base or business interface to supplement data, adapting to processing needs for abnormal tickets |
AIPROXY_API_ENDPOINT | Official access address provided by the corresponding service provider | Ensures stable model call links, adapting to network access specifications for government scenarios |
AIPROXY_API_TOKEN | Exclusive key verified through permission checks | Isolates call permissions for different business systems, ensuring access security of ticket data |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Misconfigurations
- Phenomenon: After the model mapping configuration is submitted, core fields such as the ticket's contractor unit and demand status cannot be correctly mapped to report output items, and the fields display as empty. Cause: The configured mapping field name does not match the actual field name exported from the 12345 citizen hotline platform, and field matching verification was not completed.
- Phenomenon: An error is triggered when calling Alibaba Cloud Bailian or a locally deployed Ollama model, using the image version pulled on the same day. Cause:
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENwas not filled in correctly, or the interface access permissions for the model service provider were not configured. - Phenomenon: Cross-mixing of ticket query records of different users occurs in business systems connected via the FastGPT API. Cause: Independent call keys were not assigned to each business user, and session-level user permission isolation was not configured.
How to Verify Successful Configuration
- Import a single-day ticket sample exported from the 12345 citizen hotline platform, run the ledger summary task, and check whether the core fields in the report match the original ticket data.
- Call the configured model interface, send a single test request, and check whether the interface returns a normal status with no error messages.
- Assign independent call permissions to different test entities, send their respective ticket query requests, and confirm that the query results only associate ticket data corresponding to the respective entity.
- Run test samples containing abnormal tickets such as returned orders and extension applications, and check whether the model can correctly identify and include them in the summary report.
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.