What the data for this category looks like
Three sources make up municipal water utility service inquiry data: official service guides, user-submitted ticket records, and real-time operational data. Official guides are divided by business modules, including standard processes such as water meter installation application, repair requests, bill payments, and water quality inquiries. Fields include business number, processing time limit, charging standards, and some indicators have standard units. Ticket records update in real time, including user contact information, problem descriptions, and processing progress. Operational data includes real-time monitoring values such as pipe network pressure and water turbidity. Static guides are revised quarterly, while real-time tickets and monitoring data sync every hour.
What constraints these characteristics impose on model integration and configuration
The above data characteristics impose three constraints on model integration and configuration: First, real-time ticket and monitoring data update frequently. The context sync efficiency of model calls must match the data update rhythm to avoid returning outdated content. Second, business data includes standardized values with units. The model must accurately identify and retain field units to prevent mismatches between values and units. Third, official guides have a fixed structural hierarchy. When processing content in chunks, the document structure must be matched to avoid mixing cross-module content and affecting response accuracy.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Water utility service inquiries often include multi-round ticket history and service guide snippets. This range covers complete context and avoids truncating critical information |
maxTokens | 2000–3000 token | Responses need to include clear service steps and query results. This range balances output length and information completeness |
quoteMaxToken | 1500 token | Official water utility guide snippets must be prioritized for citations. Controlling citation length ensures the authority of responses |
temperature | 0.2–0.4 | Service inquiries require rigor and accuracy. This value range reduces the randomness of generated content and aligns with the formal style of official documents |
AIPROXY_API_ENDPOINT | Fill in the internal exclusive proxy address | Isolate public calls and internal business data to ensure access security for sensitive water utility information |
AIPROXY_API_TOKEN | Use regularly rotated tokens | Prevent unauthorized access to internal business data caused by token leakage |
Three common mistakes
- Phenomenon: After restarting the deployment environment, model channel configuration and token information are empty, and normal calls cannot be initiated. Cause: Configuration is only stored in temporary memory cache, and no persistent storage path is configured. The cache is automatically cleared after restart.
- Phenomenon: After uploading a water utility ticket PDF containing water quality test images, the model does not recognize the test results in the images. Cause: The
visionparameter is not enabled, or a model version that supports visual recognition is not bound. - Phenomenon: The service steps returned by the model do not match the official guide, with redundant or incorrect content. Cause: The
temperaturevalue is too high, causing the generated content to deviate from the rigor requirements of official documents.
How to confirm the configuration is correctly applied
- Submit a test water utility inquiry question, such as "How to apply for resident water meter installation", and check whether the returned result includes the accurate process and requirements from the official guide.
- Restart the deployment environment, check whether the channel information on the configuration page is retained, and verify that the persistent configuration takes effect.
- Upload a test PDF file containing water quality test images, and check whether the model can correctly recognize the text or test data in the images.
- Adjust the
temperatureparameter to 0.1, submit the same test question, and verify whether the rigor of the generated content is further improved.
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.