What data for this use case includes
This category of data primarily comes from the 12345 citizen hotline platform, contractor business systems, government knowledge bases, public feedback and evaluation data, and local hotline management regulations. Data is updated daily. Daily processed documents include request registration forms, dispatch records, handling responses, return visit records, delay applications, return explanations, and knowledge base entries. Common data fields include request ID, request content, responsible entity, handling progress, response guidelines, and public evaluation tags.
Constraints imposed by data characteristics on model access and configuration
Because data sources are scattered and updated daily, model access must support permission isolation and incremental synchronization configuration for multi-source data. Daily documents typically combine structured fields with unstructured request content. Model input must prioritize matching core ticket fields to avoid interference from redundant information. Ticket handling also has clear time requirements, so model calls must set reasonable timeout thresholds to prevent ticket overdue from response delays. Additionally, response content must comply with local hotline regulations, and model outputs must be traceable to knowledge base entries to avoid non-compliant generated content.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of core ticket fields plus associated documents, avoiding truncation of key request and response content |
maxTokens | 1500–2000 token | Meets the output length requirements for documents such as handling responses and delay applications, aligning with the typical length of daily ticket responses |
temperature | 0.2–0.4 | Reduces generation randomness, ensuring response content complies with government regulations and avoiding non-compliant statements |
AIPROXY_API_ENDPOINT | Official API access address provided by the model service provider | Ensures stability of model calls, preventing inaccessibility due to incorrect address configuration |
AIPROXY_API_TOKEN | Valid access key obtained from the model service provider | Completes identity verification for model access, preventing unauthorized calls |
toolChoice | none | No tool calls required, focusing on document drafting and content generation, avoiding irrelevant logic |
Three common configuration mistakes
- Phenomenon: An error stating the channel cannot be found occurs when calling the AWS Claude3 model. Cause:
AIPROXY_API_ENDPOINTis not correctly configured to the official AWS access address, or the corresponding model channel mapping is not added in FastGPT. - Phenomenon: After accessing the GLM model via One API, only GPT-series models appear in the model selection list of the text extraction module. Cause: The model list is not refreshed synchronously in FastGPT's model access configuration, or the GLM model is not categorized under text generation tasks.
- Phenomenon: Non-compliant government regulation content appears after generating ticket responses. Cause: The
temperatureparameter is set too high, leading to excessive generation randomness that deviates from established response guidelines.
How to verify successful configuration
- Navigate to the FastGPT model management page, view the dedicated 12345 ticket model that has been accessed, and confirm the target model is displayed in the list.
- Upload a test ticket data entry, trigger the document drafting function, and check whether the generated content includes core ticket fields and complies with government regulations.
- Call the model interface, check whether the returned response duration meets the preset threshold, and no timeout errors occur.
- Check the key and address configuration for model access, ensure there are no spelling errors, and verify connectivity via a simple test request.
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.