What this category of data looks like
Reference only; verify against the target FastGPT release and deployment documentation.
What constraints these characteristics impose on the model integration and configuration link
The data sources for this category are scattered and cover multiple systems, requiring the model integration link to support unified connection configuration for multi-source data. The daily update frequency requires parameters configured to adapt to high-frequency synchronization. The diverse document types require the model to handle text content in different formats. Unlike other citizen hotline services, this category places greater emphasis on statutory handling procedures, so the model must strictly follow the expression standards of official documents. Core handling links rely on accurate request identification and policy interpretation, so the model's output randomness must be limited, and model output must correspond to the statutory handling process of the petition to avoid content inconsistent with business specifications. Data fields are designed around the full petition process, so the model must accurately extract key node information and avoid missing core handling elements.
How to configure settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Petition-related documents are mostly long texts, and need to accommodate complete historical data such as petition registration forms and handling opinions to ensure the model can obtain sufficient context |
temperature | 0.1–0.3 | Petition responses must strictly follow statutory procedures and policy requirements. A low temperature value reduces output randomness to ensure responses are accurate and rigorous |
quoteMaxToken | 2000–3000 characters | Need to ensure the model can fully reference official document content such as acceptance notices and handling opinions, avoiding truncation of key information |
maxTokens | 2000–3000 token | Petition responses need to be concise and clear, controlling output length to improve response speed |
AIPROXY_API_ENDPOINT | Official access address corresponding to the deployed model | Ensure the model call link is correct and avoid URL errors |
AIPROXY_API_TOKEN | Valid key assigned by the platform | Complete identity verification for model access and ensure normal call permissions |
Three common configuration errors
- Issue: An
Invalid URL (POST /v1/rerank)error occurs when calling the model. Cause: The rerank model access address is not configured correctly, or the proxy address does not match the model deployment path. - Issue: Uploaded historical petition archives are not included in the model's response context. Cause: The
maxContextparameter is not set correctly, or the uploaded documents are not associated with the corresponding knowledge base, causing the model to fail to obtain historical data. - Issue: Response speed is too slow after deploying a 70B parameter model. Cause: The value range of
maxContextis not reasonably limited, causing the model to process excessive historical petition text each time and increasing inference time.
How to confirm successful configuration
- Call the model test interface, input a simulated petition request, check whether the returned result includes the key nodes of the corresponding handling process, and confirm that the context is loaded correctly.
- View the model call logs to confirm that there are no format errors in the configuration of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, and no records of connection timeouts or identity verification failures. - Upload a standard petition document, test whether the model can correctly extract fields such as the responsible unit and request content from the document, and confirm that the knowledge base association configuration takes effect.
- Adjust the
temperatureparameter to different ranges, test the change in response randomness, and confirm that the impact of the parameter configuration on the output content meets expectations.
The values provided here 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.