Reference only; verify against the target FastGPT release and deployment documentation.
What data for this category looks like
This category’s data originates from government portals, document management systems, government information disclosure directories, policy document repositories, and press conference materials. The update frequency is daily. Commonly processed documents include policy documents, policy interpretation drafts, public disclosure directories, information release approval forms, press releases, and website column update records. Most document structures include fields such as document number, issuing authority, effective date, public disclosure attribute, and main content. No additional custom units are used. Content primarily consists of normative texts related to government public disclosure. Some documents include a small number of auxiliary explanatory images.
Constraints on the model access and configuration link
The daily update requirement means the model access link must support incremental data synchronization and high-frequency call adaptation to ensure the timeliness of government public disclosure content. Documents include government-specific fields such as document number, effective date, and public disclosure attribute, so the configuration link must adapt to structured field recognition and classification logic to ensure the model can accurately identify information related to policy compliance. The content of policy documents and interpretation drafts directly links to government public disclosure compliance, so the model’s output caliber consistency must be constrained to avoid policy misreading caused by parameter setting deviations. Additionally, multi-channel synchronization requirements mean the configuration must support unified access and distribution adaptation for multiple data sources to meet content distribution needs across different public channels.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Government documents and associated content have relatively long length, and need to accommodate full policy texts, interpretation drafts, and public disclosure directory content to avoid context truncation |
maxTokens | 2000–3000 token | Party building study Q&A requires generating accurate policy interpretations or public guidance, and controlling output length to avoid redundant information |
quoteMaxToken | 1500 token | Core fragments of policy texts need to be retained as reference basis to ensure the compliance and credibility of responses |
temperature | 0.1–0.3 | Ensure consistent caliber of policy responses, and avoid policy misreading caused by randomized output |
vision | Enable as needed | The core of this category is text-based government public disclosure and party building Q&A. Only enable when documents with attached images need to be processed |
AIPROXY_API_ENDPOINT | Fill in according to the deployed model service address | Adapt to the access requirements of Qwen3 series models deployed via VLLM |
AIPROXY_API_TOKEN | Fill in according to the access key generated during deployment | Ensure the security of model access and access permission control |
Three common configuration mistakes
- Symptom: When a configured vision model is called, a prompt stating that image content cannot be provided is returned. Cause: The
visionparameter was not correctly set to enabled, or the image recognition function was not enabled in the AI conversation node. - Symptom: An error occurs when connecting to a Qwen3-Embedding-8B model deployed via VLLM. Cause: The
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENparameters were not filled in correctly, or the model deployment port was not granted public network access permission. - Symptom: The generated policy interpretation content deviates from the core caliber of the original text. Cause: The
temperatureparameter was set too high, causing the model to output randomly and deviate from the accurate wording of the original policy text.
How to confirm successful configuration
- Test context loading for a single policy document to confirm no truncation occurs, then adjust the
maxContextparameter to cover the required content range. - Submit a party building study Q&A request to verify that the returned content includes reference fragments of the original policy text, and confirm the
quoteMaxTokenparameter is properly configured. - Submit a request for policy interpretation, check that the output caliber matches the original text, then adjust the
temperatureparameter to a range that meets requirements. - If the vision function is enabled, upload a test image and submit a request to verify that the model can correctly recognize the image content, and confirm the
visionparameter is enabled correctly.
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.