What data in this category looks like
The core documents for this category include public data resource catalogs, data open lists, data item descriptions, desensitization rules, interface documents, and update frequency descriptions. Data sources include local public data open platforms, integrated big data platforms, departmental business databases, and electronic certificate repositories. The data update cycle is monthly. Most documents contain structured fields such as data item codes, open scope, interface addresses, desensitization requirements, and update frequencies. Some documents also include interface parameters and authorization boundary descriptions.
What constraints these characteristics impose on the model access and configuration link
FastGPT v4.17.0 adapts to the configuration needs of this category. Monthly updated data requires configuring parameters that support periodic synchronization to prevent generated catalogs from being out of sync with actual data. Documents with a high proportion of structured fields require the model to accurately match fields and content to ensure field completeness of drafted documents. Integrating data from multiple sources requires configuring parameters adapted to different data formats to ensure that the sorted list matches the original data. Content involving interfaces and authorization boundaries requires the model to strictly follow compliance requirements to avoid generating non-compliant content.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Public data catalogs and open documents include multiple monthly updated materials, requiring coverage of complete context information |
maxTokens | 2000–3000 characters | Standard output length for single documents such as data item descriptions, preventing generated content from exceeding format requirements |
quoteMaxToken | 500–800 token | Must retain key fields and interface information of original data items, avoiding truncation of important compliance content |
temperature | 0.1–0.3 | Must ensure accuracy and compliance of generated content, avoiding arbitrary changes to authorization boundaries for data openness |
AIPROXY_API_ENDPOINT | Fill in the exclusive address allocated by the platform | Ensure normal model call link to achieve a stable connection with the large language model |
AIPROXY_API_TOKEN | Fill in the valid token allocated by the platform | Complete identity verification to finalize model access and configuration |
Three common mistakes
- Symptom: After configuring the model and knowledge base, a new conversation Agent tests normally, but after refreshing the page, the prompt "No available index model detected" appears. Cause: The configured model is not associated with the knowledge base index of the corresponding public data catalog, or the index synchronization has not been completed.
- Symptom: When using paragraph chunking and enabling model paragraph recognition, after setting the maximum paragraph depth to 3, maximum chunk size to 1000, and index size to 128, the generated document has field misalignment. Cause: The field boundaries of structured data in this category are not matched, and the model's paragraph recognition logic is not adapted to the multi-field document structure.
- Symptom: Attempting to access the qwen3-max model in FastGPT v4.11.0 returns a 404 status code (no body) error. Cause: This version does not have built-in interface mapping for the corresponding model, or the API endpoint configuration is incorrect, preventing normal model calls.
How to confirm the configuration is complete
- Check whether the configurations of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatch the information provided by the platform to ensure normal model call links. - Create a test conversation, enter a query related to public data catalogs, and verify whether the generated document contains correct fields and compliant content.
- Check the synchronization status of the knowledge base index to confirm that monthly updated data has completed index construction, preventing generated catalogs from being out of sync with actual data.
- After adjusting the
temperatureparameter, compare the generated document content to confirm that the impact of parameter adjustments on generation results meets expectations.
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.