What the data for this category looks like
Data comes from three main sources: internal project declaration systems, state-owned asset supervision reporting platforms, and historical case archive databases of state-owned capital investment companies.
Each single declaration batch includes structured declaration forms and unstructured documents such as project feasibility reports and scanned qualification certificates. Cases are categorized and archived by project ID, declaration subject, and review node.
Document updates follow declaration cycles: regular monthly or quarterly batch updates. Historical archives are fully synced periodically after archiving.
Available fields include project ID, declaration amount (unit: ten thousand yuan), review status, submission time, and other standard items. Some materials include multi-page scanned documents and table-style content.
What constraints do these characteristics impose on model integration and configuration
Mixed multi-source data structures require model integration to support structured field parsing, unstructured text processing, and image content processing.
The long text attribute of declaration materials and cases requires configuration to have sufficient context capacity, to avoid truncating key review information.
The proportion of image content in declaration materials requires models with visual capabilities.
The batch update rhythm requires model call trigger logic to match declaration cycles, to avoid unnecessary real-time resource consumption.
The need to associate historical cases for review requires context configuration to cover sufficient historical data volume, to ensure consistent and accurate review results.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 12000–15000 token | Declaration materials and cases are mostly long texts, so this range covers complete project and historical case information to avoid losing key review content |
maxTokens | 2000–3000 token | Review results need to include detailed judgment basis, and this value range can cover complete review conclusions and explanations |
vision | true | Declaration materials often include scanned qualification documents and financial report screenshots, so visual model parsing of image content is required |
temperature | 0.1–0.3 | Review work requires strict consistency, and this value range reduces output randomness to ensure consistent judgment standards |
AIPROXY_API_ENDPOINT | Official access address corresponding to the Qianwen 72B VL model | Need to call visual large models to parse image-based declaration materials, so correct model service endpoints must be configured |
AIPROXY_API_TOKEN | Exclusive service key applied for | Ensure identity verification and permission control for model calls, and prevent unauthorized access |
Three common configuration mistakes
- Phenomenon: After uploading declaration materials with scanned documents, the model only returns text review results without parsing image content. Cause: The
visionparameter is not set totrue, or no model service with visual capabilities is bound. - Phenomenon: Review results only include content wrapped in
<think>tags, with no final judgment output. Cause: ThemaxTokensvalue is too low to fit complete output content, or the full result return switch is not enabled in model call configuration. - Phenomenon: Model call logs return a 401 status code, prompting authentication failure. Cause: The
AIPROXY_API_TOKENconfiguration is incorrect, no correct exclusive service key is filled, or the key has expired and become invalid.
How to confirm the configuration is correctly set
- Upload a single declaration material containing scanned documents, check if the model correctly parses image content and generates complete review opinions.
- View model call logs, confirm there are no format errors in
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations, and no abnormal status codes such as 401 or 404 appear. - Test batch calls for multiple declaration batches, confirm that the
maxContextandmaxTokensconfigurations do not cause output truncation. - Check the final output result, confirm that the
<think>tag is automatically hidden, and only clear review conclusions and supporting explanations are displayed.
The configuration values listed here are common starting points. Adjustments should be made based on sample data and actual usage scenarios.
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.