What Data for This Category Looks Like
Data for this category comes from new energy development project declaration materials and approval review cases. This includes official documents such as project filing applications, environmental impact assessment approvals, land pre-examination opinions, and grid connection plans.
Data updates follow the progress of individual project declaration workflows. Materials for a single project are gradually updated from submission until approval is completed. Most documents are in multi-page PDF format. Some review cases include scanned proof attachments.
Fixed fields are included in all materials: project name, construction location, installed capacity (unit MW), total investment (unit ten thousand yuan), declaration date, and approval document number.
Reference only; verify against the target FastGPT release and deployment documentation.
Constraints Imposed on Model Access and Configuration
The multi-page PDF format and long text content of new energy development declaration materials and review cases require models to adapt to long context processing capabilities. This prevents key approval node information from being truncated.
Standardized units for fixed fields (such as MW and ten thousand yuan) require models to accurately identify and associate corresponding values. Failure to do this will lead to deviations in review results.
Some review cases include scanned proof attachments. Visual recognition capability must be enabled to extract text from these images.
The update rhythm of individual project materials requires models to continuously load newly submitted supplementary materials. A stable context synchronization mechanism must be configured. This ensures the review process can associate historically submitted materials, while avoiding loading redundant content repeatedly.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the average length of a single new energy project declaration material document, prevents core approval information from being truncated |
maxTokens | 2000–3000 token | Controls the output length of a single review round, ensures case review opinions are complete and not redundant |
quoteMaxToken | 1500 token | Limits the maximum length of quoted materials, prevents overly long quoted content from interfering with review logic |
vision | Enabled | Adapts to scanned attachments such as land use certificates and grid connection agreements included in review cases, extracts text from images |
temperature | 0.1–0.3 | Reduces output randomness, ensures review results meet standardized requirements of official approval rules |
AIPROXY_API_ENDPOINT | Fill in the exclusive address provided by the platform | Ensures model requests are correctly routed to the specified AI service interface |
Three Common Misconfigurations
- Phenomenon: API calls return an error stating the model does not support the current task, with status code 400. Cause: The
toolChoiceparameter is not configured correctly, or the selected model does not support multi-round call requirements for case review. - Phenomenon: Text from scanned attachments cannot be extracted correctly, and review results lack attachment information. Cause: The
visionconfiguration item is not enabled, or the model interface does not have visual recognition permissions enabled. - Phenomenon: Context is lost during multi-round review, and subsequent steps cannot associate previous approval opinions. Cause: The
maxContextconfiguration value is too small, causing previously submitted supplementary materials to be automatically truncated.
How to Verify Proper Configuration
- Upload the longest single declaration material PDF, trigger a review process, check if the output result includes review opinions for all core fields.
- Upload a case with scanned attachments, check if the review result extracts key text information from the attachments.
- Call the API with test data containing multiple fields, check if the returned result accurately associates values with their corresponding units.
- View the model call logs in the FastGPT backend, confirm that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations have no format errors.
The values provided here are common starting points, and should be evaluated against the samples used in the specific deployment.
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.