What data for this category looks like
Declaration materials and file review data for power generation groups primarily come from internal unit operation logs, project approval archives, environmental protection declaration documents, and file archiving records. Data update frequency follows business processes: project-based declaration materials are updated quarterly or annually in batches. Daily files are updated in real time as review and approval are completed. Most documents use fixed template structures. Declaration materials include fields such as unit number, power generation, coal consumption rate, and approval document number. Files include fields such as file number, involved project, review opinion, and completion date. Some fields have fixed formats and units: power generation is measured in megawatt-hours, and approval document numbers follow the format "XX Electric Review [Year] No. XX".
What constraints do these characteristics impose on model integration and configuration
The characteristics of declaration materials and file review data for power generation groups impose multiple constraints on model integration and configuration. Batch-updated structured data requires model configuration to support batch data recall, to avoid missing cross-batch file comparison information in single calls. Documents with fixed templates but many fields require sufficient context length configuration to accommodate complete field information, preventing the model from truncating critical parameters. Fields with fixed formats require model configuration to adapt to specific extraction rules, ensuring field accuracy of review results. Internally deployed API nodes require dedicated access addresses and tokens to keep data within the organization. The configuration must also adapt to the group’s internal time synchronization mechanism to ensure time accuracy of output results.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Declaration materials and files often include multi-page logs and approval documents, requiring complete context fragments to be retained |
maxTokens | 2000–3000 token | Single review conclusion must be output clearly, avoiding exceeding the model’s output limit |
quoteMaxToken | 1500 token | Quoted content such as file numbers and unit parameters accounts for a fixed proportion, limiting the length of referenced text |
temperature | 0.1–0.3 | Review requires rigor, reducing randomness to ensure consistency of field extraction and judgment |
AIPROXY_API_ENDPOINT | Fill in according to the group’s internally deployed nodes | Adapt to privately deployed API nodes, keeping data within the organization |
AIPROXY_API_TOKEN | Dedicated token bound to the group’s internal IP whitelist | Ensure only authorized nodes can call the model interface, improving security |
Three Common Mistakes
- The model’s calculated total power generation value does not match the logs. The symptom is field calculation deviation in review results. The cause is insufficient
maxContextconfiguration, leading the model to truncate part of the log data and miss critical parameters. - After calling a local model, the latest declaration time from the server cannot be obtained. The symptom is time fields in results lag behind actual submission dates. The cause is incorrect configuration of
AIPROXY_API_ENDPOINTto point to the group’s internal time synchronization node, or failure to enable the model’s real-time data call permission. - Custom API access returns status code 403. The symptom is model call failure. The cause is incorrect
AIPROXY_API_TOKENconfiguration, not bound to the group’s allowed IP whitelist.
How to Confirm Proper Configuration
- Upload a single complete unit declaration material, check if the model output context includes all key fields, confirm that the
maxContextconfiguration covers the target text length. - Submit a review request including historical file comparison, check if the model correctly references relevant file content, confirm that
quoteMaxTokenis set appropriately. - Call the configured API interface, check if the time field in the returned result matches the current server time, confirm that the time synchronization configuration takes effect.
- Adjust the
temperatureparameter to 0.2, submit the same material multiple times, check the consistency of output results, confirm that the parameter value meets the review rigor requirements.
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.