What the Data for This Category Looks Like
Core data sources for new energy development scenarios include project survey reports, wind turbine and photovoltaic equipment operation and maintenance logs, grid connection approval documents, employee attendance and reimbursement documents, and more. Update rhythms vary significantly: survey reports are updated irregularly as projects advance, operation and maintenance logs are generated daily or per shift, grid connection approval documents are updated at project milestones, and employee-related documents are updated monthly or quarterly. Document structures cover long-text feasibility study reports, structured equipment parameter tables, and semi-structured operation and maintenance records. Fields include project number, equipment serial number, operation and maintenance duration (hours), power generation (kilowatt-hours), and more. Units are tightly bound to business scenarios.
What Constraints These Characteristics Impose on Model Integration and Configuration
Reference only; verify against the target FastGPT release and deployment documentation.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Meets the context reading needs of long documents such as feasibility study reports and operation and maintenance log summaries in new energy development scenarios, avoiding truncation of key business parameters |
maxTokens | 2000–3000 token | Matches the reply length required for internal office and employee service-related questions, balancing information output volume and resource usage |
quoteMaxToken | 1500 characters | Retains complete fields and units of key fragments such as equipment parameters and grid connection requirements in new energy project documents, avoiding incomplete parsing |
temperature | 0.3–0.5 | Meets the demand for information accuracy in internal office scenarios, reducing reply randomness and avoiding content that does not comply with business rules |
toolChoice | auto | Supports automatically selecting whether to call the knowledge base or business tools based on employee questions, adapting to multiple types of internal service needs |
AIPROXY_API_TOKEN | Fill in the exclusive assigned key | Ensures the security of model calls and prevents unauthorized access |
Three Common Configuration Mistakes
- Symptom: Refreshing the page displays "No available index model detected", but creating a new conversation agent works normally. Cause: The index model configuration has not been synchronized to global service nodes, or the index update task has not completed to trigger status verification.
- Symptom: FastGPT v4.11.0 returns a 404 status code with no response body when configuring the qwen3-max model. Cause: This version does not support the qwen3-max interface path, or the key permission does not cover this model version.
- Symptom: The model’s generated reply contains mismatched business field units, such as incorrectly using "megawatt-hours" instead of "kilowatt-hours". Cause: The model’s structured field recognition configuration is not enabled, leading to incorrect parsing of content with units such as equipment parameters and power generation.
How to Confirm the Configuration Is Successful
- Verify that the values for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatch exactly the configuration documents provided by the service provider. - Submit a test question that includes keywords related to new energy project equipment parameters, confirming the reply includes correct fields and their corresponding units.
- Refresh the page and check the model configuration status, confirming there are no error prompts such as "no available index model".
- Adjust the
temperatureparameter to 0.7 and submit a test question, observing changes in reply randomness to confirm the configuration takes effect.
The values provided here are common starting points and should be evaluated 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.