What this category of data looks like
Target users for this category include housing and urban-rural development bureaus, administrative examination and approval bureaus, and comprehensive engineering construction project approval windows. Data is sourced from engineering construction project approval management systems, construction drawing review systems, enterprise qualification databases, and public credit platforms, and is updated daily. Daily documents include construction permit applications, construction drawing review qualification certificates, quality and safety supervision procedures, and completion record forms. Fields cover project numbers, participating unit qualifications, approval node statuses, record completion statuses, and more. Each document is linked to attributes from multiple systems, requiring cross-source integration to form a complete ledger.
What constraints these characteristics impose on model access and configuration
Reference only; verify against the target FastGPT release and deployment documentation.
How to set the configuration
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the number of fields and associated content of multi-source approval documents. It prevents context overflow that truncates key approval node information. |
maxTokens | 2000–3000 token | Matches the generation length of a single ledger summary report. It ensures coverage of all integrated approval material results. |
quoteMaxToken | 1500–2000 token | Limits the length of referenced original approval document fragments. It prevents overly long reference content from interfering with model generation logic. |
temperature | 0.2–0.4 | Reduces generation randomness. It ensures consistency between ledger data and approval document fields, and avoids fictional approval statuses. |
AIPROXY_API_ENDPOINT | Fill in according to the actual deployed model service address | Adapts to locally or privately deployed model interfaces, and reduces reliance on public models. |
AIPROXY_API_TOKEN | Fill in according to the applied private model token | Ensures legitimate identity for model interface calls, and prevents unauthorized access. |
Three Common Configuration Mistakes
- Phenomenon: Default prompt text appears in generated ledgers, such as "FastGPT is a knowledge base question answering system based on large language models (LLM)". Cause: The default initialization text was not removed from the system prompt, so the model carries redundant content during generation.
- Phenomenon: Non-streaming call errors occur when calling the Qwen3 model, and Agent node behavior is abnormal. Cause: The model's streaming call requirements were not adapted. Forced use of non-streaming requests causes the model interface to return errors, which affects Agent node execution logic.
- Phenomenon: Participating unit qualification information is missing from ledger summary results. Cause: Appropriate context parameters were not configured. Cross-system qualification database data is truncated, and cannot be fully integrated into the ledger.
How to confirm the configuration is complete
- Review the system prompt configuration. Confirm no default initialization text remains, and verify no redundant prompt content appears after triggering a test generation.
- Call the private model interface. Use
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENto send a test request, and confirm the interface returns normally with no identity verification errors. - Upload a single construction permit application document. Trigger ledger generation, and check that the result includes all required fields with no missing or incorrect content.
- Simulate a daily data update scenario. Trigger ledger regeneration, and confirm the system can pull the latest approval management system data and complete integration normally.
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.