What this category of data looks like
This category of data typically comes from the dual random, one public supervision platform, enterprise credit information disclosure system, industry supervision system, and law enforcement officer directory. Daily documents include inspection plans, inspection target lists, inspection item lists, on-site inspection records, rectification notices, and inspection result disclosures. Fields include unified social credit codes, law enforcement officer numbers, inspection item entries, and others. The data update frequency is monthly. Core documents revolve around the circulation of inspection task generation and result disclosure.
What constraints these characteristics impose on model access and configuration
Monthly updated data sources require the model access link to support incremental data synchronization, avoiding resource consumption from full retraining. Differences in multi-type document structures require configuration to adapt to mixed input of structured lists and unstructured inspection records, avoiding parsing deviations. Compliance requirements for inspection target extraction require model matching logic to strictly align with supervision rules, without additional adaptation of penalty discretion logic, simplifying configuration items. Daily high-frequency task generation and result entry links require model call delay to match business circulation rhythm, avoiding slowing the overall process.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the average length of single documents such as inspection plans and inspection records, avoiding truncation of core supervision information |
maxTokens | 2000–3000 token | Meets the output length requirement for single inspection result disclosure, while controlling video memory usage for a single call |
quoteMaxToken | 1500 characters | Matches the length limit for referenced materials in case file review, ensuring complete extraction of inspection target information |
toolChoice | auto | Allows the model to automatically select tools based on review tasks, adapting to multiple subtasks such as inspection matching and content extraction |
temperature | 0.1–0.3 | Reduces randomness, ensuring that results such as inspection target extraction and item matching comply with the rigor of supervision rules |
AIPROXY_API_ENDPOINT | Fill in the dedicated address according to the deployment environment | Adapts to access restrictions of internal supervision department networks, ensuring data transmission security |
Three common errors
- Phenomenon: Video memory usage continues to rise and fails to release after a rerank model call, eventually triggering an out-of-memory error. Cause: No automatic video memory recycling strategy is configured for the model, and video memory is not released promptly after long-context calls.
- Phenomenon: The text content extraction node in the workflow cannot select the target model. Cause: A dedicated model adapted for supervision document parsing is not mounted in system settings in advance, or the model format is incompatible with the current node.
- Phenomenon: The LLM model mounted via Ollama outputs content unrelated to the supervision knowledge base. Cause: The dedicated supervision knowledge base is not correctly bound to the model call link, or the context recall logic does not match business requirements.
How to confirm the configuration is complete
- Run a model parsing test for a single inspection plan, verify that the output inspection target list fields are complete and have no abnormal truncation.
- Trigger a batch inspection record review task, confirm that peak video memory usage falls within the measured and calibrated safe range.
- View the model call log, confirm that configuration parameters such as
maxContextandtemperaturetake effect as preset. - Initiate a query after binding the dedicated supervision knowledge base, verify that the model's output inspection results match the disclosure data in the system.
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.