What data for this category looks like
The data sources for this category are urban management law enforcement platforms, urban operation grid platforms, video surveillance, and public complaint work orders. The update frequency is daily. Daily documents include inspection records, case filing approval forms, on-site photos, rectification orders, penalty decisions, and case closure reports. Core fields cover case number, violation location, violation type, responsible entity, rectification requirements, and disposal progress. Some documents include multimedia evidence materials.
What constraints these characteristics impose on model access and configuration
This category has scattered data sources and includes multimedia evidence materials. Model access must support visual information parsing capabilities. The daily updated dataset has a stable scale. A reasonable knowledge base synchronization cycle must be configured to avoid index overload. Structural differences between multiple document types require chunking logic to adapt to different document formats. There are many closely associated core case fields. Context length must be limited to avoid redundant information interfering with model judgment. Some data comes from internally deployed law enforcement platforms, so custom API endpoint configuration must be supported.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
vision | Enabled | This category includes multimedia evidence materials such as on-site photos, so visual information parsing support is required |
maxContext | 8000–12000 token | There are many associated case documents, so context length must be limited to avoid redundant model judgment |
maxTokens | 2000–3000 token | Responses need to cover case details and disposal suggestions, so sufficient output space must be reserved |
quoteMaxToken | 1500–2000 token | Sufficient original quote space must be retained to support law enforcement basis verification |
temperature | 0.3–0.5 | Law enforcement responses require rigor, so reduce randomness to avoid uncertain statements |
AIPROXY_API_ENDPOINT | Fill in the internal network or mapped external network address according to the deployment environment | Adapt to most internally deployed law enforcement platforms and model services for this category |
Three common mistakes
- Issue: The knowledge base remains in the indexing state with no progress. Cause: The daily updated dataset scale of this category is not adapted to, the synchronization cycle does not match the data update frequency, or the chunking parameter settings exceed the model's processing limit.
- Issue: The model access prompt indicates an incorrect access point. Cause:
AIPROXY_API_ENDPOINTis not filled with a valid external network address after internal network mapping, or the format does not meet the requirements of the model service provider. - Issue: A network access failure error is returned after calling the model. Cause: The internally deployed model service is not correctly mapped to an external network address, or the
AIPROXY_API_TOKENconfiguration is invalid, causing identity verification failure.
How to confirm the configuration is complete
- View FastGPT system logs and model service logs to confirm there are no identity verification failure or endpoint unreachable errors.
- Upload a typical inspection record document of this category to trigger a model call, and check whether the core content of the document can be parsed correctly.
- After enabling the
visionconfiguration, upload an on-site photo to confirm that the model can identify details of the violation scene in the photo. - Adjust the
temperatureparameter, compare the response content of two identical inputs, and verify that the parameter takes effect as expected.
The values provided 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.