What the Data for This Category Looks Like
This category of data comes from administrative law enforcement case handling systems, law enforcement recorder footage, the national enterprise credit information publicity system, and departmental supervision databases. Data updates occur daily. Daily documents include on-site inspection records, interrogation records, evidence lists, prior administrative penalty notification letters, hearing notification letters, penalty decisions, and delivery receipts. The core structure centers on illegal fact determination, penalty basis, and decision documents. A traceable penalty evidence chain must be established.
What Constraints These Characteristics Impose on the Model Access and Configuration Link
Individual law enforcement documents have lengthy content. This consumes model context quota. Total input and output token volume must be limited. Data updates daily. Incremental sync logic must be configured to maintain Q&A content timeliness. Penalty case files must strictly match evidence and decisions. Model-returned references must accurately link to original document fragments. Irrelevant content must be avoided. Document formats differ across law enforcement scenarios. Chunking and retrieval rules for multiple law enforcement document types must be adapted. This ensures Q&A content complies with law enforcement norms and the strict standards of party building learning.
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 typical length of individual law enforcement documents, avoiding context overflow that interrupts Q&A |
maxTokens | 2000–3000 token | Controls the output length of a single response round, ensuring penalty-related content is fully expressed and compliant with standards |
quoteMaxToken | 1500–2000 characters | Limits the length of referenced document fragments, matching the standard paragraph length of law enforcement documents |
temperature | 0.1–0.3 | Reduces response randomness, ensuring Q&A content is rigorous and meets law enforcement and party building learning requirements |
AIPROXY_API_ENDPOINT | Fill in the official address provided by the platform | Adapts to network path requirements for model calls, ensuring requests are delivered normally |
AIPROXY_API_TOKEN | Fill in the key applied for during actual use | Completes identity verification for model calls, preventing request rejection |
Three Common Errors
- Phenomenon: When calling an accessed OpenAPI model, the system prompts that the model stream response is empty. Cause:
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENis not configured correctly, or the model itself does not have streaming response output enabled. - Phenomenon: After enabling the reranking function, the response speed of knowledge base Q&A decreases significantly. Cause: The number of retrieved and recalled documents is not limited, or
maxContextis not adjusted to adapt to the context load after reranking. - Phenomenon: System logs show that the model actually used for Q&A does not match the model configured in the application. Cause: The independent model setting is not turned off in the knowledge base association configuration, causing the knowledge base to prioritize using the model configured by itself.
How to Confirm the Configuration Is Correct
- Check the content filled in
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, initiate a test call to confirm there are no identity verification or network errors. - Upload a typical administrative penalty decision letter to the test knowledge base, initiate a Q&A that combines party building learning and law enforcement business, and check whether the referenced fragments in the returned result match the original document content.
- View the system logs to confirm that the parameters used during model calls match the configured items, with no unexpected parameter overrides.
- Simulate daily incremental data synchronization, confirm that the knowledge base can normally update newly added law enforcement documents and party building learning content.
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.