What data for this category looks like
Data sources for this category include regulatory and rule databases, higher-level law databases, departmental policy document repositories, and administrative regulatory document filing systems. Update frequency is irregular. Daily processed documents include regulatory document drafts, drafting explanatory notes, legality review opinions, summaries of solicited comments, and filing reports. Most documents contain fields such as article content, issuing authority, effective date, and basis for authority. Individual document lengths vary widely. Some draft documents may include multi-chapter provisions and supporting explanatory materials.
What constraints these characteristics impose on the "model integration and configuration" workflow
Reference only; verify against the target FastGPT release and deployment documentation. Data sources are scattered and updates follow no fixed cycle. Configure access logic that adapts to multi-source heterogeneous data. Document lengths vary widely and include specialized legal provisions. Model context carrying capacity must match long-text processing requirements. Review work requires strict comparison with higher-level laws and authority bases. This sets clear requirements for the accuracy of retrieval and recall results. Document fields differ significantly across scenarios. Configure flexible metadata parsing rules to ensure accuracy and compliance in the review stage.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–16000 token | Matches long-text processing needs for regulatory document drafts and supporting materials, avoids truncating core review basis |
maxTokens | 2000–3000 token | Adapts to output length requirements for review opinion generation, ensures complete presentation of legality review conclusions |
quoteMaxToken | 1000–1500 token | Limits the length of retrieved and quoted legal provision texts, ensures multiple sets of higher-level law comparison content can fit in the context window |
temperature | 0.1–0.3 | Reduces generation randomness, ensures review conclusions are rigorous and standardized, meets the certainty requirements of legal review |
AIPROXY_API_ENDPOINT | Fill in the dedicated address provided by the deployment service provider | Adapts to external API model call links, ensures correct request routing |
AIPROXY_API_TOKEN | Fill in the obtained authentication key | Completes identity verification for model calls, ensures interface access security |
Three Common Mistakes
- Symptom: First legality review request for regulatory documents takes more than 3 seconds to complete, subsequent requests respond normally. Cause: Session cache configuration is not enabled. Each request reloads model weights, leading to excessively long first call latency.
- Symptom: Out-of-memory error occurs when calling external models, or smooth operation fails in ARM soft router deployment scenarios. Cause: Memory isolation rules for multi-model loading are not configured. Loading multiple models simultaneously depletes hardware resources, and model compilation configuration for ARM architecture is not adapted.
- Symptom: Retrieved higher-level legal provisions do not match review basis, and the number of results does not meet expectations. Cause:
quoteMaxTokenconfiguration value is too small. Truncates quoted content of some associated legal provisions, leading to incomplete recall results.
How to Confirm Configuration Is Complete
- Initiate a review test for a single regulatory document draft. Check that the context window loads all core document content completely, with no truncation prompts.
- Call the model interface. Verify that returned review results include clear higher-level law reference markers, and that retrieved and recalled content meets review requirements.
- Verify external API authentication configuration. Use a test tool to send a request. Confirm that the returned status code is 200 and no authentication errors occur.
- Observe model loading and call logs. Confirm that response time after the first request meets expectations, with no abnormal latency.
The values provided are common starting points and should be evaluated against relevant deployment 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.