What the data for this category looks like
The service subjects for this category are judicial administrative departments, department legal affairs offices, and policy and legal affairs positions in government offices. Data sources include regulatory and rule databases, higher-level law databases, departmental policy document databases, and administrative regulatory document filing systems. Update frequency is irregular. Daily processed documents cover draft regulatory documents, drafting explanations, legality review opinions, solicitation opinion summaries, and filing reports. Documents include fields such as text content, issuing authority, basis of authority, and opinion solicitation results. The number and scale of documents involved in a single batch of review vary by task.
What constraints these characteristics impose on the "model access and configuration" link
Decentralized data sources and irregular update cycles require access configurations that support dynamic pulling of multi-source data and format adaptation. Daily documents contain specialized legal texts and administrative process records. The text length involved in a single review is relatively long, so model parameters adapted to long contexts must be configured. Ledger summary needs to integrate fields from multiple types of documents. This requires the model to accurately identify and align standardized content fields across different documents. Filing-related documents must strictly comply with compliance requirements, and model outputs must be traceable. Therefore, parameters that support context reference recording need to be configured.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to long-text review scenarios such as draft regulatory documents and drafting explanations, covering all core document content for a single batch of review |
maxTokens | 2000–3000 token | Controls the output length of a single-round review report, ensuring complete content that meets the format requirements of ledger summary |
quoteMaxToken | 1500 token | Limits the length of referenced content such as higher-level legal provisions and policy documents, avoiding context overload |
toolChoice | auto | Allows the model to independently choose whether to call retrieval tools, adapting to the legality comparison requirements of multi-source data |
temperature | 0.1–0.3 | Reduces output randomness, ensuring the rigor and consistency of legality review conclusions |
AIPROXY_API_ENDPOINT | Fill in according to the deployed model service address | Connects to compliant large model services, ensuring data transmission complies with government network security requirements |
AIPROXY_API_TOKEN | Fill in according to the key provided by the service provider | Completes identity verification for model services, ensuring access security |
Three common configuration errors
- A test returns
404 status code (no body). The cause is that the address configured inAIPROXY_API_ENDPOINTcannot be accessed normally, or theAIPROXY_API_TOKENwas entered incorrectly, causing identity verification failure. - After deploying a model locally, it cannot generate responses based on document content. The cause is that the
maxContextconfiguration value is too small to load the parsed document content, or the multi-source data recall logic was not configured correctly. - The model test reports a
Message field is requirederror. The cause is that themaxTokensortemperatureparameters were not configured correctly, causing the request body format to not meet the model service requirements.
How to confirm the configuration is complete
- Verify the configuration of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, send a test request, and confirm there are no identity verification or address errors. - Adjust the values of
maxContextandmaxTokens, use a single draft regulatory document for testing, and confirm that the model can fully load and process the document content. - Review the model output results, confirm that they include referenced document fragments, and verify whether the
quoteMaxTokenconfiguration meets requirements. - Simulate a multi-document ledger summary scenario, test whether the model can align fields across different documents and generate a summary report that conforms to the format.
The values provided here are common starting points and should be measured against the user'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.