What Data Looks Like for This Category
This category’s data mainly comes from enterprise contract management systems, compliance risk databases, law and regulation libraries, case management systems, and reporting platforms. Daily generated documents include compliance risk lists, legal review opinions for contracts, legal opinions on major matters, compliance review forms, and violation report investigation results. The data update frequency is monthly. Documents typically contain fields such as subject information, compliance judgment basis, risk level, and handling suggestions. There is no unified fixed unit; fields and units are adjusted based on document type.
What Constraints These Characteristics Impose on the "Model Integration and Configuration" Link
Monthly updated data requires configuring periodic synchronization incremental pull logic during model integration, to avoid excessive enterprise internal storage and network resource usage from full pull operations. Heterogeneous documents from multiple sources require configuring parsing rules adapted to different formats, to ensure accurate field extraction from documents such as compliance review opinions and risk lists. Accuracy requirements for compliance-related content are high. The context and results of model calls must be strictly linked to the original data source. The content scale of a single call must also be limited to prevent exceeding processing limits. Additionally, the high compliance requirements of legal scenarios require ensuring tool call traceability, to avoid context interruptions affecting result coherence.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
maxContext | 8000–12000 token | Fits the long-text context requirements of compliance documents, avoiding loss of key compliance judgment basis due to context truncation |
maxTokens | 4000 token | Controls the length of a single model output, ensuring the readability and completeness of compliance review opinions and risk analysis results |
quoteMaxToken | 2000 token | Limits the length of referenced original documents, ensuring matching accuracy between retrieval results and context, avoiding redundant content interfering with model judgment |
toolChoice | auto | Allows the model to independently choose whether to call tools, adapting to scenarios where compliance review requires retrieval of law libraries and case data |
temperature | 0.2–0.4 | Reduces output randomness, ensuring accuracy and rigor of compliance-related responses, meeting the high certainty requirements of legal scenarios |
AIPROXY_API_ENDPOINT | Fill in according to the deployment environment | Adapts the API access path to the enterprise internal network environment, ensuring security and stability of data transmission |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Misconfigurations
- Phenomenon: The model context is interrupted after calling the MCP tool, and subsequent responses cannot link to previous compliance review content. Cause: The context retention range of
maxContextis not configured correctly, or the context continuation logic is not configured after tool calls. - Phenomenon: The model response speed slows down after calling the MCP tool, and a single response times out. Cause: The
maxContextsetting is too large, increasing the time required for the model to load the context, or the API endpoint configuration does not adapt to the bandwidth restrictions of the enterprise internal network. - Phenomenon: The reranking model returns false as the result, and retrieval results are not sorted correctly by relevance. Cause: The calling parameters of the reranking model are not configured correctly, or the document format of the data source does not meet the input requirements of the reranking model, causing the model to fail to properly parse compliance-related documents.
How to Confirm Successful Configuration
- Initiate a context call that includes a compliance risk list and contract review opinions, then check whether the model output fully links to the key content of the original document.
- Call the MCP tool to retrieve the law library, then check whether the context retains the previous review conversation content after the tool call, with no context loss.
- Submit compliance documents in different formats, then check whether the model can correctly extract core fields such as risk level and handling suggestions from the documents.
- Test the reranking model calling process, then confirm that retrieval results can be sorted correctly by relevance, with no abnormal false returns.
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.