What this category of data looks like
Data for this category comes from the regulatory and rule database, higher-level law database, departmental policy document database, and administrative regulatory document filing system. Update cycles are irregular. Daily processed documents include regulatory document drafts, drafting explanations, legitimacy review opinions, solicited opinion summaries, and filing reports. Data fields include document title, issuing authority, effective date, article content, review opinions, and other fields. There are no unified fixed units; values change dynamically based on document type. Processing requires associating cross-source legal articles and policy basis. Data volume fluctuates with policy updates and review task volume. In FastGPT v4.17.0, the data source configuration for this category supports batch import of data from filing systems.
What constraints do these characteristics impose on model access and configuration
Scattered data sources and irregular update cycles require configuration that supports incremental synchronization for multiple data sources and index construction. This avoids full repeated data pulls that consume resources. Diverse document types include non-standard texts such as drafts, explanations, and review opinions. Configuration must support multi-format text parsing and chunking rules to ensure complete extraction of core content. Cross-database comparison of higher-level laws and authority basis requires the model’s context association logic to adapt to cross-source data association. Configuration must ensure the accuracy of retrieval and recall. Review errors directly impact policy release and implementation. Configuration must include result traceability and verification links to ensure traceability of the basis source.
How to set the configuration
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to long-text association requirements for cross-database legal articles and policy documents, avoids truncation of core basis content |
maxTokens | 4000–6000 token | Controls the output length of a single review response, ensures completeness and readability of legitimacy review opinions |
quoteMaxToken | 1500–2000 token | Limits the text length of cited basis, ensures cited content is accurate and non-redundant, complies with format requirements of review documents |
temperature | 0.1–0.3 | Reduces generation randomness, ensures rigor of review conclusions, complies with deterministic requirements for policy and institutional basis |
AIPROXY_API_ENDPOINT | Fill in the actual interface address based on the deployment environment | Adapts to model access requirements for local or cloud deployments, ensures normal model call connectivity |
AIPROXY_API_TOKEN | Fill in the key assigned by the platform | Ensures identity verification security for model calls, complies with data security specifications for government affairs systems |
Three common configuration errors
- Model calls return an error stating that only stdio processes are supported, and MCP encapsulated with the SSE protocol cannot be used. The cause is that the access configuration does not follow the platform’s default process communication specification, and a non-standard communication protocol was mistakenly enabled.
- Model icons do not display in the workflow interface, and a network error prompt appears when uploading files. The cause is that the configured
AIPROXY_API_ENDPOINTaddress does not correctly point to the deployment node, or network access permissions between nodes are not open. - The number of cited basis entries retrieved by the index does not match the actual data source. The cause is that the irregular update feature of category data is not adapted, and incremental synchronization rules are not configured, causing the index to be out of sync with the latest policy documents.
How to confirm successful configuration
- Run a model call test to verify that legitimacy review-related basis association results can be returned normally, and check that there are no errors in the call link logs.
- Check the index configuration to confirm that the data sources specified for the category, such as the regulatory and rule database and higher-level law database, have been associated, and verify that the data synchronization status is normal.
- Adjust the test
temperatureparameter, observe whether the rigor of the generated results meets review requirements, and confirm that the parameter configuration is suitable for the business scenario. - Check network and key configuration, verify the validity of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, and ensure that there are no identity verification or network issues during model calls.
The values listed above are common starting points. Adjust them based on your own specific samples and use cases.
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.