What this category's data looks like
Data for this category primarily comes from the regulatory and rule database, higher-level law database, departmental policy document database, and administrative normative document filing system. Update frequency is irregular. Daily processed documents include normative document drafts, drafting explanations, legal compliance review opinions, solicitation opinion summaries, and filing reports. Most documents contain fields such as article text, issuing body, effective date, and review annotations. There is no unified fixed unit, and processing follows the expression standards set forth in the original document text.
What constraints these characteristics impose on model access and configuration
Data sources for this category are scattered, and updates follow no fixed cycle. Model access must support batch import and incremental synchronization of multi-source data. Documents have diverse structures and contain specialized legal provisions, which requires the model to adapt to context window constraints. The text length processed in a single round must be reasonably limited. The review link requires strict comparison with higher-level laws and authority basis, so model call accuracy must meet high standards. Context overflow must be avoided to prevent review conclusion deviation. Therefore, priority should be given to configuring context and token limit parameters during setup.
How to set configurations
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
maxContext | 8000–12000 token | Normative document drafts usually include multi-chapter articles, and need to fully load higher-level laws and the original document text for comparison. This range covers the context requirements of most single review documents |
maxTokens | 4000–6000 token | A single review round handles content such as article comparison and opinion summary. Limiting token count prevents model output truncation or timeout |
quoteMaxToken | 2000–3000 token | Legal compliance review opinions or higher-level law articles must be referenced as a basis. This range retains sufficient reference text length |
temperature | 0.1–0.3 | Reviews must strictly follow legal provisions. A lower temperature improves output accuracy and standardization |
AIPROXY_API_ENDPOINT | Fill in according to the deployed model service address | Must match the actual accessed model service endpoint to ensure correct request routing |
AIPROXY_API_TOKEN | Fill in according to the key provided by the model service | Used for identity verification to ensure legitimate model service calls |
Three common configuration mistakes
- Model tests return
connector error, or no response after a call. Cause:AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENis not configured correctly, the model service port is not open, or network policies restrict access. - Model tests succeed, but no streaming output appears in conversations. Cause: The model's streaming return configuration is not enabled, or FastGPT v4.17.0 does not support streaming transmission logic.
- Review results are empty or contain a large number of incorrect comparisons. Cause:
maxContextis set too small, preventing complete loading of normative documents and higher-level law content, ortemperatureis set too high, leading the model to generate conclusions that deviate from legal provisions.
How to confirm configuration is complete
- Run a model connectivity test to confirm no
connector errortype errors appear in returned results. - Enable the streaming test switch to verify if word-by-word returned content appears during conversations.
- Upload a standard normative document draft to trigger a review task, and confirm the returned result includes core content such as article comparison and authority verification.
- Check the model call log to confirm token usage does not exceed the configured
maxTokensandmaxContextlimits.
The values provided are common starting points and should be evaluated 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.