What this category of data looks like
Data for this category originates from the randomized dual inspection and public disclosure supervision platform, enterprise credit information disclosure system, industry supervision systems, and law enforcement officer directory. Updates occur monthly. Daily generated documents include inspection plans, inspection target lists, inspection item lists, on-site inspection records, rectification notices, and inspection result announcements. Each document typically includes fields such as the unified social credit code of the supervised entity, inspection item entries, law enforcement officer numbers, and rectification time limits. There is no unified fixed unit field; different document types correspond to different business attributes.
Constraints on model access and configuration
This category of data is updated monthly, has heterogeneous sources, and diverse document structures. Model access configuration must match the monthly synchronization rhythm to avoid frequent data pulling that increases system load. Fields vary widely across different business documents, so context processing parameters adapted to multi-structure text must be configured. This ensures key fields such as supervised entity information and inspection item entries are fully retained. Policy basis Q&A must strictly match original document content, so a token limit for generated content must be set. The knowledge base update cycle must also align with business data update frequency to ensure Q&A results are timely and accurate.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | This category of documents contains business data with combined multiple fields, requiring sufficient context to ensure Q&A matches complete business logic |
maxTokens | 1000–1500 token | Policy basis Q&A requires concise and accurate content, avoiding redundant generation that exceeds business scenario requirements |
quoteMaxToken | 2000–3000 token | Long text fragments such as inspection item lists and rectification notices must be fully cited as Q&A basis |
temperature | 0.1–0.3 | Policy basis Q&A must strictly match original document content, reducing randomness to ensure result accuracy |
AIPROXY_API_ENDPOINT | Fill in according to the deployed model service address | Points to the actual deployed large model or reranking model service address to ensure normal request routing |
AIPROXY_API_TOKEN | Fill in according to the credential generated by the deployed service | Used to verify request legitimacy and ensure service access security |
Three common configuration errors
- Issue: The locally deployed rerank model does not appear in the FastGPT interface. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not correctly filled in the configuration file, or the deployed model service has not opened the corresponding port. - Issue: Policy Q&A content returned by model calls deviates from the original document. Cause: The
temperatureparameter is set too high, leading to overly random model generation that does not strictly match the original policy basis text. - Issue: Q&A results do not include complete inspection item information. Cause: The
maxContextorquoteMaxTokenparameter is set too low, causing context truncation that loses key business data.
How to confirm correct configuration
- Use a curl command to test the address configured in
AIPROXY_API_ENDPOINT, and confirm that a normal model service response is returned. - Import a monthly updated inspection target list document, initiate a policy basis Q&A test, and verify that the returned result includes key field information from the document.
- Adjust the
temperatureparameter to the 0.1–0.3 range, initiate more than 3 identical Q&A tests, and confirm consistency of returned results. - View the model management interface of FastGPT v4.17.0, and confirm that the loaded model name matches the deployed model version.
The values provided here 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.