What the data for this category looks like
The party building learning Q&A scenario for this category sources data from social insurance handling systems, tax social insurance premium collection systems, market entity registration information, and shared data from public security and civil affairs departments. The update frequency is daily. Daily processed documents include insurance registration forms, payment base declaration forms, personnel adjustment forms, social insurance premium verification forms, and personal benefit records. Q&A requirements that combine social insurance policies related to party building must also be addressed. Data fields include personnel identity identifiers, labor relationship status, payment base, unit registration information, and more. Each field uses a dedicated unit: for example, payment base uses currency units, and personnel count uses counting units.
What Constraints These Characteristics Impose on Model Access and Configuration
Multi-source data updated daily has a large scale. This requires limiting total context and output tokens during model access to avoid exceeding model capacity limits. Structured documents have fixed field formats. This requires model configuration to support structured data extraction and Q&A that combines policy content. Cross-system shared data has format differences. This requires model configuration to support data alignment verification. This scenario is party building learning Q&A. It must cover content that combines social insurance business and party building policies to ensure response compliance. Unlike the benefit verification category, this scenario focuses on basic data in the insurance registration and premium verification stage. Core fields such as personnel identity and payment base must be processed first. Errors will directly affect insurance registration, premium payment, and personal benefits. Configuration must prioritize response accuracy while controlling token consumption to avoid resource waste.
How to Set the Configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Documents under this category are mostly structured tables and bulk personnel data, requiring space to accommodate multiple forms, cross-source data fragments, and party building policy-related Q&A context |
maxTokens | 4000–6000 tokens | Needs to cover the full context and response length of Q&A for insurance registration and premium verification, avoiding truncation of critical business and policy information |
temperature | 0.1–0.3 | This scenario requires ensuring response accuracy and compliance, reducing randomness to avoid incorrect policy or business content |
toolChoice | auto | Needs to automatically call tools to complete data verification, form validation, and policy query, adapting to the Q&A needs of combining multi-source data and party building policies |
AIPROXY_API_ENDPOINT | Locally deployed model service address (e.g., http://127.0.0.1:8000/v1) | Adapts to the locally deployed model requirements of this scenario, supporting access to compliant model services such as ollama and VLLM |
AIPROXY_API_TOKEN | Exclusive access key generated by the service | Ensures the security of model calls, prevents unauthorized access, and meets compliance requirements for government affairs scenarios |
Three Common Misconfigurations
- Phenomenon: Token consumption statistics for model calls cannot be split by application. Only global consumption is displayed. Cause: Context isolation parameters for model calls are not configured correctly, leading to shared token counting logic across multiple applications.
- Phenomenon: Locally deployed ollama or VLLM models cannot be accessed normally, returning connection timeout or authentication errors. Cause:
AIPROXY_API_ENDPOINTis not configured to the actual network address and port monitored by the model, the corresponding port is not opened in the firewall, orAIPROXY_API_TOKENis not filled in correctly. - Phenomenon: Model responses contain incorrect personnel identity or payment base information, and content combining party building policies has deviations. Cause: The
temperatureparameter is set too high, leading the model to generate randomized incorrect field or policy interpretation content that does not meet the high accuracy requirements of this scenario.
How to Verify Successful Configuration
- Submit a Q&A request combining a single insurance registration form and party building policies, check whether the fields returned by the model match the document content, and adjust
maxContextandmaxTokensparameters until no critical information is truncated. - Call the locally deployed model service, verify that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations are correct, and check whether the model returns a response status code of 200. - Submit a Q&A request for bulk personnel adjustment data, check whether the
temperatureparameter setting meets accuracy requirements, and adjust it until no obvious random errors appear. - View model call logs to confirm that token consumption statistics are split by application, verifying that the context isolation configuration has taken effect.
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.