What data is covered by this category
This category covers documents including procurement plans, requirement specifications, bidding documents, bid evaluation records, contracts, acceptance sheets, supplier complaint materials, and more. Data is primarily sourced from enterprise procurement platforms, public resource trading platforms, supplier databases, contract management systems, and audit problem databases, and is updated daily. Documents include fixed fields such as procurement method, budget basis, scoring method, supplier qualification conditions, performance clauses, and others. Some documents must be linked to group centralized procurement rules and disciplinary inspection and audit requirements.
What constraints these characteristics create for the model access and configuration step
Decentralized data sources updated daily require model access to support multi-platform data synchronization. Configuration must include stability guarantees for high-frequency calls. Document fields are fixed and linked to compliance rules, so model input must strictly match specified field formats such as procurement method and budget basis. The workflow must adapt to both internal enterprise rules and state-owned asset supervision requirements, so configuration must support multi-dimensional compliance verification logic. Errors may lead to audit rectification and accountability, so configuration must include call logging and result traceability capabilities.
How to set up the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapt to the context requirements of long documents such as bidding documents and bid evaluation records, avoiding truncation of historical clauses required for compliance verification |
maxTokens | 4000–6000 token | Cover the output length of a single compliance review document, ensuring complete generation of content such as scoring methods and qualification conditions |
temperature | 0.1–0.3 | Maintain the rigor of compliance documents, avoiding generating statements that do not conform to enterprise systems |
toolChoice | auto | Automatically select tool calls required for compliance verification, adapting to the associated verification needs of multi-source data |
AIPROXY_API_ENDPOINT | Fill in the China-region exclusive node according to the deployment environment | Adapt to the network requirements of large model access in the Chinese region, avoiding cross-region call delays |
AIPROXY_API_TOKEN | Fill in the exclusive key allocated by the platform | Ensure the security of access permissions, preventing unauthorized calls |
Three common configuration mistakes
- An error with return format
{"id":"wwNHJoTgNfAF","nodeId":"4487"}occurs during calls. The cause is that themaxContextconfiguration does not adapt to long documents, resulting in abnormal node parameters after context truncation. - A request error occurs when uploading compliance-related PDF documents with no return result. The cause is that
AIPROXY_API_ENDPOINTis not configured with a China-region exclusive node, leading to cross-region network interception. - The generated compliance document contains statements that do not match the enterprise's centralized procurement rules. The cause is that the
temperaturevalue is too high, causing the generated content to deviate from rigor requirements.
How to confirm the configuration is complete
- After filling in
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, initiate a test call to confirm that the return result has no permission or network error prompts. - Import a standard bidding document, set
maxContextandmaxTokens, then check that the generated compliance review result has no content truncation. - Adjust
temperatureto around 0.2, generate a procurement plan document, and confirm that the statement is rigorous and meets compliance requirements. - Enable call logs to confirm that each model call records complete context and output content, facilitating subsequent audit traceability.
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.