What this category of data looks like
This category of data originates from China Government Procurement Network, local government procurement networks, fiscal integration systems, procurer budget systems, and supplier credit records. It is updated daily. Daily documents cover procurement requirements, procurement plans, bidding documents, tender documents, bid evaluation reports, winning bid announcements, procurement contracts, and question responses, covering all stages of the full procurement workflow. Most document fields include project numbers, procurement content, qualification requirements, evaluation criteria, and other items. These follow fixed norms for government procurement-related texts, with no custom special units.
Constraints imposed by these characteristics on model access and configuration
The daily update feature requires access configuration to support incremental synchronization mechanisms, preventing full-volume repeated pulls from occupying computing resources. Diverse document types with large structural differences require multi-format text parsing rules to be configured, ensuring correct content extraction for all procurement documents. All data sources are official public or internal compliant content. Model calls must be associated with traceability information for the corresponding data source, to avoid referencing non-compliant content. Most data fields involve compliance-related requirements. Model output must be limited to referencing only official document content, and generation of unverified rules is prohibited.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long-text context requirements of government procurement documents, avoiding truncation of key policy clauses |
maxTokens | 2000–3000 token | Controls the output length of single-round responses, ensuring policy basis explanations are clear and comply with text norms |
quoteMaxToken | 1500–2000 token | Limits the total length of referenced documents, ensuring referenced content accurately matches queried policy clauses |
temperature | 0.1–0.3 | Reduces response randomness, ensuring policy basis expressions remain consistent with official texts in rigor |
AIPROXY_API_ENDPOINT | Fill in the access address of the corresponding model according to the deployment environment | Adapts to the access paths of domestic models, avoiding cross-region access restrictions |
AIPROXY_API_TOKEN | Fill in the exclusive token generated by the platform | Verifies model call permissions, ensuring only authorized users can use the accessed models |
Three common configuration errors
- Phenomenon: GPU memory usage grows rapidly after calling the reranking model, and usage appears on multiple GPUs. Cause: The
maxContextlimit for context length is not configured, leading to large amounts of government procurement document text being loaded into GPU memory. - Phenomenon: Invalid token prompt appears when accessing domestic models. Testing tools show normal functionality but direct connection fails. Cause: The corresponding relationship between
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENis not correctly matched, and the direct connection path does not adapt to the authentication rules of domestic models. - Phenomenon: After adding a domestic model, the corresponding model cannot be displayed when creating a knowledge base or application. Cause: Visibility permission for the corresponding model is not enabled in the model access configuration, or the configured model name does not match the platform's recognition rules.
How to confirm successful configuration
- Initiate a single policy basis query, and check whether the reference source of the returned result matches the official government procurement document.
- Check GPU resource usage, confirming that the GPU memory usage of the reranking model falls within the available range of the deployment environment.
- Review the token and endpoint in the model access configuration, and use a testing tool to verify whether the call link is working properly.
- Create a test knowledge base and import government procurement bidding documents, confirming that the model can normally recall relevant policy clauses.
The values provided here are common starting points, and should be evaluated against the samples specific to the actual deployment scenario.
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.