What data for this category looks like
Data sources for this category include the national public resource trading platform, local trading platforms, engineering approval systems, and bidding and tendering supervision systems. The update frequency is daily. Daily documents include tender announcements, qualification pre-qualification documents, tender documents, bid documents, bid evaluation reports, successful bid candidate notices, and successful bid result announcements. The data structure combines structured table fields and unstructured text paragraphs. Fields cover bid section identifiers, qualification requirements, bid evaluation records, successful bid information, and other relevant content. No unified fixed format template exists for this data.
Constraints for model access and configuration
Data for this category is scattered and updated daily. Model access must support multi-source interface pulling and incremental synchronization logic. Documents include both structured tables and unstructured text, so model processing rules compatible with mixed formats must be configured. Fields have no unified fixed template, so models must support flexible extraction of non-standard business fields. The recognition accuracy of core documents such as bid evaluation reports and successful bid results directly affects project compliance. Strict control of field accuracy in model outputs is required to avoid compliance risks.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long text length of tender and bidding documents, avoiding loss of key fields due to context truncation |
maxTokens | 4000 token | Controls the output length of single-round responses, ensuring that ledger summary and report content is complete and meets format requirements |
temperature | 0.1–0.3 | Reduces randomness, ensuring consistent recognition of key information such as qualifications and bid section numbers |
AIPROXY_API_ENDPOINT | Fill in the complete official interface path of the corresponding model service provider | Adapts to routing requirements for multi-source data pulling and model invocation |
AIPROXY_API_TOKEN | Fill in the call key applied for in practice | Ensures the identity legitimacy of model invocation, avoiding unauthorized access |
Three common configuration errors
- After configuring the model channel, test requests return "404 page not found". The cause is an incorrect interface address configured in
AIPROXY_API_ENDPOINT, or the missing correct path suffix. - After uploading tender and bidding documents, the request fails. The cause is that the
maxContextconfiguration value exceeds the maximum context limit supported by the model, resulting in the request being intercepted by the server. - Recognition results for qualification and performance fields vary across multiple calls. The cause is an excessively high
temperaturevalue, which leads to excessive randomness in model outputs and unstable matching of fixed business fields.
How to confirm successful configuration
- Call the interface address corresponding to
AIPROXY_API_ENDPOINT, use the configuredAIPROXY_API_TOKENto send a test request, and confirm that the returned model response format meets requirements. - Upload a single typical tender and bidding document, trigger the ledger summary task, and verify that the fields output by the model cover the core business content of the document.
- Adjust the
temperaturevalue, compare the output results of the same document across multiple rounds, and confirm that the consistency of key field recognition meets business verification requirements. - Simulate the daily incremental data pulling process, and check whether the incremental synchronization logic of model access is normally triggered and executed.
The values provided here are common starting points and should be measured 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.