What this category of data looks like
This category of data originates from national public resource trading platforms, local public resource trading platforms, engineering approval systems, and bidding supervision systems. The update frequency is daily. Daily processed documents include bidding announcements, qualification pre-qualification documents, bidding documents, tender documents, bid evaluation reports, successful bid candidate notices, and successful bid result announcements. These documents cover exclusive fields such as qualifications, performance, bill of quantities, and construction periods. Document structure varies by section division and project type.
Constraints These Data Characteristics Impose on Model Integration and Configuration
These data characteristics impose clear constraints on the model integration and configuration phase. Multi-source data updated daily requires adapting to multi-interface synchronization logic during integration to prevent data lag or omission. Diverse document structures and specialized fields require binding context processing rules for engineering bidding-specific terminology during configuration to ensure recognition accuracy. Errors in successful bid result documents may lead to project delays or accountability. Configuration must include a strict result verification mechanism. Differences from government procurement category businesses require explicitly binding engineering bidding-specific judgment rules to avoid cross-category rule confusion. FastGPT v4.17.0 configuration options support fine-tuning for these scenarios.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to long context needs for multi-document combinations related to bidding, covering associated document content for complete project sections |
maxTokens | 2000–3000 token | Meets reply length requirements for single-round process guidance, avoiding exceeding model interface limits |
quoteMaxToken | 1500 token | Retains sufficient original reference fragments to support verification of core content such as qualification requirements and bill of quantities |
temperature | 0.1–0.3 | Reduces reply randomness, ensuring accurate and consistent process guidance steps and avoiding ambiguous guidance |
AIPROXY_API_ENDPOINT | Fill in the corresponding interface address based on the deployment environment | Adapts to access requirements for local or cloud deployment, ensuring network connectivity for model calls |
toolChoice | auto | Automatically selects tools to handle process tasks such as section splitting and expert record verification, improving guidance efficiency |
Three Common Configuration Mistakes
- Phenomenon: The model returns authentication failure after integration, and logs show a 401 status code. Cause:
AIPROXY_API_TOKENis not configured correctly, or the key does not match the deployment environment. - Phenomenon: The model configuration entry cannot be accessed after local deployment, and the interface displays a blank or unloaded page. Cause: The service was not started according to the deployment process, or the access port of the API proxy was not bound correctly.
- Phenomenon: The guidance content generated after adding the model does not include bill of quantities-related information. Cause: The
maxContextvalue adapted to bidding documents was not configured, causing the context to be truncated and core field information to be lost.
How to Confirm the Configuration Is Complete
- Verify that the
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTconfiguration values exactly match the access information provided by the platform. - Upload a complete bidding announcement document to trigger a process guidance test, and confirm that the reply includes core content such as section division, qualification requirements, and bidding requirements.
- Check the model call logs to confirm that the values of
maxContextandmaxTokensmeet the preset configuration, with no over-limit error records. - Simulate a complaint handling scenario to test whether the model generates corresponding guidance content according to process steps, with no abnormal jumps or missing links.
The values provided 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.