Reference only; verify against the target FastGPT release and deployment documentation.
What the Data for This Category Looks Like
Documents covered in this category include bidding announcements, pre-qualification documents, bidding documents, tender documents, bid evaluation reports, public notices of successful candidate bidders, and successful bid result announcements, covering all process documents. Data sources include national public resource trading platforms, local trading platforms, engineering approval systems, and bidding supervision systems. The update frequency is daily. Document structures include professional fields such as bid section division, qualification requirements, bill of quantities, and construction period. Typical entities that generate and circulate these documents include development and reform departments, industry competent departments such as housing and urban-rural development, transportation, and water conservancy, public resource trading centers, and bidding agencies.
What Constraints These Characteristics Impose on the Model Access and Configuration Link
Full-process bidding documents contain long text. Model context configurations must support multi-link coherent content. Daily updated data from multiple sources requires knowledge base synchronization mechanisms to match the update frequency. Professional fields require vector models and retrieval configurations to accurately match engineering construction domain terminology. Bidding documents demand strict output. Control output parameter randomness to avoid content that violates bidding specifications. Additionally, errors can lead to risks such as bid suspension and project delay. Interface permissions and configuration verification must be more strict.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long text context requirements of full-process bidding documents, covering multi-link coherent content |
maxTokens | 2000–3000 token | Meets the output length requirement for a single drafted document, avoiding exceeding model limits |
temperature | 0.2–0.4 | Ensures the rigor and standardization of bidding documents, reducing the risk of random output |
AIPROXY_API_ENDPOINT | Exclusive interface address for the deployment node | Ensures correct model request routing, matching the access path of the deployment environment |
AIPROXY_API_TOKEN | Exclusive key issued by the platform | Ensures interface access permissions, avoiding unauthorized access issues |
toolChoice | auto | Supports the model to automatically call tools to handle professional tasks such as bid section splitting and qualification condition verification |
Three Common Misconfigurations
- A 401 unauthorized error appears when accessing the vector model. Cause:
AIPROXY_API_TOKENis not filled in correctly, or the key has not been granted access permission for the corresponding model. - The deployed oneapi container continues to restart indefinitely. Cause:
AIPROXY_API_ENDPOINTis configured incorrectly, or there is a port binding conflict. - After adding the whisper model, the interface prompts that the browser does not support speech input. Cause: Front-end call parameters for the speech model are not configured, or the browser’s microphone permission is not enabled.
How to Verify That Configurations Are Correct
- Initiate a document drafting test for a bidding announcement, and check whether the output content includes correct bid section division and qualification condition fields.
- View the interface call log, and confirm that the request return status code for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENis 200. - Adjust the
temperatureparameter, compare the rigor differences between the two outputs, and confirm that the parameter takes effect. - Test the knowledge base synchronization function, and confirm that daily updated bidding documents can be updated to the vector database on time.
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.