This section uses the model access and configuration interface from FastGPT v4.17.0.
What the data for this category looks like
Data for this category comes primarily from the national government procurement website, local government procurement websites, fiscal integration systems, purchaser budget systems, and supplier credit records. Updates occur daily. Commonly processed documents include procurement requirements, procurement plans, bidding documents, tender documents, bid evaluation reports, winning bid announcements, procurement contracts, and responses to supplier inquiries. Data fields cover procurement project ID, budget amount, supplier qualifications, evaluation results, contract amount, and more. Each document type has dedicated fields: bid evaluation reports include expert evaluation opinion fields, while winning bid announcements include winning supplier information fields.
Constraints on the model access and configuration workflow
Dispersed data sources and daily updates require model access to support synchronized data pulling from multiple sources. Configuration must adapt to the interface formats of different data sources. Diverse document types and significant field differences require model configuration to support structured extraction across multiple document types and cross-document association. High-frequency updates require setting a reasonable context window to prevent repeated processing of old data. Government procurement documents must follow strict regulatory rules. Model configuration must ensure output content meets compliance requirements. When processing dynamic documents such as responses to inquiries, response timeliness must be maintained to avoid compliance issues caused by processing delays.
How to set the configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Government procurement project document summary content per batch is large, needs to accommodate context information from multiple core documents such as bidding documents and bid evaluation reports |
maxTokens | 4000–6000 token | The output content of ledger summary and report generation must cover complete end-to-end project information, so output length must meet document requirements |
temperature | 0.1–0.3 | Government procurement-related content must remain rigorous and compliant. A lower temperature reduces randomness in generated content to ensure output aligns with regulatory rules |
quoteMaxToken | 1000–2000 token | Original quoted fragments from government procurement documents must be retained. Limit the length of quoted content to avoid context overflow |
AIPROXY_API_ENDPOINT | Fill in the corresponding interface address based on the deployment environment | Adapt to the multi-source access requirements of government procurement data sources, and ensure normal model call links |
AIPROXY_API_TOKEN | Fill in the exclusive key allocated by the platform | Ensure identity verification security for model calls, and meet the confidentiality requirements of government procurement data |
Three common configuration mistakes
- Symptom: An error occurs after adding a locally deployed large model to the configuration, prompting that the model only supports streaming output. Cause: The call format supported by the model is not matched in the model access configuration, or related settings for enabling streaming calls are not correctly configured.
- Symptom: Missing core fields appear in results returned when calling the model to generate project ledgers. Cause: The
maxContextparameter is not correctly configured, so the context window is too small to accommodate associated information from multiple government procurement documents. - Symptom: A code 500 error is returned when using aiproxy to access a deployed model. Cause:
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENis not correctly filled, causing abnormal identity verification or interface call links.
How to confirm successful configuration
- Send a model access test request, verify that the interface returns a status code of 200, and confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations are valid. - Upload a single government procurement project document, trigger the ledger summary function, and check that the returned content includes core document fields. Confirm that
maxContextandmaxTokensparameters adapt to the current document length. - Adjust the
temperatureparameter to the preset range, verify that the generated report content is rigorous and compliant, with no redundant or randomized expressions. - Simulate a multi-source data synchronization scenario, trigger high-frequency data updates, and confirm that the model does not repeatedly process old data.
The values listed above 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.