What the data for this category looks like
Data for this category comes from central state-owned enterprise procurement platforms, public resource trading platforms, supplier databases, contract management systems, and audit issue databases, and is updated daily. Daily documents include procurement plans, requirement specifications, bidding documents, bid evaluation records, contracts, acceptance documents, and supplier complaint materials. Each document type includes fixed fields such as procurement method, budget amount, scoring rules, supplier qualifications, performance clauses, and audit issues. Unstructured text accounts for a large share of the data, and format differences between document types are notable.
Constraints imposed on model access and configuration by these characteristics
Data is updated daily and sourced from multiple dispersed locations. This requires the model access link to support incremental synchronous pulling of multi-source data. Diverse document types with significant field differences require configuring models that support multimodal processing to adapt to content in different formats such as bidding documents and bid evaluation records. Historical compliance data from the audit issue database must be linked to current processes, which requires models to be able to call tools to query associated records. High-risk audit links require strict alignment with enterprise rules, and model outputs must retain traceable contextual basis.
How to set the configuration
This configuration is designed for FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context length requirements of end-to-end tendering and procurement process documents, covering the total content of multiple document types including procurement plans, bidding documents, and bid evaluation records |
maxTokens | 2000–3000 token | Controls the length of compliance review results output in a single model round, avoiding exceeding display limits in business process interfaces |
quoteMaxToken | 1500 token | Limits the length of quoted procurement document fragments, ensuring review basis is clear without occupying excessive context space |
toolChoice | auto | Supports automatic calling of compliance query tools, adapting to multi-step processes in tendering and procurement that require checking budget basis and supplier qualifications |
vision | enabled | Supports processing multimodal attachments in bidding documents, such as drawings, stamped documents, and other non-pure text content |
temperature | 0.2–0.4 | Reduces output randomness, ensuring consistency of compliance review results and meeting the strictness requirements of central state-owned enterprises |
Three common configuration errors
- Issue: Cannot connect multimodal data to a multimodal model and plain text data to a Qwen2 tool-calling model in a single workflow. Cause: The linkage logic between the
visionparameter andtoolChoiceparameter was not configured correctly, and model routing rules for different data types were not distinguished. - Issue: An error indicating the model is not supported appears after configuring
toolChoice. Cause: The selected model was not confirmed to be compatible with thetoolChoicefunction; some lightweight models do not offer tool-calling capabilities. - Issue: The prompt "No available channel for model gpt-4o-mini under current default group" appears during QA search testing. Cause: The API channel for the gpt-4o-mini model was not configured in the corresponding group, or the channel configuration information is incorrect.
How to confirm configuration is complete
- Upload multimodal attachments, confirm the model can correctly parse images, stamped documents, and other content, verifying that the
visionparameter configuration takes effect. - Initiate a test process that includes a compliance query requirement, confirm the model automatically calls tools to obtain data from the historical audit issue database, verifying that the
toolChoiceparameter configuration takes effect. - Review the contextual reference fragments included in model outputs, confirm that the reference length conforms to the limits set by
quoteMaxToken. - Adjust the
temperatureparameter to different ranges, compare changes in the randomness of output results, confirming that the parameter takes effect. - Check the model API channel configuration in the corresponding group, confirm that the access information for the selected model is correct, verifying that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations take effect.
The values provided are common starting points and should be evaluated against the deployer’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.