What This Category of Data Consists Of
Data sources include the China Government Procurement Network, local government procurement networks, fiscal integration systems, procurer budget systems, and supplier credit records. Updates are synchronized daily. Commonly processed documents cover procurement requirements, procurement plans, bidding documents, tender documents, bid evaluation reports, winning bid announcements, procurement contracts, and objection responses. Core fields include procurement budgets, supplier qualifications, and bid evaluation item scores. The dataset includes business data for procurers, fiscal regulatory authorities, procurement agencies, and review experts.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Multi-source, scattered data sources require dialogue flows to call data sources from different systems in stages. This prevents data confusion. Daily data updates require dialogues to pull real-time latest data. This ensures review results are timely. Diverse document types require prompts to adapt to each document’s field rules. This ensures the model accurately identifies core review points. Differing business needs across stakeholder groups require dialogue configurations to support role differentiation. This meets the review logic of users such as procurers and review experts.
How to Configure Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context Length | 8000–12000 characters | Adapts to the average length of government procurement documents, avoiding truncation of core review content |
| Dialogue Timeout Threshold | 600 seconds | Adapts to the average response duration of government procurement reviews |
| Question Optimization | Enabled | Optimizes question logic for government procurement-specific terminology to improve retrieval matching accuracy |
variables Binding | Procurement project number, regulatory authority code | Accurately calls exclusive data for the corresponding project |
messages Context Retention | First 10 dialogue turns | Focuses on the current review phase, filtering redundant historical information |
chatId Association | Project unique identifier | Achieves data isolation for single-project dialogues, ensuring information security |
Three Common Configuration Errors
- Multi-module output on the front-end dialogue page is not isolated by project. Review results from different procurement projects are mixed. This occurs when the unique procurement project identifier is not bound via
variables, leaving no project-level data isolation for the dialogue context. - Configured prompts fail to take effect. Model output does not align with government procurement file review rules. This occurs when prompts are not configured to match the field requirements of specific documents such as procurement requirements and tender documents, and the model’s review responsibilities are not clearly defined.
- LaTeX formats display correctly in debug previews, but only show raw LaTeX code after publication. This occurs when LaTeX rendering adaptation is not enabled in the production environment dialogue configuration, leading to inconsistent rendering logic between debug and production environments.
How to Confirm Successful Configuration
- Enter a test procurement project number. After binding via
variables, confirm the dialogue only returns document data for the corresponding project, with no cross-project content. - After configuring prompts, enter a review-related question in the debug environment. Confirm the model output follows government procurement file review logic, with no off-script content.
- Enter test content containing LaTeX formats. Check rendering results in both debug preview and published dialogue pages to confirm consistent formatting.
- Initiate a dialogue after deployment. Confirm response duration meets expectations, with no persistent lag. This verifies the context configuration does not overload redundant data.
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.