What the data for this category looks like
Data sources for this category include enterprise procurement platforms, public resource trading platforms, supplier databases, contract management systems, and audit issue databases, with a daily update frequency. Daily processed documents include procurement plans, demand specifications, bidding documents, bid evaluation records, contracts, acceptance slips, and supplier complaint materials. Document fields cover procurement methods, budget basis, scoring methods, supplier qualification requirements, performance clauses, and more. Each document has exclusive fields and associated business units: for example, procurement plans link to budget amounts and procurement methods, while bidding documents link to bid evaluation rules and pre-qualification requirements.
Constraints for Multi-turn Dialogue and Prompt Engineering
The scattered, frequently updated data sources for this category require multi-turn dialogue to accurately distinguish field formats across different business systems, to avoid cross-data-source format conflicts. The diverse range of daily processed documents requires prompt engineering to adapt to exclusive rules for scenarios including procurement plan preparation, bidding document review, and contract drafting. Additionally, the compliance review process must retain complete dialogue tracks to ensure every adjustment is traceable. It must also limit context length to prevent mixing expired historical business data, ensuring generated content aligns with the latest procurement rules and supplier database information.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the exclusive ID of the procurement project | Ensure each round of dialogue is associated with exclusive data of the corresponding procurement project, avoiding cross-project context confusion |
messages | Retain the last 3 rounds of business dialogue content | Cover the complete context of the current procurement phase, while avoiding interference from redundant historical data on compliance review logic |
variables | Inject the daily updated supplier database and budget data | Ensure generated content uses the latest business data, meeting the daily updated data source requirement |
| Maximum context length | 800–1200 characters | Adapt to the average field length of documents in this category, avoiding exceeding the model's processing limit |
| Context traceability switch | Enabled | Meet the track traceability requirements of audit and discipline inspection departments, complying with compliance management specifications for Chinese state-owned enterprises and public sector entities |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Misconfigurations
- Symptom: LaTeX formatting displays correctly in debug previews, but shows only raw code after publishing. Cause: Format rendering rules for dialogue content are not configured, or Markdown format parsing is not enabled in the publishing environment.
- Symptom: Dialogue experiences persistent lag after deployment, and cannot return normal results. Cause: Context length is not limited, so excessive historical procurement data loads for each dialogue, exceeding processing thresholds.
- Symptom: AI dialogue content inserted in the workflow appears in the final output. Cause: Output hiding rules for workflow nodes are not configured, so content from intermediate steps is automatically included in the final result.
How to Verify a Successful Configuration
- Check whether
chatIdis bound to the exclusive identifier of the current procurement project, ensuring that dialogue context is consistently associated with the business data of the corresponding project. - Verify whether
variablesare injected with the daily updated business data. This can be done by viewing the actual content after variable replacement in the debug preview. - Run a test dialogue to confirm that LaTeX formatted content renders correctly in the publishing environment.
- Test workflow node configurations to confirm that intermediate AI dialogue output is not included in the final result.
The configuration values provided here are common starting points, and should be evaluated against local 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.