What Data This Category Includes
The data for this category comes from 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. Data fields cover procurement subjects, budget basis, bid evaluation criteria, supplier qualifications, performance and acceptance status, and audit rectification records. The size of individual data entries fluctuates with the scale of procurement projects, and core fields are associated with specific links in the procurement process.
What Constraints These Characteristics Impose on Conversation Logs and Auditing
Data sources are scattered and cover the entire procurement process. Conversation logs must be bound to the exclusive identifier of the corresponding procurement project to avoid mixing data across projects. The daily updated data source requirement means log auditing must be archived daily to align with the daily workflow of procurement business. Differences in fields across multiple document types require the auditing link to match the field rules of the corresponding documents, ensuring that procurement basis, bid evaluation results, and other information recorded in logs are traceable. Additionally, for sensitive data related to disciplinary inspection and auditing, logs must retain the complete interaction chain to meet compliance traceability requirements, and must be associated with unique records of specific procurement links to facilitate subsequent problem location and rectification.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind to the unique number of the procurement project | Ensure conversation logs are associated with the full-process data of the corresponding procurement project, avoiding confusion across projects |
responseChatItemId | Bind to the record ID of the corresponding procurement link | Facilitate quick location of interaction content for the corresponding procurement link during auditing |
detail | Enable full recording | Cover user questions, data sources called by AI, returned results, and intermediate steps to meet compliance traceability requirements |
| Log archiving cycle | Automatically execute at 00:00 daily | Align with the daily data update rhythm, facilitating monthly auditing and problem traceability |
| Maximum log storage capacity | 100–200 GB | Adapt to the data volume of annual procurement projects, avoiding storage resource overflow |
| Maximum single log length | 8000 characters | Adapt to the content volume of single procurement-related Q&A, avoiding log truncation |
| Log refresh interval | 30 seconds | Ensure auditors can view the latest interaction logs in real time |
Three Common Misconfigurations
- Phenomenon: Workflow conversations show failures, but response logs exist in the model backend. Cause: The association between
chatIdand the procurement project is not configured correctly, causing logs to fail to sync to the workflow session. - Phenomenon: Application conversations throw abnormal errors, and the log panel does not record complete error information. Cause: The
detailfull log recording option is not enabled, and only successful interaction content is recorded. - Phenomenon: The
responseChatItemIdof the last AI reply cannot be obtained, making it impossible to locate the corresponding reply during auditing. Cause: The configuration to returnresponseChatItemIdis not enabled in the conversation node, or the field is not correctly extracted for auditing association.
How to Confirm Proper Configuration
- Initiate a party building learning Q&A conversation related to procurement, and check whether the conversation panel displays the complete interaction chain and the associated procurement project number.
- Enter the system log panel, retrieve logs corresponding to the
chatId, and confirm that user questions, AI replies, and called data source information are included. - Trigger an abnormal error, and check whether the log records complete error stacks and context content.
- Use a debugging tool to view interface return fields, and confirm that
responseChatItemIdis returned along with the AI reply.
The values given are common starting points and should be evaluated against relevant 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.