What data for this category looks like
Core data for this category comes from the unified budget management system, centralized treasury payment system, accounting system, and asset management system. The update frequency follows an annual cycle. Daily documents include department final account statements, balance sheets, income and expenditure final account statements, final account explanations, and government financial reports. The structure primarily combines structured tables and written explanations. Fields cover income and expenditure details, asset and liability balances, budget execution variances, and more. Units are mostly ten thousand yuan or yuan. No incremental data is updated on a quarterly or monthly basis.
What constraints these characteristics impose on the conversation logs and auditing workflow
The annual centralized update feature means final account-related Q&A data is generated primarily at the end of the calendar year. Conversation logs must store full-year Q&A chain data, which places requirements on storage capacity and retrieval efficiency. Data from multiple systems requires association with corresponding system identifiers in logs to ensure data sources can be traced during audits. Q&A for core links such as account statement verification and variance explanation involves sensitive financial data. Logs must retain complete detail field content to meet audit compliance requirements. The serious consequences of errors require logs to have traceable unit identifiers to avoid mixing Q&A data from different budget units.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Conversation log storage granularity | Aggregate storage by chatId | Associate full-chain interactions and data sources for a single final account Q&A, facilitating audit tracing |
| Embedding task thread count | 2-4 threads | Adapt to the annual centralized final account document processing scenario, avoid exceeding embedding rate limits |
| Q&A pair extraction timeout threshold | 600 seconds | Final account documents contain multiple tables and explanatory text, leading to long processing times, prevent premature task termination |
| Log field identifier | Bind unique unit identifier | Distinguish Q&A data from different budget units, meet permission isolation and audit requirements |
| Call failure retry count | 2 retries | Reduce task interruptions caused by temporary network fluctuations or model call exceptions |
Three common misconfigurations
- Phenomenon: The Q&A pair extraction task status shows as training in progress, with no progress updates over time, and no execution record for the corresponding task appears in the call logs. Cause: Permissions to access data sources such as the unified budget management system are not configured, causing the task to fail to read final account document data.
- Phenomenon: The embedding task fails to execute, and the logs prompt that the rate limit has been exceeded. Cause: The embedding task thread count is set too high, exceeding the carrying capacity of the system's embedding resources.
- Phenomenon: Conversation logs do not carry the unique unit identifier field, making it impossible to distinguish Q&A data from different budget units. Cause:
chatIdis not bound to the unique budget unit identifier, and the logs do not associate corresponding subject information.
How to confirm the configuration is correct
- In FastGPT v4.17.0, run a single final account-related Q&A, check whether the
chatIdandresponseChatItemIdfields are included in the conversation logs. - Submit a batch embedding task for final account documents, observe whether system resource usage matches the load range corresponding to the configured thread count.
- Trigger a Q&A pair extraction task, confirm that execution records and progress updates for the corresponding task appear in the call logs.
- Simulate a temporary call failure scenario, check whether the system executes the retry logic according to the configuration, and does not terminate the task directly.
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.