What the Data for This Category Looks Like
The data for this category primarily comes from the unified budget management system, departmental staffing system, prior-year final accounts, project repository, and the annual budget preparation notice issued by the Ministry of Finance. Data updates follow an annual cycle. Corresponding documents include departmental budget drafts, project application forms, performance target forms, personnel expense calculation forms, and budget approval documents. Data fields comply with fiscal management standards, including unit code, project name, performance target, expense type, amount unit, and other content. Document structure follows unified templates set by fiscal authorities.
Constraints Imposed by These Characteristics on the Multi-turn Dialogue and Prompt Engineering Link
Static data updated annually requires that dialogue context be limited to the current year’s policies and budget scope to avoid confusion across annual data. Multiple heterogeneous data sources require prompts to explicitly specify prioritizing structured data from the unified budget management system, while matching unstructured application documents from the project repository. The fixed field structure of individual documents requires that multi-turn dialogue follow the field order specified by fiscal standards for gradual verification, to avoid missing key items. The rigor requirement for fiscal data requires that prompts preset basic audit logic, triggering a secondary confirmation step when field conflicts appear in dialogue, to prevent subsequent audit failures caused by data deviations.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context Length | 8000–12000 characters | Single-turn context for documents related to department budget preparation typically falls within several thousand characters, to avoid exceeding model processing limits |
variables | Bind the unique identifier of the current year’s budget preparation notice | Limit the Q&A scope to the current fiscal year’s policy to avoid confusion across annual data |
chatId | Bind the unique identifier of the current budget preparation project | Distinguish dialogue contexts for different projects to avoid cross-project data confusion |
messages | Append user and assistant interaction content by conversation turn | Multi-turn Q&A requires retaining historical context to ensure logical coherence, meeting the gradual verification needs of budget checks |
| Single Search Limit | Top 3 results | Department budget policy references are usually concentrated in 3 or fewer official documents; excessive results will disrupt context accuracy |
| Multi-turn Dialogue Timeout | 600 seconds | Fiscal data verification requires multiple steps; a longer timeout prevents interruptions that would invalidate the Q&A process |
Three Common Misconfigurations
- Symptom: The dialogue call returns a prompt indicating the knowledge base is empty, with no matching data displayed in the interface. Cause: The
variablesparameter was not used to bind the current year’s budget document identifier, causing the model’s retrieval scope to cover historical data and fail to match policy basis for the current budget cycle. - Symptom: Multi-turn dialogue automatically interrupts within 600 seconds without completing the full policy verification process. Cause: The multi-turn dialogue timeout was not set to a duration suitable for business requirements; the default threshold cannot cover the full steps of fiscal data verification.
- Symptom: AI reply annotations in the workflow only contain partial reply fragments. Cause: The annotation trigger timing was not set to after the full reply is output, causing annotations to trigger during the reply process and capture incomplete content.
How to Confirm the Configuration Is Correct
- Initiate a query regarding the current year’s budget preparation policy, verify that the model’s returned results only include official document content from the current year.
- Initiate progressive multi-turn queries, such as first asking about the basis for personnel expense calculation, then following up with questions about budget standards for specific projects, verify that the model retains historical dialogue context and maintains logical coherence.
- Adjust the knowledge base matching threshold, then initiate repeated queries, verify that the number of matching results changes as expected based on the threshold adjustment.
- Call the dialogue interface and pass the
messagesparameter, verify that the context returned by the interface includes complete historical interaction content.
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.