What the data for this category looks like
Data for this category comes from the integrated budget management system, centralized treasury payment system, accounting system, and asset management system, and is updated annually. Daily produced documents include department final account statements, balance sheets, income and expenditure final account statements, final account explanations, and government financial reports. Core fields include actual income and expenditure details, asset and liability balances, budget execution difference explanations, and more. Each document has a fixed structure, and must be filled out and aggregated according to a unified standard.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The annual update requirement means multi-turn dialogue must limit the dataset scope to the current year, to avoid mixing cross-year data. The combination of multiple document types and multiple system data sources means prompts must explicitly specify the document type and data source scope for retrieval, to ensure accurate retrieval results. For core processes such as verifying account statements against treasury data and explaining budget execution differences, multi-turn dialogue must progress step-by-step according to business processes, gradually guiding the use of knowledge base content for corresponding modules, to avoid information overload. At the same time, compliance check prompts must be added to the prompt to ensure output complies with business specifications.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context | Previous 10 turns of dialogue + 8000–12000 characters | Department final account-related documents are lengthy, so sufficient historical dialogue and knowledge base retrieval results must be retained |
| Single Retrieval Count | Top 6 entries | Covers core entries from multiple system data sources, avoiding information overload |
| Prompt Variables | Bind current year, specify document type | Matches the annual updated data characteristic, limiting retrieval scope |
| Dialogue History Storage | Bind chatId per session | Ensures coherent context for multi-turn dialogue, avoiding cross-session information mixing |
| Question Optimization | Enabled, adapted for fiscal professional terminology | Optimizes the professionalism of questions, improving knowledge base retrieval accuracy |
| Variable Transfer Link | Pass variables according to business steps | Adapts to the step-by-step business process of account statement verification and difference explanation |
Three Common Misconfigurations
- Symptom: Dialogue history records do not appear in the specified storage medium, or the
messagesfield is empty. Cause: ThechatIdparameter is not correctly bound, resulting in session context not being persistently stored. - Symptom: Knowledge base search debugging returns correct results, but AI dialogue output does not reference knowledge base content. Cause: Rules for calling the knowledge base are not explicitly specified in the prompt, or the
variablesparameter does not pass retrieved document fragments. - Symptom: Multiple consecutive questions fail to merge results as expected, or program execution results cannot be passed to the next AI dialogue module. Cause: The
variablesparameter is not configured to pass intermediate results, resulting in inability to share context data between modules.
How to Verify Correct Configuration
- Initiate a query targeting department final account statements, check whether the output includes content from the specified scope of documents, and confirm that retrieval rules comply with configuration requirements.
- Enable multi-turn dialogue, initiate 2-3 consecutive related queries, check whether dialogue history is correctly retained, and that the
messagesfield has no missing entries. - After configuring the variable transfer link, run a workflow that includes program execution and AI dialogue, check whether intermediate results are correctly passed to subsequent modules.
- View dialogue records in the storage medium, confirm that session data bound to
chatIdis fully present.
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.