What This Category of Data Includes
Data for policy and regulatory basis Q&A for urban investment platforms is sourced from publicly available normative documents published by local state-owned assets supervision and administration commissions, housing and urban-rural development departments, and local governments. It covers specialized areas including special bond management, urban infrastructure investment and financing, and platform transformation. Data updates align with the schedule of local policy adjustments, with no fixed cycle. Core policies are sorted and archived once per quarter. Each individual document contains fields such as release document number, issuing authority, effective date, applicable region, and clause details. Clauses are layered by article, paragraph, and subparagraph. Some cross-regional policies include applicable boundary descriptions.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
Policy documents have long length and clear clause layering. If context becomes too long during multi-turn dialogue, the model cannot focus on core policy clauses. Context length must be strictly controlled. Policy data includes time-sensitive fields such as effective date and applicable region. During multi-turn dialogue, the model must be able to associate the current question with time and geographic dimensions, to avoid referencing expired policies. Policies vary across different regions. Prompt settings must explicitly specify matching geographic parameters for the current session, to prevent confusion between cross-regional policies.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context | First 6 turns of dialogue + associated policy document snippets | Urban investment policy documents have long length. 6 turns of dialogue covers context needs for conventional multi-turn interactions, avoiding redundant information diluting model focus |
| Single Search Limit | Top 8 matching results | Urban investment policy entries are numerous. 8 results cover core matching content while reducing model processing load |
| Question Optimization | Enabled | User questions often omit policy document numbers or regional information. Optimization supplements key parameters to improve retrieval accuracy |
| Timeout Threshold | 30 seconds | Policy retrieval spans multiple data sources. 30 seconds covers most retrieval scenarios, avoiding prolonged unresponsiveness |
| Context Splicing Length | 1000–1500 characters | Core content of individual policy clauses falls within this range, retaining key information fully without exceeding the model's maximum context limit |
Three Common Misconfigurations
- Symptom: Dialogue remains in a searching state indefinitely, eventually returning no results or a timeout error. Cause: No reasonable timeout threshold set, or single search limit set too high, leading to retrieval time exceeding expected limits.
- Symptom: Model fails to recognize context from the previous dialogue turn, even when chat history turn count is configured. Cause:
messagesparameter not properly bound, or context splicing length exceeds the maximum token range supported by the model. - Symptom: Generated policy basis content has messy formatting, failing to extract required fields such as document number and effective date. Cause: Prompt engineering does not explicitly specify output field requirements, or
variablesparameter is not enabled to bind session context variables.
How to Verify Proper Configuration
- Submit a query that includes a specific policy document number, verify that the model accurately matches clause content from the corresponding document.
- Submit 3 consecutive related queries, verify that the model correctly references policy information and geographic parameters from the previous dialogue turns.
- Check system session logs, confirm that the
chatIdparameter is generated normally, and context for each dialogue turn is not cleared prematurely. - Simulate a high-concurrency iframe embedding scenario, verify that system load has no abnormal fluctuations and no sessions freeze.
Reference only; verify against the target FastGPT release and deployment documentation. The values listed above are common starting points, and should be evaluated against your own specific samples and use cases.
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.