Reference only; verify against the target FastGPT release and deployment documentation.
What the Data for This Use Case Looks Like
Data for this use case primarily comes from highway maintenance management systems, bridge and tunnel monitoring systems, road patrol inspection systems, and traffic flow monitoring data. The update frequency is daily. Regularly produced documents include maintenance plans, patrol logs, disease disposal forms, road-related construction permits, and road property damage compensation forms. Each document typically includes fields such as facility number, patrol time, disease type and location, disposal measures, construction scope, and compensation reason. Some documents involve physical units such as length and area.
Constraints on Multi-turn Dialogue and Prompt Engineering Workflows
Daily updated data requires prompts to clearly guide the model to call the latest knowledge base content, and avoid using outdated information. Mixed recall of multiple types of documents requires configuring retrieval rules adapted to different document structures to ensure that professional fields are prioritized for identification. Data integration from multiple sources requires confirming the consistency of data sources during the dialogue process to prevent cross-system information conflicts. The high field density of individual documents requires prompts to clearly specify the fields to be extracted, and avoid returning redundant content.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | Adapts to the single-item content length of patrol logs and disease disposal forms, ensures complete information |
| Overlap length | 100–150 characters | Avoids context breaks after chunking, ensures content coherence for multi-turn dialogue |
| Single search limit | Top 5 entries | Matches the effective recall volume of individual documents, avoids redundant information interference |
| Maximum context | 4000 token | Carries the historical messages records of multi-turn dialogue, retains complete interaction context |
| Question optimization | Enabled | Improves understanding of highway maintenance professional terminology, enhances retrieval accuracy |
| Knowledge base association | Triggered by specified files | Ensures the dialogue only calls target documents such as xlsx files, avoids interference from irrelevant data |
Three Common Misconfigurations
- When requesting to repeat the content of an xlsx file in the knowledge base during a dialogue, a prompt indicating that the file cannot be read is returned. The cause is that the spreadsheet format parsing rule is not enabled in the knowledge base configuration, or the target file is not correctly associated with the current dialogue application.
- The results returned after calling the knowledge base do not match the specified file content, and the number of returned entries is far less than expected. The cause is that the single search limit is set too low, failing to cover all valid content in the file.
- Subsequent questions in a multi-turn dialogue cannot associate historical context, and the returned content is unrelated to previous interactions. The cause is that the
variablesparameter for storing dialogue context is not correctly configured, or the maximum context setting is too small, resulting in historical records being truncated.
How to Verify Successful Configuration
- Upload the target xlsx file to the knowledge base, check whether the file parsing status shows success, and confirm that format parsing is normal.
- Enter a specified query targeting the file in the knowledge base on the dialogue interface, verify whether the returned content includes the field information in the file, and confirm that data calls are correct.
- Initiate two or more consecutive questions, verify whether subsequent questions can associate the context of previous interactions, and confirm that the multi-turn dialogue logic works properly.
- Check the interface call parameters, confirm that
chatIdis correctly passed, ensuring the uniqueness and coherence of the dialogue session.
The values provided here are common starting points, and should be evaluated against the samples relevant to the actual deployment.
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.