What Data for This Category Looks Like
Visible and infrared imagery captured by inspection drones, real-time sampling data from structural stress sensors, and paper or electronic records from manual inspections. Updates follow the established inspection schedule. Imagery data from a single inspection is updated per capture batch, while sensor data is pushed at fixed sampling intervals. Document structure falls into three categories:
- Image files include capture time, capture coordinates, and inspector identification
- Sensor data files include timestamp, stress value, and ambient temperature value
- Inspection record files include disease location description and preliminary judgment result
Available fields include image resolution, WGS84 coordinate reference system, stress unit MPa, and temperature unit ℃. No unified text format requirements apply.
Constraints for Multi-turn Dialogue and Prompt Engineering
Single drone images have large file sizes. Sending too many images at once during multi-turn dialogue will exceed the context limit. Implement batched calls, and clearly specify the batch order in the prompt. Sensor data is in time-series format. Multi-turn dialogue must pass context sorted by timestamp, and the prompt must specify prioritizing the latest alarm data. Inspection records are semi-structured text, with inconsistent description formats across different inspectors. The prompt must include clear rules for field extraction to ensure consistency in subsequent analysis. Significant format differences exist across multi-source data. Multi-turn dialogue must first complete format alignment, and the prompt must include guided steps for data conversion to avoid retrieval failures caused by format mismatches.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
messages | Retain no more than 20 historical messages per dialogue turn | Bridge disease data is mostly time-series or multimodal. Excessive historical messages will exceed the context limit; 20 messages covers the context needs of routine inspection dialogues |
variables | Bind two custom variables: disease location and capture time | Inspection data must be associated with specific locations and times to accurately match disease identification specifications and historical cases in the knowledge base |
chatId | Generate a unique chatId per inspection batch | Dialogues from the same inspection batch must share context, while dialogues from different batches must isolate data to avoid context confusion |
| Question Optimization | Enabled, optimization threshold calibrated via actual testing | Inspection alarm descriptions often have colloquial characteristics; optimization improves the accuracy of knowledge base retrieval |
| Single Search Limit | Top 8 results | Knowledge base entries for bridge diseases are mostly image annotations and inspection specifications. Too many entries will cause context overload and affect the accuracy of judgment results |
| Maximum Context | 8000 token | Multi-turn dialogue needs to retain inspection history and current alarm information; 8000 token covers the context needs of routine inspection dialogues |
Three Common Misconfigurations
- Symptom: Inspection image upload fails after upgrading to FastGPT v4.17.0, with the API returning a 413 status code. Cause: The multi-turn dialogue file upload limit was not adjusted. Single bridge disease image files are larger than the default upload limit.
- Symptom: Unable to specify a specific disease dataset for search in multi-turn dialogue, and returned knowledge base results contain irrelevant content. Cause: The target dataset identification parameter was not bound via
variables, and the prompt did not clearly specify the search scope. - Symptom: The dialogue window prompts context overflow, and interaction cannot continue. Cause: The number of historical messages for
messageswas not limited, and excessive unnecessary inspection history data was retained.
How to Verify Proper Configuration
- Initiate a single-turn dialogue, upload a single bridge disease image, and verify that the capture time, coordinates, and other metadata of the image are correctly carried in
messages. - Set
variablesto bind the target dataset identification, initiate a search, and verify that the returned results only contain content from the target dataset. - Initiate 5 consecutive dialogue turns, verify that
chatIdremains consistent and that no context is lost. - Upload multi-batch inspection data, verify that dialogue contexts for different
chatIdare isolated, with no mixed content across batches.
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.