What the data for this category looks like
Data sources for bridge defect identification include high-resolution image slices collected by bridge inspection drones, real-time sensor data from structural health monitoring systems, and defect annotation documents archived during daily maintenance.
Update frequency follows three schedules: image data is updated with quarterly inspections, sensor data refreshes every 10 minutes, and maintenance ledgers are updated synchronously when defect handling is completed.
Document structure is split into two categories: structured metadata fields (including point latitude and longitude, capture time, sensor number, defect type, and severity level) and unstructured image slices plus annotation description text. Some fields include units: for example, stress monitoring values use MPa, and displacement uses millimeters.
Constraints on Knowledge Base Retrieval and Recall
Bridge defect identification data contains a high proportion of unstructured image transcription text, structured records bound with multi-dimensional metadata, and real-time updated sensor data. These characteristics create the following requirements for the retrieval link:
- Support metadata-associated recall, returning image feature text bound with metadata such as point coordinates and defect level.
- Adapt to incremental update logic, avoiding full re-import of real-time monitoring data.
- Support filtering recall results by classification fields such as defect type and severity level, reducing the return of irrelevant content.
- Adapt to feature transcription text of different lengths, avoiding disruption to the association logic between titles and subordinate samples.
Configuration Guidelines
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Similarity threshold | 0.70–0.82 | Matches the semantic similarity between defect feature text and queries, filtering low-relevance non-target defect samples |
| Single search limit | Top 6–10 entries | Controls the number of defect samples returned per round, avoiding exceeding the information processing load of analysis personnel |
| Reranking switch | Enabled | Re-sorts initial recall results, prioritizing returning defect samples with higher severity levels |
| Query optimization | Enabled | Expands the semantics of natural language queries to match implicit keywords such as defect type and point location |
| Collection ID | Configured by bridge inspection zone | Limits retrieval scope to the defect knowledge base of the target zone, reducing recall noise |
| Search mode | Hybrid retrieval (full-text + vector) | Combines full-text retrieval and vector retrieval, adapting to recall requirements for unstructured image transcription text |
Three Common Misconfigurations
- Phenomenon: Too many results are returned in a single retrieval round, and the large model’s response is truncated mid-way. Cause: The single search limit is not configured, and a large number of defect samples exceeding the large model’s context window are recalled.
- Phenomenon: Retrieval results do not include metadata fields, only returning image transcription text. Cause: The metadata binding recall configuration is not enabled, and only vector text is retrieved without associating structured fields.
- Phenomenon: Logical structure is lost after importing Markdown-formatted defect annotation documents, making retrieval by defect type impossible. Cause: Blocks were not split according to document structure, and direct segmented import broke the association between titles and subordinate defect samples.
How to Confirm Successful Configuration
- Submit a query that includes defect type and severity level, and verify that returned results include both image transcription text and corresponding metadata fields.
- Adjust the similarity threshold range, observe changes in the number of recall results, and confirm that the threshold configuration takes effect.
- Import a single Markdown-formatted defect annotation document, and verify that corresponding defect samples can be recalled by title during retrieval.
- Submit a cross-zone query, and confirm that only defect knowledge base content under the corresponding collection ID is returned, with no data from other zones included.
The values provided here are common starting points, and should be evaluated against local deployment 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.