What This Category of Data Looks Like
Data sources include high-resolution RGB and infrared images captured by bridge inspection drones, time-series data from stress/displacement sensors in structural health monitoring systems, and defect record documents filled out during manual inspections. Update frequency: drone inspection data is updated in batches quarterly, sensor data is pushed in real time at minute-level intervals, and manual records are synchronized immediately after a single inspection is finished. The standard document structure for a single record includes defect location (corresponding to bridge pile number or longitude and latitude), defect type, defect size, collection time, collection device number, and associated image file path. Field units: pile numbers use meter-level integers, longitude and latitude use decimal fractions, sizes use millimeters, time adopts ISO 8601 format, and device numbers are string type.
What Constraints These Characteristics Impose on the "Vector Models and Indexes" Link
The data contains multimodal content (images, time-series numerical values, text descriptions), so vector models must support multimodal feature embedding to cover all types of defect data. Structured spatial location fields require indexes to support spatial vector hybrid retrieval, to accurately match defect records in the same region. Minute-level sensor data pushes create high-concurrency write requirements, so indexes must support incremental construction and real-time updates. Professional engineering terminology and fixed field formats require vector models to adapt to semantic features in the engineering field, avoiding semantic bias from general-purpose models. Single records associated with multiple fields require retaining metadata during chunking, to ensure the relevance of retrieval results and avoid splitting the complete characteristics of defects.
How to Configure Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Bridge defect description text contains professional terms and size parameters. Excessively long chunks will destroy context association of terms, while excessively short chunks will split defect features. This is a reasonable interval calibrated through actual testing. |
| Overlap Length | 100–200 characters | Retain metadata association information of adjacent chunks, preventing key features such as defect location and type from being split across chunks. |
| Similarity Threshold | 0.75–0.85 | Distinguish feature differences between defects and normal structures. Adjustments must be made in combination with alarm standards in actual inspection scenarios. |
| Single Search Limit | Top 10 entries | Prioritize matching highly relevant historical inspection records. Too many results will increase analysis burden, while too few may cause key information to be missed. |
| Maximum Context | 3000–4000 tokens | Adapts to the default intervals of defaultToken and maxToken for FastGPT v4.17.0, ensuring retrieval results can fully cover the multi-dimensional characteristics of defects. |
Three Common Mistakes
- Phenomenon: Index tasks remain in the "indexing" state for a long time with no progress updates. Cause: The vector model is not adapted to the multimodal characteristics of bridge defect data, and cannot handle mixed input of image metadata and text, causing embedding tasks to block.
- Phenomenon: No matching items appear in retrieval results after custom index configuration, or matching results are irrelevant to the query. Cause: Metadata fields such as defect pile number and collection time are not retained during chunking, so vectors cannot be associated with correct defect records, and the retrieval logic fails.
- Phenomenon: A large number of non-defect normal structure data are mixed in retrieval results. Cause: The similarity threshold is set too low, failing to distinguish vector differences between normal structures and defect features, leading to false matches.
How to Confirm Proper Configuration
- View vector embedding task logs to confirm that all fields of bridge defect data are correctly extracted and embedded, with no field missing or format error reports.
- Submit a query containing specific defect types and locations, and check whether the matching degree of retrieval results falls within the preset threshold range.
- Test the incremental index function, upload new inspection data, and confirm that the index task can be completed normally and new data can be retrieved.
- Check the connection status of the vector database, confirm that the read/write permissions and data format of the custom vector database match the requirements of FastGPT.
The values provided here 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.