What the data for this category looks like
Business data in the special equipment safety supervision field mainly comes from special equipment safety supervision systems, inspection and testing institution systems, user unit ledgers, and operator certificate databases. Daily business documents include use registration certificates, periodic inspection reports, maintenance records, hidden danger rectification notices, accident reports, and operator certificates. Each document contains fixed fields such as equipment number, inspection cycle, maintenance unit, and operator qualifications. The data update frequency is monthly, requiring synchronization of the latest inspection results, maintenance records, and personnel qualification changes.
What constraints these characteristics impose on the vector models and indexes link
Monthly updated data sources require indexes to support incremental synchronization, avoiding computing resource consumption from full reconstruction. Differences in structure across multiple document types require chunking strategies adapted to different text lengths, avoiding over-truncation of long inspection reports or loss of key information from short certificates. The presence of fixed fields requires indexes to retain metadata associations, ensuring retrieval results can directly link to the unique identifier of the corresponding equipment. High-risk supervision attributes require accuracy in retrieval results, requiring strict control of matching thresholds to avoid invalid results interfering with business judgments.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-section content length of documents such as special equipment periodic inspection reports and maintenance records, avoiding over-truncation of key information |
| Overlap Length | 100–200 characters | Retains contextual association between adjacent chunks, preventing cross-chunk logic from being interrupted |
maxToken | 4096 | Adapts to the token limit of general embedding models, ensuring complete vector generation for long documents |
| Similarity Threshold | 0.75–0.85 | Calibrated based on actual testing, balancing recall rate and accuracy of retrieval in supervision business |
| Single Search Limit | Top 10 entries | Covers the multi-equipment association verification needs in supervision business, avoiding excessive results interfering with manual review |
defaultToken | "No valid metadata" | Processes documents missing fields such as equipment number and inspection cycle, preventing index tasks from failing due to empty fields |
Three Common Mistakes
- An index task throws an error after uploading documents, with prompts related to empty fields. This occurs because
defaultTokenis not configured to process documents with missing metadata, causing the index process to interrupt. - No image indexing model option is available when creating a knowledge base. This occurs because the currently deployed FastGPT v4.17.0 is the community edition, and the image indexing function is only supported in the commercial edition.
- Retrieval results only return a small number of entries and cannot cover relevant equipment. This occurs because the similarity threshold is set too high, filtering most valid matching results.
How to Confirm the Configuration is Correct
- Upload a periodic inspection report with complete metadata, check the index task log to confirm that vector data for the corresponding chunks has been generated.
- Retrieve a known equipment number, confirm that the corresponding use registration certificate, maintenance records and other associated documents can be matched.
- Adjust the similarity threshold, observe changes in the number of retrieval results to verify the filtering effect of the threshold on matching results.
- Upload a test document missing the equipment number, confirm that the metadata field displays the preset
defaultTokenvalue after the index is completed.
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.