What Data for This Category Looks Like
The typical entities in this category are cabinet chassis, ventilation equipment, electrical cabinets, and non-standard equipment manufacturers. Daily generated documents include part unfolding drawings, CNC cutting programs, bending process cards, nesting diagrams, first-piece records, and process flow cards. Data mainly comes from CAD/CAM software, order BOMs, sheet metal inventory systems, laser cutters, bending machines, and MES systems. Update frequency varies with orders, daily cycles, or each production changeover. Most documents include fields such as bending coefficients, sheet metal specifications, and processing steps.
What Constraints These Characteristics Impose on Vector Models and Indexing
Multi-variety, small-batch production causes document volume to grow rapidly alongside orders. It needs vector indexes that adapt to high-frequency updates. Mixed document formats demand vector models that balance semantic understanding of structured fields and unstructured text. Single production changeovers generate temporary data that needs fast index updates to avoid disrupting real-time retrieval. Accurately capture semantic associations between key fields such as bending coefficients and sheet metal thickness. Failure to do this causes process matching deviations and disrupts subsequent production links.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the text length of bending process cards and nesting diagrams, avoids semantic breaks within chunks |
| Overlap Length | 100–150 characters | Retains associated information such as bending processes and sheet metal specifications across adjacent chunks |
maxToken | 8192 | Adapts to the maximum input length of Qwen series embedding models, avoids truncation of critical process parameters |
| Similarity Threshold | 0.72–0.80 | Distinguishes similar bending processes from unrelated documents, fits the process matching scenario of multi-variety, small-batch production |
| Single Recall Limit | Top 10 entries | Controls the scale of retrieval results, avoids information overload during process document retrieval |
Three Common Misconfigurations
- Files stuck in "indexing" status show no progress updates on the interface. The cause is failing to match the
maxTokenrange supported by the current embedding model, which triggers a retry loop after document chunk parsing fails. - Batch updating embedding vectors causes the retrieval recall rate of some historical documents to drop sharply. The cause is failing to adjust the chunking strategy according to document update frequency, and the old document chunk length does not match the new embedding model.
- Indexes generated from manually uploaded process documents disappear automatically several hours later. The cause is failing to configure the index persistence storage path, so temporary indexes are cleared after service restarts or cache cleanup.
How to Confirm Configurations Are Properly Set
- Upload a typical bending process card, check that vector generation progress completes within a reasonable timeframe, and confirm no parsing errors occur.
- Retrieve bending process documents for the same sheet metal type, verify the relevance of returned results, and adjust the similarity threshold to a range that fits business requirements.
- Batch import process documents from 10 different orders, verify that after index generation completes, the number and matching degree of retrieval results meet preset requirements.
- Restart the FastGPT v4.17.0 service, check that already generated indexes are not cleared, and confirm the persistence configuration takes effect.
The values provided here are common starting points, and should be evaluated against the user's own samples.