What the Data for This Category Looks Like
Business data for metal cutting machining comes from product drawings and 3D models, CAPP/CAM systems, MES, tool management systems, machine tool collection equipment, and inspection and measuring tool records. Daily documents include process routes, operation cards, tool lists, CNC programs, first-piece inspection records, and patrol inspection forms. Work orders, single-piece machining, spot checks, and tool changes trigger data updates. Document fields include tool model, cutting speed, feed rate, tolerance values, inspection results, and other details. Common units include millimeters, revolutions per minute, millimeters per minute, and similar units.
What Constraints These Characteristics Impose on Vector Models and Indexing
Multi-source heterogeneous data formats require indexes to support text, CNC code, and table content, and prevent cutting that breaks process logic. High-frequency work order data requires incremental index support to reduce time spent on full index reconstruction. Professional terms such as clamping datum, tool compensation, and dimensional drift require accurate understanding from embedding models. Otherwise, matching deviations will occur. Quotation preparation for bidding scenarios requires association with specific process parameters. Indexes must retain associations between fields to avoid losing context after splitting.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the typical paragraph length of process routes and operation cards, avoids cutting that breaks process connection logic |
| Overlap Length | 50–80 characters | Retains process-related information across chunks, such as front and rear process requirements for clamping schemes |
| Similarity Threshold | 0.72–0.85 | Distinguishes bidding requirements from general process data, filters low-relevance irrelevant entries |
| Single Search Limit | Top 8 entries | Matches the number of process library entries commonly used in machining bidding, avoids redundant results |
| Maximum Context | 6000 characters | Adapts to the splicing length of process descriptions in bidding documents, aligns with the conventional adjustment range of FastGPT v4.17.0 |
maxToken | 8192 | Meets the embedding requirements for long industrial texts, matches the input limit of general embedding models |
Three Common Mistakes
- The number of search return results is far lower than expected. The cause is that the similarity threshold is set too high, filtering process data that meets bidding requirements.
- Search speed becomes too slow after knowledge base indexing. The cause is that the chunk length is set too large, leading to excessive single-batch embedded data volume and increased vector calculation time.
- The prompt "No matching content" appears during invocation. The cause is that the
defaultTokenconfiguration of the embedding model does not match the parameters used during indexing, or the embedding model interface is not correctly associated.
How to Confirm the Configuration Is Correct
- Upload a single operation card document, check if the complete clamping scheme description is retained after index chunking, with no key content truncated.
- Enter bidding-related queries such as "Cast iron part milling quotation", check if the returned results include matching tool lists and process parameters.
- Batch import process documents from 10 different work orders, verify that the index completion time meets expectations with no obvious lag.
- Check the embedding model interface call logs, confirm that the
maxTokenparameter has taken effect as configured, with no parameter conflict errors.
The values provided are common starting points and should be validated against applicable in-house samples.