What Data for This Category Looks Like
This category’s data originates from product drawings and 3D models, CAPP/CAM systems, machine tool collection terminals, tool management systems, MES systems, and measurement and inspection tool data. Daily documents cover process routes, operation cards, tool lists, CNC programs, first-piece inspection records, patrol inspection forms, and measuring tool calibration records. Data updates trigger per work order, individual workpiece, sampling batch, or tool replacement. Document fields include tool numbers, cutting speeds, feed rates, dimensional tolerance values, and similar parameters. Common units are millimeters, revolutions per minute, and millimeters per minute.
Constraints Imposed on Vector Models and Indexing
This category’s data has scattered sources and diverse formats, including structured cutting parameters, semi-structured process documents, and CNC program code text. Vector extraction rules must support mixed formats. Update frequency is high, with dispersed trigger nodes. Indexing tasks must support incremental updates to avoid redundant computation caused by full reconstruction. Individual document content varies widely: CNC programs may contain large numbers of code lines, while process cards use paragraph-style descriptions. Chunking rules must be flexibly adjusted to fit different text lengths. Fields include precise numerical parameters. Vector extraction must retain numerical correlation features to prevent loss of semantic meaning from professional process logic.
Configuration Settings
The following configurations are formulated based on the interface rules of FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Fits the mixed text length of CNC programs and process cards, avoids overloading single chunks |
| Overlap Length | 100–200 characters | Retains cross-chunk process step correlation information, prevents critical logic from being broken by chunking |
defaultToken | Calibrated based on actual measurements | Adapts to vector encoding thresholds for professional terminology and numerical parameters within documents |
maxToken | 4096 token | Matches the context window limit of mainstream vector models, avoids truncating critical process parameters |
| Single Recall Limit | Top 8 entries | Matches the correlation density of machining documents, covers cross-review requirements for adjacent processes |
| Reranking Switch | Enabled | Filters irrelevant tool parameters or machine tool logs, improves accuracy of cross-review |
Three Common Configuration Errors
- Indexing tasks show no progress for extended periods, with an incomplete status displayed in the interface. This occurs when incremental indexing trigger rules are not configured, and full scanning of frequently updated process documents and CNC programs exhausts computing resources.
- After calling the file upload API, no basis for confirming index completion is available. This happens when the polling parameter for index status is not enabled, and the interface rules for task completion callbacks are not bound.
- Vector recall results deviate significantly from actual process requirements. This occurs when the vector model’s encoding rules are not adjusted for numerical fields in machining documents, leading to weakened semantic correlation of professional cutting parameters.
How to Verify Proper Configuration
- Upload a mixed document containing a CNC program and a process card, check the content integrity of the chunking preview interface, and confirm that the chunking rules fit the document’s characteristics.
- After calling the file upload API, use the system-provided task status interface for polling, and verify whether an index completion status identifier can be retrieved.
- Switch between different vector models, compare the correlation of cutting parameters in recall results, and confirm that the model adapts to the encoding effect of professional machining text.
- Submit a cross-review task, check the number of associated documents in the returned results, and confirm that the single recall limit meets the needs of the business scenario.
The values provided here are common starting points and should be evaluated against the reader’s own samples.