What Data for This Category Looks Like
This category's data primarily comes from CAD/CAM software, order BOM systems, sheet metal inventory ledgers, laser cutter and bending machine equipment logs, and MES production management systems. Daily generated documents include 3D part unfolding drawings, CNC cutting programs, bending process cards, nesting diagrams, first-piece inspection records, and process transfer cards. Each document contains fields such as sheet metal specification, bending coefficient, cutting size, process number, and transfer status. Units include millimeters, square meters, kilograms, and piece count. Data updates are triggered by each order, daily production scheduling, and each production line changeover, with no fixed batch update cycle.
Constraints Imposed on Vector Models and Indexing Workflows
This category's data characteristics impose multiple constraints on the vector model and indexing workflow. First, documents contain multiple types of content such as engineering drawings, process parameters, and transfer records, requiring indexes to support combined semantic encoding of structured fields and unstructured text. Second, because orders, scheduling, and line changeovers cause frequent updates, use the processing status and task logs actually reported by the deployed version to measure the time from submission to searchability. Do not infer the existence of a configurable incremental-indexing switch from the update frequency, or assume that every update rebuilds the entire index. Third, each document contains specialized engineering parameters such as bending coefficients and cutting sizes, so the vector model must have engineering domain semantic understanding capabilities to ensure retrieval accuracy for parameter associations.
Configuration Settings
The following configurations are compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Setting Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the length of single-segment engineering text in documents such as bending process cards and nesting diagrams, avoiding semantic fragmentation |
| Overlap Length | 50–80 characters | Retains cross-chunk associated information for key parameters such as bending coefficients and cutting sizes |
| Similarity Threshold | 0.72–0.85 | Filters low-match irrelevant production records, ensuring retrieval results align with ledger summary requirements |
| Single Search Limit | Top 10 entries | Controls the scale of retrieval results, adapting to the batch summary processing logic of production ledgers |
maxToken | 4096 | Adapts to the total length of engineering text and parameters, avoiding truncation of critical process information |
| Reranking | Calibrated via actual testing | Optimizes reranking logic for sheet metal domain terminology, improving retrieval priority for core parameters |
Three Common Configuration Errors
- No image-indexing option is available when creating a knowledge base: First confirm the deployed FastGPT version and edition. In v4.17.2, the Dataset OpenAPI marks
imageIndexas Business Edition only. If the deployment is Business Edition and the option is still missing, check the matching configuration, release package, and API version; do not assume a missing multimodal plugin or disabled switch. - A large number of process card data from non-current orders is mixed in retrieval results. Cause: The similarity threshold is set too low, failing to effectively filter unmatched production records.
- New or updated process cards cannot be retrieved promptly after a line changeover. Diagnosis: review the status and logs reported for that task, then query a field unique to the batch after completion. Use the logs to locate any delay; do not automatically attribute it to a missing incremental-indexing setting or a full rebuild.
How to Verify Proper Configuration
- Upload a mixed document of a bending process card and a nesting diagram, check if the parsed fields in the index include key production parameters such as sheet metal specification and bending coefficient.
- In a test dataset, submit a new process card with a unique field. Follow the task status and logs actually reported by the deployment; after the task completes, query that field to confirm the new content is searchable. If the delay is unexpected, use the logs to locate it instead of assuming an incremental-indexing switch or a full rebuild.
- Submit a retrieval request containing "bending sequence" and "remaining material management", verify that the relevance of returned results meets business expectations.
- Confirm that configuration items such as chunk length and overlap length have been adapted to document types, with no obvious semantic fragmentation issues.
- If image indexing is required, first verify
imageIndexsupport for the deployed version/edition. The v4.17.2 OpenAPI marks it as Business Edition only. If the deployment supports the feature, submit a test image with distinctive visible content, inspect the task status and logs, then use a retrieval method supported by that deployment to confirm the image content is searchable. Confirm any required setting or API against that deployment's documentation.
The values provided are common starting points and should be measured against the reader's own samples.