What data looks like for this category
This category of data covers documents including coating process cards, paint batch sheets, paint mixing records, film thickness and color difference reports, oven curves, defect repair orders, VOCs ledgers, and more. Data sources include TDS/SDS documents from paint suppliers, data collected by spray booth and oven equipment, colorimeters, film thickness gauges, environmental monitoring systems, and MES systems. Data update frequencies cover per-paint-batch, per-shift production, and continuous equipment collection scenarios. Fields include quantitative indicators such as film thickness, color difference ΔE, oven temperature, and paint viscosity, as well as text descriptions such as process steps and defect types.
Constraints on vector models and indexing
Multi-source heterogeneous data characteristics in spraying and coating scenarios impose multiple constraints on the vector models and indexing workflow. Documents vary widely in length. Long documents such as oven curves and process cards require adaptation for long-text encoding. Frequently collected equipment data needs to avoid excessive chunking that breaks process windows. Batch-level association requirements demand that indexing supports metadata filtering by paint batch and production shift, to prevent incorrect cross-batch matching. Defect analysis requires associating appearance descriptions with quantitative detection data. Vector encoding needs to balance semantic alignment between text and structured fields, and support incremental updates to adapt to continuously collected equipment data.
Configuration settings
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | Adapts to the average length of single documents such as coating process cards and paint mixing records, prevents irrelevant content across pre-treatment, spraying, and curing windows from being merged |
| Chunk overlap length | 100–200 characters | Covers key information for continuous process steps, prevents chunk truncation of associated data from pre-treatment, spraying, and curing stages |
| Similarity threshold | 0.75–0.85 | Matches the similarity of quantitative detection data such as color difference and film thickness. Combines commercial semantic enhancement indexing to improve cross-document association accuracy, avoids incorrect association of non-similar defect cases |
| Single search limit | Top 10 results | Controls the number of recalled defect cases, adapts to the small-sample association requirements of coating defect analysis |
defaultToken | 4096 | Adapts to the vector encoding requirements of long documents such as oven curves and process cards |
maxToken | 8192 | Matches the context recall limit for large process documents |
Three common configuration mistakes
- Phenomenon: The knowledge base shows the document is split into 8 ready segments, then changes to 13 segments, with duplicate index entries. Cause: Document-level deduplication verification is not enabled. When continuously collected spray booth logs are uploaded repeatedly, data from the same batch is not automatically merged.
- Phenomenon: Existing queryable defect traceability results are lost after upgrading from v4.9.0 to v4.9.3. Cause: The token truncation logic of the default vector model changed after version iteration. Old data that was not re-indexed cannot match the new encoding rules.
- Phenomenon: Vector model batch training only supports single-file parameter adjustments, and cannot perform unified batch configuration. Cause: No batch indexing task queue is configured, and the batch-level unified parameter adjustment function is not enabled.
How to confirm correct configuration
- Upload a single coating process card, check the match between the number of chunks and the document structure, confirm there is no incorrect merging across process windows.
- Initiate a defect traceability query, verify that the recalled results include corresponding film thickness and color difference data, and that the similarity threshold falls within the preset range.
- Execute a batch indexing task, confirm that all documents from the same batch have the same number of chunks, and there are no duplicate index entries.
- Adjust the
defaultTokenparameter, re-encode long documents, check the integrity of vector encoding, and confirm there are no truncation errors. - Confirm that FastGPT v4.17.0 is compatible with the current configuration parameter range, and there are no version compatibility errors.
The values provided are common starting points and should be evaluated against the user's own samples.