What the data for this category looks like
This category’s data mainly comes from supporting systems and equipment of coating lines for automobiles, home appliances, construction machinery, and metal furniture. It includes documents such as coating process cards, paint batch sheets, paint mixing records, film thickness and color difference reports, oven curves, defect repair orders, and VOCs ledgers. Data sources cover paint supplier TDS/SDS, real-time collected data from spray booths and ovens, colorimeters, film thickness gauges, environmental monitoring equipment, and MES systems. Update frequency aligns with each paint batch, shift production cycles, and continuously collected equipment data. Documents contain fields with clear units such as viscosity, film thickness, color difference, oven temperature, and VOCs concentration.
Constraints imposed by these characteristics on the vector models and indexes link
The multi-source and heterogeneous data characteristics of this category require the vector indexing link to support mixed chunking logic for structured documents and time-series equipment data. Frequently updated equipment collected data and batch documents need to adapt to incremental indexing logic to avoid the time cost of full reconstruction. Fields with clear units (such as film thickness μm, color difference ΔE) must retain field associations to improve vector matching accuracy. Long time-series oven curve data has higher requirements for chunk length flexibility. Compliance ledgers need strict control over duplicate indexing to prevent data redundancy from affecting retrieval efficiency.
How to set the configuration
This document is based on the configuration logic of FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the paragraph length of structured documents such as coating process cards and paint mixing records, while accommodating time-series segments of oven curves |
| Overlap Length | 50–80 characters | Prevents long time-series oven curve data from being split, retaining contextual association between adjacent segments |
| Similarity Threshold | 0.72–0.85 | Filters low-correlation batch data when matching fields with clear units, reducing mismatches |
| Single Recall Limit | Top 10 entries | Adapts to the batch audit scenario of coating inspection, controlling redundancy of retrieval results |
defaultToken | 4096 | Adapts to vector generation for long documents, covering complete oven curve or VOCs ledger content |
maxToken | Set to the selected embedding model/service maximum supported input tokens | maxToken is the input-token maximum supported by the selected embedding model/service; verify it in that provider configuration. See the v4.17.2 model-field reference. It is not retrieval/LLM context or a FastGPT-wide fixed cap. |
Three Common Misconfigurations
- Phenomenon: When uploading documents to the knowledge base, the system shows 8 segments. After displaying as ready, viewing again shows 13 segments, with duplicate index entries. Cause: No deduplication rules for incremental indexing are configured. The system misjudges different batch uploads of the same document or chunk splitting logic, leading to duplicate vector fragment generation.
- Phenomenon: ollama qwen2.5 and bge-m3 models have been added, but no text understanding model options appear in the available model list. Cause: The indexing model is not bound to the text understanding model channel for the knowledge base, or the channel configuration is not synchronized to the knowledge base creation page.
- Phenomenon: Only single-file adjustment of training parameters followed by re-upload is supported. Batch triggering of vector model retraining is not available. Cause: Batch index update configuration is not enabled. The system defaults to only supporting single-file incremental updates, with no global retraining entry enabled.
How to confirm the configuration is correct
- Upload a typical oven curve document, check that the number of chunks matches expectations, with no duplicate segments.
- Enter the knowledge base settings page, confirm that the added indexing model appears in the dropdown options of text understanding models.
- Initiate a coating defect retrieval request, check that the number of returned results matches the configured single recall limit.
- Upload a new paint batch sheet, verify that the system only generates vector fragments for new content, with no prompt for full reconstruction time consumption.
The values provided here are common starting points and should be measured against the reader's own samples.