What this category of data looks like
This category of data covers automotive, home appliance, construction machinery, and metal furniture painting line scenarios. Daily documents include painting process cards, coating batch sheets, paint mixing records, film thickness and color difference reports, oven curves, defect repair sheets, and VOCs ledgers. Data comes from coating supplier TDS/SDS, spray booth and oven equipment, colorimeters, film thickness gauges, environmental monitoring systems, and MES systems. Update frequency varies with each coating batch, each shift’s operations, and continuously collected equipment data. Fields include process parameters, detection values, batch information, and others. Units include micrometers, seconds, milligrams per cubic meter, and more.
What constraints do these characteristics impose on the vector models and indexes link
Coating data uses a mixed structure of static process documents and real-time equipment collection data. Indexes must support multi-field associated recall and multimodal content adaptation. Frequently updated coating batch and equipment operation data requires incremental index logic. This avoids the time cost of full index reconstruction. Business needs for multi-field association—such as matching film thickness values to spraying parameters—require indexes to retain field-level association rules. This prevents recall results from being disconnected from business context. Document lengths vary widely. Paint mixing records are as short as tens of characters, while oven curves can reach thousands of characters. This requires flexible chunking logic.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to varying document lengths such as painting process cards and oven curves, avoiding information breakage in single chunks |
| Overlap Length | 100–150 characters | Retains cross-chunk process parameter associations, ensuring continuous information such as film thickness and curing temperature is not split |
| Similarity Threshold | 0.75–0.85 | Filters low-relevance historical data, meeting the precise Q&A needs of coating scenarios |
| Single Search Limit | Top 8 entries | Covers associated data for major process links, avoiding redundant information interfering with results |
maxToken | 3000–4000 token | Accommodates complete long document fragments such as oven curves and paint mixing records, meeting context recall requirements |
defaultToken | Set from representative sample documents | defaultToken is the default token count for direct text chunking; tune it on representative source documents. See the v4.17.2 model-field reference. It is not a vector dimension, embedding-model input cap, or retrieval/LLM context. |
Three common misconfigurations
- Symptom: The knowledge base returns coating data unrelated to the query. Cause: No field-level filtering rules are configured, leading to full index recall of non-associated VOCs ledgers or irrelevant oven historical data.
- Symptom: The index status remains unready for a long time. Cause: Incremental index mode is not enabled. Full reconstruction is performed on continuously collected equipment data, causing index construction timeout.
- Symptom: Retrieval results only return a single short document, failing to cover the complete process window. Cause: The chunk length is set too small, splitting the complete spraying and curing process into too many fragmented chunks, leading to incomplete recalled information.
How to confirm correct configuration
- Import a single painting process card, run a retrieval test for the corresponding process question, and check if the returned results include matching process requirements and parameters.
- Upload continuously collected oven curve data, verify that the index completes updates within a reasonable time frame, with no timeout or error prompts.
- For coating defect-related questions, check if the recalled results include corresponding defect repair sheets and process adjustment records.
- View the index status panel, confirm that the index is marked as ready, with no abnormal error messages.
The values listed above are common starting points. Adjust settings based on testing with specific operational samples.
This document is compiled based on the configuration instructions for FastGPT v4.17.0.