Data for This Category
Data comes from coating supplier TDS/SDS, spray booth and oven operation data, colorimeters, film thickness gauges, environmental monitoring equipment, and MES systems. Daily documents include coating process cards, paint batch sheets, paint mixing records, film thickness and color difference reports, oven curves, defect repair sheets, and VOCs logs. Update frequency adjusts per batch of paint, shift production, and continuously collected equipment data. Most documents include fields such as process parameters, batch identifiers, test values, and environmental records. There is no unified fixed format, and it changes with production batches and test items.
Constraints on Vector Models and Indexing
The multi-source and high-frequency update characteristics of this category impose multiple constraints on the vector model and indexing workflow. Mixed document types exist, including plain text process descriptions, structured test data, and time-series operation curves. Chunking rules must adapt to varying content lengths to avoid truncating critical parameters. High-frequency updated batch data and continuously collected equipment data quickly accumulate large volumes of incremental content. Incremental indexing must be supported to avoid resource consumption from full reindexing. Additionally, demand for correlation analysis between process parameters, appearance defects, and curing effects requires the index to retain cross-document association features to improve matching accuracy.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average content length of documents such as coating process cards and paint mixing records, avoids including off-process unrelated information in a single chunk |
| Overlap Length | 100–200 characters | Retains process association information between adjacent chunks, avoids breaking the correlation between paint viscosity and spray parameters during matching |
| Similarity Threshold | 0.75–0.85 | Matches association features between coating processes and defect reports, filters low-correlation non-process documents |
| Single Recall Limit | Top 10 entries | Controls the number of documents returned per retrieval, adapts to the multi-parameter matching requirements of spray processes |
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. |
defaultToken | 200 token | Used for standardized chunking of short documents, adapts to short-content documents such as paint batch sheets and VOCs logs |
Three Common Misconfigurations
- Issue: After a server crash and restart due to a large number of file uploads, the knowledge base status remains "not ready", no automatic indexing is triggered, and the index progress displays as empty in the interface. Cause: Automatic recovery configuration for incremental indexing is not enabled. After a full indexing task is interrupted by a restart, it does not automatically resume. Batch uploads of short files do not trigger breakpoint resuming.
- Issue: After upgrading to FastGPT v4.17.0, existing knowledge bases with previously created indexes cannot return search results, and search results are empty. Cause: The embedding dimension of the vector model changes after the version update. The vector features of the original index do not match the new model, and automatic reindexing is not triggered.
- Issue: Indexing tasks time out, and the interface displays an "index failed" error. Cause: The values of
defaultTokenandmaxTokenare not adjusted. Chunk splicing for long documents such as oven curves exceeds system limits, leading to indexing task timeouts.
How to Verify Proper Configuration
- Upload a single typical document (such as a coating process card) to the FastGPT v4.17.0 platform. View the chunk preview interface to confirm that the chunk length matches the preset value, and no critical content is truncated.
- Trigger an incremental indexing task. After the task completes, view the index statistics panel to confirm that the number of indexed documents matches the number of uploaded documents.
- Enter process-related search terms, view the number and relevance of returned results, and adjust the similarity threshold to a range that meets business requirements.
- Restart the FastGPT service. View the knowledge base status to confirm that the automatic recovery indexing task starts normally, and no manual triggering is required.
The values provided are common starting points and should be measured against the reader's own samples.