What data for this category looks like
Business data for spraying and coating scenarios comes from paint supplier TDS/SDS documents, real-time operation data of spray booths and ovens, test reports from colorimeters and film thickness gauges, environmental monitoring records, and MES system work orders. Data update cadence covers each paint batch, each shift process adjustment, and continuously collected equipment operation parameters. Document types include coating process cards, paint batch sheets, paint mixing records, film thickness and color difference reports, oven temperature curves, defect repair orders, and VOCs ledgers. Fields include process parameter values, test results, batch identifiers, and equipment numbers. Some specialized fields use industry standard units.
What constraints these characteristics impose on the knowledge base retrieval and recall link
Multi-source and frequently updated data requires the retrieval pipeline to support near-real-time document synchronization, to avoid retrieving outdated process parameters or equipment records. Diverse document types and specialized fields require the retrieval logic to balance semantic matching and precise keyword matching, ensuring that specialized terms such as film thickness, oven temperature, and color difference ΔE are correctly identified. Coating data from different batches has strong correlation, so retrieval results must cover related documents under the same process window, preventing split parameter information from forming an incomplete solution. At the same time, errors in coating scenarios are directly linked to batch rework and compliance risks, so retrieval result accuracy is higher than general scenarios, and irrelevant non-coating data must be strictly filtered.
How to set the configurations
The following configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Specialized parameter matching in the coating industry requires a high similarity threshold to filter low-relevance general equipment documents |
limit | 8–12 items | Coating equipment inspection issues involve multi-link process parameters, requiring a sufficient retrieval volume to cover the complete process window |
searchMode | mixedRecall | Balances semantic matching for specialized terms and precise keyword matching, adapting to multi-type documents in coating scenarios |
usingReRank | Enabled | Parameter correlation is high in coating scenarios, reranking can filter redundant results and improve the ranking of highly relevant documents |
datasetSearchUsingExtensionQuery | Enabled | Coating issues often involve associated information of process parameters, extended queries can cover relevant data of related batches and equipment |
collectionIds | Bind dedicated datasets for coating equipment inspection | Isolate knowledge base content from other categories to avoid retrieving irrelevant non-coating documents |
Three common configuration errors
- Phenomenon: Neither full-text retrieval nor semantic retrieval can retrieve the specified paint batch sheet or oven curve document, and a large number of irrelevant general equipment documents are returned. Cause: The dedicated dataset is not bound via
collectionIds, resulting in the retrieval scope covering knowledge base content from unrelated categories. - Phenomenon:
usingReRankis configured but does not take effect during online retrieval testing. Cause: The reranking switch is not enabled during the knowledge base indexing stage, or the reranking model is not adapted to the semantic matching logic of specialized coating text. - Phenomenon: After setting a large
limitparameter, the large language model cannot receive the context content. Cause: The total length limit of the knowledge base context is not adjusted, exceeding the input token limit of the large language model, resulting in the context being truncated or discarded.
How to confirm the configuration is correct
- Upload a dedicated coating process card document, perform targeted retrieval, and confirm that the document can be retrieved without irrelevant results.
- After enabling
usingReRank, compare the ranking of retrieval results before and after enabling, and confirm that the ranking of highly relevant documents changes reasonably. - Adjust the
limitparameter to a test value, check the logs of the large language model's input context, and confirm that the content is complete and not truncated. - Test queries containing specialized terms, such as "repair process for unqualified film thickness", and confirm that the retrieved documents contain corresponding defect repair order content.
The values provided are common starting points and should be measured against the reader's own samples.