What This Category’s Data Includes
The data for this category comes from customer color swatches, formula libraries, dye chemical batch records, dye vat operation data, colorimeter reports, setting machine parameters, environmental monitoring logs, and similar sources. Daily documents include dyeing formulas, chemical weighing logs, dye vat temperature curves, proofing confirmation sheets, color difference analysis reports, setting process sheets, and sewage discharge records.
Data is updated on a per-vat, per-batch, per-shift basis. Each individual document contains fields such as process parameters, batch numbers, test values, and operating personnel. Covered units include temperature, concentration, batch numbers, and emission indicators.
What Constraints Do These Traits Impose on Knowledge Base Retrieval and Recall
The high-frequency updates and strict batch-binding traits of this category require knowledge base retrieval to prioritize recalling documents from the most recent batches, to avoid using expired process data.
Multi-structured process parameter and test report documents require retrieval to support hybrid queries that combine numerical range matching and text semantic matching.
Data sources that mix direct device connections and manual records create differences in field formats. Compatible recall rules must be configured to cover the same type of data across different formats.
A single dye vat’s full processing cycle is linked to multiple documents. Retrieval must support cross-document associated recall to prevent critical process information from being fragmented.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches the precision requirements for dyeing process parameters, and avoids recalling irrelevant old batch data |
limit | Top 8 entries | Covers the number of documents associated with a single dye vat’s processing cycle, while controlling the context length of retrieval results |
searchMode | embedding | Adapts to the semantic matching needs of dyeing formulas and color difference reports, and supports cross-document process association |
usingReRank | Enabled | Reorders recall results by process relevance, correcting ranking biases from pure semantic matching |
datasetSearchUsingExtensionQuery | Enabled | Expands semantic queries for process keywords, covering the same type of process parameters with different phrasing |
collectionIds | Knowledge base collections bound by business line | Isolates process data across different orders and workshops, to avoid invalid recall across batches |
Three Common Misconfigurations
- Symptom: Overall low relevance of knowledge base search results, with some results unrelated to the current dyeing batch. Cause: No reasonable
similaritythreshold set, orusingReRankreranking not enabled, leading to low-relevance old batch data being recalled first. - Symptom: Unable to accurately match temperature curve data for a specific dye vat via keywords. Cause:
datasetSearchUsingExtensionQuerynot enabled, no semantic expansion applied to process keywords, so different phrasing for the same type of parameter cannot be covered. - Symptom: Uploaded chemical weighing log documents cannot be called by the retrieval scenario for the corresponding workshop. Cause:
collectionIdsnot bound by business line, so documents are assigned to the wrong knowledge base collection and cannot be accessed by retrieval for the corresponding order or workshop.
How to Verify Proper Configuration
- Run a keyword query for a single dye vat’s processing cycle, and confirm that the batch numbers of returned results match the order batch of the current query.
- Upload a new dye vat temperature curve document, run a retrieval for the corresponding parameters, and confirm that the new document is recalled normally.
- Adjust the
similaritythreshold, observe changes in the number of returned results, and confirm that the threshold configuration takes effect. - After enabling
usingReRank, compare the order of results before and after reranking, and confirm that the reranking function operates normally.
The values provided here are common starting points, and should be evaluated against the reader’s own samples.