What this category of data looks like
Data for this category primarily comes from daily production documents of cotton spinning, chemical fiber spinning, woven, and knitted enterprises. These documents include cotton blending or batching lists, process setup sheets, equipment operation records, yarn inspection reports, grey fabric inspection reports, and defect records. Data sources cover raw fiber inspection, ERP systems, spinning and loom collected data, evenness testers, fabric inspectors, and order specification documents. Update frequency is typically synced per raw material batch, per production shift, or per finished fabric piece. Each individual document usually contains fields such as fiber indicators, process parameters, equipment operation data, and inspection results. Common units include weight, length, rotational speed, and piece count, which are standard for production work.
What constraints do these characteristics impose on the knowledge base retrieval and recall link
Multi-source, high-frequency updated data for this category requires knowledge base retrieval to support fast recall of multi-format documents. High-frequency updates per raw material batch, shift, and fabric piece create data timeliness constraints. Retrieval must prioritize recalling the latest batch cotton blending and process parameter data. Multi-field production documents need retrieval logic that matches specific business fields. For example, defect records must be linked to process parameters of the corresponding grey fabric batch to avoid returning irrelevant cross-batch results. Equipment operation records and inspection data have long-text attributes, so retrieval must accurately locate core parameter segments. The setup must also adapt to mixed structured and semi-structured data recall requirements collected by different types of equipment.
How to set the configurations
These configurations are optimized for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.85 | Matches the precise matching needs of spinning process parameters and defect records, avoids recalling production data from unrelated batches |
limit | Top 8 entries | Covers core associated data for a single production batch, while controlling context length within model limits |
searchMode | embedding | Adapts to mixed structured and semi-structured production documents of this category, prioritizes precise parameter matching via vector recall |
usingReRank | Enabled | Filters irrelevant results via re-ranking for long-text segments like defect records and process sheets, improves accuracy of report generation |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands retrieval keywords for production ledgers; for example, links "yarn strength" to fiber indicators on corresponding cotton blending sheets |
collectionIds | Configured by business domain | Only recalls documents from corresponding business collections such as cotton blending, process, and inspection data, avoids interference from cross-category data |
Three Common Misconfigurations
- Retrieval result re-ranking marked as false. Phenomenon: The interface shows the re-ranking function is deployed, but the re-ranking flag of retrieved results is false, and re-ranking logic cannot be triggered. Cause: The collection ID of the corresponding re-ranking model is not bound in the
usingReRankconfiguration item, or the deployment path of the re-ranking model is not synchronized to the knowledge base configuration. - Retrieval fails to match specified production ledger files. Phenomenon: The content returned by the large model during conversation does not reference uploaded specified files such as cotton blending sheets and process sheets. Cause: The corresponding business collection is not specified via the
collectionIdsconfiguration, or thedatasetSearchUsingExtensionQueryis not enabled to expand the retrieval scope during retrieval. - Number of retrieved results does not match the configured value. Phenomenon: The
limitis set to top 8 entries, but the actual returned results are fewer or more than this number. Cause: Thesimilaritythreshold is not set correctly, leading to some low-match results being filtered out or retained, or the collection scope of the current business domain is not limited during retrieval.
How to confirm the configuration is correct
- Enter the knowledge base management page, check the
usingReRankswitch status of the corresponding business collection, confirm it is enabled and bound to a deployed re-ranking model. - Initiate a simulated retrieval, input "XX batch cotton blending sheet parameters", verify that the
similarityscore of returned results falls within the 0.72–0.85 range, and the number of results matches thelimitconfiguration. - Export the retrieval configuration log of the current knowledge base, confirm that
datasetSearchUsingExtensionQueryis enabled, andcollectionIdsonly includes target business collections such as cotton blending, process, and inspection data. - Upload a test defect record document, initiate retrieval, and verify that it can accurately recall process parameter content of the corresponding grey fabric batch.
The values provided here are common starting points, and should be measured against the reader's own samples.