What the Data for This Category Looks Like
It typically covers production data from cotton spinning, chemical fiber spinning, woven and knitted fabric enterprises. Daily documents include cotton blending lists, process setup sheets, equipment operation records, yarn and grey fabric inspection reports, defect ledgers, and more. Data sources include raw fiber test results, ERP systems, real-time data from spinning and weaving looms, evenness testers, fabric inspection machine test data, and order specification parameters. Update frequency adjusts synchronously with each batch of raw materials, each shift’s production, and each piece of fabric output. Most documents contain fields like fiber indicators, twist parameters, loom weave patterns, defect types, and corresponding treatment plans. Units include metric count, twist, centimeters, kilograms, and other standard units.
Constraints Imposed on Knowledge Base Retrieval and Recall
The high-frequency updates for each batch of raw materials, each shift, and each piece of fabric require the knowledge base to support incremental indexing and real-time recall. This prevents data lag from impacting the timeliness of process and exception reports. Multiple heterogeneous data sources need support for different document parsing formats. For example, table-based documents like blending lists and inspection reports require precise field extraction to avoid content truncation or loss of critical parameters. The strong correlation between defect records and process parameters requires retrieval results to match the context of the corresponding production link. This prevents irrelevant cross-category content from interfering with drafting. The massive volume of data from large numbers of equipment requires limiting the number of single-retrieval results. It also ensures the recall accuracy of core parameters to prevent retrieval result overload from reducing drafting efficiency.
Recommended Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.80 | Matches the accuracy requirements for spinning process parameters and defect handling, prevents low-relevance results from being included in drafting scenarios |
limit | Top 8 entries | Adapts to the context length of a single process or exception report, prevents retrieval result overload |
searchMode | embedding | Matches the semantic association between process parameters and defect types, adapts to recall needs for multi-source heterogeneous data |
usingReRank | Enabled | Performs secondary sorting on recall results, prioritizes returning core content matching the current drafting scenario |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands retrieval keywords, covers derivative query scenarios such as cotton blending indicators and loom weave patterns |
collectionIds | Collection IDs grouped by production link | Isolates production data from office documents, narrows the retrieval scope, and improves recall accuracy |
Three Common Mistakes
- Retrieval results are empty after importing Feishu spreadsheets and multi-dimensional documents: The parsing switch for the corresponding document type is not enabled, or documents are not grouped into the specified business dataset.
- Knowledge base files cannot be opened from retrieval results after import: File external link permissions are not configured, or file metadata is lost during parsing.
- Knowledge base data is missing after restoring a backup: Only project files were restored, and the index storage data of the dataset was not synchronized. Back up the storage directory associated with the dataset at the same time.
How to Verify Proper Configuration
- Log in to the FastGPT v4.17.0 backend, enter the corresponding business dataset, and verify that Feishu Excel and multi-dimensional documents have completed parsing and indexing, with a status of "Completed".
- Submit a test query such as "Process setup sheet with 32-count combed cotton blending parameters", check that returned results include core fields from the corresponding document, and the number of results matches the
limitsetting. - Trigger an incremental sync operation, verify that newly uploaded defect records can be recalled quickly, with no noticeable data delay.
- Attempt to restore a specified backup node, verify that the dataset and project files are restored synchronously, with no content missing or abnormal error messages.
The values listed here are common starting points, and should be measured against the reader’s own samples.