Cross-review of technical documents for spinning and weaving: Knowledge base retrieval and recall

What the data for this category looks like

Cotton spinning, chemical fiber spinning, woven and knitted fabric enterprises form the main entities of this category. These enterprises generate daily documents including cotton blending formulas, process setup sheets, equipment operation records, yarn and grey fabric inspection sheets, and defect records. Data sources include raw fiber inspection data, ERP systems, real-time data from spinning and weaving machines, data collected by evenness testers and fabric inspection machines, and order specification documents. Data updates synchronize with each batch of raw materials, each production shift, and each roll of fabric produced. Most individual documents contain fields such as fiber indicators, process parameters, defect types, and inspection values, with some including specialized units such as twist, width, and weight.

What constraints do these characteristics impose on the knowledge base retrieval and recall link?

This category’s data updates trigger frequently in line with production rhythms. The retrieval and recall pipeline must support near-real-time knowledge base synchronization to avoid returning outdated process parameters and inspection results. Multi-source, heterogeneous document formats include structured cotton blending formulas and unstructured defect records. These formats require adaptation to different chunking rules to ensure retrieval does not split key information from the same document. Specialized fields such as twist, width, and evenness CV value require retrieval to prioritize matching specialized terminology, while supporting synonym expansion. A single production batch links to multiple cross-process documents. Retrieval must associate all relevant data for the same batch to avoid fragmented results that reduce cross-review efficiency.

How to set the configurations

The following configurations are formulated based on FastGPT v4.17.0.

Three common errors

• Phenomenon: After importing an Excel file, multiple lines of content from a single cotton blending formula are merged into one chunk, making it impossible to accurately match individual process parameters during retrieval. Cause: Custom delimiters were not configured correctly, the default general chunking rule was used, and single Excel records were not split by line.
• Phenomenon: Irrelevant dyeing and finishing process documents appear in retrieval results, which do not match the current spinning and weaving retrieval requirements. Cause: The exclusive knowledge base collection collectionIds was not bound, causing the retrieval scope to cover document data from other categories.
• Phenomenon: Retrieval recall rate for specialized terminology such as "evenness CV value" is low, failing to match corresponding inspection records. Cause: datasetSearchUsingExtensionQuery was not enabled, and no synonym expansion was performed for specialized terminology, causing retrieval to only match documents with literal expressions.

How to confirm the configuration is correctly set

• Upload a test Excel cotton blending formula sheet, check if the chunking result shows one record per line, confirming that the custom delimiter configuration is effective.
• Enter a search term containing specialized terminology, such as "32-count pure cotton yarn twist parameters", check if synonyms are automatically expanded and relevant documents are recalled, confirming that datasetSearchUsingExtensionQuery is enabled.
• After initiating a retrieval, view the number of recall results, confirm that the returned results meet the quantity requirements specified in the limit configuration, and that the similarity threshold matches the precision needs of the current retrieval.
• After enabling usingReRank, compare the order of initial recall results and re-ranked results, confirming that the relevance ranking aligns with business logic.

The values provided are common starting points and should be measured against the reader's own samples.