What Data Looks Like for This Category
This category’s data comes from daily production and inspection operations at carbonated beverage, juice, tea, functional beverage, and packaged drinking water factories. It includes core documents such as formula sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspection reports, and finished product inspection reports. Teams collect data from water source and raw material testing, formula systems, online sugar and acidity monitoring tools, filling equipment, visual inspection systems, and LIMS systems. Data updates happen per batch, per shift, or via continuous collection. Core documents include fields like specific feeding values, water quality indicators, filling parameters, and inspection batches. Units cover concentration, temperature, liquid level, total bacterial count, and other metrics. Retrieval must align with real-time or batch-level data tied to production links.
What Constraints Do These Characteristics Impose on Knowledge Base Retrieval and Recall
The category’s mixed batch-level and real-time data characteristics require retrieval systems to support matching both fixed batch documents and continuously collected real-time data. High-speed filling and multi-product switching business scenarios require retrieval systems to quickly locate exclusive formulas and process parameters for different products, avoiding interference from cross-category data. High-priority requirements for water treatment and aseptic control require retrieval results to first associate documents related to water quality and sterilization parameters, while restricting recall of non-essential fields. The high batch update frequency requires retrieval systems to adapt to frequently updated datasets, avoiding recall of expired historical feeding or inspection data.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches the mixed numeric and text fields of this category, filters low-correlation general production documents |
limit | Top 8 entries | Adapts to multi-document parallel retrieval scenarios, prevents context overflow caused by too many batch data recalled in a single retrieval |
searchMode | mixedRecall | Combines full-text retrieval and vector retrieval, covers different types of business documents such as formula values and process records |
usingReRank | Enabled | Re-ranks recalled batch documents, prioritizes matching core parameters of the current audit batch |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands related process terms based on user audit questions, improves recall accuracy of formulas and inspection records |
| Single search context upper limit | 1500 token | Matches the context window limit of FastGPT v4.17.0, adapts to the single-batch document length of this category |
Three Common Misconfigurations
- Uploading PDF documents of formula sheets or water treatment records leads to low retrieval accuracy. This occurs because chunking optimization is not applied to the numeric fields of this category, causing key feeding parameters and water quality indicators to be split across different chunks.
- Setting
limitto Top 15 entries results in insufficient actual recalled entries. This happens becausecollectionIdsis not correctly configured to bind the production document collection of the corresponding factory, leading the retrieval scope to cover irrelevant datasets. - Retrieving long chunks of 5000 tokens causes the final output to exceed the set context upper limit. This is because long chunks are not automatically truncated after retrieval, leading to context overflow that affects answer accuracy.
How to Verify the Configuration is Correct
- Upload a single-batch formula sheet document of this category, perform retrieval related to audits, and check whether the recalled results include the corresponding feeding values and batch information.
- Adjust the
similaritythreshold, verify whether low-correlation non-production documents are filtered, and ensure that recalled results are concentrated on business documents related to audits. - Test long-document retrieval, confirm that when the length of recalled chunks exceeds the set context upper limit, the system automatically truncates excess content.
- Check the
collectionIdsconfiguration, confirm that only the production dataset of the current audit factory is bound, avoiding cross-factory data interference with retrieval results.
The values provided are common starting points and should be measured against the reader's own samples.