What the Data for This Category Looks Like
This category’s core data covers daily production documents from carbonated drink, juice, tea, functional beverage, and packaged drinking water factories. Documents include formula sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspection reports, and finished product inspection reports. Data sources include water source and raw material testing systems, formula management systems, online sugar and acidity monitoring equipment, filling equipment, visual inspection devices, and LIMS systems. Data update frequency changes with production batches and shifts. Some equipment data collects continuously. Fields include concentrate dosage (kilograms), sugar content (°Brix), turbidity (NTU), sealing pressure (kilopascals), liquid level height (millimeters), inspection batch number, and other relevant fields, with standard units for all corresponding production parameters.
How Do These Characteristics Create Constraints for Knowledge Base Retrieval and Recall
The data characteristics of this category create multiple constraints for the retrieval and recall workflow. First, data sources are scattered, and update frequencies vary widely. Real-time equipment data collected continuously and batch-level static documents need clearly divided retrieval scopes. This prevents cross-batch data mixing, which would harm cross-review accuracy. Second, documents include professional production parameters such as sugar content, turbidity, and sealing pressure. Retrieval must support precise field matching to avoid generic results that cannot support compliance reviews. Third, documents from aseptic filling and water treatment links have high mutual relevance. Recall must prioritize associating multiple document types from the same production batch to ensure full contextual integrity during cross-review. Fourth, for production scenarios with multiple lines and switched product categories, knowledge base data from different production lines must be strictly separated. This prevents cross-line misuse of retrieval results.
How to Configure Settings
FastGPT v4.17.0 supports the following configuration items. Below are recommended settings for beverage and bottled water production scenarios:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | This category requires precise matching of production parameters and batch information to avoid irrelevant low-similarity documents interfering with cross-review |
limit | Top 8–12 | The number of compliance documents linked to a single production batch is limited. Excessive recall results add unnecessary screening burden for review personnel |
searchMode | mixedRecall | Balances semantic recall for professional terminology and precise matching of fields such as batch number and concentrate dosage, meeting the retrieval needs of production documents |
usingReRank | Enabled | Production documents contain many professional terms. Reranking filters results that are semantically similar but business-irrelevant, improving review efficiency |
datasetSearchUsingExtensionQuery | Enabled | Automatically supplements extended query terms such as production batch number and production line number, strengthening associated recall of multiple document types from the same batch |
collectionIds | Bind the knowledge base collection corresponding to the production line | Separates production data from different production lines to prevent document misuse caused by cross-line retrieval |
Three Common Misconfigurations
- Some equipment operation diagrams and packaging sample images in production documents fail to display normally in retrieval results. The cause is that image resources were not correctly associated during knowledge base upload, or images were not embedded in the document body. This leads to failure to load associated content during retrieval and recall.
- When connecting to an enterprise’s existing Confluence or Feishu knowledge base via API, sugar and acidity test data in blending records cannot be fully pulled. The cause is that structured data adaptation for knowledge base extended retrieval was not enabled, so only plain text fragments were pulled.
- Retrieval results include concentrate feeding records unrelated to the current batch, leading to misjudgment of feeding compliance during cross-review. The cause is that the retrieval scope was not limited to the current production line’s knowledge base collection via
collectionIds, mixing in historical data from other production lines.
How to Confirm the Configuration Is Correct
- Upload complete production documents for a single batch, initiate a query that includes the batch number, and verify that recall results only include associated documents from the current batch.
- Import test text containing professional production parameters, review the similarity distribution of retrieval results, and confirm that the
similarityvalue meets business requirements. - After enabling
usingReRankanddatasetSearchUsingExtensionQuery, initiate a query that includes the production line number, and verify that reranked results prioritize associating documents from the same production line. - After connecting to an external knowledge base source, initiate a query for documents containing image references, and confirm that associated equipment diagrams and packaging sample images load normally in retrieval results.
The values provided are common starting points and should be measured against the reader's own samples.