What the Data for This Category Looks Like
The typical entities in this category are carbonated drink, juice, tea, functional beverage, and packaged drinking water factories. Daily documents include formula sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspection records, and finished product inspection records. Data sources cover water source and raw material testing, formula systems, online sugar and acidity monitoring equipment, filling equipment, visual inspection systems, and LIMS systems. Data is updated continuously per batch or shift. Individual records include fields such as batch number, feeding parameters, test values, and exception types.
Constraints on Vector Models and Indexes
This category has high data update frequency and scattered data sources. Indexes must support incremental updates to avoid resource consumption from full reconstruction. Different document types have significant field differences. Different chunking rules must be configured per document type to prevent mixing of cross-batch formula and filling records. Data has strong correlation. A single exception report must be linked to the process records of the corresponding batch. Metadata such as batch number and timestamp must be retained during chunking to ensure accurate matching of related content during retrieval. In addition, false recalls of production exceptions can cause serious consequences. Retrieval precision thresholds must be strictly controlled to avoid including irrelevant data in the context.
How to Set Configurations
The following configurations are based on FastGPT v4.17.0, and are adapted to the process document processing needs of beverage and bottled water production scenarios.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the field length of individual process records to avoid mixing cross-batch content |
| Overlap Length | 100–150 characters | Retains cross-chunk key information such as batch number and timestamp to prevent retrieval gaps |
maxToken | 8192 token | Adapts to the input limit of mainstream embedding models, covering complete single-batch process documents |
| Similarity Threshold | 0.85–0.92 | Meets the high-precision requirements of beverage production, avoiding false recalls of abnormal batch data |
| Single Search Limit | Top 10 entries | Balances retrieval efficiency and recall completeness, covering the volume of abnormal records for a single shift |
defaultToken | Calibrated based on actual testing | Adapts to model token limits for local deployments, avoiding interface timeouts |
Three Common Configuration Mistakes
- Retrieval results cannot include associated visual inspection images. This occurs because multi-modal vectorization configuration is not enabled, or the association metadata between images and batch numbers is not retained during chunking.
- The system cannot select a specified embedding model. This occurs because the access path for the corresponding model is not added to the configuration, or the interface token does not have model call permissions.
- Imported process document fields are truncated or lost. This occurs because the chunk length is set too small, causing key values such as concentrate feeding amount to be lost after long fields are split.
How to Verify Correct Configuration
- Upload a single filling exception report that includes visual inspection images. Perform a retrieval, then check whether the returned results include associated image assets.
- View the list of connected embedding models on the model configuration page, and confirm whether the target model is displayed as selectable.
- Import a batch of test process documents, and check whether the indexed fields completely match the batch numbers and feeding parameters of the original documents.
- Initiate a batch indexing task, and check the system operation logs for timeout or error messages to confirm that the configuration complies with interface limits.
The values provided are common starting points and should be measured against the reader's own samples.