What Data Looks Like for This Category
This category covers carbonated beverage, 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 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 production lines, visual inspection devices, and LIMS systems. Data updates follow per-batch production, shift-based operations, and continuous collection modes. Fields include feeding volume, test values, equipment operating parameters, batch numbers, and test times, with corresponding units of measurement.
Constraints Imposed on Vector Models and Indexing by This Category's Data Features
Multi-source heterogeneous data sources result in inconsistent field formats. Define standardized field mapping rules to ensure consistency of embedded vectors. High-frequency continuous production data generates many small documents. Adjust chunking logic to avoid redundant index storage. Production data linked to batches requires indexes to support precise recall by batch dimension. This allows quick association of full-process records for the same batch during ledger summary. Documents mixing high-precision measured values and text descriptions need embedding models that handle both structured and unstructured data. Abnormal data in core links requires real-time retrieval. Index recall latency must match the response requirements of production and operations.
Configuration Settings
This configuration is compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the complete content of a single batch production record, avoiding splitting of associated data for the same batch |
| Overlap Length | 100–200 characters | Retains cross-chunk associated information such as batch numbers and core parameters, preventing truncation of key fields |
| Similarity Threshold | 0.75–0.85 | Filters low-correlation historical ledgers, avoiding mixing of unrelated batch records during summary |
| Maximum Context | 4000–6000 tokens | Adapts to combined recall of multiple inspection records and production parameters, meeting report generation requirements |
defaultToken | 1024 | Controls the output length of a single embedded vector, balancing index storage and retrieval accuracy |
maxToken | 8192 | Adapts to the upper limit of vector embedding for batch ledgers, meeting batch processing requirements for high-frequency data |
Three Common Misconfiguration Issues
- A large number of unrelated batch records are returned when searching production ledgers. The cause is a similarity threshold set too low, which fails to filter low-correlation historical data.
- The vector model fails to start normally after configuration, with a configuration error displayed in the interface. The cause is failure to adjust embedding mapping rules to match the multi-source heterogeneous field structure of this category, leading to incompatible field formats.
- A timeout error occurs when importing multiple inspection records in batches. The cause is failure to adjust the
defaultTokenandmaxTokenparameters. The single embedding length exceeds the system default limit, causing processing delays.
How to Confirm Successful Configuration
- Import the complete production record for a single batch, run a ledger summary test, and check that recalled documents only include data related to this batch.
- Check index storage usage, and confirm that chunk length and
defaultTokensettings do not cause storage resource overload. - Simulate an abnormal data retrieval scenario, and verify that the similarity threshold setting filters unrelated historical ledger content.
- Import multiple production records from different shifts in batches, and confirm that the index supports precise recall by batch dimension.
The values provided are common starting points and should be evaluated against the reader's own samples.