What the data for this category looks like
Factories producing carbonated drinks, juice, tea, energy drinks, and packaged drinking water are the main entities in this category. They generate daily documents including formula sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspection reports, and finished product inspection reports. Data comes from water source and raw material testing, formula systems, online sugar and acidity monitoring equipment, filling equipment, visual inspection systems, and LIMS systems. Updates occur per batch, per shift, or through continuous collection. Individual records include fields such as sugar content, acidity, filling liquid level, sealing parameters, and batch number, tied to different business units.
Constraints on Vector Models and Indexing From These Characteristics
High-frequency continuous updates require indexes to support incremental triggering, avoiding full re-execution that consumes system resources. Mixed data types from multiple sources require vector models to support structured parameters and unstructured logs, ensuring all types of inspection data generate valid representations. Single-batch records have high field density, so cross-link context association must be retained during chunking; otherwise, the correspondence between formulas and filling parameters is lost. Frequent batch switching from high-speed filling requires index retrieval speed to adapt to single-shift abnormal troubleshooting needs.
How to Set Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the field density of single-batch inspection records, avoids mixing content across batches |
| Overlap Length | 100–150 characters | Retains connection information between adjacent links, prevents key parameters from being truncated |
| Similarity Threshold | 0.75–0.85 | Matches subtle deviations in inspection results, distinguishes normal and abnormal business parameters |
| maxToken | 4096 | Adapts to complete inspection logs for long batches, aligns with the default compatibility range of FastGPT v4.17.0 |
| Single Search Limit | Top 10 entries | Covers the number of abnormal records per shift, avoids redundant results interfering with troubleshooting |
| defaultToken | Set from representative sample documents | defaultToken is the default token count for direct text chunking; tune it on representative source documents. See the v4.17.2 model-field reference. It is not a vector dimension, embedding-model input cap, or retrieval/LLM context. |
Three Common Mistakes
- Index tasks always show as indexing with no progress updates. Cause: Incremental index triggering rules are not configured for high-frequency continuously collected data. Repeated full index execution causes system blocking.
- Search results generally have low relevance and fail to match target inspection parameters. Cause: Context for core business fields such as sugar content and acidity is not retained during chunking, so key matching information is lost from vector representations.
- The final set of indexed data fails to complete storage. Cause: Field formats for filling liquid level and sealing parameters are not verified. Illegal characters cause the indexing process to interrupt.
How to Confirm the Configuration Is Correct
- Upload complete inspection records for a single batch. Check whether the chunk preview retains complete field information for formulas, water treatment, and filling.
- Trigger an incremental index task. Confirm that the task completes within the preset time, with no timeout error prompts.
- Enter search terms for abnormal parameters. Check whether returned results prioritize matching inspection records for the corresponding batch.
- Check index storage logs. Confirm that there are no prompts for field format errors or data truncation.
The values listed above are common starting points and should be evaluated against the reader's own samples.