What Data Looks Like for This Category
Core data for this category includes animal quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature logs, microbial test reports, and more. Data comes from animal quarantine information systems, supplier batch ledgers, production traceability platforms, real-time temperature collection devices, metal detection equipment, and laboratory testing systems. Updates run per production batch, shift operations, and continuous temperature control processes. Individual records include fixed fields such as batch number, test time, temperature value (℃), quarantine number, and others. Field structures vary across different documents. Some records require association with multi-dimensional batch information.
Constraints Imposed on Vector Models and Indexes by This Category's Characteristics
This category has high-frequency data updates and tight batch associations. Index systems must support incremental updates and batch-related retrieval to prevent split loss of associated information for individual records. Variations in field structures across different documents require indexes to support multi-format data. The system must also filter abnormal temperature control or test records to avoid low-quality data interfering with retrieval results. Continuously collected temperature data has many temporal duplicate features. Chunking logic must be optimized to avoid redundant indexing, while ensuring rapid location of temperature control records for the corresponding batch during retrieval. Additionally, the data involves biosecurity-related content. The indexing process must protect sensitive information from leaks. Retrieval results must meet compliance requirements.
How to Configure
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the typical length of individual quarantine or temperature control records, retains complete context for batch associations |
| Similarity Threshold | 0.72–0.85 | Filters low-match non-associated inspection records, adapts to semantic differences in multi-dimensional data for this category |
maxToken | 4096 | Adapts to the total length of single-batch documents imported in bulk, avoids truncation of critical batch and test information |
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. |
| Single Search Limit | Top 6 results | Balances retrieval efficiency and associated information completeness, adapts to multi-dimensional reference needs for equipment maintenance |
| Reranking | Calibrated via actual testing | Adapts to semantic features of maintenance documents for different equipment, optimizes ranking logic for recall results |
Three Common Mistakes
- Phenomenon: After importing to the knowledge base, the dataset status remains stuck in "Indexing" for a long time. Cause: The bulk temperature control record format for this category is not adapted, and the indexing process times out due to duplicate batch field verification.
- Phenomenon: Custom vector model requests always route through the LLM channel without triggering vector retrieval. Cause: The channel binding for the vector model is not configured correctly, and the vector call switch for the equipment inspection scenario is not enabled.
- Phenomenon: Semantic similarity return values fall outside the 0-1 range. Cause: Normalization parameters for the vector model are not adjusted, leading to abnormal similarity calculation results.
How to Confirm Proper Configuration
- Upload a single animal quarantine certificate and slaughter record, check if the index completion status in FastGPT v4.17.0 updates to "Completed" within 1 minute.
- Enter an equipment inspection-related query, verify that retrieval results include temperature control and test records for the corresponding batch.
- Adjust the similarity threshold, confirm that the matching degree of retrieval results changes appropriately with the threshold.
- Import bulk cold chain temperature logs, check that resource usage for the indexing process stays within a reasonable range.
The values provided here are common starting points, and should be evaluated against the reader's own samples.