What Data Looks Like for This Category
The primary entities in this category are slaughterhouses, cooked meat and frozen prepared food enterprises. Their daily documents include quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature records, and microbial test reports. Data is sourced from animal quarantine information, supplier batches, production traceability systems, temperature collection equipment, metal detection equipment, and laboratory test results. Update frequency follows per batch and per shift cycles. Cold chain temperature records are collected continuously. Documents must associate carcass or raw material batches with quarantine information, and include fields and corresponding units such as batch number, temperature value, and test result.
Constraints Imposed on Vector Models and Indexes
This category's data characteristics impose multiple constraints on the vector model and index workflow. Documents linked to multiple batches require precise filtering and recall by batch fields to avoid mixing documents from different production batches. Continuously collected cold chain temperature data involves high-frequency, small-batch writes, so indexes need to support low-latency incremental updates. Production records divided into raw and cooked areas require strict isolation in vector space to prevent compliance risks from cross-zone recall. Significant differences in field structures across different document types require unified vectorization and index construction for multi-structure documents.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average field length of single documents such as quarantine certificates and test reports. Avoids semantic fragmentation from too short chunks, and reduces vectorization overhead from too long chunks. |
| Overlap Length | 100–150 characters | Covers key cross-chunk fields such as batch numbers and temperature thresholds. Prevents critical information from being split across different chunks. |
| Similarity Threshold | 0.75–0.85 | Matches strongly associated features of production batches and quarantine information. Filters low-relevant historical documents, and avoids redundant recall data. |
| Single Search Limit | Top 10 entries | Controls the context scale during tender quotation preparation. Prevents excessive historical data from interfering with decisions for the current batch. |
defaultToken | 8192 | Adapts to the total length requirement after concatenating multiple documents. Supports context integration for multi-batch data in tender documents. |
maxToken | 16384 | Reserves sufficient token space to accommodate vectorization and recall of long texts such as cold chain temperature records and test results. |
Three Common Misconfigurations
- Issue: A 404 status code is returned when configuring Baidu Embedding-v1, and the vector model cannot be added. Cause: Third-party model mapping parameters for OneAPI are not configured correctly, resulting in an incorrect interface request path.
- Issue: A connection timeout error occurs after the locally deployed index model starts, and the second 3090 GPU cannot be called normally. Cause: Multi-card load balancing configuration is not enabled, so the system does not correctly identify and allocate GPU resources.
- Issue: Deployment of the m3e vector model fails in a non-GPU environment, and an error is reported when the container service starts. Cause: The GPU forced loading parameter is not disabled, so the model attempts to call non-existent GPU resources, causing startup failure.
How to Verify Successful Configuration
- View the running logs of FastGPT v4.17.0 vector model service, and confirm there are no error messages related to configuration errors, insufficient resources, or missing dependencies.
- Import a single quarantine certificate document, test the chunking and vectorization process, and confirm that the generated vector data format and fields match expected results.
- Initiate a search request associated with a batch number, and check that the number and relevance of recall results conform to preset filtering rules.
- Import new cold chain temperature records, test the index incremental update function, and confirm that the index can normally synchronize and update the latest data.
The values provided here are common starting points, and should be evaluated against the reader's own samples.