What the Data for This Category Looks Like
The typical entities in this category are slaughterhouses, cooked meat and frozen prepared food enterprises. Daily generated documents include animal quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature records, microbial test reports, and more. Data sources cover animal quarantine information, supplier batch ledgers, production traceability systems, temperature collection equipment, metal detection equipment, and laboratory test results. Data is updated per production batch, per shift operation, and for continuous temperature control collection. Document fields include batch numbers, quarantine identifiers, temperature values and corresponding units, and other relevant fields.
Constraints Imposed on Vector Indexing by This Category’s Data Characteristics
This category’s data characteristics impose three core constraints on the vector indexing process. First, data is strongly linked to production batches. Indexing must retain metadata such as batch numbers and quarantine identifiers to avoid cross-batch matching errors. Second, high-frequency continuous temperature control data and low-frequency quarantine report updates have large differences in update rhythm. Split chunking rules by data type to prevent low-frequency documents from being overwritten by high-frequency updates. Third, documents contain numeric fields with specific units. Retain complete field and unit information during chunking to ensure semantic accuracy during vector matching.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Fits the typical length of single-batch slaughter records and thermal processing records, avoids splitting metadata linked across batches |
| Overlap Length | 100–150 characters | Retains critical cross-chunk metadata such as batch numbers and quarantine identifiers, prevents loss of association logic after chunking |
defaultToken | 8192 | Fits the total token count of multiple spliced cold chain temperature records, meets long-text embedding requirements |
maxToken | Calibrated via actual testing | Matches the maximum supported limit of the currently used embedding model, avoids invocation errors |
| Similarity Threshold | 0.75–0.85 | Defines the semantic similarity boundary to distinguish documents linked to the same batch from unrelated cross-batch documents |
| Single-Search Limit | Top 6 results | Matches the maximum number of linked documents involved in a single-batch production process, avoids redundant retrieval results |
Three Common Mistakes
- Phenomenon: The imported knowledge base dataset.csv only displays the index field and no content field. Cause: Content extraction rules during chunking were not configured correctly. Only metadata fields such as batch numbers were captured, and the complete text content of the original records was not associated.
- Phenomenon: Vector retrieval returns a large number of unrelated cross-batch documents. Cause: The similarity threshold was set too low, or the retrieval scope was not filtered using production batch as metadata, leading to incorrect matching of documents from different batches.
- Phenomenon: Vector model calls return a 400 status code error. Cause: The
maxTokensetting exceeds the supported limit of the current embedding model, or long continuous temperature control log text was not split by batch.
How to Confirm Proper Configuration
- Upload single-batch slaughter records and corresponding quarantine certificates, check if the chunked content field contains complete record text and linked metadata.
- Submit a retrieval request for a specific batch number, confirm that the returned results only include linked documents for that batch, with no cross-batch unrelated content.
- Test embedding model calls, verify that returned vector results fall within the preset similarity threshold range.
- Confirm that the FastGPT v4.17.0 indexing configuration has been synchronized to the service nodes deployed via docker-compose.
The values provided are common starting points and should be measured against the reader's own samples.