What the Data for This Category Looks Like
The main entities in this category are slaughterhouses, cooked meat and frozen prepared food enterprises. Daily documents include animal quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature records, and microbial test reports. Data sources cover animal quarantine information, supplier batches, production traceability systems, temperature collection equipment, metal detection equipment, and laboratory test results. Update frequency follows production batches and work shifts. Cold chain temperature records are collected continuously. Document fields include batch number, quarantine number, timestamp, temperature value, total microbial count, and some documents require association with biological risk labels and raw/cooked zoning tags.
What Constraints These Characteristics Impose on Vector Models and Indexes
Multi-source heterogeneous data structures require indexes to support mixed vector encoding of text and numeric fields. Using a single vector encoding for all data types will cause matching bias. High-frequency updated batch data requires an incremental indexing mechanism to ensure newly generated ledgers can be quickly included in the retrieval scope. The strong association between batch and quarantine information requires indexes to be stored in batch groups, preventing irrelevant records from different batches from being retrieved. Continuously collected temperature data has a large number of temporal duplicate entries, so chunking rules must be used to avoid redundant vector generation. The control requirement for raw/cooked zoning requires indexes to support filtered retrieval by zoning fields, narrowing the retrieval scope.
How to Set Configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average text length of a single batch ledger, avoiding truncation of cross-batch information |
| Overlap Length | 50–100 characters | Preserves contextual continuity of batch-related information, preventing chunking from breaking association logic |
| Similarity Threshold | 0.75–0.85 | Filters low-match unrelated test records, improving retrieval accuracy |
| Single Search Limit | Top 10 entries | Controls the number of records returned for a single batch retrieval, avoiding context overload |
defaultToken | 8192 | Adapts to the total token length after combining multiple ledgers for a single batch |
maxToken | 16384 | Reserves context expansion space for the report generation phase |
Three Common Mistakes
- Symptom: The console returns a 503 error, prompting that no available
text-embedding-v3channel exists under the default group. Cause: No channel key for the corresponding embedding model is configured in FastGPT v4.17.0, or the key configuration format does not meet requirements. - Symptom: Retrieval results include irrelevant ledger records from other batches. Cause: No index filtering rules are configured based on batch fields, leading to retrieval scope exceeding the target production batch.
- Symptom: Index matching rules cannot be adjusted directly on the retrieval test page; configuration must be done in the knowledge base content management page. Cause: Old version operation logic is used, and the interface update of FastGPT v4.17.0 is not adapted.
How to Confirm the Configuration Is Complete
- Check the embedding service running logs to confirm there are no 503 errors or prompts for key loading failures.
- Enter the quarantine number of a single batch as the retrieval keyword, verifying that returned records only include ledger data for that batch.
- Test the total token count after combining multiple ledgers for a single batch, confirming it does not exceed the set
maxTokenvalue. - Adjust the similarity threshold, verifying that the matching degree of retrieval results meets the preset filtering standards.
The values provided here are common starting points and should be evaluated against the reader's own samples.