What Data Looks Like for This Category
Typical data for this category comes from slaughterhouses, cooked meat and frozen prepared food enterprises. Daily documents include quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature logs, 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. Updates follow per-batch or per-shift schedules. Continuous temperature logs update in real time. Document fields include batch number, test values (such as temperature in ℃, total bacterial count CFU/g), test time, quarantine number, and others. Some fields are structured, some are free text.
Constraints on Embedding Models and Indexes
Data for this category is linked by batch and has varied update rhythms. Index systems must support metadata filtering by batch dimension to prevent mixing of document vectors across different batches. Continuous temperature logs are high-frequency time-series data. Index update frequency must match per-batch or per-shift update rhythms to avoid data lag that reduces review accuracy. Documents contain quarantine and test content related to biological risks. Vector matching needs higher semantic precision to prevent incorrect association of risk data from unrelated batches. Documents from raw and cooked product zones have clear scene differences. Indexes must support classification by production scene to improve retrieval targeting. Some documents are free text. A flexible chunking strategy is required to avoid semantic breaks.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average text length of single documents in this category to avoid semantic breaks from chunking |
| Overlap Length | 100–150 characters | Retains contextual association between adjacent chunks, supporting the long sentence structure of slaughter records and test reports |
| Similarity Threshold | 0.75–0.85 | Filters low-match results to prevent accidental recall of quarantine documents from non-corresponding batches |
| Single Search Limit | Top 20 results | Meets the document quantity requirement for single reviews in this category, avoiding redundant results |
| Embedding Model | Ali-emb3 | Open-source version offers high semantic precision to match the semantic matching needs of biological risk and production documents for this category |
defaultToken | 8192 token | Supports vector generation for long documents such as complete quarantine certificates |
maxToken | 16384 token | Supports the upper limit of long text processing during batch indexing |
Three Common Configuration Mistakes
- Phenomenon: Index tasks stay stuck in "processing" for extended periods, and the interface returns a timeout status code. Cause: Index tasks are not split by batch. A single batch contains too many temperature logs and test documents, leading to index processing time exceeding a reasonable range.
- Phenomenon: Cross-batch documents appear in retrieval results. The batch number field is empty or does not match query conditions. Cause: Batch number metadata is not added to documents. Vector matching does not associate the batch dimension, leading to mixing of documents from different batches.
- Phenomenon: Retrieval accuracy does not improve after adjusting index configuration. Returned results include unrelated raw and cooked product zone documents. Cause: The chunking strategy is not adjusted for document types in raw and cooked product zones, leading to semantic matching deviations.
How to Confirm Proper Configuration
- Access the FastGPT v4.17.0 admin backend. View the index task list to confirm single-batch document processing time meets expectations.
- Submit a retrieval condition with a specified batch number. Verify that the batch number of returned results fully matches the query condition.
- Retrieve continuous temperature logs. Confirm that the time order of returned results matches actual production time.
- Access the embedding model configuration page. Confirm that the values of
defaultTokenandmaxTokenmatch the preset configuration.
The values provided are common starting points and should be measured against the reader's own samples.