What This Category’s Data Looks Like
Typical entities in this category are slaughterhouses, cooked meat and frozen prepared food manufacturers. Daily generated 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 systems, supplier batch ledgers, production traceability systems, temperature collection terminals, metal detection equipment, and laboratory test systems. Data update frequency is synchronized for each batch of raw materials and each shift of production links. Continuous temperature control data is updated in real time. Document fields include batch number, quarantine number, temperature threshold, test results, and other items. Some documents require association with batch and supplier information.
Constraints Imposed on Knowledge Base Retrieval and Recall
Data in this category has high update frequency and is strongly bound to production batches. This requires the retrieval system to support precise recall of associated documents by batch dimension, to avoid cross-batch data confusion. Continuous temperature control data is generated in real time, so incremental synchronization and low-latency retrieval must be supported. Documents include both structured fields and unstructured text, so both keyword and semantic matching capabilities need to be supported. Some documents involve biological risk control, so recall result accuracy must be ensured to avoid missed detection or false recall.
How to Set Configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
similarity | 0.75–0.82 | Balances recall precision and recall volume for core documents such as animal quarantine certificates and microbial test reports, and adapts to the high-risk control retrieval requirements of this category |
limit | Top 6–8 results | Matches the usage scenario of quickly viewing core batch-associated documents during production audits, avoiding excessive results interfering with on-site judgment |
searchMode | mixedRecall | Matches both structured fields such as batch number and quarantine number, and unstructured text such as test results and operation records, covering all retrieval dimensions required for full audits |
usingReRank | Enabled | Performs secondary ranking on recall results, reduces the false recall probability of documents related to cross-contamination, and improves audit efficiency |
datasetSearchUsingExtensionQuery | Enabled | Supplements extended queries associated with supplier batches, strengthening cross-document batch association recall capabilities |
collectionIds | Bound to two collections: quarantine records and production records | Covers core document types for supplier audits and process quality control, avoiding irrelevant data interfering with retrieval |
Three Common Misconfigurations
- Issue: Knowledge base content fails to synchronize with production batch updates in a timely manner. Cause: No incremental update task triggered by production batches is configured, and real-time generated documents such as cold chain temperature records and microbial test reports are not associated.
- Issue: Table datasets cannot be selected during retrieval. Cause: The index switch for table datasets is not enabled in FastGPT v4.17.0, or the table lacks retrievable structured fields including batch number and temperature value.
- Issue: Batch re-embedding tasks fail after switching embedding models. Cause: Re-embedding tasks are not split by batch dimension, causing large continuous temperature control datasets to exceed processing time limits.
How to Verify Configurations Are Correctly Set
- Submit a test query containing a specific batch number and cold chain temperature threshold, check whether the returned results include the quarantine certificate and temperature records of the corresponding batch.
- Upload newly generated batch documents, verify whether the knowledge base completes synchronization updates within 15 minutes.
- After switching the embedding model, trigger a batch re-embedding task, check whether the task status is completed normally.
- Test the hybrid retrieval mode, confirm that both keyword and vector retrieval results are matched.
The values given are common starting points and should be measured against the reader's own samples.