What this category’s data looks like
Typical entities are 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 records, and microbial test reports. Data sources cover animal quarantine information, supplier batch ledgers, production traceability records, real-time temperature collection data, metal detection results, and laboratory test reports. Updates are performed per production batch and shift; cold chain temperature records are updated via continuous collection. Most documents are associated with unique identifiers such as batch numbers and quarantine numbers. Fields include timestamps, numeric parameters, and compliance notes. There is no unified fixed format, and it varies across production links.
What constraints do these characteristics impose on the vector model and indexing workflow
The high-frequency update feature of this category requires indexes to support incremental updates, avoiding excessive server resource usage from full reconstruction. Strongly associated batch and quarantine identifiers require retaining contextual associations during chunking, preventing loss of critical matching information after splitting. Multiple document types and unit-bearing numeric fields require vector models to adapt to vectorization processing of mixed text and numeric content. Key compliance-related information requires high-accuracy recall, so index filtering rules must strictly match business requirements and cannot rely on generic thresholds. Additionally, continuously collected temperature control data contains a large number of time-series fields, and chunking must be prevented from destroying time-series associations to ensure that complete temperature change chains can be matched during retrieval.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Most documents in this category are structured records with unique identifiers. Excessively long chunks will destroy batch associations, while excessively short chunks will split single complete records |
| Overlap Length | 100–200 characters | Key cross-chunk information such as batch numbers and quarantine numbers must be retained to ensure context can still be matched after chunking |
| Similarity Threshold | 0.75–0.85 | Compliance-critical information involving quarantine, temperature, etc., requires strict filtering of low-match results to avoid mistakenly recalling irrelevant records |
| Single Search Limit | Top 8 results | Balances recall coverage and response speed, avoiding excessive results leading to context overflow |
maxToken | 8000 token | Compliance documents in this category are mostly multi-page records, requiring the context to fully carry key batch and test information |
defaultToken | 1500 token | Daily process Q&A mostly involves single-record queries, matching the vector length of conventional single-document chunks |
Three common mistakes
- Scenario: After FastGPT is updated to v4.17.0, the knowledge base of the original index cannot return search results, and the interface displays "No vector retrieval results". Cause: The vector model input format of the new version is not adapted, and the chunk token length of the old index does not match the current configuration.
- Scenario: After the server restarts, the knowledge base stays in the "Not Ready" state and cannot complete indexing automatically. Cause: The incremental index automatic recovery configuration is not enabled, and the full index task is not automatically triggered when the service starts.
- Scenario: After batch uploading multiple production batch records, the system reports a timeout error, and the log displays "Chunk processing timed out". Cause: The chunk length is set too large, exceeding the time threshold for single-task processing, leading to excessive server load during batch upload.
How to confirm the configuration is correct
- Upload a single quarantine certificate document, initiate a query of "Does this batch meet quarantine requirements", and confirm that the returned results contain key quarantine information from the document.
- Check the knowledge base index status, and confirm that after restarting the FastGPT v4.17.0 service, index reconstruction is completed automatically without manually triggering chunking and vectorization operations.
- Batch upload 10 consecutive batch production records, verify that the system has no timeout errors and the index progress bar advances normally.
- Adjust the similarity threshold, and verify that low-match irrelevant records are no longer recalled.
The values provided are common starting points and should be measured against the reader's own samples.