What Data for This Category Looks Like
This category of data covers the full production chain of liquid milk, yogurt, milk powder and dairy ingredients. Daily documents include raw milk acceptance records, standardized batching sheets, pasteurization/UHT sterilization records, CIP cleaning records, filling operation records, sample retention and microbial test reports. Data sources cover raw milk testing systems, manufacturing execution systems (MES), homogenization and heat treatment equipment, online temperature and flow acquisition modules, CIP cleaning systems, and laboratory information management systems (LIMS). Updates trigger in real time per milk tank or production batch. Each document includes structured fields such as raw milk antibiotics and physicochemical indicators, heat treatment parameters, and cleaning verification data.
What Constraints These Characteristics Impose on Vector Models and Indexing
Real-time single-tank or single-batch data updates require indexes to support high-frequency incremental writes, and avoid excessive time consumption caused by full reconstruction. Multi-source heterogeneous data includes time-series parameters collected by equipment, qualitative and quantitative results from laboratory tests, and historical records converted from paper to electronic format. Vector models must adapt to semantic and numerical associations of mixed fields. Documents from core links such as raw milk acceptance and sterilization verification have high relevance. Indexes must support precise filtering by production batch and raw material batch, to avoid interference from irrelevant data during cross-review. The production chain of perishable raw materials has no redundant batch isolation. The accuracy of index recall is directly linked to production compliance, so the false recall rate must be strictly controlled.
How to Set Configurations
These configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the content density of typical documents such as raw milk acceptance records and sterilization records, and avoids breaking field associations during splitting |
| Overlap Length | 100–200 characters | Retains semantic continuity across chunks, and adapts to long parameter sequences in batching sheets and filling records |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Matches the semantic matching accuracy of raw milk indicators and sterilization parameters, and controls the false recall rate |
| Single Search Limit | Top 8 entries | Adapts to the number of associated documents for a single batch of production data, and avoids redundant loading |
defaultToken | 8192 | Adapts to long-text content such as laboratory test reports and CIP cleaning records |
maxToken | Set to the selected embedding model/service maximum supported input tokens | maxToken is the input-token maximum supported by the selected embedding model/service; verify it in that provider configuration. See the v4.17.2 model-field reference. It is not retrieval/LLM context or a FastGPT-wide fixed cap. |
| Vector Database Selection | Databases compatible with vector retrieval such as PostgreSQL, Milvus | Adapts to the storage and retrieval needs of multi-source heterogeneous data in dairy processing |
Three Common Misconfigurations
- The interface displays the error "503 Current group default has an issue with model text-embedding". The cause is vector model call timeout or quota exhaustion, and the model call retry strategy has not been adjusted for the high-frequency real-time data of dairy processing.
- The knowledge base remains in the indexing state continuously. The cause is that incremental indexing mode is not configured, full reconstruction is triggered every time data is updated, and the real-time update rhythm of single tanks or single batches is not adapted to.
- Irrelevant food production documents are mixed into retrieval results. The cause is that index filtering rules by production batch and raw material batch are not set, and dedicated data source tags for dairy processing are not bound.
How to Verify Proper Configuration
- Check index logs to confirm that the incremental write time of single batch production data meets real-time update requirements.
- Initiate a cross-review search for raw milk antibiotic indicators, and verify that returned results only include relevant documents from the same batch.
- Test retrieval of long-text CIP cleaning records, and confirm that no context truncation errors occur.
- Verify that the return results of vector model calls fall within the set similarity threshold range.
The values provided here are common starting points, and should be measured against the reader's own samples.