What Data Looks Like for This Category
Typical enterprises in this category produce tires, seals, rubber hoses, and other rubber products. Daily generated documents include formula sheets, weighing records, mixing curves, rubber compound quick test reports, vulcanization process cards, mold records, and finished product performance reports. Data sources cover formula systems, raw material batch records, internal mixers, Mooney viscometers, rheometers, vulcanizers, and laboratory testing equipment.
Documents are generated at a frequency of one per batch of rubber, per batch of raw materials, and per mold production operation. Each single document contains fields such as specific parameters, batch identifiers, and test values, with units including professional measurement standards like weight, temperature, time, and mechanical performance indicators.
Constraints on Vector Models and Indexes Stage
The data characteristics of this category impose multiple constraints on the vector models and indexes stage. The high-frequency, small-batch update rhythm requires indexes to support incremental synchronization, avoiding full reconstruction that occupies production resources. Multi-source heterogeneous data sources require vector models to be compatible with both structured equipment parameters and semi-structured process text, adapting to document inputs in different formats. Each single document contains a large number of professional terms and associated fields, so chunk processing must retain field associations to prevent retrieval failure caused by semantic splitting. Strict quality control requirements require that retrieved documents must accurately match business scenarios, reducing interference from invalid results.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | This category’s documents contain professional formulas and process parameters. Excessively long chunks will lose semantic associations, while excessively short chunks will damage field integrity |
| Overlap Length | 100–150 characters | Continuous parameters exist in mixing curves, vulcanization process cards, and other documents. Overlapping chunks can retain contextual coherence and avoid parameter splitting breaks |
| maxToken | 8192 | This category’s documents contain many professional terms, requiring sufficient tokens to carry complete semantics and adapt to long text vectorization requirements |
| defaultToken | 2048 | Retrieved document fragments should not be excessively long, to avoid exceeding the context processing limit of FastGPT v4.17.0 |
| Similarity Threshold | Calibrated based on actual testing | Different enterprises have varying precision requirements for process parameters, and matching accuracy must be adjusted based on business scenarios |
| Single Search Limit | Top 8 results | Cross-review for this category requires comparing multiple identical-type documents. Excessively many retrieved results will increase computational burden and reduce review efficiency |
Three Common Misconfigurations
- The symptom is that only vector fields are returned during retrieval, and original document content cannot be associated. The cause is that the foreign key association between vectors and original documents is not configured. Only vector data is stored in the pg vector column, and the path or content reference of the original document is not stored.
- The symptom is that when using the bge-m3 vector model, semantic retrieval scores are generally high. The cause is that no domain adaptation is performed for professional terms of this category, such as mixing temperature and vulcanization time. General-purpose models have deviations in matching thresholds for professional semantics.
- The symptom is that no data is displayed after index creation. The cause is that the trigger rule for incremental synchronization is not configured, or the collection frequency of the data source does not match the index update frequency, resulting in newly generated formulas and process cards not being synchronized to the index.
How to Verify Configurations Are Properly Set
- Randomly select a production document to perform vectorization operations, check that the vector generation result retains complete fields of the original document, and confirm that key parameters are not lost during chunking.
- Initiate a cross-review query, check that the retrieved original documents are associated with vector data, and confirm that the foreign key configuration is correct.
- Submit a newly generated mixing curve document, wait for index synchronization via FastGPT v4.17.0, then perform retrieval, and confirm that the new data can be retrieved normally.
- Adjust the calibration parameters of the similarity threshold, and verify that the matching accuracy of retrieval results meets business expectations.
The values provided are common starting points and should be evaluated against the reader’s own samples.