What Data for This Category Looks Like
This category’s data covers production process specifications, batch production records, cleanroom zone records, sterilization batch records, inspection reports, software version records, and adverse event and traceability materials. Data sources include design history files, device and material batch ledgers, MES systems, clean environment monitoring equipment, sterilization equipment, and inspection and calibration systems. Data updates are performed per batch or per product, with regular updates following clean environment monitoring plans. Individual documents include fields such as unique device identifiers, material batch numbers, sterilization parameters, and test results. Units include degrees Celsius, minutes, and particles per cubic meter, among others.
Constraints These Characteristics Impose on Vector Models and Indexes
Data for this category is updated per batch and sourced from multiple locations. Indexes must support incremental synchronization and multi-source data field mapping. Documents contain specialized production parameters and unique identifier fields. Vector models must adapt to medical device domain terminology and the vectorization logic for numeric fields. Individual documents link to multi-dimensional production information. Index recall must prioritize matching unique device identifiers and material batch fields to ensure retrieval accuracy. Data update frequency fluctuates with production batches. This requires adapting to indexing rhythms for small-batch, high-frequency updates.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxToken | 8192 | This category’s documents include multiple sections of production parameters and traceability information. The 8192 token length covers the content of a single core document |
defaultToken | 512 | The core fields of a single batch record require a stable base token length for vectorization, adapting to the information density of numeric fields |
| Chunk Length | 800–1200 characters | The length of single-section process descriptions or batch record entries for this category fits this range, avoiding chunking that breaks field integrity |
| Overlap Length | 100–150 characters | Prevents chunking from truncating critical unique identifiers or parameter-related content, ensuring information continuity across chunks |
| Similarity Threshold | 0.75–0.85 | This requirement distinguishes batch-specific and cross-batch production records, avoiding mismatches with documents from non-target batches |
| Single Search Limit | Top 10 results | This category requires precise matching of batches and parameters. Excessive results increase subsequent verification and review costs |
Three Common Mistakes
- Symptom: Vectorization results fail to accurately match specialized production parameters, leading to large deviations in retrieval results. Cause: A generic vector model was used, without adapting to medical device domain terminology and numeric parameters.
- Symptom: Indexes cannot be added in a docker-compose deployment environment, with the interface displaying an "Invalid index configuration" error. Cause: The vector index service’s mount path and environment variables were not configured in docker-compose.yml, and the local document storage volume was not linked.
- Symptom: Index construction times out continuously, with logs showing token limit exceeded. Cause: A reasonable
maxTokenparameter was not set, and the token length of a single document exceeds the model’s upper limit.
How to Verify Proper Configuration
- Upload a single batch production record document, and check if the vectorized fields include complete unique device identifiers and sterilization parameters.
- Initiate a search for a specific batch, and verify if the number of returned results and batch matching meet expectations.
- Run an incremental update test, upload a newly generated cleanroom zone record, and check if the index completes synchronization within the preset time.
- View the vector index monitoring panel in FastGPT v4.17.0, confirm that the service is running normally, and there are no error logs.
The values provided here are common starting points, and should be evaluated against the reader’s own samples.