What the data for this category looks like
Photovoltaic module encapsulation data originates from cell grading, glass, adhesive film and backsheet batch records, MES systems, string welding and lamination equipment, EL/IV testers, and insulation and voltage withstand test equipment. Daily documentation includes layout drawings, material batch records, lamination process records, EL defect images, IV test reports, insulation and voltage withstand records, and packing lists. Data updates occur once per manufactured module, and once per material batch when placed into storage. Data fields include cell current grade, lamination temperature and pressure parameters, EL crack location, IV test power value, insulation and voltage withstand value, and other metrics covering the full technology and testing workflow.
Constraints imposed by this category’s data on vector models and indexing
The data types for this category include structured process parameters, unstructured test reports, and supporting text for images. Update frequency is extremely high. Each module corresponds to an independent business data unit. Unlike semiconductor surface technology in cell manufacturing, this category focuses on the association between lamination processes and power grading. Indexes must prioritize linking lamination parameters to IV test results. This requires vector indexes to split data by business unit. This prevents mixing data across batches or modules. Most data fields are precision-sensitive numerical values and batch identifiers. Embedding and retrieval processes must maintain matching precision, and must not lose critical batch association information. Data sources are scattered across multiple devices and systems. Vector indexes must support access to multi-format data sources. This ensures all document types can be correctly embedded and retrieved.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the typical length of photovoltaic module process records and supporting text for EL images, avoids splitting across business units |
| Overlap Length | 100–150 characters | Ensures continuity of process parameters and test data, prevents critical associated information from being broken |
| Similarity Threshold | 0.75–0.85 | Meets the precision requirements for matching cell current grades and lamination parameters, filters irrelevant retrieval results |
| Single Search Limit | Top 10 results | Corresponds to the scale of associated data for a single batch of modules, avoids redundant retrieval affecting retrieval efficiency |
maxToken | 8000 token | Adapts to the default limit of FastGPT v4.17.0, accommodates all associated data for a single module |
defaultToken | 1500 token | Matches the typical length of a single process record, ensures the processing effect of the embedding model |
Three common configuration errors
The first common error: The symptom is a 404 error returned when calling the embedding model API. The cause is incorrect configuration of the embedding model’s API endpoint and key, or use of an unsupported model identifier. The second common error: The symptom is failure to load the vector model service in a hardware environment without a GPU. The cause is failure to select a lightweight embedding model compatible with CPU operation, or failure to correctly configure relevant parameters for CPU execution. The third common error: The symptom is that associated data retrieved by the vector index does not match the target module. The cause is failure to split data by the complete business data unit of a single module during chunking, resulting in process records from different batches being mixed into the same index block.
How to verify correct configuration
- Review embedding model call logs. Confirm each request returns valid vector data with no error status codes.
- Manually input typical process parameters or test data for a single module. Verify retrieved associated documents belong to the same business link and batch.
- Adjust the similarity threshold. Verify whether the matching precision of retrieval results meets preset business standards.
- Check the index update configuration. Confirm its refresh frequency matches the production and update rhythm of modules.
The values provided are common starting points, and should be evaluated against the reader’s own samples.