What This Category of Data Looks Like
Data for the photovoltaic module encapsulation scenario originates from cell grading, glass adhesive film backsheet batches, MES systems, string welding and lamination equipment, and EL/IV testers. Daily generated documents include layout diagrams, material batch records, lamination process records, EL images, IV test reports, insulation pressure resistance records, and packing lists. Data is generated synchronously for each module and each batch of materials. Fields cover cell current gear, lamination temperature and pressure, EL crack location, IV power values, and other items, with professional process units attached.
What Constraints Do These Data Characteristics Impose on the Vector Model and Index Workflow
The data characteristics of this scenario create multiple constraints for the vector model and index workflow. First, data is updated at high frequency for each module and each batch of materials. Indexes must support incremental synchronization and low-latency updates to avoid data lag that affects process judgment. Second, documents contain mixed text and image content. Vector encoding must adapt to both text parameter records and multimodal data such as EL images and IV test reports, covering all types of business documents. Third, there are many professional fields with strict semantics. Chunk processing must retain the integrity of field associations to prevent cross-chunk semantic breaks that cause incorrect parameter matching. Fourth, matching accuracy between process parameters and exception reports is high. Recall logic must be optimized for core fields such as cell current gear and lamination parameters.
How to Configure the Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the paragraph length of process documents, balancing semantic integrity and index storage efficiency |
| Overlap Length | 100–200 characters | Prevents cross-chunk semantic breaks, retaining associated information from adjacent paragraphs |
| Similarity Threshold | 0.75–0.85 | Meets precision requirements for matching professional process parameters, filtering low-relevance recall results |
| Rerank Function | Enabled | Optimizes result ranking rationality for mixed retrieval of exception reports and process documents |
defaultToken | 4096 | Adapts to the typical paragraph length of a single process document, meeting basic context requirements |
maxToken | 8192 | Integrates context from multiple associated test reports, supporting semantic reasoning for complex scenarios |
Three Common Configuration Mistakes
- When deploying FastGPT v4.17.0 locally, no image index model option appears after uploading image documents such as EL images to the knowledge base. The cause is that the local deployment package of this version does not include multimodal index dependency components, and the corresponding plug-in must be installed manually.
- After setting up a Rerank model and enabling the global index configuration, no rerank results appear in online recall tests. The cause is that the rerank switch was not enabled separately in the recall settings of the target knowledge base. The global configuration only acts as the default effective condition.
- A connection error occurs when configuring an Embedding model for calls via OneAPI. The cause is that the locally deployed FastGPT did not correctly configure the OneAPI interface address and access key, resulting in model call timeouts or rejections.
How to Confirm Successful Configuration
- Upload a lamination process record document, wait for the indexing progress to complete, and retrieve the keyword "cell current gear" to confirm that returned results include matching content for the corresponding fields.
- After enabling the rerank function, retrieve an exception report containing the description "EL cracks" and check whether returned results are sorted by relevance.
- View model call logs in the FastGPT backend to confirm that there are normal request records for both the Embedding model and the rerank model.
- Incrementally upload a new material batch record and confirm that the indexing task is automatically triggered and completed within a short period of time.
The values provided are common starting points and should be measured against the reader's own samples.