What the Data for This Category Looks Like
Inspection data for PV module encapsulation comes from battery cell grading records, glass adhesive film backplane batch ledgers, workshop MES systems, string welding and lamination equipment logs, and EL/IV tester output files. Each individual module generates a separate inspection record, and batch ledgers are updated synchronously for each raw material batch. Daily documents include layout drawings, material batch records, lamination process records, EL crack images, IV test reports, insulation voltage resistance records, and packing lists. Fields cover battery cell current gear, lamination temperature and time, EL crack coordinates, IV test power values, and insulation voltage resistance values. Corresponding units include amperes, degrees Celsius, seconds, watts, kilovolts, and others.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
The separate data for each module requires dialogues to be bound to a unique serial number, to avoid context confusion. Multiple document formats (images, tables, text) require prompts to differentiate processing logic for different data types. For example, EL images should guide the model to identify crack locations, and IV reports should extract power values. The high-frequency update feature requires the knowledge base to support incremental synchronization, to ensure the latest batch and equipment data is used during dialogue calls. The most time-consuming matching and interpretation steps require multi-turn dialogue to gradually guide users to provide information such as batch and equipment parameters. This avoids model errors caused by inputting too much content at one time. Consequences of errors are directly linked to customer claims, so prompts must clearly define verification rules to ensure audit results meet business standards.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the unique serial number of a single PV module | Each module’s inspection data is independent, preventing confusion of dialogue contexts across different modules |
variables | Preset battery cell current gear thresholds, lamination temperature ranges, and EL crack determination rules | Business audit rules are fixed, reducing the workload of repeated prompt input |
messages | Limit the context window to the first 15 dialogue records | The audit process for a single module has limited steps; overly long contexts will interfere with the model’s judgment of current issues |
| Maximum context length | 800–1200 characters | The amount of associated document data for a single module is moderate; excessive length will cause model processing overload |
| Single recall limit | Top 5 matching documents | Core reference documents for PV module audits are concentrated in batch records, EL images, and IV reports; too many documents will distract the model |
Three Common Misconfigurations
- Phenomenon: Calling the historical dialogue interface returns "No permission to operate this dialogue record". Cause: The
chatIdis not bound to the serial number of the currently audited PV module, so the system blocks cross-module dialogue access. - Phenomenon: Knowledge base answers automatically append document citations at the end of paragraphs, and cannot be hidden. Cause: The prompt does not explicitly require omitting citations, or the answer format switch parameter is not configured correctly.
- Phenomenon: During voice interaction, the model cannot correctly match the current module’s EL image and IV test data. Cause: No batch verification step is included in the multi-turn dialogue process, so the context detaches from the current module’s business data.
How to Verify Proper Configuration
- Initiate an inspection dialogue for a single module, enter the module’s serial number, and confirm that
chatIdis correctly bound to the serial number. Ensure subsequent dialogue contexts only associate data for the current module. - Submit preset audit variables, and confirm that the model can directly use preset parameters for judgment, without repeated input of business rules.
- Initiate a multi-turn dialogue, and test whether old dialogue content is automatically truncated when the number of records exceeds the set window limit.
- Trigger knowledge base recall, and confirm that the number of returned matching documents matches the set single recall limit, with no additional redundant content.
The values provided are common starting points and should be measured against the reader's own samples.