What the Data for This Category Looks Like
Core documents for this category include welding procedure qualification records, welding procedure specifications, welding consumable issue records, welder qualification records, weld inspection reports, and repair orders. Data is sourced from design drawings, material quality certificates, welding equipment parameters, welding material warehouses, non-destructive testing systems, and personnel qualification ledgers. Data updates occur per weld seam, per batch, or per shift. Single documents contain fields such as base material grade, welding consumable batch number, welder qualification number, non-destructive testing results, and number of repairs. Data uses a mix of structured tables and unstructured text formats.
Constraints Imposed on Model Integration and Configuration
This category has high data update frequency, and single-batch data is closely linked. Model integration and configuration must adapt to requirements for high-frequency calls and context association. Multi-source data access requires adapting to different data source formats, and configuring access parameters for corresponding channels. Mixed structured and unstructured documents require model processing tailored to different formats. For example, enable vision processing parameters when handling unstructured content such as design drawings. Some data fields, including qualification numbers and test results, have rigid validation requirements. Adjust the model’s output format constraints to avoid irrelevant content output. Inspection results directly relate to structural safety. Ensure the stability and consistency of model calls to prevent incorrect validation results.
How to Set the Configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the document context linked to multiple batches for this category, covering associated data for a single batch of weld seams |
temperature | 0.1–0.3 | Reduces randomness to ensure consistency in qualification verification and test result interpretation |
vision | Enabled | Adapts to unstructured content processing such as design drawings and weld appearance images |
AIPROXY_API_ENDPOINT | Fill in according to the model deployment address | Connects to the access address of a local or third-party model |
AIPROXY_API_TOKEN | Fill in according to the key assigned by the model platform | Completes identity verification for model access |
maxTokens | 2000–3000 token | Adapts to the content length of a single inspection report or repair order |
Three Common Mistakes
- Empty content returned after accessing DeepSeek-style models: The phenomenon is empty output after model calls. The cause is failure to properly handle inference content wrapped in the model's
<think>tag, leading to FastGPT being unable to recognize this output format. - Locally deployed vector models always display indexing status: The phenomenon is the system remains in the indexing state for a long time after vector model access. The cause is incorrect configuration of vector model access parameters, or the locally deployed model not starting normally.
- Configured
maxTokensexceeds the model's own maximum output limit: The phenomenon is content truncation or parameter error when calling the model. The cause is the setmaxTokensvalue exceeding the model's maximum output upper limit.
How to Confirm the Configuration is Complete
For FastGPT v4.17.0, use the following actions to verify the configuration:
- Upload a single welding consumable issue record, and check if the model correctly extracts fields such as welding consumable batch number and issue quantity.
- Call the model to process welding procedure qualification documents, and confirm the output includes welder qualification verification and welding material matching inspection conclusions, with no empty content.
- Check the model access status page, confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly, and the connection status is normal. - Test high-frequency model calls, and check that the output format meets preset inspection and audit requirements, with no abnormal truncation.
The values provided are common starting points and should be measured against the reader's own samples.