What this category of data looks like
This category of data is primarily collected from daily production documents of forging plants, aerospace and automotive forging suppliers. Documents include forging process specifications, cutting lists, heating furnace records, forging process cards, mold life records, heat treatment work orders, and ultrasonic testing reports. Data sources cover raw material quality certificates, heating furnace systems, press collection data, MES systems, and non-destructive testing instruments, alongside other production equipment and management systems. Data updates are triggered upon completion of each individual forging piece or each furnace production batch. Documents contain dedicated fields and units such as forging ratio, fiber direction, furnace batch number, and testing parameters.
What constraints these characteristics impose on model integration and configuration
The high-frequency update requirement of the data means model integration configuration must support real-time or near-real-time knowledge base synchronization, to prevent Q&A content from lagging behind the latest production batches. Documents include industrial-specific terms including forging ratio and fiber direction. Embedding models must adapt to semantic understanding of professional industrial text, to ensure Q&A content is professional and accurate. Individual process specification documents have a long length, so configuration must support long-text vector generation and context truncation rules, to avoid losing key process parameters. Multi-source heterogeneous data integration requires configuration items compatible with import logic for different format data sources, to ensure all types of production records can be correctly indexed.
How to set configurations
The following configurations are formulated for FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long text content of a single forging process specification, covering complete process steps and parameter information |
maxTokens | 2000–3000 token | Meets the detailed response needs for process specification Q&A, avoiding truncation of key technical explanations |
quoteMaxToken | 1500–2000 characters | Limits the length of quoted document fragments, ensuring the context window is not filled by a single long document |
temperature | 0.1–0.3 | Reduces random output, ensuring the accuracy of process parameters and standard content |
AIPROXY_API_ENDPOINT | Fill in according to the actual interface address of the deployed model | Adapts to locally or third-party deployed embedding and generation models, ensuring correct request routing |
AIPROXY_API_TOKEN | Fill in according to the exclusive token generated by the platform | Completes identity verification for model integration, securing interface calls |
Three common configuration mistakes
- Issue: Truncation errors occur when the embedding model generates vectors, or the generated vectors fail to cover the complete content of the process specification. Cause: The
maxContextparameter is not adjusted to adapt to long documents, and the default short context configuration is used for forging process documents with thousands of words. - Issue: The process parameters returned by the model do not match the actual standards, with deviations or errors. Cause: The
temperatureparameter is set too high, causing the generated content to deviate from fixed technical standards. - Issue: Locally deployed embedding models fail to integrate normally, with interface request timeouts or identity verification errors returned. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not filled in correctly, or network permissions are not configured to allow the model to access internal interfaces.
How to confirm configuration is complete
- Upload a typical forging process specification document, trigger a Q&A test, and check whether the response includes dedicated parameters such as forging ratio and fiber direction from the document.
- View the model call log, confirm that the actual values of parameters such as
maxContextandmaxTokensmatch the configured values, with no default values overriding the settings. - Test the integration of the locally deployed model, initiate a test call, and confirm that the interface returns normally, with no identity verification or routing errors.
- Simulate a production batch update, trigger knowledge base synchronization, and check whether newly uploaded production records can be correctly retrieved and referenced.
The values provided are common starting points and should be measured against the reader's own samples.