What the data for this category looks like
Data for this category comes primarily from raw material quality certificates, heating furnace systems, press collection systems, MES systems, non-destructive testing instruments, and customer forging drawings. Daily documents include forging process specifications, cutting cards, heating furnace operation records, forging process logs, mold life ledgers, heat treatment entrustment forms, and ultrasonic testing reports. Data updates follow the rhythm of single-batch forgings or single-heat batches. Each document corresponds to a specific forging batch or heat batch. Fields include forging number, heat batch number, process parameters, testing results, and similar items. No additional statistical data is included.
Constraints for model access and configuration
This category has high data update frequency and strong ties to single batches or heat batches. Model context must match the scope of single-batch data to prevent cross-batch mix-ups. Data sources are scattered. Multi-data source access must be supported to adapt to different system formats. Daily document fields mostly cover process and testing parameters. The model must accurately recognize key identifiers such as forging number and heat batch number. Errors in this scenario lead to serious consequences. Model output must link to specific batch data to avoid vague conclusions. This category of data has no universal statistical template. Retrieval logic adapted to structured fields must be configured to ensure matching results align with actual production batches.
Configuration Setup
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Single-batch data for this category includes multiple associated documents. This range accommodates all record content for a single heat batch or forging batch. |
maxTokens | 2000–3000 token | Documents such as forging testing reports and process specifications have moderate length. This range ensures output includes complete conclusions and correction suggestions. |
quoteMaxToken | 1500 token | Prioritize returning core testing and process document fragments matching the forging batch, to avoid redundant content. |
temperature | 0.1–0.3 | This scenario requires precise matching of process parameters and testing results. Vague or divergent output should be avoided. |
AIPROXY_API_ENDPOINT | IP:port address for locally running models | Adapt to locally deployed large models, and ensure production data is not transmitted externally. |
AIPROXY_API_TOKEN | Access key generated by the local model service | Verify call permissions and prevent unauthorized access to production system data. |
Three common configuration errors
- Symptom: After uploading attachments such as forging process specifications and ultrasonic testing reports, the model cannot generate corresponding summaries or answers. Cause: The
maxContextparameter is not configured correctly. Attachment data exceeds the context capacity limit, so the model cannot read the content. - Symptom: The model returns only one knowledge base text block, and cannot output multiple matching blocks. Cause:
quoteMaxTokenor related retrieval logic is not explicitly configured, causing retrieval results to be forcibly truncated to a single entry. - Symptom: Connection timeout or no response occurs when calling a locally deployed QWQ-32B model. Cause:
AIPROXY_API_ENDPOINTis configured incorrectly, and does not point to the actual port address where the local model is running.
How to confirm correct configuration
- Upload a single heating furnace operation record document, trigger a query that includes furnace temperature parameters, and check whether the model return content includes specific furnace temperature, holding duration, and other parameters from the document.
- Enter a query that includes a specific forging number, confirm that all matching results returned by the model are linked to this number, and there is no irrelevant cross-batch data.
- Call the locally deployed large model, check that the interface returns a status code of 200, with no permission error or connection abnormality prompt.
- Set
temperatureto 0.1, trigger the same query multiple times, and confirm that the output content has high consistency, with no obvious divergence.
The values listed above are common starting points. They should be evaluated against the reader's own samples.