What the data for this category looks like
The ledger data for metal material processing comes primarily from real-time sensor collection by production equipment, electronic inspection reports from quality inspection departments, material warehouse management systems, and production scheduling logs. Data updates trigger when a processing batch finishes. Daily ledgers sync daily or per shift. A complete single ledger includes fields such as furnace number, batch number, material grade, processing process parameters (such as heating temperature, rolling pressure, holding duration), core quality inspection indicators (hardness, tensile strength), inbound and outbound quantities and flow directions. Field units follow industrial standards: temperature in degrees Celsius, pressure in megapascals, duration in minutes, tensile strength in megapascals, and quantity in tons.
Constraints on model access and configuration
Multi-source heterogeneous data format differences require configuring field alignment rules. This prevents the model from confusing identical-named fields from different systems. For example, map "hardness" collected by equipment and "hardness value" from the quality inspection system to the same field name. Data updates use two modes: batch-triggered and scheduled synchronization. Match model invocation trigger timing parameters to adapt to different summarization frequency needs. Ledgers contain both long-text quality inspection reports and short numeric fields. Adjust context window parameters to accommodate input content of varying lengths. This avoids truncating long text or wasting context resources on short inputs. Professional terms for metal material processing such as grade and heat treatment process require the model to have domain recognition capabilities. Configure model parameters adapted to professional scenarios to improve the accuracy of summarization reports.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to long-text quality inspection reports and multi-batch ledger summarization, avoiding truncation of core process data |
maxTokens | 2000–3000 token | Controls the output length of a single report, matching the conventional output scale of ledger summarization |
quoteMaxToken | 1500 token | Limits the length of referenced original ledger data, ensuring the model focuses on valid processing parameters and quality inspection results |
temperature | 0.1–0.3 | Reduces generation randomness, ensuring consistent output of professional values and fields |
AIPROXY_API_ENDPOINT | Locally deployed vllm/ollama service address, such as http://192.168.1.100:8000/v1 | Adapts to internally deployed metal material domain fine-tuned models, reducing public network call latency |
AIPROXY_API_TOKEN | Access key corresponding to the deployed service | Completes identity verification for model access, ensuring secure call permissions |
Three common configuration mistakes
- Symptom: Empty content returns when calling an ollama model, with no error logs. Cause: Port mapping for the ollama service is not configured correctly, or the FastGPT container and ollama container are not on the same network. This prevents requests from reaching the model service normally.
- Symptom: Only system default models appear on the model management page, and configured external models cannot be found. Cause: The AIPROXY feature is not enabled, or the correct
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not filled in the configuration items. - Symptom: The visual input option cannot be enabled after selecting an access item labeled as an image model. Cause: The
visionconfiguration item is not set to enabled, or the accessed model itself does not support visual processing capabilities.
How to confirm the configuration is complete
- Navigate to the FastGPT model management page, confirm the configured external model appears in the available model list.
- Upload test data for a single metal material processing ledger, initiate a model call, and check that the returned summary content includes complete process parameters, quality inspection indicators, and summarization conclusions.
- View the model call logs, confirm the request was successfully sent to the configured API endpoint, with no connection timeout or identity verification error prompts.
- Adjust the value of the
temperatureparameter, observe whether the generated report content maintains consistency of professional values, and verify that the parameter configuration takes effect.
The values given are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.