What this category of data looks like
Data for this category comes from customer surface treatment specifications, tank solution testing, rectifier data, online water quality monitoring, and hazardous waste management ledgers. Daily documents include tank solution analysis records, process parameter cards, fixture records, film thickness and adhesion reports, hazardous waste ledgers, and wastewater monitoring records. Fields in individual documents include tank number, analysis time, metal ion concentration, pH value, tank temperature, current density, and other items. Some fields have dedicated units. Data update frequency follows per tank batch, per shift, or as required by monitoring protocols. Data related to a single production batch is gradually generated as the process progresses.
Constraints Imposed on Model Access and Configuration
This category has data with high update frequency, scattered sources, and professional parameters. These characteristics create three constraints for model access and configuration:
- High-frequency incremental synchronization must be supported. Knowledge base configuration must adapt to real-time updates of multi-source data.
- Many professional fields have dedicated units. The model must accurately recognize professional terms. Set temperature parameters appropriately to ensure rigorous output.
- Data involves compliance content such as hazardous waste and emissions. Sensitive field leakage must be avoided. Configure compliance check rules for output content.
Additionally, splicing multiple types of documents increases total context length. Set context limit parameters appropriately to avoid exceeding model capacity limits.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | This category requires integrating multiple short documents such as tank solution records, process parameters, and online monitoring data. The total context length must cover all data for a single production batch to avoid losing key parameters |
temperature | 0.1–0.3 | Equipment inspection and maintenance require rigorous professional expressions. Lowering the temperature value reduces model parameter hallucinations and ensures output consistency with document data |
quoteMaxToken | 500–800 token/entry | Single documents for this category, such as tank solution analysis records, are mostly under 500 characters in length. Limiting the token count per quoted entry avoids redundant content interfering with responses |
| Workflow/API tool selection | auto | Multi-source data including rectifier current and tank solution test results must be called. Automatic tool selection integrates scattered inspection and maintenance related data |
AIPROXY_API_ENDPOINT | http://127.0.0.1:11434/v1 | Some sensitive data for this category, such as hazardous waste ledgers, requires local processing. Using a local API endpoint ensures data security |
AIPROXY_API_TOKEN | Fill in according to deployment requirements | Verifies access permissions for local model services and prevents unauthorized calls |
Three Common Configuration Mistakes
- Symptom: Output still contains
<think>tag content after calling the model. Cause: The option to disable thinking process output is not correctly enabled in the model configuration, or the configuration is not synchronized to workflow nodes. - Symptom: An uncaught exception occurs when calling a local model after deploying FastGPT v4.17.0 via Ubuntu Docker. Cause: The port mapping for
AIPROXY_API_ENDPOINTis not correctly configured, or the local model service has not finished starting. - Symptom: Workflow question classification results have low matching accuracy with actual documents. Cause: Tank solution parameters and process specifications exclusive to this category are not used as core dataset content, leading to the model being unable to recognize professional scenarios.
How to Verify Successful Configuration
- Upload a single tank solution analysis record for this category, trigger model question and answer, and check whether the response accurately references professional parameters such as metal ion concentration and pH value in the document.
- After configuring the local model service, call the test interface and check whether the returned result contains no
<think>tag content. - View the model call log to confirm that the context length of each call does not exceed the set
maxContextvalue, and that tool calls are triggered normally. - Simulate an abnormal scenario, such as inputting tank solution parameters beyond the conventional range, and check whether the model can provide compliant maintenance suggestions based on document data.
The values given are common starting points and should be measured against the reader's own samples.