What the data for this category looks like
Data in pulp and paper scenarios originates primarily from wood pulp and waste paper batches, DCS/QCS systems, on-paper machine measurement equipment, laboratory testing, energy and water treatment systems. Daily business documents include pulp preparation sheets, beating records, paper machine operation records, online quality curves, laboratory reports, sheet break and product change records. Data update frequencies cover three levels: real-time, per shift, and per roll. Fields include pulp ratio, grammage, moisture, thickness, paper defect type, and more. Corresponding units are typically percentage, grams per square meter, millimeters, and others.
What constraints do these characteristics impose on model access and configuration
The multi-dimensional, multi-update-frequency data characteristics of this category impose multiple constraints on model access and configuration. High real-time requirements for paper machine operation data and sheet break records require models to quickly respond to and process short context content. Coexisting multiple document types require configuring indexing rules adapted to different document structures to avoid retrieving irrelevant content. Massive real-time data generated by high-speed continuous web production requires limiting the model context length to avoid processing overload. Exclusive documents from different departments require configuring precise retrieval rules to match corresponding business scenarios, ensuring drafted content aligns with departmental needs.
Configuration Setup
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
AIPROXY_API_ENDPOINT | Fill in the deployment address and append /v1 | Adapt to the API call format of general proxy services, comply with industry standard path specifications |
maxContext | 8000–12000 token | Adapt to the context length after splicing multiple documents such as pulp preparation sheets and paper machine operation records, avoid model processing overload |
maxTokens | 2000–3000 token | Match the standard drafting length of process documents and exception reports, ensure complete output content that complies with business formats |
temperature | 0.2–0.4 | Reduce random output probability, ensure accuracy and consistency of process parameters and exception handling suggestions |
quoteMaxToken | 1500–2000 characters | Limit the reference length of retrieved documents, ensure a reasonable ratio between context and output content, avoid interference from redundant information |
Three Common Mistakes
- After uploading process documents, the model prompts that null was not uploaded, but the file can be downloaded within the system. The cause is failing to correctly fill in
AIPROXY_API_ENDPOINTin the docker compose configuration, and not adding the/v1suffix, which causes abnormal API call links. - After configuration, triggering an exception report drafting task retrieves documents that do not match the current business scenario. The cause is failing to set dedicated retrieval rules for different document types such as pulp preparation sheets and beating records, leading to insufficient retrieval accuracy.
- The workflow still triggers the search step when the knowledge base is empty. The cause is failing to configure the workflow’s pre-judgment logic, so the search step is not skipped before directly proceeding to the large model call phase.
How to Confirm the Configuration Is Correct
- Upload a single pulp preparation sheet document, wait for indexing to complete, initiate a matching query, check if the target document can be retrieved normally, and verify that the
AIPROXY_API_ENDPOINTconfiguration is correct. - Trigger an exception report drafting task, check the context reference range of the output content, and confirm that the values of
maxContextandquoteMaxTokenmeet the expected context length requirements. - Simulate a scenario where the knowledge base is empty, start the workflow, check if the search step is skipped and directly enter the large model call phase, and verify that the pre-judgment logic is configured correctly.
- Adjust the
temperatureparameter and trigger multiple drafting tasks, compare the consistency of the output results, and confirm that the parameter values meet the accuracy requirements for the pulp and paper industry scenario.
The values provided are common starting points and should be measured against the reader's own samples.