What Data for This Category Looks Like
Business data for pulp and paper operations comes primarily from wood pulp and waste paper batch ledgers, DCS/QCS control systems, on-paper machine measurement equipment, laboratory testing systems, and energy and water treatment systems. Daily documentation includes pulp preparation sheets, beating records, paper machine operation logs, online quality curves, laboratory test reports, and sheet break and product change ledgers. Data update cycles include real-time, per-shift, and per-roll batch updates. Fields include pulp ratio, beating degree, grammage, web width, sheet break duration, and more. Corresponding units include g/㎡, seconds, meters, and others.
Constraints on Model Access and Configuration
The multi-source heterogeneous data sources, differentiated update cycles, and diverse document structures of pulp and paper impose multiple constraints on model access and configuration. Disparate data formats across multiple systems require configuring access rules adapted to multi-source data to avoid data parsing deviations. Real-time, per-shift, and per-roll update cycles require vector synchronization frequencies to match business nodes, preventing delayed recall of data. Diverse documents include tabular quality curves and textual operation logs, so chunking strategies must balance adaptability for structured and unstructured content. Unit requirements for specific business fields require retaining original field identifiers in configurations to prevent unit confusion in semantic retrieval.
The following configurations are developed based on the logic of FastGPT v4.17.0.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Pulp and paper process specifications include multi-section operation records and quality curves, requiring sufficient context to carry complete process logic |
maxTokens | 4000 token | Single-round process Q&A needs to cover core content of links such as pulp preparation, beating, and paper machine adjustment, to avoid exceeding the model's processing limit |
quoteMaxToken | 2000–3000 token | Sufficient original process document fragments need to be recalled to support responses, while controlling the length of a single citation |
temperature | 0.2–0.4 | Q&A for process specifications and technical standards needs to maintain rigor, avoiding generating suggestions that deviate from standards |
AIPROXY_API_ENDPOINT | Fill in the access address provided by the deployment service provider | Ensure correct model request routing to avoid connection failures |
AIPROXY_API_TOKEN | Fill in the valid token allocated by the service provider | Complete identity verification to enable legitimate model calls |
Three Common Mistakes
- Phenomenon: Semantic retrieval returns generally high scores for candidate results, with no improvement after replacing the vector model. Cause: The matching logic has not been adjusted for pulp and paper-specific business fields such as beating degree and grammage. General vector models cannot accurately distinguish between business-related and irrelevant content.
- Phenomenon: A
tls: failed to verify certificateerror occurs when calling an overseas-deployed model. Cause: Certificate verification parameters for the proxy service are not configured correctly, or the certificate of the accessed third-party service is abnormal. - Phenomenon: A single piece of business data cannot generate multiple sets of matching vectors, and cannot cover retrieval needs for different process links such as pulp preparation, beating, and paper machine adjustment. Cause: Multi-vector grouping configuration is not enabled, and only a single vector model is bound. This cannot adapt to the differentiated characteristics of multi-link documents for pulp and paper operations.
How to Verify a Successful Configuration
- Submit a test process question, and verify that the document fragments referenced in the returned results originate from the configured pulp and paper business data sources.
- View the system synchronization log to confirm that business data with different update cycles completes vector synchronization in accordance with configuration requirements.
- Test different types of business documents to confirm that they are all correctly parsed and included in the retrieval scope.
- Call the model interface to confirm that the returned content does not exceed the configured context and token limits.
The values provided are common starting points and should be measured against the reader's own samples.