This document is written based on FastGPT v4.17.0
What this category of data looks like
Core data for pulp and paper scenarios comes from preparation, pulping, paper making, quality and energy departments. It covers documents such as mix stock sheets, beating records, paper machine operation records, online quality curves, lab reports, sheet break and product change records. Data sources include wood pulp and waste paper batches, DCS/QCS systems, paper machine online measurement equipment, lab testing systems, energy and water treatment systems. Update frequencies include real-time, shift-by-shift, and per roll. Fields include material batch identifiers, process parameters, equipment operation metrics, test results, and more. There is no unified fixed format, and it includes structured tables and unstructured charts.
What constraints these characteristics impose on model access and configuration
Multiple scattered data sources require configuration to support cross-system data pulling and unified access. High-frequency update rhythms (real-time, shift-by-shift, per roll) require that context configuration not be too large, to avoid loading timeouts and resource waste. Documents include structured tables and unstructured charts, requiring configuration to support multi-type data parsing. Bidding documents and quotation preparation require precise matching of process data, requiring reduced output randomness to avoid parameter deviations. Data with different update frequencies needs to adapt to different context caching strategies to ensure data timeliness and accuracy.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context length of multi-source process data, avoids loading timeouts for high-frequency updated data |
maxTokens | 4000 token | Controls single-round output length, ensures complete output of detailed process data for bidding quotations and documents |
quoteMaxToken | 1500 token | Limits the length of referenced document fragments, matches the single-segment data scale of mix stock sheets and operation records |
vision | Enabled | Supports parsing unstructured chart data such as online quality curves and lab reports |
temperature | 0.2–0.4 | Reduces output randomness, ensures accuracy of process parameters and quotation data in bidding documents |
AIPROXY_API_ENDPOINT | Enterprise intranet proxy address | Adapts to the network environment of internal enterprise data access, ensures data security |
AIPROXY_API_TOKEN | Access key assigned by the enterprise | Verifies access permissions for internal interfaces, ensures data interaction security |
Easy-to-Make Mistakes
- Issue: Multi-column process Excel files are imported with fragmented segments, and a single mix stock sheet is split into multiple unrelated fragments. Cause: The chunking logic is not adjusted for multi-column structured documents of this category, and the default automatic segmentation does not match the requirement of one process record per row.
- Issue: Frequent timeout errors occur during interface calls, and logs show context loading timeout. Cause: The value of
maxContextis too large, exceeding the response threshold of enterprise intranet data pulling, and cannot adapt to real-time updated process data. - Issue: After uploading quality curve images, the model cannot identify test indicator data in the charts. Cause: The
visionconfiguration is not enabled, so unstructured chart data parsing is not supported. - Issue: Interface calls fail, returning status codes related to proxy service errors. Cause: The
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENconfiguration is incorrect, and the enterprise internal proxy service is not properly connected.
How to Confirm the Configuration is Correct
- Import typical documents of this category, such as mix stock sheets and paper machine operation records, verify that the model's output segments match the expected single process record, adjust the corresponding configuration until requirements are met.
- Upload online quality curve images, verify that the model can correctly extract test indicator data from the charts, confirm that the
visionconfiguration takes effect. - Initiate a test call using the enterprise intranet proxy address and key, verify that the interface can connect normally, confirm that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations are correct. - Simulate high-frequency data pulling, check whether the interface response time meets business requirements, confirm that the
maxContextvalue is reasonable.
The values provided here are common starting points, and should be evaluated against the user's own samples.