What the data for this category looks like
Data sources for papermaking intelligent due diligence reports include monthly production monitoring data released by industry associations, import and export statistics for logs and waste paper from the General Administration of Customs, public environmental assessment and capacity disclosure documents from listed papermaking enterprises, and monthly shipment reports submitted by enterprises themselves. Update rhythms vary across data sources: import and export data is updated weekly, industry association reports are released quarterly, and enterprise disclosure documents are updated alongside financial reporting cycles. Document structures typically follow four main sections: raw material supply, production capacity, product shipments, and environmental protection indicators. Fields include log import volume (unit: cubic meters), paper machine design capacity (unit: tons/year), unit product energy consumption (unit: kg standard coal/ton), COD emission concentration (unit: mg/L), and others. No unified standardized field format exists.
How these characteristics impose constraints on tool calling and plugins
Dispersed data sources and inconsistent update rhythms require the tool calling chain to support simultaneous connection to multiple third-party data source plugins, and configuration of differentiated cache update cycles to avoid pulling outdated data. Long document length and inconsistent structure require pre-processing long texts into chunks before tool calling, and limiting the context length of single-round tool calling to prevent exceeding model processing limits. Differences in field units and statistical calibers require the tool calling module to include built-in unit conversion and caliber alignment logic, to avoid parameter extraction errors caused by inconsistent data formats. Cross-verification requirements for multi-source data require tool calling to support multi-round parameter verification, to ensure the accuracy and reliability of extracted papermaking industry data.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
embedding_model | text-embedding-ada-002 or a locally deployed model with the same parameters | Adapts to the long-text vector extraction requirements of papermaking due diligence reports, and ensures recognition accuracy for core fields such as raw materials and production capacity |
custom_embedding_url | Custom embedding service address | Bypasses the one API link to directly call a dedicated embedding model, adapting to the processing requirements of professional papermaking text |
embedding_chunk_size | 800–1200 characters | Adapts to the chapter-based structure of papermaking due diligence reports, avoiding context loss caused by overly fragmented chunking |
tool_call_timeout | 120 seconds | Adapts to the time required to pull papermaking raw material, production capacity, and shipment data via multi-source plugins, avoiding mid-run interruptions |
enable_manual_confirm | Enable when the extracted parameter matching score is below a preset threshold | Addresses differences in multi-source fields for papermaking data, reducing the risk of incorrect tool call execution |
max_tool_steps | 6–8 | Limits the number of tool calling rounds for papermaking due diligence reports, avoiding resource consumption caused by circular calls |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: A 403 permission error or request block occurs when calling the embedding model. Cause: The
custom_embedding_urlparameter is not configured, and the one API link is used for calls, resulting in permission verification failure. - Phenomenon: A user confirmation pop-up does not appear after tool calling extracts parameters and executes directly. Cause: The
enable_manual_confirmconfiguration is not enabled, or the trigger condition is set to always off, failing to match the verification requirements of papermaking data. - Phenomenon: Automatically generated AI explanation content cannot be hidden after tool calling is completed, leading to redundant output. Cause: The
show_tool_call_resultconfiguration is not turned off, or no result filtering node is set in the workflow, failing to adapt to the concise output requirements of papermaking due diligence reports.
How to Confirm Configuration Is Complete
- Upload a sample document of a single papermaking due diligence report, and check whether the length of the vector chunks falls within the configured
embedding_chunk_sizerange. - Trigger the tool calling process, and verify whether a confirmation pop-up appears when the parameter matching score does not reach the preset threshold.
- Check the tool calling logs to confirm that the embedding model request directly points to the custom address and does not pass through the one API link.
- Run the full workflow, and verify that only the results from the final step are displayed, with no intermediate step processing results shown.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.