What the data for this category looks like
Data sources for papermaking industry intelligent due diligence reports include corporate annual reports, monthly production and operation reports, raw material procurement contracts, environmental monitoring public notices, capacity adjustment announcements, and similar documents. Update frequency varies significantly by document type: annual reports are updated per calendar year, monthly reports are released monthly, and procurement contracts and capacity announcements are updated irregularly based on actual business operations.
Document structures typically include modules such as company overview, raw material supply and demand data, capacity and production metrics, energy consumption and environmental emission data, financial operation status, and more. Core fields are often tied to specific units, such as water consumption per ton of paper (cubic meters per ton), production capacity (ten thousand tons per year), raw material procurement volume (ten thousand tons), emission concentration (mg/L), and others. Some documents also include cross-page structured tables and chart descriptions.
What constraints these characteristics impose on document parsing and chunking
The characteristics of papermaking due diligence documents directly impose constraints on the parsing and chunking stage. First, documents often contain cross-page structured tables. Parsing must ensure table integrity, and avoid splitting the same cross-page indicator data into different chunks. Second, core indicators are tightly bound to specific units. The parsing process must accurately associate indicators with their corresponding units, and prevent separation of units and indicators. Third, the length of individual reports varies widely. Some annual reports can reach hundreds of pages, so reasonable chunk boundaries must be set to ensure each chunk contains complete indicator descriptions and corresponding data. Fourth, document structures differ significantly based on update frequency. Monthly reports are mostly standardized tables, while annual reports mix narrative content and tables. Parsing rules must adapt to the differences between these two structure types.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_TABLE_ENABLE | Enabled | Papermaking due diligence documents include a large number of structured tables for capacity, energy consumption, and procurement volumes. Enabling this setting allows complete extraction of table content and header binding relationships |
PARSE_CHUNK_SIZE | 800–1200 characters | Individual chapters of papermaking due diligence documents have lengthy content. This setting ensures each chunk contains complete indicator descriptions and corresponding data, and avoids splitting content across multiple indicators |
PARSE_CHUNK_OVERLAP | 100–150 characters | Adapts to context association for long text chunks, and prevents cross-chunk indicator descriptions from being truncated |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Individual papermaking due diligence reports often contain multiple pages of tables and long paragraphs. This timeout setting ensures large files complete parsing |
extract_field_unit | Enabled | Most indicators in papermaking documents are tied to specific units (such as cubic meters per ton, ten thousand tons). Enabling this setting preserves the correspondence between units and indicators |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Some papermaking enterprise due diligence reports include multiple attachments. This setting supports large-volume batch uploads |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: After server deployment, uploading a docx file results in a 404 error returned by the document parsing node, and the file content cannot be read. Cause: The server environment is not configured with read/write permissions for the temporary file storage directory, or the front-end upload path does not align with the back-end parsing path.
- Symptom: Parsed chunk content loses chapter title binding, making it impossible to associate corresponding indicators during retrieval. Cause: Chapter title extraction configuration is not enabled, or the chunk size is set too small, splitting titles and main text into different chunks.
- Symptom: When deploying Qwen3-14B with Vllm 0.10, specified fields (such as water consumption per ton of paper) cannot be extracted from parsing results. Cause: Compatibility issues between the model version and the field extraction plugin, or the field extraction prompt not adapted to the dedicated indicator naming rules of the papermaking industry.
How to confirm configurations are set correctly
- Upload a standard papermaking enterprise monthly due diligence report, view the parsed chunk list, and confirm that the complete content of each table is contained in a single or adjacent chunks.
- Randomly select one chunk of content, check that the indicators and their corresponding units are fully bound, with no separation between units and indicators.
- Upload a single papermaking annual report document with a volume of 1500 MB, and confirm that the parsing task completes within 300 seconds with no timeout errors.
- Test field extraction across different model deployment environments, and confirm that specified papermaking industry indicators can be correctly identified.
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.