What This Category's Data Looks Like
Data for the plastics and rubber category comes primarily from daily spot quotes from commodity exchanges, monthly supply and demand reports from industry associations, regular annual reports of listed companies, third-party investment research special reports, and monthly customs import and export statistics. Update frequencies vary significantly: spot prices update daily, industry supply, demand and policy data update weekly or monthly, and investment research reports are released on a non-fixed schedule tied to project timelines. Document formats include structured tables (with fields such as grade, physical and chemical parameters, transaction prices), long-text analysis sections, PDF files with embedded charts, and some files converted from scanned paper documents. Common field units include yuan/ton, cubic meter, gram/10 minutes, and similar units.
What Constraints Do These Characteristics Impose on Document Parsing and Chunking
The category's multiple structured tables, varied document lengths, and specialized field traits create multiple constraints for document parsing and chunking. Structured tables contain detailed grades, physical and chemical parameters, and transaction prices. Parsing must retain table row and column associations to avoid breaking field correspondences. Document lengths vary widely: from single-page spot quote sheets to dozens of pages of in-depth investment research reports. Chunking strategies must adapt to different document lengths. Some scanned documents require OCR recognition, which can lead to misplaced table layouts and incorrect field recognition. High-volume small files uploaded in batches (such as daily quote sheets) require efficient parsing scheduling logic.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_TABLE_ENABLE | Enabled | Meets the parsing needs of the large number of structured tables in the category, retains table fields and row-column associations |
PARSE_OCR_ENABLE | Configured per document type, enabled for scanned PDFs | Covers recognition scenarios for documents converted from scanned paper documents |
CHUNK_SIZE | 800–1200 characters | Adapts to widely varying document lengths, balances context completeness and recall accuracy |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports batch uploads of dozens of pages of in-depth investment research reports |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to time requirements for batch parsing of multiple files, prevents normal parsing from being interrupted |
SIMILARITY_THRESHOLD | 0.75 | Filters low-relevance recall results, fits the precise investment research scenario for specialized categories |
The parameter values provided on this page are common 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 Misconfigurations
- Phenomenon: When a PDF is uploaded, the data processing result is empty, and no content is returned during search tests. Cause: The
PARSE_TABLE_ENABLEconfiguration is not enabled. Structured table fields are not correctly extracted, resulting in no valid retrieval content after chunking. - Phenomenon: When small files are uploaded in batches after local deployment, some files fail to parse. Cause: The
PARSE_FILE_TIMEOUT_SECONDSconfiguration value is set too low, which cannot adapt to the parsing time required for batch high-volume small files. - Phenomenon: Field recognition errors appear after uploading a scanned PDF. Cause:
PARSE_OCR_ENABLEis not enabled for scanned documents, so OCR recognition is not triggered, and table layout information is lost.
How to Verify Configuration Correctness
- A single-page structured table PDF is uploaded. Confirm that complete row and column field associations are retained after parsing, with no missing content.
- A PDF converted from a scanned paper document is uploaded. Confirm that OCR recognition is triggered during parsing, with no obvious text recognition errors.
- Documents of varying lengths are uploaded. Verify the integrity of content after chunking, with no splits across critical information.
SIMILARITY_THRESHOLDis adjusted. Conduct search tests to confirm that the relevance of recall results meets expected standards.
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.