What the Data for This Category Looks Like
Data sources for thermal coal investment research include production ledgers from producing areas, weekly coastal port inventory reports, daily market reports from futures exchanges, quarterly research reports from industry associations, and long-term contract documents. Update frequencies vary significantly: port inventory is updated weekly, futures market data is updated in real time, industry research reports are updated monthly, and long-term contracts are archived once. Document types include structured PDF research reports, Excel inventory and price ledgers, CSV daily market reports, and some documents contain tables with merged cells. Core fields include calorific value (unit: kcal/kg), sulfur content (unit: %), ash content (unit: %), producing area, port arrival price (unit: yuan/ton), plus delivery terms and pricing rules in long-term contracts.
Constraints on Document Parsing and Chunking
Multi-source and heterogeneous document types require parsing workflows to adapt to different format handling logic. This avoids issues such as lost CSV market data or unextractable chart text from PDF research reports. Clear field and unit requirements mean chunking must preserve the binding between fields and their corresponding units. Splitting a field from its associated unit across separate chunks will damage the integrity of investment research logic. High-frequency real-time and weekly updated data require incremental parsing capabilities. This prevents resource waste and delays caused by full reprocessing. Mixed structured and unstructured content in long-term contracts requires parsing workflows to distinguish between table clauses and textual descriptions. This adapts to the needs of structured extraction and semantic chunking respectively.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
OCR_PROVIDER | pytesseract | Most thermal coal investment research documents are Chinese research reports and ledgers. This engine supports Chinese language recognition needs |
PARSE_TABLE_STRATEGY | structured_extract | Thermal coal documents contain a large number of structured tables. This strategy preserves cell associations and field integrity |
CHUNK_SIZE | 800–1200 characters | Thermal coal research reports have moderate paragraph density. This range covers a single core investment research logic and avoids chunk breakage |
CHUNK_OVERLAP | 100–150 characters | Preserves contextual connections for industry terms across chunks, such as the coherence of "benchmark calorific value" |
PARSE_TIMEOUT | 300 seconds | Meets parsing time requirements for large industry research reports and Excel ledgers with multiple worksheets |
UPLOAD_FILE_MAX_SIZE | 500 MB | Matches the common maximum file size limit for single large industry research report PDFs |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Symptom: Garbled Chinese text or missing rare characters after OCR parsing. Logs show recognition error code
1001. Cause: The Chinese language pack was not specified, or thetesseract-ocr-chi-simlanguage pack was not installed on the system, leading to failed Chinese recognition. - Symptom: After importing an Excel table into the knowledge base, metadata displays normally but no table content appears in search results. Parsing task status shows
TABLE_PARSE_FAILED. Cause: ThePARSE_TABLE_CONTENTparameter was not enabled, or the table has multiple layers of merged cells that the parsing logic cannot correctly extract content from. - Symptom: Auxiliary data such as document update time and file path appears before core investment research logic in search results. Chunk logs show auxiliary fields have higher scores than business fields. Cause: The
RERANK_SCORE_THRESHOLDparameter for filtering low-value fields was not configured, or no weight marker was set for core business fields during chunking.
How to Verify Successful Configuration
- Upload a thermal coal port inventory Excel ledger, check the parsed table details to confirm all fields retain their corresponding units, such as "port arrival price" marked with "yuan/ton".
- Run a parsing task for an industry research report PDF under 500 MB, check that the task status has no timeout errors, and parsing time falls within the range set by
PARSE_TIMEOUT. - Search for "thermal coal benchmark calorific value standard", check that the chunked content covers the complete logic with no term breakage or lost context.
- View the Chinese text fragments after OCR parsing, confirm there are no garbled characters, and verify that the language pack configured for
OCR_PROVIDERis working.
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.