Data Characteristics of This Category
Data sources include daily quote files from commodity spot trading platforms, monthly reconciliation documents from upstream and downstream steel industry enterprises, and analysis reports from industry research institutions. Update frequencies range from daily spot quotes to monthly industry trend reports. Document formats include structured Excel tables, PDF reports with embedded charts, and scanned paper ledger transcription files. Fields include origin identifiers, iron content metrics, pricing benchmarks, transaction volumes, delivery grades, and more. Units include dry ton, wet ton, ton, and others. Marketing documents also include customized quote comparisons for specific clients.
Constraints Imposed by These Characteristics on Document Parsing and Chunking
The data characteristics of the iron ore category impose multiple constraints on the parsing and chunking process. First, structured Excel documents often contain merged cells across rows and columns to mark associated information such as pricing benchmarks and delivery grades. If field associations are not preserved during chunking, critical semantic information will be lost. Second, marketing documents often include customized client quote comparisons. Chunking must retain the full context of individual quotes to avoid splitting client-specific information. Third, offline ledgers and paper quotes in scanned format require OCR parsing, and documents often include embedded professional charts. Standard parsing workflows may fail to extract text descriptions associated with charts. Fourth, documents with different update frequencies have significant length differences. Short documents do not need splitting, while long research reports require precise chunking to avoid breaking industry logical chains.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_OCR_ENABLE | Enabled | Iron ore marketing documents often include scanned paper ledgers and offline quotes, requiring OCR to extract text content from images |
chunk_size | 800–1200 characters | Iron ore marketing documents contain professional terminology and multi-field associations. Chunks that are too long will lose context, while chunks that are too short will break logical chains |
chunk_overlap | 100–150 characters | Professional terminology in iron ore documents often spans multiple chunks. Retaining overlap ensures semantic coherence |
PARSE_EXCEL_MERGE_CELL | Preserve merged cell associations | Iron ore Excel documents often include cross-row and cross-column pricing benchmarks and grade descriptions. Preserving associations avoids field semantic breaks |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing and OCR processing for large monthly industry research reports takes significant time. Extending the timeout prevents parsing failures |
PARSE_TABLE_RETAIN_HEADER | Retain table headers | Iron ore tabular data often includes multi-dimensional fields. Retaining headers ensures clear field semantics after chunking |
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 case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- After uploading scanned iron ore research reports, the parsed results do not include text descriptions associated with charts. This occurs because the
PARSE_OCR_ENABLEconfiguration is not enabled, and only plain text content is parsed without extracting OCR text from images. - When configuring chunk length, token units are selected, but the proportion of professional terminology tokens in iron ore marketing documents varies widely, leading to uneven chunk lengths. This happens because the document's chunk unit requirements are not clearly defined, and tokens are incorrectly selected as the measurement basis.
- After uploading large iron ore monthly ledgers, the parsing task times out and fails. This occurs because the
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and the default timeout duration is insufficient to complete parsing and OCR processing for large tables.
How to Verify Correct Configuration
- Upload a scanned iron ore spot quote sheet, and check if the parsed results include OCR text from the image. For version v4.8.12-alpha, the
PARSE_OCR_ENABLEparameter can be found in the parsing configuration section of the knowledge base settings page to confirm the configuration is active. - Upload an iron ore Excel ledger with merged cells, and verify that the parsed table retains cross-row and cross-column field associations, confirming that the
PARSE_EXCEL_MERGE_CELLconfiguration meets requirements. - Test iron ore industry research reports of different lengths, adjust the
chunk_sizeandchunk_overlapparameters, and ensure that professional terminology and context are not split during chunking. - Upload a single iron ore monthly report, check that the parsing task completes within the set timeout duration, confirming that the
PARSE_FILE_TIMEOUT_SECONDSconfiguration is reasonable.
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.