Document Parsing and Chunking for Iron Ore Investment

What This Category of Data Looks Like

Iron ore investment research data primarily comes from public data on domestic spot trading platforms, delivery warehouse receipt data from futures exchanges, public capacity announcements from mining enterprises, procurement ledgers of steel enterprises, and survey data from industry associations. Update cycles include daily updated spot price data, delivery warehouse receipt data updated during trading hours, monthly released industry survey data, and irregularly released mining and steel plant announcements. Document types include structured tables such as spot quotation sheets and delivery lists, semi-structured supply and demand analysis research reports, and unstructured third-party quality inspection reports. Fields include origin, dry basis iron content value, FOB price, CIF price, loading and unloading port, delivery batch, and more. Units mostly adopt general commodity measurement standards such as dry tons and wet tons.

Constraints Imposed on the "Document Parsing and Chunking" Link

The multi-type characteristics of iron ore investment research documents impose multiple constraints on the parsing and chunking process. Structured table documents have multi-level headers and multi-dimensional fields. Accurately identify header levels and data column associations to avoid field misalignment or loss. Semi-structured research reports contain professional terminology and cross-chapter supply and demand related content. Retain terminology integrity and contextual coherence during chunking. Unstructured quality inspection reports contain complex professional test descriptions. Avoid splitting core test terminology and data. Documents with different update frequencies require matching parsing strategies. Ensure frequently updated spot data and low-frequency released announcement documents can be processed accurately.

How to Set Configurations

Configuration Item	Recommended Setting	Rationale
`PARSE_PDF_USE_MARKER`	Enabled	Most iron ore-related documents include scanned quality inspection reports and research report PDFs. Marker's high-precision OCR can accurately identify tables and professional terminology, avoiding OCR recognition errors
`PARSE_FILE_TIMEOUT_SECONDS`	600 seconds	Parsing large iron ore research reports or multi-page quality inspection reports takes a long time. 600 seconds covers most long document parsing needs, and adapts to resource usage in private deployment scenarios
`CHUNK_SIZE`	800–1200 characters	Iron ore documents contain multi-dimensional fields and professional terminology. This range retains the logical integrity of a single chunk, avoiding splitting cross-field associated information
`CHUNK_OVERLAP`	10–15%	Chunk overlap retains contextual association across chunks, adapting to the connection logic between supply and demand data and policy interpretations in iron ore research reports
`EXCEL_PARSE_MODE`	Retain complete header and row-column structure	Excel documents such as iron ore spot quotation sheets and delivery lists have multi-level headers and field associations. This mode avoids field misalignment or loss
`PARSE_TABLE_STRUCTURE`	Enabled	Structured tables in iron ore documents need to retain row-column relationships to facilitate field matching during subsequent recall

The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.

Three Common Mistakes

Scenario: When uploading a scanned iron ore quality inspection report or research report PDF, the interface returns an OCR Error alert, and the parsing result lacks professional terminology and table data. Cause: The PARSE_PDF_USE_MARKER configuration is not enabled. The default OCR engine cannot adapt to iron ore professional terminology and complex table structures.
Scenario: When uploading an iron ore industry research report PDF with more than 80 pages in a private deployment scenario, FastGPT returns a 504 Gateway Timeout error. The parsing service log shows parsing succeeded but the content was not synchronized to the knowledge base. Cause: The PARSE_FILE_TIMEOUT_SECONDS configuration value is lower than the actual parsing time, and does not cover the long document processing cycle.
Scenario: After uploading an iron ore spot quotation Excel file, the parsed chunk content lacks core fields such as origin and price, or fields are misaligned and chaotic. Cause: The EXCEL_PARSE_MODE is not set to retain complete header and row-column structure, or the CHUNK_OVERLAP value is too low, causing cross-field associated content to be split.

How to Confirm Configurations Are Set Correctly

Upload a scanned iron ore quality inspection report PDF, check if the parsing result includes complete quality inspection indicators and table structures, to confirm the PARSE_PDF_USE_MARKER configuration is effective.
Upload an iron ore research report PDF with more than 100 pages, wait for parsing to complete and check the task status, to confirm the PARSE_FILE_TIMEOUT_SECONDS value adapts to the current document length.
Upload an iron ore spot quotation Excel file, check if the parsed chunk content retains complete headers and field associations, to confirm the EXCEL_PARSE_MODE and PARSE_TABLE_STRUCTURE configurations are correct.
Call the knowledge base recall function, enter iron ore professional terminology, check if the recalled chunk content includes the corresponding terminology and associated information, to confirm the CHUNK_SIZE and CHUNK_OVERLAP configurations are 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.