Knowledge Base Retrieval and Recall for Iron Ore Intelligent

What the data for this category looks like

Data for iron ore due diligence reports comes primarily from domestic spot trading platforms, customs clearance systems, publicly disclosed documents from global mining enterprises, and monthly supply and demand reports from industry associations. Update frequencies are grouped into multiple tiers: spot price data updates daily, monthly supply and demand reports are released monthly, customs clearance data updates weekly, and production capacity data updates quarterly. Most documents are structured tables, with fields including origin, ore type, iron content metrics, transportation routes, cost structure, compliance labels, and more. Units include ten thousand tons, yuan per ton, dry-based iron content, and others. Some documents also include unstructured content such as scanned customs declarations and on-site port photos.

Constraints imposed by these characteristics on knowledge base retrieval and recall

Diverse, heterogeneous data sources with widely varying update frequencies require knowledge base configurations to use batch synchronization tasks. Set different synchronization cycles based on data type to avoid mixing old and new data that harms retrieval accuracy. Documents with a high proportion of structured tables require retrieval support for field-level precise matching, to prevent full-text search from breaking associated information within tables. Diverse field units require unit normalization during document parsing, to avoid recall failures caused by inconsistent units. Significant differences in data dimensions across iron ore from different origins and grades require filtering by business dimensions during the recall phase, to narrow the recall scope and reduce irrelevant data interference. Unstructured image content requires configuring OCR recognition to convert image content into searchable text. The high proportion of long documents requires setting a reasonable segmentation strategy, to avoid single segments being too long and exceeding model context limits.

How to set the configurations

Configuration Item	Recommended Value Range	Rationale
`maxContext`	8000–12000 characters	Iron ore due diligence reports are mostly structured tables plus long-form text analysis, requiring coverage of the full context of a single report
`Max knowledge base citations`	Top 6–8 results	Iron ore data is clustered by origin and grade; too many citations will lead to redundant context and reduce model output accuracy
`Similarity threshold`	0.72–0.80	Precise matching of field-level features is required. A threshold that is too low will introduce irrelevant category data, while a threshold that is too high will miss valid matching results
`Chunk size`	1000–1500 characters	Adapt to the block content length of iron ore structured tables, avoiding segmentation that destroys the associated structure of tables
`PARSE_FILE_TIMEOUT_SECONDS`	300 seconds	Large monthly supply and demand reports take longer to parse, requiring an extended timeout to avoid parsing failures
`Knowledge Base File Format Whitelist`	.docx, .pdf, .xlsx	Covers common document formats used in due diligence reports, including table files and scanned document formats

The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.

Three common configuration mistakes

Phenomenon: Uploaded iron ore port photos and scanned customs declarations in the knowledge base cannot be retrieved. Cause: The Enabled图片解析 parameter was not configured, and the knowledge base image OCR recognition switch was not turned on, resulting in image content not being converted into searchable text.
Phenomenon: A large number of irrelevant coal and steel category data are returned when searching for iron ore data. Cause: The Similarity threshold parameter was not set, or the threshold was not set to meet business requirements, and recall results were not filtered by business dimensions.
Phenomenon: A timeout error is triggered when parsing large monthly iron ore supply and demand reports. Cause: The PARSE_FILE_TIMEOUT_SECONDS parameter was not adjusted, and the default timeout period was insufficient, leading to parsing interruption.

How to confirm the configuration is complete

Navigate to the knowledge base configuration page, and verify that the values of parameters such as Chunk size and Similarity threshold match the document characteristics of the current business.
Upload a standard docx file of an iron ore due diligence report, check whether the parsed structured fields are complete, and confirm that table content is not broken up.
Initiate a simulated search, enter keywords for iron ore indicators from a specified origin, and verify whether the number and relevance of recall results meet expectations.
Check the configuration of multi-data source synchronization tasks, confirm that data sources with different update frequencies have been assigned corresponding synchronization cycles.

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.