What This Type of Data Entails
Data sources for iron ore research reports include domestic steel industry information platforms, international commodity news institutions, broker research institute industry reports, and public futures warehouse receipt data. There are three update schedules:
- Spot market data is updated daily
- Weekly industry reports are released at fixed weekly times
- Monthly supply and demand and policy reports are updated in the late part of each month
- Sudden international market developments or domestic policy changes are released immediately
Typical document structures include four sections: market summary, core data tables, supply and demand analysis, and future outlook. Core data tables mark information such as variety specifications, pricing units, and statistical cycles. Common units include yuan/wet ton, dry ton, and port inventory tons.
Constraints Imposed on Model Access and Configuration
Mixed structured and unstructured data from multiple sources requires configuring segmentation rules optimized for table parsing during model access. This prevents information association breaks caused by splitting core data table cells. High-frequency updated data sources require setting valid document period limits in recall configurations. This filters out outdated market and analysis data. Dense commodity-specific professional terms require embedded models and large language models to support industry vocabulary semantic alignment. Without this, semantic matching deviations will occur. Multi-dimensional quantitative data and exclusive units in research reports require parameter configuration to preserve the correspondence between data and units during retrieval. This prevents loss of critical information.
How to Set the Configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 1200–1800 characters | Adapts to the mixed table and text structure of iron ore research reports, avoids splitting professional term combinations and complete data cells |
embedding_model | bge-large-zh-v1.5 or commodity-specific embedding model | Adapts to iron ore industry-specific terms, improves semantic matching accuracy |
recallCount | Top 8–12 results | Balances information completeness and result redundancy, adapts to the high information density of research reports |
document_valid_days | 7–14 days | Matches the timeliness requirements of iron ore market data, filters outdated reference content |
parse_table_enable | Enabled | Preserves the structure and unit association of structured data tables in research reports, improves retrieval precision |
max_context | 8000–16000 tokens | Accommodates multiple retrieval results and research report analysis logic, supports models in integrating complete information |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfiguration Scenarios
- Scenario: Calling baidu embedding-v1 via a configured proxy returns a 404 error. Cause: Model interface path is not correctly configured, or the proxy forwarding does not include valid authentication tokens.
- Scenario: After accessing bge-large-zh-v1.5 deployed via ollama, the conversation step returns a 404 error. Cause: The local port of the ollama model is not correctly mapped, or the model address configured in FastGPT does not include the complete API path.
- Scenario: When using deepseek-distill-qwen-32b as the classification model, classification results for iron ore research reports vary excessively. Cause: No dedicated prompt is configured for commodity industry terms. The model lacks sufficient understanding of industry-specific analysis logic, leading to blurred classification boundaries.
How to Verify Successful Configuration
- Upload a single iron ore research report. Check if the parsed text retains the complete structure of the data table and pricing units. Confirm the table parsing configuration is active.
- Submit industry-related queries. Verify that the returned research report release times align with preset timeliness rules. Confirm the document valid period configuration is correct.
- Adjust the recall count parameter. Compare the number of retrieval results across different configurations. Confirm the parameter settings function normally.
- Call the test interface. Verify connectivity between the large language model and embedding model. Confirm there are no authentication or path errors in the proxy configuration.
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.