What This Category's Data Entails
Iron ore investment research data includes structured and semi-structured content from multiple sources. Data sources cover commodity spot trading platforms, public futures exchange data, industry association survey data, and ocean shipping logistics data. Update frequencies differ: spot trading data updates daily, weekly industry supply and demand reports release weekly, and monthly supply and demand reports and research reports update monthly. Documents fall into three categories:
- Daily spot reports include origin identifiers, grade identifiers, transaction prices, and port stock figures
- Cross-category supply and demand reports include upstream and downstream linked data fields
- Research reports include supply and demand balance and trend analysis content
Price and stock units are uniformly yuan per metric ton and ten thousand metric tons.
Constraints for Context and Token Management
The multi-source heterogeneous nature and varying length of iron ore investment research data create multiple constraints for context and token management. Daily spot reports are relatively short, while monthly research reports can reach tens of thousands of characters. Single-file upload and chunk processing must accommodate these size differences. Frequently updated spot data requires regular re-chunking and re-ingestion to prevent outdated information from being included in context recalls. Investment research questions often require associating multi-dimensional data, such as linked analysis of port inventory and futures price spreads. This requires retrieving multiple documents from different sources, leading to higher total context token consumption than general single-category scenarios. Continuous follow-up research questions require retaining historical conversation context, which further increases token usage requirements.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
chunkSize | 1000–1500 characters | Fits the coverage needs of core information per segment for iron ore data, avoids overly long segments that exceed model context limits, and reduces the number of chunks to lower redundant token consumption |
maxContextToken | 8000–12000 | Covers the total token requirements for combined retrieval of iron ore research reports and spot data, and reserves space to handle conversation context for continuous follow-up questions |
retrieval count | Top 8–10 results | Meets the needs of multi-dimensional linked analysis for iron ore investment research, balances effective information retrieval and token usage limits |
similarity threshold | 0.75–0.85 | Filters cross-category documents with mismatched formats, avoids invalid token usage, and accurately matches iron ore-specific data fields |
conversationContextMaxTurns | 3–5 turns | Fits the logical needs of continuous follow-up questions in investment research, retains complete conversation context while controlling historical token consumption |
UPLOAD_FILE_MAX_SIZE | 200 MB | Fits the single-file size range of iron ore monthly research reports, avoids triggering file size limit errors during upload |
The parameter values provided on this page are common starting points for configuration setup. 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 Misconfigurations
- Issue: When uploading iron ore industry research reports larger than 200 MB, the interface throws an
UPLOAD_FILE_MAX_SIZEerror, or parsed single-segment text exceeds model context limits. Cause: Upload thresholds and chunk length are not adjusted for iron ore long documents, generic category low configuration parameters are used, and the size characteristics of category documents are not accommodated. - Issue: When continuously asking follow-up iron ore investment research questions, answers to the second and subsequent questions are not linked to historical queries, resulting in off-topic responses. Cause: The
conversationContextMaxTurnsparameter is not configured or is set too low, or the workflow does not correctly bind conversation context variables. This causes each request to only use the current query text and not include historical context tokens. - Issue: When calling a FastGPT workflow configured with context via API, returned results do not include historical conversation context. Cause: The
chatHistoryfield is not included in the API request, or the workflow does not correctly bind conversation context variables, resulting in context tokens not being passed.
How to Confirm Proper Configuration
- Upload a single iron ore monthly research report of approximately 150 MB, check the number of parsed segments and the character count per segment to confirm they fall within the
chunkSizeconfiguration range. - Initiate 3 consecutive rounds of iron ore investment research questions, such as first querying the current day's spot price, then asking about the 7-day trend, and finally associating port inventory data. Check if answers are consistently linked to historical query content.
- Call the API to send a request that includes the
chatHistoryfield and the current query, check if returned results include historical conversation context information. - View the
maxContextTokenparameter in the knowledge base configuration interface, test retrieval of multiple iron ore documents, and verify that total token usage does not exceed the configured limit.
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.