What the data for this category looks like
Energy metal investment research data comes from spot and futures trading platforms, industry associations, listed company earnings reports and professional investment research reports. Update frequencies include real-time spot prices, daily inventory data, weekly industry updates, quarterly earnings reports and annual industry analysis. Document structures include structured price tables with fields such as commodity code, delivery date, price and inventory, semi-structured research report paragraphs and unstructured industry analysis text. Field units include yuan/ton, USD/ton, percentage and 10,000 tons. Some cross-source data has unit discrepancies, which require unified conversion before ingestion.
Constraints on retrieval and recall
The multi-source nature, varied update frequencies and structured traits of energy metal data create multiple constraints for the retrieval and recall link. Real-time spot data requires priority recall of the latest versions to avoid stale data impacting investment research decisions. Structured price tables require field-level filtering for accuracy, to avoid fuzzy matching interfering with result precision. Cross-source data with mixed units needs unified conversion during preprocessing, otherwise numerical comparisons in retrieval results will fail. Long research reports with dense technical terms require retaining context windows around terms during segmented retrieval, to avoid semantic loss from truncated terms. Cross-document supply chain related data, such as mine production capacity and downstream smelter operating rates, requires entity-associated recall, otherwise complete investment research logic chains cannot be covered.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Energy metal research reports and price documents have dense technical terms. 800–1200 characters covers the complete meaning of a single professional analysis segment, avoiding term truncation |
recall_top_k | Top 10–15 results | Energy metal investment research needs to cover multi-dimensional data including spot, futures, research reports and supply chains. This range covers all relevant information without increasing downstream processing load |
similarity_threshold | 0.72–0.85 | Technical terms for energy metals have high semantic similarity, such as lithium carbonate and lithium hydroxide. This threshold filters irrelevant results while retaining relevant data |
rerank_top_n | Top 3–5 results | The reranking stage focuses on high-relevance core investment research data, filtering low-value redundant information from initial recall |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing large industry research reports or batch structured price tables takes significant time. 300 seconds covers parsing needs for most conventional documents |
ENABLE_FIELD_FILTER | Enabled | Energy metal data includes multi-field structured information. Enabling this allows precise location of price data for specified commodities and time ranges |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: A
400 Bad Requesterror is returned when calling the knowledge base, with the error message containingThe dollar ($) prefixed field '$schema'. Cause: The imported structured price data includes the$schemafield, and this field was not filtered or escaped during data preprocessing, causing a FastGPT interface validation failure. - Symptom: Unparseable image placeholders or unrecognized chart content appear in retrieval results. Cause: No dedicated parsing logic was configured for image files such as industry charts and capacity distribution maps in energy metal research reports. Directly uploading image files without extracted text causes abnormal retrieval results.
- Symptom: Retrieval returns price data that is outdated relative to actual trading times. Cause: No incremental update strategy was configured, and the full knowledge base refresh cycle is too long to meet real-time spot data update requirements.
How to verify correct configuration
- Upload a test document containing a structured lithium price table and professional research reports, confirm that parsed text blocks retain complete technical term context, with no truncation or formatting errors.
- Submit a retrieval request for "lithium prices on domestic futures trading platforms", verify that recall results include precisely matched data for specified fields, and that sorting follows relevance hierarchy.
- Check the knowledge base update logs, confirm that real-time price data update cycles match preset configurations, with no delays or duplicate updates.
- Trigger a knowledge base call, check that returned results have no formatted field errors or error messages, verifying that interface validation logic works correctly.
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.