What the data for this category looks like
Inquiry data for commodity trading originates from spot price sheets, futures contracts, trade contracts, warehousing and logistics documents, customs declarations, and industry research reports. Update rhythms vary significantly: spot prices and real-time market quotes update minute-by-minute or hourly. Trade contracts are created and archived when transactions are finalized. Customs declarations are synced to storage after customs clearance is completed. Industry research reports are released weekly or monthly.
Document structures include structured tables (such as price sheets with product name, specification, origin, price, and unit of measure), semi-structured contract text (with clauses, amount, and delivery terms), and plain-text research reports. Core fields include product name, specification/model, origin, unit of measure (tons, barrels, cubic meters, etc.), transaction price, and delivery location.
What constraints do these characteristics impose on the vector model and index process
The data characteristics of commodity trading impose multiple constraints on the vector model and index workflow.
Many structured fields and diverse units require the vector model to have semantic recognition capability for technical terms and units. Select an embedding model that supports commodity trading technical terms to avoid confusing different specifications of products during matching.
Wide variation in data update frequency requires support for incremental indexing and batch synchronization, to avoid excessive resource usage from full indexing.
Long-text trade contracts and research reports require proper chunking without destroying clause integrity. Otherwise, key transaction information cannot be retrieved.
Large volumes of price sheets and contract data require the index structure to have efficient sharding and retrieval capabilities, to ensure retrieval speed.
Reference only; verify against the target FastGPT release and deployment documentation.
How to set the configuration
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the paragraph length of commodity trading contracts and research reports, avoids splitting core transaction clauses |
| Overlap Length | 50–100 characters | Preserves cross-block contextual connections, such as payment and delivery clauses spanning multiple paragraphs in a contract |
| Similarity Threshold | 0.75–0.85 | Differentiates commodities with the same product name but different specifications or origins, reduces false recall probability |
| Single Search Limit | Top 10 results | Covers inquiry needs for multi-supplier quotes and multiple delivery locations |
| maxToken | 8000 token | Adapts to long-text context recall, meets the requirement of fully referencing trade contract clauses |
| defaultToken | 4096 token | Controls the output length of single-round responses, avoids redundant references to historical data |
Three common mistakes
- Recall results include non-target category product information, or matching fields are empty. Cause: No dedicated semantic matching rules are configured for commodity product names, specifications, and units of measure, leading to the vector model’s inability to accurately recognize technical terms.
- Knowledge base retrieval returns empty results, or fails to retrieve key transaction information. Cause: Chunk length is set too long, leading to core clauses being split and omitted, or similarity threshold is set too high, filtering out valid matching results.
- Retrieval results do not show specific document sources, making traceable responses to inquiry requests impossible. Cause: Document traceability configuration is not enabled, or document metadata fields (such as contract number, price sheet ID) are not correctly bound, leading to inability to associate with the original document.
How to confirm the configuration is correctly set
- When a commodity trading contract and spot price sheet are uploaded, the chunked text is checked to confirm it retains complete clauses and price fields with no key content truncated.
- When an inquiry for a specific product name and specification is triggered, the similarity of recall results is verified to fall within the preset threshold range, avoiding irrelevant matches.
- The retrieval result display area is checked to confirm each matching result shows the corresponding document name and metadata information to enable traceable display.
- When a long-text inquiry scenario is tested, the response context length is checked to conform to the maxToken setting with no content truncation or redundant output.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.