What the data for this category looks like
Core data for bulk commodity trade comes from spot price sheets, futures delivery documents, trade contracts, customs declarations, and logistics tracking records. Update schedule: Spot prices update daily. Futures position data updates weekly. Annual trade contracts are archived monthly. Document types include structured tables (for example, price sheets include fields such as product name, specification, origin, unit price, and trading volume), long-text compliance manuals, and scanned customs declarations and warehouse receipts. Field and unit standards vary. Unit prices mostly use yuan/ton and US dollars/barrel. Trading volume uses tons and cubic meters as units. Unique identifier fields such as contract numbers and customs declaration numbers are also included.
What constraints these characteristics impose on knowledge base retrieval and recall
Structured table data has strong field correlation. Fixed-length chunking breaks the correspondence between product name and unit price, and between specification and origin. This leads to inaccurate retrieval result matching. Scanned customs declarations and warehouse receipts cannot generate vector embeddings directly. OCR text extraction must be completed first, adding a data preprocessing step. Frequently updated spot price data requires the knowledge base to support incremental synchronization. This avoids excessively long full index processing time. Multi-unit trade fields (such as tons, kilograms, barrels) can cause semantic matching deviations. Unit normalization processing must be completed during the retrieval stage.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Bulk commodity trade inquiries mostly focus on precise matching of quotes and compliance clauses. A threshold that is too low introduces irrelevant historical quotes. A threshold that is too high misses recent valid matching results |
limit | Top 8 entries | Valid information for trade inquiries is usually concentrated in quotes and standard contract templates from the past 7 days. Too many results exceed the context window limit and increase the model's processing burden |
searchMode | Hybrid vector retrieval + keyword retrieval | Balances semantic matching and precise keyword matching. Adapts to precise retrieval needs for structured fields such as product name and specification. Prevents missing precise field content when relying only on semantic matching |
usingReRank | Enabled | Bulk commodity data has many similar entries, such as quotes for the same type of steel from different origins. Reranking filters redundant results and raises the display priority of core information |
datasetSearchUsingExtensionQuery | Enabled | Trade inquiries often use abbreviations, such as "rebar" for HRB400 rebar and "WTI oil" for New York Mercantile Exchange light crude oil. Extended queries add synonym matching and improve recall coverage |
collectionIds | Bound to trade-specific collections | Prevents mixing of business data from other categories. Ensures the retrieval scope only covers bulk commodity trade-specific datasets and improves result accuracy |
Three common configuration mistakes
- Empty retrieval results for scanned trade contracts and customs declarations. Cause: The image text recognition capability of FastGPT v4.17.0 is not enabled. OCR preprocessing is not completed for scanned documents. This prevents extraction of valid text to generate vector embeddings.
- Too many retrieved results cause model context overflow. Cause: A reasonable
limitparameter value is not set. Excessive redundant historical quote data is recalled, exceeding the context window limit. - Structured price sheet retrieval results show separated product names and unit prices. Cause: The default fixed-length chunking logic is used. The row-by-row chunking rule is not configured for table documents. This breaks the correlation between fields.
How to confirm the configuration is correct
- A scanned customs declaration is uploaded, and a retrieval test is run. Text content such as customs declaration number and product name is confirmed to be extractable.
- A trade inquiry question with units is entered, such as "What is the price of rebar at Shanghai Port today". Unit matching of returned results is checked. Correct association between product names and unit prices is confirmed.
- The knowledge base dataset configuration is viewed. Only bulk commodity trade-specific
collectionIdsare confirmed to be bound. No datasets from other business categories are included. - The
similarityparameter is adjusted. Changes in retrieval result relevance are compared. The threshold setting is confirmed to meet the business's precise matching needs.
The values listed here are common starting points. They should be evaluated against samples specific to the target deployment.
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.