What Data for This Category Looks Like
Commodity trading business data primarily comes from internal trade contract systems, warehouse receipt management platforms, logistics tracking systems, and business process manuals. Data update cycles cover daily spot price trends, weekly contract archive updates, and monthly process node adjustment notices. Document formats include structured Excel tables (with fields such as product category, transaction quantity, unit price, delivery cycle), standardized processing guides in PDF format, and approval flow configuration information stored in databases. Fields mostly use dedicated units of measurement such as tons, yuan per ton, and workdays.
What Constraints These Characteristics Impose on Knowledge Base Retrieval and Recall
Multi-column row-spread data in structured Excel requires precise matching of single-row business fields during retrieval, to avoid cross-row confusion. Dedicated units of measurement require the retrieval model to recognize industry terms such as tons and yuan per ton, to prevent semantic matching deviations. High-frequency updated spot price data requires recall results to align with the data update cycle for timeliness, to avoid returning outdated prices. Multi-source document formats require the retrieval system to support both structured data matching and unstructured text retrieval, covering business data in different formats such as contracts and guides. The frequency of business process node changes requires the knowledge base to quickly synchronize and update approval node information, to ensure guidance content matches current processes.
How to Configure Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches specialized terminology and structured fields for commodity trading, avoids irrelevant results from too low a threshold, and avoids insufficient recall from too high a threshold |
limit | Top 8 entries | Covers multiple approval nodes and contract terms that may be involved in a single batch of business processing, avoids missing key information from too few recall results |
searchMode | hybrid | Supports both structured field matching and unstructured text retrieval, adapting to the multi-format characteristics of trading data |
usingReRank | Enabled | Reorders recall results by business relevance, prioritizes frequently used content such as approval processes and contract terms |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands industry terms for business questions, such as associating "pickup" with "warehouse receipt outbound", to improve retrieval accuracy |
collectionIds | Bind three dedicated collections: trade processes, warehouse receipts, and contracts | Limits the retrieval scope, avoids mixing in business data from other categories |
Three Common Misconfigurations
- After importing multi-column Excel trading data, automatic segmentation results mix cross-row and cross-column fields into one segment, and cannot split by single business entry. No row-wise segmentation rule is configured for structured table data, and the default automatic segmentation logic does not recognize the row and column structure of tables.
- The knowledge base retrieval function returns the error
undefined model must match "^(text". This occurs when an embedding model not supported by FastGPT is used, or a non-text embedding model is incorrectly selected in the configuration. - Retrieval results mix non-trading process administrative office documents. This happens when
collectionIdsis not configured to bind dedicated business datasets, so the retrieval scope covers content from other unrelated collections.
How to Verify the Configuration
- Upload a test Excel containing a single business entry, check if the segmentation result splits the single row and column completely, to confirm the segmentation rule is active.
- Enter a test question containing industry terms, such as "What are the approval nodes for copper trading?", check if retrieval results prioritize content related to trading processes, to confirm reordering and extended query functions are active.
- Check the retrieval logs, confirm that only the bound dedicated business datasets are called, and no content from other collections is retrieved, to confirm the
collectionIdsconfiguration is correct. - Adjust the value range of retrieval parameters, test the change in recall result quantity across different thresholds, to confirm the parameter configuration meets business needs.
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.