What the Data for This Category Looks Like
Core data for commodity trading comes from spot warehouse receipts, futures contract texts, cross-border freight rate index reports, trade purchase and sales contracts, logistics ledgers, and warehouse inventory records. Data update rhythms vary by type. Spot warehouse receipts and real-time freight rates are updated synchronously. Purchase and sales contracts are filed immediately after signing. Warehouse ledgers are updated with daily inventory checks. Document structures include fields such as contract number, goods category, transaction quantity (unit: tons, cubic meters), delivery cycle, warehouse location, and carrier qualification number. Some long documents include multi-page attachment scans and signature information.
Constraints These Characteristics Impose on Knowledge Base Retrieval and Recall
The multi-type data characteristics of commodity trading impose multiple constraints on retrieval and recall. First, mixed structured fields and unstructured long documents require the retrieval system to support both precise field matching and full-text semantic recall. Second, data includes clear unit identifiers such as tons and yuan per ton. Retrieval must associate unit information to avoid matching invalid results. Real-time updated freight rate data and low-frequency archived contract data coexist. An incremental update mechanism must distinguish data update frequencies to avoid full retrieval redundancy. In addition, some documents include signature scans. The system must support OCR recall of image content, and handle context association issues after long text splitting.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.85 | Matches the precise field and semantic requirements of commodity trading, and avoids mixing low-relevance results |
limit | Top 8–12 entries | Adapts to the multi-dimensional content of trade documents, balancing recall coverage and context length |
searchMode | hybrid | Supports both structured field matching and full-text semantic retrieval, covering different types of data such as contracts and freight rates |
usingReRank | Enabled | Reranks recall results to correct semantic drift issues after long document splitting |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands trade-related terms, such as associating "iron ore" with synonyms like "iron ore fines" |
collectionIds | Collection IDs divided by business module | Distinguishes knowledge bases for different businesses such as spot, futures, and logistics, avoiding cross-module interference |
Three Common Configuration Mistakes
- Phenomenon: Retrieval results include fixed guiding text such as "This answer is generated based on FastGPT knowledge base retrieval and recall". Cause: The system's default reply prefix configuration is not disabled, and redundant content interferes with employee query experience.
- Phenomenon: Image resource URLs returned by retrieval are truncated, and images cannot load completely. Cause: The correct image hosting domain name is not configured, or long URLs exceed the system's default truncation length limit.
- Phenomenon: Retrieval speed slows significantly, and the number of returned results is less than the set value. Cause:
collectionIdsare not divided by business module, leading the retrieval scope to cover too many irrelevant datasets and increasing computational load.
How to Verify Successful Configuration
A test query containing trade terms is initiated, and returned results are checked to confirm they include corresponding category field information such as goods name and transaction unit. A test image containing a warehouse receipt number is uploaded, and retrieval results are confirmed to recall the OCR content of the image, with the URL fully displayed. The similarity parameter range is adjusted, and the relevance of returned results is verified to meet business expectations, without relying on fixed values. Retrieval logs are viewed, and it is confirmed that only the collectionIds of the current business module are called, with no cross-module retrieval of irrelevant data performed.
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.