What this category of data looks like
Bulk commodity trade ledger data comes mainly from paper or electronic documents including formal trade contracts, warehouse receipts, logistics waybills, and settlement vouchers. The data updates in sync with the completion of individual transactions or daily settlement cycles. A single ledger document usually includes structured fields such as product name, specification model, transaction quantity (unit: ton, barrel, or cubic meter), transaction unit price, transaction date, and information of both paying and receiving parties. Some supporting documents embed warehouse receipt photos and quality inspection report images. Individual document lengths range from hundreds to thousands of characters.
What constraints these characteristics impose on knowledge base retrieval and recall
The large number of clearly defined structured fields requires retrieval to balance text semantic matching and precise field matching. This prevents recalling irrelevant category trade data. Frequently updated transaction data needs regular refreshes of the knowledge base index. Without such refreshes, retrieval results will lag behind the latest transactions. Supporting documents with embedded images require full extraction of text and image content. Missing this step will lose key supporting information such as quality inspection and warehouse receipt data. Individual documents have long lengths. Chunked retrieval must avoid splitting that breaks structured field integrity, which reduces matching accuracy.
Configuration settings
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Similarity threshold | 0.75–0.85 | Core fields of commodity ledgers have high matching precision requirements, filter out irrelevant data with low matching degrees |
| Single search limit | Top 10 entries | Control the number of documents returned per retrieval, avoid context window overflow that affects report generation |
| Retrieval mode | Semantic retrieval + field matching | Adapt to structured fields and free text descriptions of ledgers, balance accuracy and recall scope |
| Reranking switch | Enabled | Reorder initial recall results by core field weights, prioritize entries matching product names and transaction amounts |
| Query expansion switch | Disabled | Ledger data has high structural complexity, no need for additional query expansion, avoid introducing irrelevant retrieval interference |
| Bound collection ID | Bind dedicated trade ledger collection | Isolate ledger data of different business lines, prevent cross-category recall of non-target documents |
Three common mistakes
- After uploading a docx format trade ledger document, the knowledge base preview interface only displays text content, no embedded warehouse receipt images, and an error occurs during database creation. The cause is that embedded images in the docx file are not correctly extracted as retrievable content, only the plain text portion is uploaded.
- In a FastGPT instance deployed on sealos, when creating a bulk commodity trade ledger knowledge base, the index creation step gets stuck at over 90% progress with no further progress. The cause is that the ledger data contains large numbers of structured tables and long text paragraphs, the default index configuration does not adapt to the processing rhythm of large-volume data.
- When retrieving ledger summary reports, the returned results include trade data from non-target categories that do not match the preset bulk commodity categories. The cause is that no dedicated collection ID is bound, leading to recall of documents from other business collections.
How to confirm correct configuration
- Upload a docx trade ledger document with embedded images, check if the knowledge base preview interface displays both text content and image thumbnails.
- Enter a combined query term of a specific product name and transaction date, verify that the number of returned results matches the single search limit setting.
- View the FastGPT instance's index logs, confirm there are no error messages about image parsing failures or index timeouts.
- Randomly select an original ledger document, compare it with the retrieved results, check if the matching degree falls within the preset similarity threshold range.
The values provided here are common starting points and should be evaluated 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.