What this category of data looks like
Declaration materials and case review data for commodity trading primarily come from system records across the full trade fulfillment workflow. These records include purchase and sales contracts, customs documents, quality inspection reports, transport bills of lading, and more. Data updates trigger when individual trade declarations are submitted or fulfillment is completed. A single declaration package contains both structured fields and unstructured text. Structured fields include commodity category, transaction quantity, unit price, customs declaration number, and contract number. Corresponding units include tons, yuan, cubic meters, and others. Unstructured text consists of long-form content such as contract clauses and quality inspection descriptions.
Constraints on vector models and indexing
The mixed structured and unstructured data characteristics of commodity trading require vector models and indexing to adapt to both field-level semantics and long-text semantics. Unstructured text lengths in individual trade declaration packages vary widely. Some large-scale trade contract clauses can reach several thousand characters. Reasonable chunk boundaries must be controlled to avoid semantic breaks. Structured fields are tied to units and commodity categories. Vector encoding must retain unit-related semantics to avoid interference from cross-category matching. The triggered update characteristic requires indexes to support incremental synchronization. This avoids resource consumption caused by full index rebuilding.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Ensures semantic integrity for long texts like commodity trading contracts and customs documents, and prevents single chunks from containing unrelated transaction clauses |
| Overlap Length | 100–200 characters | Compensates for semantic breaks at chunk boundaries, and ensures contextual coherence for long texts |
| Similarity Threshold | Calibrated based on actual testing | Must match the semantic similarity characteristics of commodity categories, and avoids misclassifying weak semantic associations across different commodity categories as high-match results |
defaultToken | 8192 | Adapts to the input token limit of most mainstream vector models, and meets encoding requirements for long text chunks |
maxToken | 16384 | Covers the total token count of large-scale trade contracts, and avoids encoding truncation that loses key fulfillment clauses |
| Single Search Limit | Top 10 results | Balances retrieval efficiency and recall completeness, and covers common multi-document associated review scenarios |
Three common errors
- Phenomenon: An undefined model error occurs when using a non-default vector model. Cause: The access key and interface address for the corresponding vector model are not correctly added in the system configuration. This prevents the system from recognizing non-preconfigured models.
- Phenomenon: Semantic retrieval scores are high but actual matching accuracy is insufficient. The issue persists after switching vector models. Cause: The similarity threshold is not adjusted for commodity category characteristics. This misclassifies weak semantic associations across different commodity categories as high-match results.
- Phenomenon: When a set of trade data is associated with multiple documents, no corresponding multiple vector groups are generated. Cause: The multi-document associated index configuration is not enabled. Only individual documents are vectorized, and one-to-many mapping between data and vectors is not implemented.
How to verify correct configuration
- Upload a standard commodity trade purchase and sales contract using the FastGPT v4.17.0 system. Check that chunked text fragments retain complete clause logic, with no obvious semantic breaks.
- Input an audit query term targeting a specific commodity category. Verify that the number of returned search results matches the single search limit configuration, with no abnormal empty results or excessive results.
- Switch between different vector models for testing. Confirm that the system can load models normally and generate vectors, with no undefined model errors.
- Import a set of trade data associated with multiple documents. Check that the index generates the corresponding number of vector entries, with no missing data.
The configuration values listed here are common starting points. They should be evaluated against the user's own sample data.
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.