Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Commodity trading business data primarily comes from internal trade contract ledgers, port warehouse inbound and outbound records, customs declaration documents, and transaction settlement vouchers. Data update frequencies are divided into multiple tiers: real-time synchronized spot trading quotes and delivery records, daily updated inventory counts and in-transit cargo statuses, and monthly archived annual trade contracts and settlement files. A standard single business document typically includes fixed fields such as contract number, commodity category, specification and model, transaction quantity (unit: ton, cubic meter, or standard container), information of both transaction parties, delivery cycle, and settlement terms. Some cross-regional trade documents also add logistics tracking numbers and insurance policy numbers.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
The multi-dimensional data characteristics of commodity trading impose multiple constraints on the multi-turn dialogue and prompt engineering process. First, business data includes multiple fields with dedicated units. Prompts must explicitly require the dialogue to identify and verify unit matching to avoid confusion between tons and cubic meters. Second, data update frequencies vary significantly. Multi-turn dialogue must include timeliness confirmation steps during interaction to distinguish real-time delivery data from historically archived contracts. In addition, business information associated across modules (such as contracts bound to logistics tracking slips) requires the dialogue context to continuously track associated identifiers, ensuring that each round of interaction can link to the complete data chain of the corresponding business entity. Different commodity categories have different field focus areas. Prompts must guide users to clarify commodity categories to match corresponding data verification rules.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind the business contract number as the unique identifier | Ensure that multi-turn dialogue in the same trade process is linked to a single business entity, avoiding context interference |
messages | Retain the last 12 rounds of interaction context | Cover the complete business nodes from contract confirmation to logistics tracking for commodity trading, while controlling the context volume |
variables | Preset three core variables: commodity category, transaction quantity, and settlement method | Match core business fields to facilitate automatic verification and filling of business parameters during dialogue |
| Maximum context length | 8000–12000 characters | Adapt to the core information volume of commodity trading business documents, while complying with the input limits of mainstream large models |
| Dialogue timeout | 600 seconds | Cover common business waiting cycles such as cross-department confirmation and logistics information query, preventing session interruption mid-process |
Three Common Misconfigurations
- Phenomenon: The AI dialogue node returns an error message stating "Input content exceeds limit", or fails to respond directly. Cause: No context truncation rule is configured, and the accumulated business descriptions and historical interaction content in multi-turn dialogue exceed the model's input limits.
- Phenomenon: Dialogue logs cannot be linked to specific trade businesses or processing entities. Cause:
chatIdis not bound to a unique business identifier such as a business contract number, resulting in logs that only retain session identifiers without business association information. - Phenomenon: After uploading a JPG format business document, the AI cannot parse fields such as transaction quantity and commodity category from the document. Cause: No tool node supporting image parsing is enabled, and the prompt does not explicitly require calling the image parsing capability.
How to Verify Proper Configuration
- Initiate a simulated trade business dialogue, check whether
chatIdis automatically filled with the preset contract number, and confirm that the context association is correct. - Input a business description of excessive conventional length into the dialogue, and verify whether the system triggers input length verification or automatically truncates the context.
- Upload a JPG format business document, check whether the dialogue node calls the image parsing tool and returns recognition results for the corresponding fields.
- View the dialogue logs, confirm that each interaction is linked to the corresponding
chatIdand business entity identifier, and the complete processing process can be traced.
The values given here 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.