What the data for this category looks like
Internal office data for commodity trading primarily comes from enterprise ERP systems, port dispatch platforms, spot price databases, and contract management ledgers. Data update frequencies cover three categories: real-time (spot transaction prices, in-transit logistics locations), daily (inventory check data), and monthly (contract execution ledgers). Each document structure includes fields such as product category, contract number, cargo quantity, unit price, delivery date, logistics nodes, and customs declaration information. Units include tons, yuan per ton, cubic meters, port codes, and more. Document fields vary across different business modules.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Business issues in commodity trading often relate to multiple batches of cargo and cross-linkage processes. Multi-turn dialogue must carry forward the business context of previous questions to avoid repeatedly asking basic information. Differences in fields across different data sources require prompts to explicitly specify corresponding fields to prevent confusion between data with different units or business dimensions. Real-time updated spot prices and in-transit logistics data require the dialogue module to quickly retrieve the latest information. Timeout settings must adapt to the duration requirements of complex accounting queries. Internal office scenarios require that conversation histories be isolated according to employee permissions to avoid sensitive data leaks.
How to configure the settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the user's unique employee ID | Ensure each employee's conversation history is independent, achieving permission isolation for internal office use |
messages | Retain the first 15–20 conversation turns | Business issues in commodity trading often relate to multiple batches of cargo and multi-linkage processes. Sufficient context avoids repeated inquiries about basic information |
variables | Bind current department and product category permissions | Used to filter inventory and contract data within the corresponding permission scope, narrowing the retrieval range |
| Single dialogue timeout | 600 seconds | Complex trade accounting and cross-node logistics queries require longer processing times |
| Context window size | 8000–12000 token | Adapt to the retrieval needs of long documents such as contracts and logistics data, calibrated based on actual testing |
Three Common Misconfigurations
- Phenomenon: Conversation histories of different employees are visible to each other, leading to permission leaks. Cause:
chatIdis not correctly bound as the user's unique identifier, resulting in multiple users sharing conversation contexts. - Phenomenon: After a workflow is executed, replies from all AI dialogue nodes are displayed in the chat interface. Cause: No output hiding rules are configured for the nodes, and all node outputs are synced to the dialogue flow by default.
- Phenomenon: Multi-turn dialogue cannot carry forward previous business information, only allowing single-turn questions without follow-up inquiries. Cause: The context retention logic for the
messagesparameter is not enabled, and only the current question is passed in each conversation, making it impossible to associate previous business data.
How to Confirm the Configuration Is Correct
- Switch between different test accounts to initiate conversations, check if each user's historical conversation records are independent, and confirm that the user identifier binding configuration is effective.
- Initiate a business question involving multi-linkage processes, such as first querying basic information of a specific batch of cargo, then asking about its subsequent process nodes, check if the dialogue carries forward previous information, and confirm that the context retention configuration is correct.
- Configure AI dialogue nodes in a workflow, test hiding the output of specified nodes, check if the corresponding replies are not displayed in the chat interface, and confirm that the output rule configuration is effective.
- Initiate a complex business query, check if the response is completed within a reasonable time frame, and confirm that the timeout setting meets the requirements of the business scenario.
The values provided 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.