What this category's data looks like
Reference only; verify against the target FastGPT release and deployment documentation.
What constraints these characteristics impose on the workflow orchestration link
Coal transportation and sales business data includes multi-dimensional business fields and involves sensitive enterprise operation information. The workflow must be configured with a permission verification node to filter unauthorized access. Real-time updated weighing and shipment data requires workflow nodes to support dynamic pulling of the latest business data, to avoid delayed results caused by static caching. Different inquiry scenarios correspond to different business data interfaces, so corresponding nodes must be routed by inquiry type. Cross-system data association logic must also be handled to ensure consistent return results. Additionally, structured business document data requires the workflow to be configured with a format verification node, to prevent non-compliant values or text from entering subsequent processing links.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Context history retention length | First 20 conversation turns | Coal transportation and sales inquiries often involve specific business documents; overly long context will interfere with accurate matching, and 20 turns can cover the historical associated information for a single inquiry |
| Variable binding scope | Only current workflow instance | Transportation and sales data involves internal enterprise business information; limiting the scope prevents data leakage |
| Node timeout period | 600 seconds | Pulling transportation and sales ledgers and settlement data across systems requires a long response time; 600 seconds covers most business scenarios |
| Context clearing trigger condition | Switch by inquiry type in variables | Different transportation and sales business inquiries require isolated conversation history; triggering clearing via the variable field accurately matches business scenarios |
| Knowledge base recall upper limit | Top 8 matching results | Coal transportation and sales documents are mostly structured business documents; too many recall results increase processing burden, and 8 results cover core matching information |
| Maximum single request token count | 8000 token | Adapt to the parsing and aggregation needs of structured business data, avoid requests exceeding platform limits |
Three Common Misconfigurations
- Phenomenon: Historical conversation remains after workflow execution, causing subsequent inquiry results to deviate from the current business scenario. Cause: No context clearing trigger logic based on
variablesconfigured, no isolation of conversation history by business type. - Phenomenon: Workflow return results include AI outputs from multiple intermediate nodes, unable to display only the final aggregated result. Cause: Did not turn off the output switch for intermediate nodes, or did not configure a result aggregation node to filter redundant content.
- Phenomenon: Document parsing node repeatedly loads previously uploaded transportation and sales documents, reducing parsing efficiency. Cause: No filtering rule configured to only process the currently uploaded attachment, no binding of the current file identification parameter in
variables.
How to Verify Proper Configuration
- Trigger different types of transportation and sales inquiries such as contract query and shipment progress query, to check whether the conversation history is automatically cleared after scene switching.
- Submit a workflow test with multiple business nodes to confirm that only the aggregated result of the last node is returned, with no intermediate AI output remaining.
- Upload a single transportation and sales document and trigger parsing, check that the parsing log only loads the currently uploaded file, with no historical attachment data.
- View the workflow node's timeout configuration log to confirm that no timeout errors occur when pulling data across systems.
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.