What Data for This Category Looks Like
This category's data primarily comes from enterprise procurement systems, supplier quotation documents, budget sheets, and contract systems. Daily circulating documents include purchase requisitions, price comparison records, formal contracts, and acceptance documents. Data is updated per individual procurement process, when a purchase is completed or during monthly reconciliations. Each document includes fields such as procurement category, budget amount, supplier qualifications, contract terms, and acceptance criteria. Some cross-system fields have inconsistent formatting.
How These Characteristics Impose Constraints on Forms and Interactions
Data for this category comes from multiple sources and is updated per individual process. It must connect to external procurement systems and supplier quotation libraries to enable linked field population. This avoids manual duplicate entry of information such as procurement category and budget amount. Because document fields change based on process stage, external systems must implement logic to dynamically load custom field groups. This adapts form structures for different stages including purchase requisition, price comparison confirmation, and contract review. The FastGPT formInput node is responsible for collecting user input fields for the corresponding stage. Data updates occur infrequently but carry high associated risks. The interaction flow must embed field validity check logic provided by external business systems. For example, matching budget amounts to corresponding budget sheets. This reduces the risk of subsequent procurement delays or overspending.
How to Configure Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
Form Input Node formInput | Test example: Select 3 fields from IT procurement and supplier management as required inputs. Actual fields are determined based on the task | This node collects user input. Missing required fields requires checking interaction status to prevent subsequent nodes from receiving incomplete input |
User Selection Node userSelect | Test example: Set 2 business options, check corresponding branches one by one | This node handles user selection. Queries and filtering of records related to IT procurement and supplier management must be implemented by subsequent interfaces or external systems |
| Field Passing After Submission | Test example: Prepare 10 input samples for IT procurement and supplier management. Cover complete filling, missing required fields, and canceled operations | Compare input and node output to verify whether subsequent nodes receive corresponding fields. Result thresholds are determined based on actual tasks |
| External Implementation Item: Knowledge Base Reference Count | Must be implemented separately by external systems; first 3 entries (test example) | Single document data volume for this category is moderate. The first 3 entries can cover core clause verification needs |
| External Implementation Item: Form Submission Trigger Logic | Must be implemented separately by external systems; auto-submit after completing required field verification (test example) | Matches the rhythm of individual process data updates, reducing manual operations |
| External Implementation Item: Tool Call Timeout | Must be implemented separately by external systems; 120 seconds (test example) | Adapts to time spent pulling cross-system data, preventing interruption of procurement processes |
Three Common Configuration Mistakes
- Phenomenon: The AI does not trigger preset tools during the tool call link, and directly generates natural language responses. Verification steps: Check whether the tool call trigger conditions are configured correctly. Verify whether the tool call timeout setting matches the time required for cross-system data pulling.
- Phenomenon: The knowledge base reference count cannot select values in the 100-900 range, only fixed preset options. Verification steps: Check the knowledge base reference count configuration item for the current FastGPT version. If custom value range 100-900 is needed, it must be implemented by external systems to restrict the range.
- Phenomenon: The judge node matching rule trigger logic is abnormal. "Equal to" and "Starts with" rules trigger the ELSE branch, while "Contains" and "Ends with" trigger the IF branch. Verification steps: Check whether the judge's branch configuration order matches the business verification logic. Adjust matching priority.
How to Confirm Proper Configuration
- Submit a simulated purchase requisition. Check whether the
formInputfields connect to external systems as configured to complete data association. Verify that rules trigger correct prompts. - Trigger tool calls. Confirm that external data sources are connected as configured to pull supplier qualification data. Verify that tool calls do not time out.
- Configure the judge to verify contract terms. Verify that different matching rules trigger corresponding branches as expected.
- View knowledge base reference results. Confirm that the number of returned documents matches the preset value range.
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 2026-10-19.