What the data for this category looks like
Data sources for this category include medical insurance intelligent monitoring systems, fund supervision rule bases, medical insurance settlement data, drug and consumable traceability codes, and complaint leads. The update frequency is once per month. Commonly processed documents include audit notices, inspection work papers, suspicious item lists, interview records, decision documents, and rectification reports. Data fields include medical treatment serial numbers, expense details, medical insurance catalog codes, violation determination clauses, complaint reasons, and other items. No additional custom statistical units are used.
How these characteristics impose constraints on workflow orchestration
The requirement for monthly data updates determines that the knowledge base synchronization cycle must match the monthly update rhythm. Avoid using overly short synchronization intervals to waste resources. The combination of multi-source data and multiple document types requires configuring multiple data source nodes in the workflow to connect to different data sources. It also requires passing fielded variables between nodes to associate relevant information across different documents. The most time-consuming suspicious item screening link must be split into independent workflow nodes. This allows separate configuration of retry and verification logic. Because errors in this link can lead to fund losses, a compliance verification node must be added at the end of the workflow.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Bind code node output and business fields | Enable passing code execution results to AI models, matching workflow variable transfer requirements |
nodeId | Name based on node function (e.g. filter_suspicious points) | Facilitate quick node location and troubleshooting of workflow connection and invocation issues |
| Single recall limit | Top 8 entries | Align with the typical number of audit suspicious items for this category, ensuring reasonable context loading |
| Context length | 700–1100 characters | Match the typical length of policy documents and audit records, ensuring answer accuracy |
| Retry count | 2 retries | Address temporary invocation failures caused by data source fluctuations, avoiding excessive retries that consume resources |
| Timeout duration | 500–700 seconds | Adapt to the time requirements of monthly batch data processing, calibrated based on actual testing |
Three Common Mistakes
- Code node execution results cannot be passed to AI models in the workflow, triggering the
chat:ai_input_is_eerror. The cause is failing to bind the result as a callable variable via thevariablesconfiguration item, preventing the AI model from reading the node output content. - When using the database connection plugin to connect to PostgreSQL, the prompt "Workflow verification failed, please check for missing or empty values, and whether connections are normal" appears. The cause is failing to correctly configure the data source connection parameters, or failing to cover all required fields in node connections, causing the verification logic to fail.
- Using the
[{datasetId: xxx}]format for knowledge base variable reference fails. The cause is failing to match FastGPT's variable reference specification, requiring the use of the platform-specified variable invocation format.
How to Confirm Proper Configuration
- Manually trigger the workflow, check the code node's output log, confirm the result has been correctly bound to the
variablesvariable. - Run the database connection node, check the verification result, confirm there are no missing fields or connection error prompts.
- Submit a test question, check whether the AI answer links to the audit data and policy content for this category.
- Check the
nodeIdnaming of the workflow, confirm all node IDs are unique and align with their functional positioning.
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.