What this category of data looks like
This data comes from the national medical insurance information platform, hospital HIS systems, medical insurance interfaces, medical insurance catalogs and coding libraries, and cross-regional medical settlement platforms. The update frequency is daily. Daily processed documents include medical insurance settlement lists, expense details, medical record homepages, prescriptions, rejection lists, and settlement reconciliation statements. Core fields cover diagnosis and treatment item and drug consumable codes, expense amounts, insured person identity identifiers, settlement status, rejection reasons, and related details. This data supports daily settlement review operations before and after payment processing.
Constraints imposed on multi-turn dialogue and prompt engineering by these characteristics
Data is updated daily and sourced from multiple locations. Prompt engineering must be synchronized with the latest medical insurance catalogs and coding libraries on a regular basis. This prevents code matching failures. Multiple related documents must be passed in conversation order. This ensures the review process can sequentially verify consistency between expense details, prescriptions, and settlement lists. Field differences between single or bulk settlement data require multi-turn dialogue to gradually guide the supplementation of missing information. For example, first confirm the settlement batch number before retrieving corresponding details. The reconciliation requirement for rejection lists requires the dialogue context to retain historical rejection records. This prevents duplicate reviews or missed appeal information.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
messages context configuration | Pass the latest 10 rounds of interaction content in conversation order | Retains historical information required for review, such as insured person identity and past rejection records, and prevents context truncation |
variables binding configuration | Bind medical insurance catalog coding library and settlement rule library | Provides real-time matching standardized data for multi-turn dialogue, ensuring accuracy of code review |
chatId session configuration | Bind exclusive session ID | Distinguishes review processes for different insured persons or settlement batches, preventing context confusion |
| Number of single-input documents | Top 5 | Matches the daily document processing scope for single or bulk settlement, avoiding overload |
| Timeout threshold | 600 seconds | Adapts to the processing duration of bulk settlement reviews, preventing process interruption due to data retrieval delays |
| Context window length | 8000–12000 token | Covers all related documents and dialogue history for a single batch of settlement review, preventing truncation of critical code and amount information |
Three Common Misconfigurations
- Symptom: Returns 400 invalid image error when calling the multimodal dialogue interface, with no valid response. Cause: No format validation was performed on uploaded medical document images, or the image resolution exceeds the interface's supported range.
- Symptom: The orchestrated workflow runs normally in the FastGPT debug interface, but only the first dialogue node can be triggered after connecting to an external dialogue platform, and subsequent interactions cannot be responded to. Cause: The session passing logic for
chatIdwas not configured correctly, leading to loss of cross-platform session context. - Symptom: After a locally deployed FastGPT connects to a custom model via a transit interface, the dialogue interface automatically switches to the built-in model, and the target model cannot be called normally. Cause: The target model identifier was not locked in the application configuration, causing the platform to call the built-in model by default.
How to Verify Successful Configuration
- Upload a single medical insurance settlement list and associated prescription, test whether multi-turn dialogue can sequentially retrieve and verify code and amount information, and confirm that context is not truncated.
- Simulate a bulk settlement review scenario, pass multiple documents, and check whether the dialogue process can process the review logic for each expense in order.
- Trigger a timeout test, confirm that the review process is completed within the preset threshold, and no interruption errors occur.
- Check the knowledge base bound to
variables, confirm that the update time of the medical insurance catalog coding library is consistent with the official data source.
The values provided are common starting points and should be evaluated against your own specific 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.