Data Characteristics
Medical insurance settlement quality documents include policy interpretations, settlement process specifications, audit rules, expense declaration guidelines, and appeal procedures. These documents originate from national and local medical insurance bureaus, hospital internal management systems, or third-party service providers. Updates occur quarterly or annually, driven by policy changes, with urgent policy adjustments triggering immediate updates. Documents are typically in PDF, Word, or structured JSON formats, covering policy terms, operating steps, and case analyses. Fields include cost codes, item names, reimbursement ratios, settlement dates, patient information (anonymized), audit statuses, and rejection reasons. Units involve RMB, percentages, dates, and text descriptions.
Constraints on Deployment and Upgrade
The dispersed sources and uncertain update frequency of medical insurance settlement documents require FastGPT deployments to support flexible data ingestion. Documents contain extensive policy text and complex logic, demanding high accuracy in text segmentation and key field recognition to ensure precise RAG recall. The deployment environment must account for data sensitivity, necessitating intranet or private deployment solutions. For document updates, efficient incremental update mechanisms are critical to avoid resource consumption from full re-indexing. Furthermore, the accuracy and timeliness of document content directly impact compliance with medical insurance settlements. Therefore, upgrades must maintain knowledge base consistency and correctness, preventing logical flaws or data inconsistencies due to version differences.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Medical insurance policy documents can be large, containing numerous charts and attachments. |
Chunk size | 800–1200 characters | Ensures completeness of medical insurance policy clauses, preventing truncation of critical information. |
Recall count | Top 5 entries | Focuses recall results, reducing interference from irrelevant information. |
Similarity threshold | 0.75 | Medical insurance policy text is highly specialized; a higher threshold improves matching precision. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing large PDF or Word documents may require extended parsing times. |
maxContext | 4000 token | Ensures the model can handle complex queries involving multiple policy clauses. |
Common Pitfalls
- FastGPT containers fail to access external interfaces after startup, with logs showing network connection errors. This indicates incorrect container network configuration, preventing proper bridging to the host or external network.
- Uploading a large medical insurance policy PDF file results in a prolonged loading state, eventually timing out during parsing. This occurs when the
PARSE_FILE_TIMEOUT_SECONDSparameter is set too low, not allowing sufficient processing time for large files. - After a knowledge base upgrade, answer accuracy for the same questions decreases, sometimes referencing outdated policies. This typically results from incorrect incremental indexing or disorganized knowledge base version management during the upgrade process.
Verification Steps
- Upload a multi-chapter, extensive medical insurance settlement policy PDF document. Verify successful parsing and indexing.
- Pose questions regarding specific clauses within the medical insurance policy. Verify FastGPT accurately recalls relevant document segments and confirm the
Similarity thresholdeffect through logs. - Simulate a medical insurance policy update by uploading a new version of the document. Observe if the knowledge base identifies incremental content and updates effectively. Also, check if questions about the old policy version still receive correct answers.
The values provided are common starting points. Measure them against your 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 2026-09-21.