Data Characteristics
Medical insurance access documents originate from policy documents issued by national and local medical insurance bureaus, application materials submitted by pharmaceutical/device companies, expert review opinions, and public announcements of negotiation results. Update frequencies vary. Policy documents typically update quarterly or annually. Specific drug access dynamics can be released at any time.
Document formats are diverse. They include PDF policy texts, Word application templates, Excel data attachments, and some web announcements. Data structures are complex, covering key fields such as drug generic name, indications, dosage form, specifications, medical insurance payment standards, payment scope restrictions, negotiation period, and access time. Payment standards may involve multiple currencies or units of measurement. Payment scope restrictions often use complex clinical indication descriptions. Field content is mostly unstructured text.
Constraints on Database and Operations
The complex data characteristics of medical insurance access documents impose multiple constraints on database and operations.
First, diverse document formats require robust document storage and parsing capabilities. This includes text extraction from PDFs and Word documents, and tabular data recognition from Excel files.
Second, irregular update frequencies require the operations system to flexibly configure timed fetching and incremental update strategies. This avoids resource waste from frequent full scans.
Unstructured text fields, especially indications and payment scope restrictions, require vector database support for efficient semantic retrieval. Traditional relational databases struggle with such complex queries.
Furthermore, varied units of measurement and payment standards demand high data cleaning and standardization. Pre-processing is necessary before data ingestion.
Finally, the sensitive nature of medical insurance data requires strict access control and data encryption mechanisms to ensure information security.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MAX_FILE_SIZE_MB | 200 MB | Medical insurance policy documents and application materials can contain many charts or scanned images, leading to large file sizes. |
CHUNK_SIZE | 800–1200 characters | Medical insurance policy descriptions are usually logically coherent. Overly short chunks lose context. Overly long chunks reduce retrieval precision. |
OVERLAP_SIZE | 100 characters | Ensures semantic continuity between chunks, especially when policy clauses cross-reference. |
VECTOR_DIMENSION | 1536 (OpenAI Ada-002) | Guarantees precise vector representation capabilities for medical insurance terminology and concepts. |
PARSE_TIMEOUT_SECONDS | 600 seconds | Handles PDF documents with complex tables or many pages, preventing parsing timeouts. |
DB_BACKUP_INTERVAL | 03:00 UTC+8 daily | Medical insurance data updates are not frequent, but policies have significant impact. Daily backups ensure data recoverability. |
Common Pitfalls
- Document parsing takes too long or fails with a
PARSE_TIMEOUT_ERROR. This happens when thePARSE_TIMEOUT_SECONDSparameter is set too low, not accounting for the complexity and size of medical insurance documents. - Retrieval results for payment scope terms are semantically inaccurate or missing. This occurs when
CHUNK_SIZEis too small orOVERLAP_SIZEis not configured correctly, leading to truncation of key information or loss of context. - Database connection fails, displaying
Connection refused for IP xxx.xxx.xxx.xxx. This indicates the database firewall has not whitelisted the FastGPT server's outbound request IP, blocking the connection.
Verification Steps
- Upload a typical medical insurance policy PDF document. Check logs for parsing failures or timeout errors. Verify that chunked content is complete and semantically coherent.
- Perform multiple semantic retrieval queries for complex terms like medical insurance payment scope and indications. Evaluate the accuracy and relevance of recalled results to ensure key information is effectively retrieved.
- Simulate database disconnections or service restarts. Verify that the database backup and recovery process executes correctly and that data consistency is maintained.
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 2026-09-21.