Data Characteristics
Medical insurance access clinical trial pre-screening data primarily comes from official documents published by national and provincial medical insurance bureaus. These include drug catalogs, payment standards, and negotiation results. Documents are typically in PDF, Word, or Excel format, with irregular update frequencies (quarterly, annually, or ad-hoc policy changes). Document structures are complex, containing numerous tables, nested lists, and unstructured text. Key fields include generic drug name, dosage form, specification, medical insurance payment category, restricted payment scope, payment standard, and effective date. Units for drug specifications include milligrams (mg), milliliters (ml), and units (U). Payment standards are in Chinese Yuan (¥).
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The multi-source and unstructured nature of medical insurance access data imposes specific requirements on tool calling and plugin design. The prevalence of PDF and Word documents necessitates robust text and table extraction capabilities from file parsing plugins, especially for complex and multi-page tables. Irregular data updates require flexible trigger mechanisms to adapt to external data source changes, preventing information lag. Restricted payment scopes often involve lengthy descriptions with complex logical conditions. Tools must convert these rules into executable query conditions, such as SQL statements or structured query languages, after parsing. Key numerical information like payment standards and effective dates requires strict data type validation and unit conversion after extraction to ensure calculation accuracy.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Allows sufficient time for processing large PDF documents or files with complex tables, preventing parsing interruptions. |
maxContext | 2000 characters | Medical insurance policy descriptions are often long, requiring a larger context window to capture complete restrictive conditions. |
Chunk size (Segment Length) | 800–1200 characters | Ensures individual segments contain complete medical insurance payment terms or drug descriptions, minimizing semantic fragmentation. |
Recall count (Recall Count) | Top 8 | Medical insurance access conditions may involve multiple related terms; increasing the recall count improves coverage. |
Similarity threshold (Similarity Threshold) | Calibrate by measurement | Medical insurance terms are precise; high semantic similarity is required, necessitating adjustment based on actual data. |
tool_code_timeout | 60 seconds | Tool calls (e.g., SQL queries or API calls) may involve data retrieval, requiring sufficient execution time. |
Common Pitfalls
- Tool calls return null values, with logs showing
null. This can occur when a knowledge base assistant is nested within a workflow, and API calls in specific versions (e.g., v4.8.10 and above) have a null return defect. - File parsing plugins do not return a link after image upload, or return a local path. This indicates incorrect file server configuration or the plugin not correctly handling the callback address after image upload.
- The application interface displays an error:
You need to use the app key rather than the account key. This means an account-level key was used instead of the correct application key (app key) for authentication.
Verification Steps
- Upload a PDF file containing complex tables and multi-page medical insurance policies. Check if the file parsing results completely extract all table data and key text segments.
- Construct a query including a medical insurance drug name and restricted payment conditions. Observe if the tool accurately calls the SQL plugin and returns matching drug information.
- Simulate a medical insurance policy update to trigger the data synchronization process. Verify that relevant knowledge base content updates as expected, with seamless transition between old and new data.
- For a specific drug, query its medical insurance payment status and restrictive conditions through the system. Compare the results with the original medical insurance document content, especially for key fields like payment scope and effective date.
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.