Data Characteristics for This Category
Medical insurance access products primarily use data from national and local medical insurance bureaus. This includes policy documents, drug and medical device catalogs, payment standards, and negotiation outcome announcements. Data updates frequently. National-level data typically updates annually or quarterly. Local policies may adjust at any time.
Document structures vary. Common formats include PDF, Word, Excel, or structured databases. PDF and Word documents often contain extensive unstructured text, such as policy interpretations, access conditions, and payment scopes. Excel files or databases list fields like drug or device codes, names, manufacturers, dosages, specifications, payment prices, and reimbursement ratios. Field names may lack uniformity. Units can differ across documents; for example, payment prices might be "yuan/unit" or "yuan/course."
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The diverse and heterogeneous nature of medical insurance access data requires flexible data parsing and extraction capabilities for tool calling. Unstructured policy documents need advanced natural language processing tools to extract key information, such as access conditions and restrictive clauses. This requires tools to process long texts, understand semantics, and perform entity recognition.
Structured data needs database connection tools or file parsing tools to accurately extract field information. Standardization is then required to resolve unit and naming discrepancies. High update frequency means tool calling needs to support scheduled tasks and incremental updates to ensure information timeliness. Data sensitivity requires permission management and data security considerations during tool calling to prevent sensitive information leaks.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
maxContext | 8192 | Accommodates the long-text nature of medical insurance policy documents, ensuring the model can process complete contexts. |
Chunk size (Segment Length) | 500-800 characters (characters) | Balances textual semantic integrity and retrieval efficiency, preventing loss of critical information due to segmentation. |
Recall count (Recall Count) | Top 8-12 entries (top 8-12 items) | Addresses the complexity of medical insurance access conditions by increasing recall to improve relevance coverage. |
Similarity threshold (Similarity Threshold) | 0.75-0.85 | Ensures highly relevant recall results and filters out unnecessary noise. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Handles the potentially long parsing time for large PDF or Word policy files. |
DB_CONNECTION_TIMEOUT_MS | 10000 ms (milliseconds) | Adapts to the response times of external databases or API services, preventing connection interruptions. |
Common Pitfalls
- The inference model stops outputting thought processes after adding tool calls. This can happen if the tool calling logic directly returns results, overriding the model's original chain-of-thought output path. Adjust the tool return format or model prompt.
- The FastGPT conversation interface does not return the referenced knowledge base ID. This is typically due to interface design or configuration not including
knowledge_base_idas part of the return value. Check the API documentation or system configuration. - A database connection tool fails to support a specific database, such as Oracle. This indicates the current tool connector module lacks adaptation for that database type. Extend or customize the connection plugin.
Verification Steps
- Construct queries containing complex medical insurance policy clauses. Check if the model accurately cites policy text and provides logical answers. This helps determine if
maxContextandChunk sizeare appropriate. - Use queries with specific drug or device codes. Verify if the tool accurately extracts corresponding payment prices and reimbursement ratios from structured data sources. This confirms correct database connection and field mapping configuration.
- Monitor system logs for frequent timeout errors related to
PARSE_FILE_TIMEOUT_SECONDSandDB_CONNECTION_TIMEOUT_MS. If errors occur, evaluate if the current configuration meets actual needs. - Regularly simulate medical policy updates. Test if the system promptly captures the latest data and reflects it in query results. This confirms the effectiveness of incremental updates and data synchronization mechanisms.
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.