Data Characteristics
Medical insurance access regulations data originates primarily from policy documents, announcements, detailed interpretations, and relevant legal texts published by national and local medical insurance bureaus. These documents update frequently, especially regarding annual medical insurance catalog adjustments, negotiated drug results, and payment standard changes. Document structures typically feature hierarchical legal texts, clause lists, or Q&A formats. They include diverse fields such as generic drug names, indications, payment scope, payment ratios, restricted payment conditions, negotiated prices, effective dates, and expiration dates. Units often involve monetary values (Yuan), percentages (%), and time (year/month/day). Numerical precision and the rigor of condition descriptions are critically important.
Constraints on "Knowledge Base Retrieval and Recall"
The hierarchical structure and high update frequency of medical insurance access regulation documents require the knowledge base to support incremental updates and version management, ensuring retrieval result timeliness. Field diversity necessitates precise identification of different entity types, such as drug names and restricted conditions, to improve recall accuracy. The rigor of legal texts means segment granularity should not be too coarse, to avoid losing critical context, nor too fine, to prevent semantic fragmentation. The precision of numerical values and condition descriptions means retrieval should prioritize exact matching and conditional logic, reducing ambiguity from fuzzy matching. High update frequency also demands robust synchronization mechanisms for the knowledge base, ensuring users always access the latest policies.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 300-500 characters | Medical insurance policy clauses are often long. This range prevents truncation of key context while ensuring an appropriate amount of information per segment. |
Chunk Overlap Length | 50-100 characters | Ensures semantic continuity between adjacent paragraphs, improving recall rate for information spanning multiple segments. |
Recall count | 8-12 entries | Given the complexity of medical insurance policies, this number increases coverage and provides sufficient options for the reranking model. |
Similarity threshold | 0.75-0.85 | Medical insurance policy queries demand high precision. A higher threshold filters out irrelevant or weakly relevant results. |
Rerank result count | 3-5 entries | After processing by the reranking model, this selects the most relevant few results for user presentation, improving user experience. |
Max Context Length | 4000-8000 Token | Ensures the Q&A model can process complex and interconnected clauses within medical insurance policies, providing complete and accurate answers. |
Common Mistakes
- Knowledge base retrieval results lack the latest policy documents. This happens when the knowledge base synchronization mechanism fails to effectively monitor policy release sources, leading to data staleness.
- When a user asks about "the medical insurance payment scope for a certain drug in a specific province," retrieval results fail to accurately present relevant restricted conditions. This occurs when document segmentation is too coarse, separating restricted conditions from the main drug information, preventing effective model association.
- After enabling the reranking model, the order of retrieval results does not match expectations. This can happen if the reranking model is not fine-tuned for the specific characteristics of medical insurance policy texts, making it unable to effectively identify logical relationships between policy clauses.
How to Verify Configuration
- Select a batch of typical questions covering new and old policies, different drugs, and various provinces. Observe if retrieval results include all relevant policy documents and clauses. Check if the number of recalled items matches expectations.
- For complex queries such as medical insurance payment conditions and restricted payment scope, verify if retrieval results accurately present all key fields and values, and check their contextual completeness.
- Compare retrieval result rankings for the same query before and after enabling the reranking model. Evaluate if the top-ranked documents after reranking better align with the query intent. Compare with expert opinions to determine if the reranking effect meets expectations.
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.