Data Characteristics
Medical insurance access policy data originates from official documents published by national and local medical insurance bureaus. These documents include notices, methods, and detailed rules. They are typically in PDF or Word format. Content structure varies; some files contain complex tabular data. Update frequency is usually quarterly or annually, with ad-hoc releases for major policy changes. Document fields cover drug names, indications, payment scope, payment standards, negotiation results, and effective dates. Units include monetary amounts (yuan), percentages (%), time (days/months/years), and various medical terms. The data volume is large and highly specialized, requiring significant effort for understanding and parsing.
Constraints on Model Integration and Configuration
The official nature of medical insurance access policy data requires models to extract and understand information accurately, avoiding misinterpretations of original policy texts. The document update frequency necessitates a knowledge base that supports regular or event-driven incremental updates to ensure information timeliness. The presence of multi-format documents (PDF, Word) challenges the compatibility and stability of file parsing components, especially for nested tables and complex mixed text/image layouts. The specialized and diverse nature of fields requires strong semantic understanding from the model to distinguish similar concepts and accurately extract numerical and textual information. These constraints ultimately influence the choice of chunking strategies, retrieval mechanisms, and model inference parameters.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 800–1200 characters (characters) | Medical insurance policy documents often have long paragraphs. Shorter chunks can break context, while longer ones increase model processing difficulty. |
Chunk Overlap Length (Chunk Overlap) | 100–150 characters (characters) | Ensures continuity of context at chunk boundaries, improving accuracy of cross-paragraph information retrieval. |
Similarity threshold (Similarity Threshold) | Calibrate through testing | Policy clauses have precise semantics. Actual testing is needed to find a threshold range that provides high discrimination and accurate retrieval. |
Recall count (Retrieval Count) | Top 5–8 entries (top 5–8) | Balances policy complexity and model processing capability, ensuring coverage of key information while reducing irrelevant interference. |
Rerank result count (Reranked Return Count) | Top 3 entries (top 3) | Further optimizes ranking based on initial retrieval, prioritizing the most relevant information. |
maxContext | 3500–4000 token | Medical insurance questions often require substantial context for judgment, ensuring the model receives sufficient relevant information. |
Common Pitfalls
- Model responses contain policy interpretations or data inconsistent with the original text. This occurs due to improper chunk size settings, leading to truncated key information or missing context.
- User queries result in long delays or timeout errors. This can happen if the
PARSE_FILE_TIMEOUT_SECONDSparameter is set too low, preventing proper processing of complex or large policy files. - The model fails to accurately extract payment standards or dates from tables. This indicates that the file parsing component has insufficient capability to handle specific table formats or date fields, resulting in incomplete or incorrect data extraction.
Validation Steps
- Randomly select 5 typical medical insurance policy documents. Upload them to the knowledge base. Verify successful file parsing status and confirm that chunk previews are complete and logically coherent.
- For complex queries, such as "medical insurance payment scope and reimbursement ratio for a specific drug in a particular province," check if the model's retrieved results include all relevant policy clauses and if key numerical values in the answer match the original text.
- Continuously monitor the average response time for user queries. Ensure it remains within a reasonable range under varying loads.
- Regularly use test sets containing the latest policy information to evaluate whether the accuracy and timeliness of model responses meet expected thresholds after knowledge base updates.
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.