Data Characteristics
High-value consumables policy documents originate from regulatory bodies like national and local medical insurance bureaus, health commissions, and drug administration agencies. They also include implementation rules from hospital internal management departments. These documents have a relatively stable update frequency, typically revised after policy releases, annual adjustments, or specific events. The document structure primarily consists of regulations, operational specifications, and catalog lists, commonly found in PDF and Word formats. Content covers consumable classification, coding, access standards, procurement processes, usage specifications, reimbursement rules, and adverse event management. Fields include consumable name, model, specification, manufacturer, registration certificate number, medical insurance payment category, price limits, and usage department. Units vary, such as "piece," "set," "milliliter," "gram," or no unit.
Constraints on Document Parsing and Chunking
The characteristics of high-value consumables policy documents impose specific requirements on document parsing and chunking. First, the authority and rigor of policy documents demand complete and accurate content parsing. Any missing information or parsing errors can lead to misunderstandings of the policies. Second, the large number of tables and lists, especially consumable catalogs and codes, tests the ability to extract structured data. Correct identification and association of table data are critical. Third, different levels of regulations may contain references and associations. The parsing tool must identify cross-references in the text to ensure complete context for knowledge chunks. Finally, the documents involve numerous professional terms and abbreviations. Accurate identification during parsing is necessary to avoid inaccurate chunking due to lexical ambiguity.
Configuration Settings
| Parameter | Recommended Value | Rationale |
|---|---|---|
Chunk size | 500–800 characters | Balances contextual completeness and retrieval granularity, preventing knowledge chunks from being too long or too short. |
Overlap Length | 50–100 characters | Ensures sufficient contextual connection between adjacent knowledge chunks, improving retrieval recall. |
OCR_ENABLED | True | Many policy documents are scanned images; OCR must be enabled to ensure image text is recognizable. |
TABLE_EXTRACTION_MODE | ROW_BASED | High-value consumables catalogs are often row-based data; row-by-row parsing maintains table structure integrity. |
EMBEDDING_MODEL | text-embedding-large | Captures semantic information in complex policy texts, improving retrieval accuracy. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Allows sufficient parsing time for large policy files or documents with complex tables. |
Common Pitfalls
- Incomplete or malformed table display in the knowledge base. This occurs when document parsing fails to correctly identify table boundaries or cell content.
- Retrieval results contain many irrelevant paragraphs. This manifests as an excessive number of recalled items with low relevance, due to overly fine-grained chunking that results in insufficient contextual information.
- Garbled Chinese text after OCR. Logs show
UnicodeDecodeError. This occurs when the OCR engine does not correctly handle Chinese encoding.
Configuration Validation
- Upload a typical high-value consumables policy PDF file. Check if the chunked content in the knowledge base is complete, especially tables and list data.
- Perform test queries using key terms and policy clauses from the document. Evaluate the relevance and accuracy of retrieval results, and verify if the number of recalled items is reasonable.
- Randomly select parsed knowledge chunks. Check if their context is coherent, if there are semantic breaks or missing information, and compare them with the original document.
The values provided are common starting points and should be measured 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.