Data Characteristics
Medical insurance access R&D document data originates from official websites of national and provincial medical insurance bureaus, centralized drug procurement platforms, pharmaceutical industry association publications, and internal corporate clinical trial reports and drug registration materials. This data updates frequently. Medical insurance catalog adjustments and drug price negotiation results, for instance, typically update quarterly or annually, with some policy changes occurring even more often. Document structures vary. They include legal clause formats for policy texts, tabular data for negotiation results, chapter-based descriptions for clinical reports, and custom formats for internal corporate reports. Specific fields include generic drug name, dosage form, specifications, manufacturer, medical insurance payment standard, payment scope, indications, negotiation cycle, and price reduction percentage. Units involve milligrams, milliliters, Yuan, percentages, and years.
Constraints Imposed by These Characteristics on Document Parsing and Chunking
The high update frequency of medical insurance access R&D documents requires the parsing system to support rapid response and incremental updates, preventing information lag. Diverse document structures demand a flexible parser that can adapt to different formats. This is especially true for unstructured or semi-structured text, where identifying key information boundaries is critical. The prevalence of tabular data necessitates accurate extraction of table content and preservation of row and column relationships, preventing data misalignment or loss. Numerical fields like medical insurance payment standards and price reduction percentages require correct identification and retention of units and precision during chunking to ensure accurate subsequent calculations and analysis. Descriptive texts such as indications and payment scopes need fine-grained chunking to capture complete semantic information, avoiding critical context loss due to excessive truncation.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 500–800 characters | Medical insurance policy clauses and clinical report paragraphs are of moderate length. This range maintains semantic integrity while controlling the information volume of each chunk. |
overlap_size | 50–100 characters | Ensures contextual continuity, especially when cross-referencing or interpreting complex policy terms, improving recall accuracy. |
parse_table_as_text | False (Parse tables independently) | Table data (e.g., negotiation results, payment standards) is core to medical insurance access documents. Independent parsing preserves table structure for subsequent structured extraction. |
extract_metadata | True (Enable metadata extraction) | Automatically extracts document titles, publication dates, and sources. This helps differentiate policy versions and trace information origins. |
max_file_size | 200 MB | Accommodates large clinical trial reports or comprehensive policy documents, ensuring no restrictions on file upload and parsing. |
timeout_seconds | 600 seconds | OCR and parsing of complex PDFs or scanned documents can be time-consuming. This provides sufficient time to prevent parsing interruptions. |
Common Pitfalls
- Document parsing errors, with a message like
{"detail":"错误信息:"}. This typically occurs due to corrupted or encrypted PDF files, or missing dependencies or misconfigurations of the PDF parsing engine (e.g., Marker) in specific environments. - Table content parsing results in misaligned fields or data loss. This happens when independent table parsing is not enabled, or the general text chunker fails to correctly identify complex table row and column boundaries.
- During answer retrieval, the system cannot pinpoint specific medical insurance policy clauses, only providing the document name. This indicates overly coarse chunking granularity or failure to store the original document's hierarchical structure (e.g., chapters, clause numbers) as metadata.
How to Verify Configuration
- Upload typical medical insurance policy documents and clinical reports. Check if the parsed chunks are logically complete and free of obvious semantic breaks.
- Randomly select documents containing complex tables. Verify that the parsed table data matches the original text, with no misalignment or omissions.
- In question-answering tests, query specific medical insurance payment standards or indications. Verify that the system accurately cites the corresponding original text chunks and document sources.
- Use the API or interface to inspect the metadata of parsed chunks. Confirm that key fields like document title, publication date, and chapter information are included.
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.