Data Characteristics
Market access regulatory documents in the biopharmaceutical sector typically originate from official bodies like the National Medical Products Administration, the National Healthcare Security Administration, and provincial/municipal health commissions, as well as industry association guidelines. These documents have relatively stable update frequencies; for example, the National Medical Insurance Drug Catalog usually adjusts annually, while local policies may be revised irregularly based on actual circumstances. Document structures are complex, commonly including PDF regulation files, Word implementation details, and web-based interpretative articles. Content often contains extensive specialized terminology, generic drug names, dosage forms, medical insurance payment standard codes, and frequently involves quantitative information such as numerical ranges, indications, and cost units (e.g., "yuan/box," "mg/tablet"). Multi-level heading structures, embedded tables, and images are prominent features of these documents.
Constraints Imposed by These Characteristics on Document Parsing and Chunking
The complex structure and specialized content of market access regulatory documents pose specific challenges for document parsing and chunking. Their multi-level headings and embedded tables require parsers to accurately identify and maintain the original logical relationships, preventing content fragmentation and semantic loss. The high frequency of specialized terminology and codes means that simple general word segmentation may not effectively extract key information; integration of glossaries or domain-specific dictionaries should be considered. The moderate update frequency but large volume of content demands a degree of automation and efficiency in the parsing process to handle rapid iterations when new policies are released. Furthermore, consistent processing capabilities for different formats like PDF and Word are prerequisites for ensuring information completeness, as different formats may vary in text extraction and image recognition.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters | Policy documents often have long paragraphs; this retains sufficient contextual information while balancing retrieval efficiency. |
Chunk Overlap Length (Chunk Overlap Length) | 100–200 characters | Ensures key information across chunks is linked, preventing important terms or clauses from being cut off. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing large regulatory files and complex tables requires longer parsing times. |
File Type Whitelist | pdf, docx, doc, html, txt | Covers common market access document formats, ensuring mainstream files can be processed. |
OCR Enabled (OCR Enabled) | True | Addresses policy documents that contain text as images or stamped documents. |
Table Content Processing | Parse by row and retain headers | Ensures table data is retrievable while preserving its semantic structure. |
Three Common Mistakes
- Document parsing takes too long or times out, with logs showing a
PARSE_FILE_TIMEOUTerror. Policy documents are often lengthy and complex, and default parsing timeout settings are insufficient to complete processing. - After parsing, some text content is missing or formatting is garbled in uploaded Word documents or PDFs with image content. The parser may have insufficient support for specific formats or embedded objects, leading to incorrect extraction of all information.
- After uploading a file via API, the call succeeds but retrieval results are empty or irrelevant. The file content may not have been correctly passed to the parsing module during the API call, or an unhandled internal error occurred during parsing.
How to Verify Correct Configuration
- Upload typical lengthy PDF policy documents and Word documents containing tables. Check if the parsed text content is complete, especially if headings, clauses, and table data are correctly extracted.
- For documents containing specialized terminology and codes, perform keyword searches. Verify that relevant paragraphs are accurately recalled and evaluate the completeness of the recalled context.
- Upload different file types (e.g., PDF, DOCX) through the FastGPT platform interface. Upload the same files via API and compare the consistency of parsing results between the two methods.
The values provided are common starting points and should be measured against specific 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.