Document Parsing and Chunking for Pharmaceutical E-commerce

Data Characteristics for This Category

Quality documents in pharmaceutical e-commerce primarily include registration certificates, production licenses, business licenses, inspection reports, product manuals, adverse event reports, GSP (Good Supply Practice) certification documents, and supplier qualification proofs for drugs, medical devices, and health products. Most of these documents are in PDF format, with some being scanned copies. Their structure is relatively fixed. For example, drug manuals typically contain standard sections like [Drug Name], [Ingredients], [Indications], [Dosage and Administration], and [Adverse Reactions]. Data sources are mainly the National Medical Products Administration database, internal enterprise management systems, and third-party testing agency reports. Update frequency varies by document type. Registration certificates and licenses have fixed validity periods and review cycles, while product manuals and adverse event reports may be updated irregularly based on regulatory requirements or actual situations. Fields and units strictly follow NMPA regulations, such as drug dosage units (mg, g, ml), batch numbers, and expiration dates.

Constraints Imposed by These Characteristics on "Document Parsing and Chunking"

The fixed structure and strict field requirements of pharmaceutical e-commerce quality documents demand high accuracy in document parsing. The presence of scanned copies necessitates high-quality OCR capabilities to ensure complete text extraction. Documents may contain numerous tables and images (e.g., drug packaging images, chemical structures, inspection chromatograms). Parsing must preserve their contextual relevance to avoid information loss or misinterpretation. For product manuals and inspection reports, critical parameters, dosages, and adverse event descriptions must be chunked precisely for accurate retrieval in subsequent question-answering or search operations. Varying update frequencies require the knowledge base to support incremental updates and version management to prevent outdated knowledge due to document changes. The standardization of fields and units allows for structured information extraction using predefined rules during chunking, improving retrieval efficiency.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	Quality documents may contain numerous images or scanned copies, leading to larger file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	OCR parsing for large PDF files or scanned copies can be time-consuming, requiring sufficient processing time.
`Chunk size` (Chunk Length)	`800–1200 characters` (characters)	Ensures the completeness of information within a single chunk while accommodating large language model input windows, especially suitable for sections in product manuals.
`Chunk Overlap Length` (Overlap Length)	`100–200 characters` (characters)	Ensures contextual continuity and prevents semantic loss due to chunk truncation.
`Enable Image Recognition`	`Yes`	Quality documents often include charts, diagrams, and chemical structures. Image content is crucial for understanding.
`Table Parsing Mode` (Table Parsing Mode)	`Structured Extraction` or `Markdown`	Documents like inspection reports contain extensive tabular data. Preserving the table structure is necessary.

Three Common Pitfalls

After uploading a PDF file, the parsing status remains "processing" for an extended period or fails directly. This often happens when the PDF file is too large or complex (e.g., many scanned images), causing PARSE_FILE_TIMEOUT_SECONDS to be set too short, leading to a timeout before processing completes.
During knowledge base Q&A, critical information like drug dosages or adverse events is missing or inaccurate. This occurs when document parsing does not adequately consider table or image content, resulting in incomplete structured information extraction, or when Chunk size (Chunk Length) is too long, diluting key information.
The knowledge base cannot parse certain external linked documents, showing "unsupported link type." This usually happens when the link points to content requiring login verification, dynamic generation, or non-standard web page formats. The knowledge base only supports publicly accessible static web page links.

How to Confirm Correct Configuration

Upload and parse representative documents such as drug manuals and inspection reports. Check if the parsed chunks are complete, especially the text descriptions corresponding to tables and images.
Query the knowledge base for critical information within the documents (e.g., dosage and administration for a specific drug, list of adverse reactions) to verify if relevant chunks are accurately retrieved and used for answers.
Upload a quality document containing scanned copies and confirm the accuracy of the OCR-identified text content, especially for critical fields like batch numbers and expiration dates.
Attempt to upload a file exceeding UPLOAD_FILE_MAX_SIZE or one expected to take a long time to parse. Observe if the system provides clear error messages or timeout notifications.

Note: 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.