Data Characteristics
Regulatory and Standard Operating Procedure (SOP) documents in the e-commerce pharmaceutical sector originate primarily from national and provincial drug administration bureaus, as well as internal quality management system files and operational procedures. These documents are typically in PDF format, with some in Word or as scanned images. The update frequency is relatively consistent: national regulations are revised or added several times a year, while internal company policies are updated annually or semi-annually based on external policy adjustments or internal business development. Document structures are rigorous, often including directories, chapter titles, clause numbers, and appendices. Fields include drug names, batch numbers, production dates, expiration dates, storage conditions, delivery requirements, and quality standards. Units cover milligrams, milliliters, degrees, and percentages.
Constraints on Document Parsing and Chunking
The prevalence of PDF and scanned image formats for e-commerce pharmaceutical regulatory documents poses challenges for accurate text extraction. This can lead to character recognition errors or layout inconsistencies. The rigorous document structure requires the parser to accurately identify chapter titles and clause numbers to maintain logical integrity and prevent mixing different clauses. The large number of specialized terms and units in regulations and SOPs necessitates accurate identification by the parser for important information chunking. The fixed update rhythm requires support for incremental updates and version management to ensure the RAG system always uses the latest regulations. Additionally, the presence of sensitive information like drug batch numbers and expiration dates requires data anonymization or access control.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters | Regulatory clauses are often long. Chunks that are too short can break semantic integrity, while overly long chunks can affect recall accuracy. |
Overlap Length | 100–150 characters | Ensures contextual continuity between paragraphs, preventing loss of important information due to chunking. |
Parsing Strategy | By Title | Regulatory documents are highly structured. Chunking by title effectively maintains logical integrity. |
OCR Recognition | Enabled (Enabled) | Addresses scanned and image-based regulatory documents, ensuring text content can be correctly extracted. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Provides sufficient parsing time for large regulatory files or SOP documents containing many images. |
maxContext | Calibrate by actual measurement (Calibrate by measurement) | Balances context length and inference cost based on specific business scenarios and model capabilities. |
Common Pitfalls
- After uploading a document, if the chat interface displays "Parsing failed" or "Document content is empty," this often indicates that the PDF document is a scanned image and
OCR Recognitionis not enabled or its recognition rate is insufficient. - If RAG retrieval results contain irrelevant content or key clauses are truncated, the
Chunk size(Chunk Length) might be set too short, leading to incorrect splitting of semantic units. - If the system updates regulatory documents but user queries still return old answers, this indicates that the incremental update process was not executed correctly, or the caching mechanism was not refreshed in time.
Verification Steps
- Upload regulatory documents in various formats (e.g., PDF, scanned images, Word). In the knowledge base management interface, check if the document status is "Parsing completed" and preview the chunking results. Confirm text extraction completeness and logical chunking.
- Ask questions about specific clauses or specialized terms within the regulatory documents. Observe the RAG retrieval results and verify that the retrieved chunks are accurate, complete, and highly relevant to the question.
- Regularly upload updated versions of regulations. Verify through questioning that the system smoothly transitions between new and old content, ensuring it correctly identifies and uses the latest regulations.
The values provided are common starting points. They 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.