Deployment and Upgrade for Drug Rationalization Policies

Data Characteristics

Drug rationalization policy data primarily originates from guidelines published by national health commissions, implementation rules from local health authorities, and internal pharmaceutical management policies and clinical pathways developed by hospitals. This data typically exists as PDF documents, Word documents, and a small number of structured Excel spreadsheets.

Update frequency varies: national guidelines are usually revised every 3-5 years, while local rules and internal hospital policies may undergo minor updates annually or as policy changes occur. Document structures often include chapters, articles, and appendices, with frequent cross-references. Fields and units involve drug dosages (e.g., mg/kg, tablet), frequencies (e.g., times/day), treatment durations (e.g., days, weeks), disease diagnoses (e.g., ICD-10 codes), and patient characteristics (e.g., age, weight).

Constraints on Deployment and Upgrade

Drug rationalization policy documents have a relatively low update frequency, but each update can involve extensive revisions. This requires efficient version management capabilities during data import. The prevalence of PDF and Word documents necessitates robust document parsing to accurately extract text content and preserve structural information, preventing parsing errors from leading to critical clause omissions or confusion.

The numerous cross-references and hierarchical structures in documents demand strong semantic understanding and contextual association from the knowledge base. This ensures the Q&A system can accurately interpret questions and synthesize answers from multiple relevant clauses. Fields with specific units, such as drug dosages and frequencies, require the system to recognize and process them correctly. For example, when a user asks a question, the system should distinguish between mg and g and retrieve relevant content accordingly.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	Drug rationalization policy documents are often large, containing numerous charts and attachments, requiring support for large file uploads.
`maxContext`	`2000 characters`	Policy documents have complex logic, requiring a longer context window to understand the full context.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Allocate sufficient time for parsing complex PDF/Word documents to avoid timeout failures.
`Chunk size` (Segment Length)	`800–1200 characters`	Maintain the integrity of text paragraphs, reduce semantic fragmentation, and balance recall efficiency.
`Recall count` (Recall Count)	`Top 8 entries`	Policy Q&A requires more comprehensive information coverage to handle cross-references and multi-perspective questions.
`Similarity threshold` (Similarity Threshold)	`0.75`	Ensure the precision of recalled content, avoiding incorrect matches due to similar terminology.

Common Pitfalls

When uploading large policy documents, the interface displays File upload failed and the log shows Request Entity Too Large. This indicates that the UPLOAD_FILE_MAX_SIZE configuration is too small to support large document transfers.
When users ask about specific drug dosages, the answer is vague or lacks critical numerical values. This occurs when document parsing fails to correctly identify and extract numerical fields with units, resulting in an incomplete knowledge base index.
After a system upgrade, some historical Q&A results show discrepancies. This may be due to an improper knowledge base index rebuilding strategy during the upgrade, failing to effectively integrate old version data or handle semantic differences between new and old documents.

Verification Steps

Upload a PDF document containing complex tables and cross-references from a drug rationalization policy. Verify that the file is successfully parsed and that the text content and structure are fully preserved.
Conduct multi-round Q&A tests on the knowledge base. Ask specific questions about drug dosages, indications, and contraindications. Check if the system's answers are accurate and cite the correct clauses.
Simulate user queries involving unit conversions or ambiguous phrasing. Observe if the system can correctly understand and provide relevant results. This helps determine if the Similarity threshold (Similarity Threshold) is appropriate.
Check system logs to confirm that no Timeout or Memory Exhausted errors occurred during document parsing and vectorization, ensuring resource configuration meets requirements.

Note: The values provided are common starting points. Measure against specific samples to determine optimal settings.

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.