Data Characteristics
DTP pharmacy product and reagent consultation data primarily comes from official product inserts provided by pharmaceutical companies, clinical research reports, drug batch information, and internal pharmacy sales records and inventory data. This data updates frequently, especially with new drug launches, batch updates, and clinical guideline revisions. Document structures typically include standardized drug insert formats, such as indications, dosage and administration, contraindications, and adverse reactions, as well as reagent specifications, expiry dates, and batch numbers. Common fields include drug generic name, brand name, ATC classification code, manufacturer, approval number, minimum packaging unit, storage conditions, shelf life, and patient-related disease codes (e.g., ICD-10) and treatment regimens. Units strictly follow pharmaceutical and biological norms, such as milligrams (mg), milliliters (ml), international units (IU), moles (mol), and concentration percentages.
Constraints from Data Characteristics on Deployment and Upgrade
Frequent updates to DTP pharmacy data require the knowledge base, post-deployment, to have efficient data synchronization and incremental update capabilities. This ensures the timeliness and accuracy of consultation information. Standardized document structures facilitate automated extraction and indexing through predefined parsing rules, reducing manual intervention costs. However, processing unstructured clinical research abstracts or patient feedback requires more flexible text processing strategies. The dynamic nature of fields like drug batch and expiry dates demands version management and obsolescence mechanisms for the knowledge base, preventing the provision of outdated or invalid product information to users. Furthermore, strict unit specifications and professional terminology necessitate precise semantic understanding during vectorization and retrieval to avoid consultation errors due to unit confusion or terminology misinterpretation. The deployment environment must handle large volumes of mixed structured and unstructured data and support high-concurrency real-time queries to meet the immediate consultation needs of pharmacists and patients.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 4000–6000 characters | DTP pharmacy consultations often involve long texts like drug inserts and patient histories, requiring a sufficiently long context window to ensure information completeness. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Product inserts and clinical reports from pharmaceutical companies may contain numerous charts and detailed descriptions, leading to large individual file sizes. |
Chunk size (Chunk Length) | 800–1200 characters | Drug inserts and clinical reports have strong logical structures; overly short chunks may break semantic integrity, while overly long chunks introduce too much irrelevant information, affecting retrieval accuracy. |
Recall count (Recall Count) | Top 8–Top 12 | DTP pharmacy consultations demand extremely high accuracy, requiring the recall of more potentially relevant document snippets. This ensures no critical information is missed after reranking and LLM filtering. |
Similarity threshold (Similarity Threshold) | 0.78–0.85 | Strict matching is required for professional terms like drug names and indications. A higher similarity threshold helps filter out irrelevant results but needs a certain range to accommodate synonyms or descriptive variations. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large PDF drug inserts or clinical trial reports can take a long time, requiring a longer timeout to avoid parsing failures due to file complexity. |
Common Pitfalls
- User query results still contain old information after a knowledge base update. This occurs when incremental indexing or caching strategies are not configured correctly, preventing new data from taking effect promptly.
- During drug dosage and administration consultations, the AI platform confuses units or values. This happens when the data preprocessing stage fails to effectively identify and standardize professional units for drug dosage and concentration.
- When uploading large PDF clinical reports, file parsing fails or the progress bar remains stuck for an extended period. This usually indicates that the
PARSE_FILE_TIMEOUT_SECONDSparameter is set too short, insufficient to process complex document structures.
Verification Steps
- Upload the latest batch of drug inserts (PDF format) and verify that the knowledge base accurately indexes key information, such as batch numbers, expiry dates, and the latest revised dosage and administration.
- For specific drugs, simulate common patient questions, such as "What are the contraindications for Drug XX?", and check if the AI's response is complete, accurate, and references the correct knowledge base sources.
- Test the knowledge base's incremental update function by uploading a drug insert with only minor revisions. Observe if the newly revised content is immediately retrievable after the knowledge base update and check if old version information is correctly overwritten or marked.
- Conduct concurrency tests, simulating multiple pharmacists or patients consulting simultaneously. Confirm that system response times are within acceptable limits and query results remain consistent.
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.