Data Characteristics in This Category
Data in pharmaceutical e-commerce primarily originates from drug manufacturers' instructions, product catalogs, NMPA (National Medical Products Administration) registration information, and supplier product details. Data updates frequently, typically on a weekly or monthly basis, triggered by new product launches, batch updates, instruction revisions, and price adjustments. Document structures are predominantly semi-structured, containing extensive textual descriptions, specifications, precautions, and contraindications. Common fields include generic drug name, brand name, dosage form, specification, manufacturer, approval number, indications, dosage and administration, and adverse reactions. Units involve milligrams (mg), milliliters (ml), tablets, pills, vials, and boxes. Unit variations are significant across different dosage forms and specifications.
Constraints on "Forms and Interactions" Imposed by These Characteristics
The semi-structured nature of pharmaceutical e-commerce data necessitates highly flexible form design to accommodate field variations across different drug types and dosage forms. For example, oral solutions may require "ml/bottle," while tablets focus on "tablets/box." High-frequency data updates mean the knowledge base must support rapid synchronization to ensure users receive the latest information, preventing product inquiry errors or user complaints due to outdated information. The complex unit system requires the system to intelligently recognize and provide correct unit options when users input or select specifications, reducing operational errors. Additionally, users often mention multiple drugs or symptoms during product inquiries, requiring interaction design to support multi-entity recognition and contextual understanding for accurate matching with product information in the knowledge base. For long-text data like drug instructions, efficient segmentation and retrieval strategies are crucial to provide precise answers within a limited number of interaction turns.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 300-500 characters | Drug instructions are dense; shorter segments improve retrieval accuracy and avoid redundant information. |
Recall count (Recall Count) | Top 5 | Balances retrieval efficiency and information comprehensiveness, providing sufficient relevant product or instruction snippets. |
Similarity threshold (Similarity Threshold) | 0.75-0.85 | Pharmaceutical terminology is highly specialized; a higher threshold ensures retrieval results are highly relevant to user intent. |
maxContext | 3000-4000 tokens | Ensures capacity for multi-turn conversation context, especially when users ask detailed questions about drugs. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing large drug instructions or batch files requires a longer parsing time to avoid timeout errors. |
Streaming Output | Enabled | Enhances user experience, particularly when responding with lengthy drug introductions or usage instructions. |
Three Common Mistakes
- The drug specification field is empty in the user's submitted inquiry form because the form does not dynamically adjust required fields or provide unit selection based on drug type.
- AI-provided drug information does not match the latest version of the instructions because the knowledge base synchronization mechanism fails to process high-frequency data updates from suppliers in a timely manner.
- When users inquire about drug interactions for multiple products, the AI cannot provide a comprehensive answer because knowledge base segments are too long, leading to context loss within individual segments.
How to Confirm Correct Configuration
- Randomly select multiple newly launched drugs. Submit inquiries through the consultation form. Verify that the AI's response matches the latest product information on the official website or in the instructions.
- Simulate user submissions containing various dosage forms and specifications. Check if the drug units and dosage descriptions in the AI's response are accurate.
- For typical long drug instructions in the knowledge base, ask detailed questions about adverse reactions, contraindications, and other specifics. Verify if the AI can accurately extract and respond from the instructions.
The values given 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.