Data Characteristics for This Category
DTP pharmacy product data originates primarily from official pharmaceutical manufacturer product manuals, drug registration approvals, clinical trial report summaries, and public approval information from regulatory bodies. This data exists as PDF documents, Word documents, and structured database exports (e.g., CSV, Excel). Data update frequency is relatively stable, with concentrated updates typically occurring during initial drug launches or regulatory changes. Routine updates are usually quarterly or annually. Document structures commonly include fields such as drug name, generic name, indications, dosage and administration, contraindications, adverse reactions, drug interactions, storage conditions, manufacturer, and approval number. Units involve dosage (mg, g, ml), frequency (times/day, week), and treatment duration (days, weeks, months), strictly adhering to national pharmacopoeia and drug manual specifications. Some specialized drugs also include additional information such as cold chain transport and special storage requirements.
Constraints Imposed by These Characteristics on "Forms and Interaction"
The authoritative and rigorous nature of DTP pharmacy product data demands highly precise and error-resistant form design. Due to patient medication safety concerns, form field input validation must strictly match the units and ranges specified in drug manuals. For example, dosage input cannot exceed safety thresholds. The low data update frequency means that knowledge base synchronization can use periodic full updates, eliminating the need for high-frequency real-time synchronization. Fixed document structures facilitate pre-planning knowledge base field mapping and extraction rules, reducing manual intervention. Standardized fields and units allow the AI to accurately understand and convert ambiguous user input during interaction, avoiding misjudgments due to unit confusion. Since data originates from official sources, responses to user inquiries must clearly cite information sources to enhance credibility.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 4000 | Ensures complete loading of critical information from drug manuals. |
embeddingModel | text-embedding-ada-002 | Balances semantic understanding capabilities with cost-effectiveness. |
recallThreshold | 0.78 | Ensures recalled results highly match the professional nature of DTP pharmacy products. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates parsing time requirements for large PDF manuals. |
Chunk size | 800 characters | Optimizes retrieval efficiency and contextual coherence for long text content. |
maxRetryAttempts | 3 | Handles temporary network fluctuations for external API calls or database connections. |
Common Mistakes
- After form submission, a "Invalid parameter input" message appears. This may be due to input field data type, format, or range failing backend validation. For example, entering non-numeric characters for dosage or exceeding the range defined in the drug manual.
- AI responses to user queries show confusion in drug dosage or usage units, such as interchanging "milligrams" and "grams." This occurs when the knowledge base fails to correctly standardize or differentiate synonymous units during data extraction or intent recognition.
- When the API retrieves application initialization information, the
opening_remarksorform_schemafields are empty. This may be due to incorrect setup of the opening remarks or form structure in the application configuration, or incomplete parameter passing in the API interface.
Verification Steps
- Simulate user input for various types of drug inquiries (including generic names, brand names, indications). Verify that AI responses accurately cite key information from drug manuals and check for information source identifiers.
- Test form input functionality. Attempt to enter data outside the specified range, in incorrect formats, or with mismatched units. Observe if the system correctly intercepts and provides clear error messages.
- Inspect the document parsing results for DTP pharmacy products in the knowledge base. Ensure that core fields such as drug name, dosage, units, and indications are accurately extracted and stored in a structured format.
- Use API calls to retrieve application initialization information. Verify that the returned opening remarks and form structure match the expected configuration.
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.