Data Characteristics
Retail chain pharmacovigilance data originates from sales systems, member management systems, customer service records, and external regulatory announcements. Sales systems record drug batch numbers, quantities, and customer information, potentially linking to adverse event reports. Member management systems provide detailed customer medication history and allergy information. Customer service records contain raw text from consultations, complaints, and adverse event feedback.
Data updates frequently. Sales data is real-time, customer service records are daily, and regulatory announcements are irregular. Document structures vary. Sales records are typically structured database tables. Customer service records are free text. Regulatory announcements are often PDFs or web pages, which may include tables or images.
Fields and units include:
- Sales records: generic drug name, brand name, batch number, production date, expiry date, unit price, quantity.
- Customer service records: symptom description, duration, medication details, medical treatment status.
- Regulatory announcements: drug name, implicated batch numbers, adverse event type, action taken.
Constraints on Citation and Traceability
These data characteristics impose specific requirements on citation and traceability. Real-time sales data requires rapid indexing of new information and instant linking to adverse event reports. This allows quick identification of problematic batches and affected customers.
Free-text customer service records and unstructured content in regulatory announcements require robust text parsing to extract key information like symptoms, drug names, and timestamps. This directly impacts knowledge base recall accuracy. Precise matching of fields like drug batch numbers and production dates is fundamental for a complete traceability chain.
The system must integrate data from various formats to ensure citations trace back to original records. For example, an adverse event report should link directly to the corresponding sales order or customer service call recording. For external information like regulatory announcements, citations must be timely and authoritative, avoiding outdated or inaccurate information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500-800 characters (characters) | Balances completeness of symptom descriptions in customer service records with vector retrieval efficiency. Avoids over-segmentation and context loss. |
Recall count (Recall Count) | Top 10 entries (top 10) | Covers multiple potential knowledge points from sales records, customer feedback, and regulatory announcements, increasing initial recall breadth. |
Similarity threshold (Similarity Threshold) | 0.75-0.85 | Ensures relevance while allowing for text variations common in free-text customer service records. |
Rerank result count (Reranked Return Count) | Top 3 entries (top 3) | Focuses on the most relevant core citations, reducing downstream processing complexity and improving result precision. |
maxContext | 3000 Tokens | Accommodates potentially long descriptions and multi-source information in retail chain adverse event reports, ensuring context completeness. |
UPLOAD_FILE_MAX_SIZE | 50 MB | Covers the typical size of PDF announcement files from regulatory agencies, preventing upload failures due to large file sizes. |
Common Pitfalls
- AI responses cite incorrect sales batch numbers or drug names. This often results from inaccurate entity recognition in free text by the parser or outdated knowledge base entries.
- The system experiences out-of-memory errors or response timeouts when processing large volumes of customer service records. This can be due to an excessively large
Chunk size(Segment Length), leading to too much data per vector block, or an insufficientPARSE_FILE_TIMEOUT_SECONDSsetting. - The conversation component cannot cite specific fields from previous knowledge base processing results, such as adverse event severity or treatment recommendations. This indicates that these fields were not correctly extracted or tagged during knowledge base indexing, making them inaccessible to downstream components.
Verification Steps
- Select customer service records with typical adverse event descriptions. Verify the system accurately identifies and cites drug names, batch numbers, and symptoms, tracing back to the original records.
- Upload a PDF regulatory announcement containing complex tables and multiple text sections. Check if the system correctly parses and indexes key information and presents it as a citation source in Q&A.
- Simulate user queries for adverse event reports of specific drugs across different sales batches. Verify that the system's returned citations include corresponding sales record IDs or timestamps.
The values provided are common starting points. Measure them 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.