Data Characteristics in This Category
Data sources for pharmaceutical e-commerce registration and declaration document preparation are highly diverse. They primarily include drug inserts, registration certificates, clinical trial reports, pharmaceutical research data, manufacturing process documents, quality standards, packaging label designs, and regulatory documents. These documents have varying update frequencies. Regulatory documents, such as policies issued by the National Medical Products Administration, typically have clear effective dates and revision cycles. Drug inserts and registration certificates update with product batches or change applications. Most documents are structured or semi-structured text. For example, drug inserts contain fixed fields like Drug Name, Indications, and Dosage and Administration. Clinical trial reports contain extensive narrative content and tabular data. Fields and units are highly standardized; for instance, dosage units (mg, g), concentration units (%), and time units (hours, days) are strictly defined. Some data may exist as scanned images or pictures, requiring OCR recognition.
Constraints Imposed by These Characteristics on "Reference Sourcing and Traceability"
The data characteristics of pharmaceutical e-commerce registration and declaration documents impose specific requirements on reference sourcing and traceability. First, the regulatory sensitivity of the data demands precise citation to the original source to avoid misinterpretation or deviation, especially for critical fields like approval numbers and manufacturing enterprise information. Second, multi-source heterogeneous data requires refined processing during knowledge base construction to ensure effective segmentation and indexing of different document types. For example, the OCR accuracy of scanned documents directly impacts subsequent text recall. Third, some data updates infrequently but has far-reaching implications when updated; the system must identify and prioritize quoting the latest version. Finally, the standardization of fields and units means that during the RAG (Retrieval Augmented Generation) process, semantic matching is necessary between terms in the query and fields in the knowledge base to prevent citation failures due to expression differences. For instance, if a user asks "daily dosage," the system must map this to the "Dosage and Administration" section in the drug insert.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
Chunk size (Segment Length) | 500–800 characters (500–800 characters) | Pharmaceutical regulatory texts often contain long sentences and paragraphs. An appropriate segment length ensures semantic completeness and prevents critical information from being truncated. |
Recall count (Recall Count) | Top 8–12 entries (Top 8–12 items) | The complexity of registration and declaration documents requires recalling a sufficient number of potentially relevant pieces of information to cover various possibly related regulatory provisions or product details. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Ensures the precision of recalled content, avoiding incorrect citation of regulatory provisions that are semantically similar but actually irrelevant. |
Rerank result count (Reranked Return Count) | Top 5 entries (Top 5 items) | After processing by the reranking model, prioritize displaying a few high-quality references most relevant to the user's query, improving the accuracy and credibility of the answer. |
OCR Recognition Accuracy | High-precision mode | Addresses the large volume of scanned and image-format regulatory documents, ensuring accurate extraction of text content. |
Document Update Frequency | Calibrate by actual measurement (Calibrate based on actual measurement) | Set differentiated indexing update strategies for various document types (e.g., regulations, drug inserts) to ensure citation of the latest versions. |
Three Common Mistakes
- Citing a large amount of irrelevant or low-relevance information. This happens when the
Similarity threshold(Similarity Threshold) is set too low, causing non-core content to be recalled. - Incorrect approval numbers or version information in the answer's citations. This may occur if document version management is not strict during knowledge base construction, leading the system to index outdated or incorrect data.
- When a user queries specific fields (e.g., "contraindications"), the system fails to recall corresponding content from the knowledge base. This might be due to an improper knowledge base segmentation strategy that separates critical fields from their context, or a failure to effectively identify fields during indexing.
How to Confirm Proper Configuration
- Select typical registration and declaration questions. Check if the system's returned citations precisely match the original text and verify the accuracy of key information like approval numbers and dates.
- Simulate updating regulatory documents or product inserts. Observe whether the system's priority for citing new vs. old versions aligns with expectations after the knowledge base update.
- For queries involving multiple data formats (e.g., text, scanned images), check if the system can effectively extract and cite required information from different sources. Manually compare to confirm the accuracy of OCR-recognized content.
The values provided are common starting points and should be measured 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.