Data Characteristics
Pharmaceutical e-commerce registration document data originates from regulatory documents, guidelines, and technical review requirements published by drug administration departments. It also includes internal submissions such as application forms, research reports, production process documents, quality standards, and stability data. This data updates frequently, especially with regulatory policy changes or optimizations in new drug review and approval processes. Documents come in various formats, including PDF for regulations, Word for application forms, Excel for statistical data, and image formats for supporting materials. Fields and units are highly specialized, covering generic drug names, brand names, dosages, specifications, registration classifications, indications, usage and dosage, adverse reactions, manufacturers, and approval numbers. Units often include milligrams (mg), milliliters (ml), and percentages (%).
Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall
The high update frequency of pharmaceutical e-commerce registration documents requires the knowledge base to quickly synchronize and index new files. This prevents errors caused by outdated information. Diverse document formats challenge file parsing and content extraction, necessitating robust multi-format parsers to ensure all critical information is effectively vectorized. Identifying and associating specialized fields and units is crucial. This requires vector models to accurately capture the semantic features of these specialized terms, improving retrieval precision. For example, retrieving different drug specifications requires distinguishing "5mg" from "10mg," and retrieving different dosage forms requires identifying "tablets" and "capsules." Furthermore, the hierarchical structure and citation relationships of regulatory documents impose specific requirements on knowledge graph construction and relation-based retrieval.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Segment Length | 500–800 characters | Ensures logical completeness of regulatory clauses or application data while preventing excessively long segments from introducing too much noise. |
Segment Overlap | 100 characters | Guarantees contextual continuity and improves semantic understanding across paragraphs, especially for connections between regulatory clauses. |
Recall Count | Top 8 | Given the strictness required for pharmaceutical application data, increasing the number of recalled items covers more potentially relevant information and reduces the risk of omissions. |
Similarity Threshold | 0.75–0.85 | The pharmaceutical field demands high accuracy. Raising the threshold appropriately filters out low-relevance results and reduces false positives. |
Rerank Return Count | Top 5 | Combined with a reranking mechanism, this further optimizes sorting, ensuring the most relevant core data is presented first and improving user efficiency in obtaining key information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses potentially long parsing times for large PDF regulatory documents or scanned images, preventing parsing interruptions. |
Common Pitfalls
- After uploading PDF files to the knowledge base, retrieval results lack critical information or have missing content. This might be because the PDF is a scanned image without an extractable text layer, preventing the parser from recognizing the content.
- When retrieving specific drug registration information, the system displays "authentication failed" or an API request error. This often occurs due to an expired API key or incorrect permission configuration, failing backend service authentication.
- After a knowledge base update, newly uploaded regulatory files cannot be retrieved immediately, or retrieval speed significantly slows down. This might be because the knowledge base indexing mechanism was not triggered in time or the indexing process took too long, failing to quickly synchronize the latest data.
Verification Steps
- Select a drug application document containing complex tables and specialized terminology. After uploading, check the parsing logs to confirm that all text content, especially key fields and values, has been fully extracted.
- For recently published pharmaceutical regulations, upload them to the knowledge base. Then, simulate real queries to verify if the corresponding regulatory clauses and associated guidelines are accurately recalled.
- Use registration application questions for different drug types (e.g., chemical drugs, traditional Chinese medicine, biological products) to perform cross-retrieval tests. Evaluate the accuracy and relevance of the retrieval results and adjust the
Similarity Thresholdbased on feedback. - Monitor the knowledge base's file indexing queue and status to ensure new files are indexed within the expected time after upload and can be retrieved normally.
Note: 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.