Data Characteristics in this Category
Drug registration documents involve diverse data types. These primarily include drug inserts, clinical trial reports, pharmacological and toxicological studies, adverse reaction monitoring data, drug interaction literature, and regulatory documents. This information typically exists as PDFs, Word documents, or structured databases. Data update frequency is relatively low, mainly occurring during pre-market approval, insert revisions, or the release of significant safety information. Document internal structures are usually highly standardized; for example, drug inserts contain fixed fields such as "Indications," "Dosage and Administration," "Contraindications," and "Precautions." Field content may include dosage units (e.g., mg, ml), time units (e.g., hours, days), and medical terminology. The data volume is substantial, with single documents potentially reaching hundreds of pages.
Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall
The data characteristics of drug registration documents impose several constraints on knowledge base retrieval and recall. First, the standardized document structure necessitates document parsing capabilities to identify and extract specific sections or field content, supporting more precise retrieval. Second, the specialized nature of medical terminology and dosage units requires the knowledge base to have strong semantic understanding. It must handle synonyms, abbreviations, and unit conversions to avoid missed retrievals due to differing expressions. Third, although data update frequency is low, any update has a wide impact. This requires the knowledge base to support version management and incremental updates, ensuring the timeliness of retrieval results. Finally, the large document size and complex internal structure make fine-grained segmentation and indexing of individual documents necessary. This improves retrieval efficiency and recall accuracy, preventing context loss from long texts.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale for this Value |
|---|---|---|
Chunk size (Chunk Length) | 500–800 characters | Ensures paragraphs contain sufficient context while avoiding excessive length that impacts embedding quality and retrieval efficiency. |
Chunk Overlap Length (Chunk Overlap Length) | 50–100 characters | Maintains contextual coherence and prevents critical information from being truncated. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Balances recall rate and accuracy, filtering out irrelevant results, and accommodating precise matching of medical terminology. |
Recall count (Number of Retrieved Chunks) | Top 5–8 | Covers potentially relevant information, reduces the model's processing burden, and avoids introducing excessive noise. |
Rerank result count (Number of Reranked Chunks) | Top 3 | Focuses on the most relevant information, improving the quality and efficiency of the final answer. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses long parsing times for large PDF or Word documents, preventing parsing failures due to timeouts. |
Three Common Mistakes
- Knowledge base retrieval results contain a large amount of irrelevant information because the system is not optimized for specialized terminology, leading to an excessive number of generalized retrieval results.
- When retrieving a specific section from a drug insert, the results return similar sections from other drugs. This occurs because document chunking granularity is too coarse, failing to effectively distinguish contextual boundaries between different drugs.
- After updating some regulatory documents, retrieval results do not reflect the latest content. This happens because the knowledge base did not undergo timely incremental updates or version management was configured incorrectly.
How to Verify Correct Configuration
- Select a batch of test questions containing medical terminology and dosage units. Check if the retrieval results accurately recall relevant document snippets and verify the completeness of critical information.
- For documents with different structural types (e.g., drug inserts, clinical trial reports), verify if the knowledge base can correctly parse and index their key fields and sections.
- Simulate a data update scenario by modifying part of a document, then perform a retrieval to confirm if the knowledge base recalls the updated information.
- Check logs for file parsing timeout error messages and adjust parameters such as
PARSE_FILE_TIMEOUT_SECONDSaccordingly.
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.