Data Characteristics
Neurodegenerative disease pharmacovigilance data originates from clinical trial reports, real-world evidence (RWE) data, adverse drug reaction (ADR) systems, academic literature, and regulatory guidelines. This data primarily consists of unstructured documents, such as clinical study reports in PDF format, case report forms in Word documents, and structured adverse event data exported as XML or JSON from online systems. Reports are frequently updated, especially after new drugs launch, as regulatory bodies continuously collect and assess adverse reactions. Documents have complex structures, often including tables, charts, and text descriptions. Text sections contain extensive medical terminology, abbreviations, and dosage units like milligrams (mg), micrograms (µg), and milliliters (mL), as well as specific time units (hours, days, weeks).
Constraints on Document Parsing and Chunking
The complexity of neurodegenerative disease pharmacovigilance data imposes specific requirements on document parsing and chunking. First, the high density of medical terms and abbreviations in documents demands strong entity recognition capabilities from the parser to avoid mis-segmentation or omission of critical information. Second, common table and chart data in clinical reports require the parser to accurately extract structured information and integrate it into the text context. High update frequency necessitates efficient incremental parsing mechanisms to avoid full reprocessing each time. Furthermore, precise recognition of dosage and time units is crucial for assessing the severity and causality of adverse reactions; any parsing error could affect subsequent risk assessment. Documents are typically long, containing multiple chapters and appendices, requiring a chunking strategy that balances semantic completeness and information granularity.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters | Balances contextual relevance of medical terms with information density per chunk. Prevents excessively long chunks that lead to inaccurate recall or excessively short chunks that lose context. |
Overlap Length | 100–200 characters | Ensures critical information spanning across chunks is not truncated, especially descriptions of adverse reactions and dosage information. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Accounts for the longer parsing time of large clinical reports, preventing processing interruptions due to timeouts. |
chunk_strategy | Smart split by paragraph and table | Ensures table data is recognized and associated with relevant text content, improving information completeness. |
embedding_model | text-embedding-ada-002 or equivalent | Addresses the complexity of medical terminology, requiring high-dimensional semantic representation capabilities to improve the accuracy of similarity matching. |
max_upload_size_mb | 200 MB | Accommodates the batch upload requirements for large clinical trial reports and multiple adverse reaction reports. |
Common Pitfalls
- After uploading a PDF to the knowledge base, parsing does not complete for a long time, and query results are empty. This occurs because
PARSE_FILE_TIMEOUT_SECONDSis set too low, and large or complex documents cannot be processed within the default time. - When querying specific drug dosage information, results are inaccurate or missing units. This happens when document parsing fails to adequately recognize or extract numbers and units from the text, leading to semantically incomplete chunks.
- After uploading multiple related documents, some documents are not vectorized, resulting in incomplete search results. This may be due to insufficient concurrent processing capability or intermediate file parsing errors without clear error logs.
Verification Steps
- Select a typical neurodegenerative drug clinical report containing complex tables and medical terminology. Upload it and check the chunk preview content to confirm that critical information (e.g., drug name, dosage, adverse event description) is semantically complete within the chunks.
- For a specific adverse reaction event in the report, perform a search using a query that includes associated drugs, dosages, and symptoms. Observe the accuracy and relevance of the retrieved results and adjust the
Similarity threshold(similarity threshold) as needed. - Batch upload multiple reports. Monitor system logs and the task queue to confirm all files complete parsing and vectorization normally, without freezing or abnormal exits. Check the knowledge base status to ensure the number of documents matches the uploaded quantity.
The values provided are common starting points and should be measured against specific 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.