Database and Operations for Pharmacovigilance R&D Document

Data Characteristics in This Category

Pharmacovigilance data primarily originates from post-market adverse drug reaction (ADR) reports, clinical trial reports, literature, and drug inserts. These documents often contain semi-structured or unstructured text, such as free-text descriptions, medical terminology, dosage information, patient characteristics, and adverse reaction codes (e.g., MedDRA codes). Data updates frequently, especially during the early stages of new drug launches or when new safety signals emerge. Document structures vary, ranging from standardized report templates to open-ended research papers. Fields include drug name, batch number, ADR occurrence time, severity, outcome, relevant medical history, and concomitant medications. Units such as dosage (mg, g), frequency (times/day), and time (days, weeks) require precise identification.

Constraints Imposed by These Characteristics on "Database and Operations"

The high update frequency of pharmacovigilance data requires the database to support efficient write and update operations, handling a continuous influx of reports. The diverse and semi-structured nature of documents necessitates a flexible document-oriented database. This allows for storing and retrieving information in various formats, avoiding structuring difficulties imposed by rigid schemas. The vast amount of medical terminology and codes demands efficient text retrieval and fuzzy matching capabilities. Precise identification of field units means the structuring process needs robust entity recognition and normalization. Furthermore, long-term storage and traceability of historical data place higher demands on database backup and recovery strategies, ensuring data integrity and compliance.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`3000 Tokens`	Pharmacovigilance reports often contain detailed descriptions; this value supports processing longer context information.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing large PDFs or scanned documents can be time-consuming; this avoids parsing failures due to timeouts.
`Chunk size`	`800–1200 characters`	Balances semantic integrity of paragraphs and retrieval efficiency, suitable for the length of medical texts.
`Similarity threshold`	`0.75`	Ensures recalled document segments are highly relevant to the query, reducing noise.
`Rerank result count`	`Top 5 entries`	After reranking, the top few items typically contain the most relevant information, improving accuracy.
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	Pharmacovigilance documents (e.g., clinical reports) may contain numerous images or charts, resulting in larger file sizes.

Three Common Pitfalls

FastGPT runs but queries show no historical conversation records. This happens when the MongoDB database is not correctly configured with a replica set, leading to data persistence failure.
Parsing timeout errors occur when uploading large PDF files. This is due to PARSE_FILE_TIMEOUT_SECONDS being set too low, not providing enough processing time for complex documents.
Retrieval results contain a large amount of irrelevant content. This happens when the Similarity threshold (similarity threshold) is set too low, recalling document segments with distant semantic meaning.

How to Confirm Correct Configuration

Upload a pharmacovigilance report with complex medical terminology and multiple pages. Check if the parse_status field displays "Completed" (completed).
Execute specific queries against the report content. Check if the recall_segments field contains accurate key information and verify that the similarity_score meets expectations.
Simulate high-concurrency document uploads and queries. Monitor the database's connection_pool_size and query_latency metrics to confirm system performance meets requirements.

The values provided are common starting points and should be measured against the reader's 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.