Data Characteristics for this Category
CDMO (Contract Development and Manufacturing Organization) pharmacovigilance data originates from clinical trial reports, real-world data (RWD), post-market surveillance reports, safety data submitted by partners, and internal batch records. This data exists in both structured formats (e.g., database records, XML/JSON files) and unstructured formats (e.g., clinical study PDF documents, scanned handwritten doctor's reports). Data updates are frequent, especially during clinical trial phases or early drug launch periods, where safety reports may be submitted weekly or even daily. Document structures vary, including ICH E2B format safety reports, PDF Case Report Forms (CRFs), and Word or Excel summary reports. Fields and units are highly specialized, covering drug names, batch numbers, dosages (mg/kg), routes of administration, adverse event terms (MedDRA codes), occurrence dates, outcomes, and often include medical abbreviations and specialized terminology.
Constraints Imposed by These Characteristics on "Deployment and Upgrades"
The diversity of CDMO pharmacovigilance data sources requires FastGPT deployments to have robust heterogeneous data access capabilities, supporting various file formats and database connectors. High-frequency data updates demand real-time indexing and incremental update mechanisms, necessitating efficient re-indexing strategies to quickly synchronize the latest safety information. Complex document structures require customized text parsers and entity recognition models to accurately extract key fields, especially for specialized terms like MedDRA codes. The specialized nature of fields and units, along with medical abbreviations and terminology, places higher demands on built-in language models and word embedding models. This may require incorporating industry-specific pre-trained models or performing domain-adaptive fine-tuning to improve recall and matching accuracy.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Clinical trial reports and safety database export files can be large, ensuring single-upload capability. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing large PDFs or complex structured data files requires longer parsing times. |
Chunk size | 800–1200 characters | Ensures completeness of contextual information such as adverse event descriptions and patient history. |
Recall count | Top 10 entries | Pharmacovigilance queries typically require comprehensive information to support decision-making. |
Similarity threshold | 0.75–0.85 | Balances recall and precision, avoiding omission of critical adverse event information. |
Rerank result count | Top 5 entries | Optimizes the final presented results, focusing on the most relevant safety information. |
Three Common Mistakes
- Model returns empty adverse event names or drug batch numbers. This happens when the custom entity recognition model lacks sufficient generalization capability for medical terminology and batch number formats, failing to correctly extract them from unstructured text.
- Processing large safety report PDFs times out after upload. This happens when the
PARSE_FILE_TIMEOUT_SECONDSparameter is set too low, not allowing enough time for complex document parsing. - Unable to access the expected knowledge base after logging into FastGPT. This happens when user and knowledge base permissions are not correctly configured during initial deployment, or an initial administrator user is not created.
How to Confirm Correct Configuration
- Upload typical clinical trial report PDFs and ICH E2B XML files. Check if they parse correctly and generate knowledge segments. Verify that segment content includes key adverse event descriptions and drug information.
- Query using MedDRA codes or drug names. Check the relevance and accuracy of recall results. Confirm that expected safety data is retrieved. Observe result changes by adjusting
Similarity threshold. - Simulate high-concurrency data update scenarios. Observe the update speed of the knowledge base index and the real-time nature of query results. Ensure the incremental indexing mechanism functions correctly.
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.