Data Characteristics in This Domain
Pharmacovigilance R&D documents include Case Safety Reports (ICSRs), Periodic Safety Update Reports (PSURs/PBRERs), Risk Management Plans (RMPs), and post-marketing study reports. These documents are typically in PDF, Word, or scanned image formats. Data sources are diverse, covering clinical trial organizations, healthcare institutions, patient reports, and internal pharmaceutical company databases. Update frequency varies by document type; ICSRs may be real-time or periodic, while PSURs/PBRERs are usually semi-annual or annual. Document internal structures are relatively fixed. For example, ICSRs contain fields such as patient demographics, drug information, adverse event descriptions, and management measures. Field content may include medical terminology, dosage units (e.g., mg, g, IU), frequency units (times/day, times/week), and event timestamps.
Constraints on Deployment and Upgrade from These Characteristics
The complexity and diversity of pharmacovigilance documents impose specific requirements on FastGPT deployment and upgrades. First, a large volume of unstructured or semi-structured documents (like scanned images) necessitates integrating a high-quality OCR module during deployment to ensure accurate text extraction. Second, precise identification of specialized medical terminology and dosage units in documents requires incorporating domain-specific dictionaries during model training and fine-tuning to enhance the model's understanding of specific entities and units. High-frequency updates of ICSR documents demand an efficient incremental update mechanism for the knowledge base, avoiding full rebuilds each time. Additionally, the coexistence of multiple document versions requires FastGPT to support version control in knowledge base management, ensuring historical data traceability. The deployment environment must consider data security and compliance, selecting appropriate storage solutions and network isolation strategies.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 200 MB | Pharmacovigilance reports can be large, containing charts and attachments |
Chunk size (Segment Length) | 800 characters | Ensures complete context while preventing overly long segments from affecting recall |
Recall count (Recall Count) | Top 10 entries | Balances recall rate with subsequent re-ranking efficiency |
Similarity threshold (Similarity Threshold) | 0.75 | Balances recall precision and generalization, reducing irrelevant results |
Rerank result count (Re-rank Return Count) | Top 3 entries | Focuses on the most relevant key information, improving final response quality |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Time allocated for parsing complex PDFs and scanned documents |
Common Pitfalls
- The content extraction module fails to operate correctly in a self-deployed environment, displaying "File parsing failed" or "Connection timeout" errors. This typically occurs due to missing essential dependency libraries or incorrectly configured OCR services within the container environment.
- After a knowledge base update, query results do not reflect the latest pharmacovigilance information, returning outdated data. This may be related to an unconfigured incremental synchronization mechanism or an improper index rebuilding strategy, preventing new data from being included in the retrieval scope in a timely manner.
- After upgrading the FastGPT version, existing knowledge base content or application configurations are lost, requiring re-importation. This often happens because data migration scripts were not executed correctly during the upgrade, or persistent storage volumes were not effectively mounted and backed up.
Verification Steps
- Upload a PDF pharmacovigilance report containing complex tables and medical terminology. Check if the text content is extracted completely and accurately, especially key fields like dosage and time.
- Create test queries for recently published adverse event reports. Verify that retrieval results include the latest relevant information and check if recalled items match expectations.
- Simulate a FastGPT version upgrade. Before and after the upgrade, use APIs to verify knowledge base integrity and application functionality availability, ensuring no data loss and retention of original configurations.
Note: The values provided are common starting points. Measure them against specific samples to determine optimal configurations.
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.