Data Characteristics
IVD diagnostic reagent pharmacovigilance data originates from clinical trial reports, post-market surveillance reports, user complaints, and regulatory submission databases. This data updates frequently, typically upon event occurrence or through regular submissions (e.g., monthly or quarterly). Document structures vary, including detailed PDF reports, Word document user manuals, Excel batch records and adverse event lists, and CSV or JSON files exported from structured databases. Fields cover reagent name, lot number, manufacturing date, expiration date, adverse event description, patient information (anonymized), event time, handling measures, and diagnostic results. Units commonly include concentration (e.g., mol/L), dosage (e.g., IU), time (e.g., days, hours), and quantity.
Constraints Imposed by These Characteristics on Deployment and Upgrade
The diverse data sources for IVD diagnostic reagents require FastGPT to have robust file parsing capabilities during deployment, especially for identifying and extracting information from unstructured documents. High-frequency updates demand efficient real-time data ingestion and index reconstruction to prevent data lag from affecting pharmacovigilance analysis. Complex document structures necessitate refined text segmentation strategies during knowledge base construction to ensure question-answering accuracy. Specific fields, particularly critical identifiers like lot numbers and expiration dates, require special handling during data cleaning and vectorization to maintain semantic integrity. These factors collectively determine that system deployment requires sufficient computing resources and flexible configuration adjustment capabilities. Upgrades must consider data migration compatibility and downtime.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | IVD reports can contain numerous images and detailed data, leading to large individual file sizes. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large PDF or Word reports can be time-consuming, requiring a longer timeout. |
Chunk size (Segment Length) | 800–1200 characters | Ensures critical information like adverse event descriptions and diagnostic results remain within the same segment, preventing semantic fragmentation. |
Recall count (Recall Count) | Top 5 entries (Top 5) | Increases initial recall coverage to address multiple relevant snippets that may appear in IVD reports. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Adjust based on actual Q&A effectiveness to balance recall accuracy and relevance, ensuring a good match with adverse event descriptions. |
Vector Database Shard Count (Vector Database Shard Count) | 4–8 Units | Addresses high-frequency data updates and query pressure, distributes data load, and improves retrieval efficiency. |
Three Common Mistakes
- Knowledge base query results fail to accurately link to specific lot numbers or expiration date information because these critical fields were not effectively identified as independent entities or metadata during data ingestion.
- After a system upgrade, some historical adverse event reports cannot be loaded or parsed correctly. This occurs because the new version's file parser has compatibility issues with older data formats, such as differences in handling specific table or chart structures.
- When batch processing newly uploaded adverse event reports, the system experiences connection timeouts or out-of-memory errors. This happens because the concurrency of batch processing tasks is set too high, exceeding the deployment environment's resource capacity.
How to Confirm Correct Configuration
- Upload a batch of IVD diagnostic reagent adverse event reports in various file formats (PDF, Word, Excel, CSV). Check if the knowledge base has correctly indexed all critical information, including reagent names, lot numbers, and adverse event descriptions.
- Simulate high-frequency data updates by uploading a batch of new adverse event reports. Observe the system's processing speed for new data and verify that the latest adverse event information can be immediately queried in the knowledge base.
- Use query statements containing specific lot numbers and adverse event characteristics to verify that FastGPT accurately recalls relevant document snippets. Manually compare to confirm the completeness and accuracy of the recalled content.
- Check system logs to confirm no abnormal errors or warnings occur during data ingestion, knowledge base updates, and question-answering processes, especially logs related to file parsing and database operations.
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.