Deployment and Upgrade for Peptide Drug Pharmacovigilance

Data Characteristics

Peptide drug pharmacovigilance data originates from clinical trial reports, real-world studies, post-market surveillance, and spontaneous reporting systems from global drug regulatory agencies (e.g., FDA Adverse Event Reporting System, FAERS; European Medicines Agency EudraVigilance). Data update frequencies vary. Clinical trial data typically releases periodically with study progress, while spontaneous reporting system data flows continuously, with daily or weekly update cycles. Document structures are diverse, including unstructured text descriptions (e.g., adverse event report details), semi-structured case report forms (CRF), and structured database records. Field characteristics include peptide sequence information, dosage units (e.g., mg/kg, IU), administration routes, MedDRA codes for adverse reactions, event onset time, duration, and patient-specific biomarkers.

Constraints on Deployment and Upgrade

The complexity of peptide drug data sources and varied update frequencies require FastGPT deployments to configure flexible data synchronization mechanisms. These mechanisms must adapt to different data source fetching frequencies and formats. Large volumes of unstructured text data (e.g., clinical descriptions in adverse event reports) demand stronger text parsing capabilities and longer segment lengths to ensure semantic completeness. The presence of specialized fields like peptide sequences requires vector models with high domain knowledge. This necessitates selecting or fine-tuning models capable of bioinformatics understanding. Additionally, adverse reaction reports often contain time-series information. This requires the knowledge base to effectively handle temporal dimensions during indexing and support time-range filtering during retrieval. Potential duplicate reports and inconsistencies in the data also impose higher demands on data cleaning and preprocessing, influencing the setting of similarity thresholds.

Configuration Recommendations

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Clinical trial reports and real-world study documents can contain numerous charts and detailed descriptions, leading to large file sizes.
`Chunk size` (Segment Length)	`800–1200 characters`	Ensures that complex clinical descriptions and sequence information in peptide drug adverse reaction reports are segmented completely, preventing semantic truncation.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDFs or documents with complex tables can take a long time.
`maxContext`	`8000`	Accommodates detailed medical history and concomitant medication information that may appear in peptide drug adverse reaction reports, ensuring complete context.
`Similarity threshold` (Similarity Threshold)	`0.78–0.85`	Descriptions of peptide drug adverse reactions can be highly similar, requiring a higher threshold to distinguish subtle differences.
`Rerank result count` (Reranked Return Count)	`Top 5`	Ensures that after initial retrieval, reranking accurately presents the most relevant adverse reaction information.

Common Mistakes

Workflow environment variable calls fail, showing variables as null or undefined. This usually occurs because environment variables are not correctly mapped into the container during Docker deployment, or FastGPT workflow configurations do not correctly reference system environment variables.
After upgrading FastGPT, specific models (e.g., m3e) fail to load, with logs indicating model loading failure or API errors. This may be due to updates in model interfaces or dependent libraries in the new version, requiring a check of model configuration files or reinstallation of relevant dependencies.
The file parsing module errors when processing specific documents, with logs indicating split exceptions or parsing timeouts. This typically happens when document formats are complex, contain special characters, or files are too large, causing the parser to encounter unexpected data structures during tokenization or text processing.

Verification Steps

Upload a typical peptide drug clinical trial report PDF file. Check if file parsing is successful and verify that the generated segments in the knowledge base contain key peptide sequences and adverse event descriptions.
For a known adverse reaction case, use a query containing key symptoms and drug information to test. Check if the retrieval results include relevant historical reports and verify the similarity score.
Build a simple Q&A workflow to simulate a user asking about peptide drug adverse reactions. Check if the AI Agent's response accurately cites data from the knowledge base and evaluate the completeness and professionalism of the response.
Monitor backend logs to ensure no significant parsing errors, model call failures, or timeout warnings occur during data import and querying.

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.