Deployment and Upgrade for Attenuated Inactivated Vaccine

Data Characteristics for This Category

Attenuated inactivated vaccine pharmacovigilance data primarily originates from clinical trial reports, post-market surveillance systems (e.g., VAERS, EudraVigilance), medical institution case records, and academic literature. This data typically exists as structured tables (e.g., adverse event report forms), unstructured text (e.g., patient medical records, medical imaging descriptions), and semi-structured data (e.g., laboratory test reports). Data update frequency in the post-market surveillance phase is usually continuous or quarterly. Clinical trial data is submitted in batches after trials conclude. Document structures are complex, containing medical terminology, dosage units (e.g., pfu, TCID50), immunization history, and complication descriptions. Fields are diverse, with extensive free-text descriptions, demanding high standardization. Data cleaning and standardization are core challenges.

Constraints on "Deployment and Upgrade" Due to These Characteristics

The complexity of attenuated inactivated vaccine data sources requires FastGPT to support multimodal data ingestion during deployment. This ensures the system can process both structured and unstructured data. Continuous or quarterly data update frequencies mean system upgrades must consider incremental data synchronization and version compatibility. This avoids data loss or format mismatches. The abundance of medical terminology and unique units in documents demands robust medical dictionaries and entity recognition capabilities for the knowledge base. Relevant models require pre-loading or training during deployment. Field diversity and free-text descriptions necessitate configuring a longer context window and stronger semantic understanding. This accurately extracts adverse event information. Accurate parsing of key fields like vaccine batch and vaccination date directly impacts the precision of pharmacovigilance analysis. Detailed field mapping rules are essential during deployment.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`1000 MB`	Clinical trial reports and medical records are often large; this ensures complete uploads.
`maxContext`	`8000 tokens`	Accommodates lengthy descriptions in medical literature, improving context understanding.
`Chunk size`	`500 characters`	Balances the integrity of medical terms with the efficiency of vector retrieval.
`Recall count`	`15 entries`	Increases the probability of hitting relevant information in complex adverse event queries.
`Similarity threshold`	`0.75`	Ensures retrieved results are highly relevant to medical query intent.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Time required to parse large PDF medical records or reports.

Common Pitfalls

Inaccurate adverse event descriptions in knowledge base retrieval results often stem from improper knowledge base chunking strategies. This leads to truncation of key medical terms or weak semantic associations.
After a system update, some previous adverse event reports fail to parse correctly. This is typically due to compatibility issues between the new model version and old data formats, without sufficient migration testing.
The M3E model fails to operate correctly in aiproxy configuration. This is usually due to a mismatch between the aiproxy version and the M3E model API protocol, or missing necessary dependencies.

Verification of Correct Configuration

Upload an attenuated inactivated vaccine adverse event report containing complex medical terminology and vaccine batch information. Verify the system's ability to accurately identify and extract key fields, comparing against expected results.
Conduct multi-turn Q&A in the knowledge base. Query the incidence of adverse reactions for specific vaccines and symptom descriptions. Check the medical accuracy and completeness of the answers. Confirm that the number of retrieved items and similarity threshold meet expectations.
Perform a small-scale incremental data synchronization. Observe if new data is correctly indexed and queried by the system. Check system logs for any error messages.
Call the M3E model via aiproxy for embedding operations. Check if the API response status code is 200. Verify the correctness of the returned vector dimensions.

The values given 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.