Deployment and Upgrade for Gene Therapy AAV Clinical Trial

Data Characteristics

Gene therapy AAV (adeno-associated virus) clinical trial prescreening data originates from clinical trial protocols, patient medical records, genetic testing reports, and biomarker assay results. This data updates frequently, especially as trials progress and follow-up data is continuously entered. Document structures vary, including structured electronic medical record data, semi-structured gene sequencing reports, and unstructured clinical notes or imaging report descriptions. Fields include demographic information, genotypes (e.g., SNP variants), viral vector dosage (vg/kg), immunogenicity indicators (e.g., neutralizing antibody titer NAb), and specific gene expression levels (mRNA copies/cell). Units involve concentrations, copy numbers, and titers. Different assay methods may use different units, requiring standardization or conversion.

Constraints on Deployment and Upgrade

The high update frequency of AAV clinical trial prescreening data requires FastGPT to have flexible data synchronization mechanisms after deployment. This avoids excessive manual intervention and ensures knowledge base timeliness. Diverse document structures mean FastGPT needs to support parsing multiple document formats, especially effective extraction and understanding of unstructured text. This directly impacts model recall quality. The presence of specific fields like genotypes and viral vector dosages requires the knowledge base to accurately identify and index these specialized terms and values. Semantic integrity must be maintained during vectorization. Varying units necessitate standardization during data preprocessing to prevent retrieval errors due to unit inconsistencies. The deployment environment must consider data security and compliance, particularly for sensitive patient privacy information. Strict access control and data anonymization policies are required.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Gene sequencing reports or image files can be large and must be uploadable.
`maxContext`	`3000 tokens`	Clinical trial protocols and medical records are information-dense, requiring a longer context window.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDF documents or complex genetic reports can be time-consuming.
`Chunk size`	`800 characters`	Ensures each text chunk contains sufficient clinical context, such as a complete medical history description.
`Recall count`	`Top 10 entries`	Increases initial retrieval coverage to capture more potentially relevant genotypes or immune indicators.
`Similarity threshold`	`0.75`	Clinical terminology and gene sequence matching require higher similarity standards.

Common Mistakes

Model responses are based on old data after a knowledge base update. This occurs when the knowledge base synchronization mechanism is not correctly configured or scheduled tasks are inactive.
Key gene mutation information is not recognized or fields are empty after uploading a gene testing report PDF. This happens when the PDF parser has insufficient capability to extract tables or specific text formats.
The system is unresponsive for extended periods or queries time out. This indicates insufficient server resources (e.g., CPU or RAM) to support high-concurrency vector retrieval and model inference.

Verification Steps

Upload real AAV clinical trial data containing various document types (structured, unstructured). Verify that key fields (e.g., 基因型, 病毒载体剂量) are accurately indexed and retrievable in the knowledge base.
Execute a series of queries containing specialized terms and values (e.g., NAb 滴度, SNP rs12345). Check the accuracy and completeness of the returned results.
Simulate high-concurrency query scenarios. Monitor system response times and resource utilization to ensure stable operation under expected load.

The values given 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.