Deployment and Upgrade for Attenuated and Inactivated

Data Characteristics

Attenuated and inactivated vaccine registration data originates from diverse sources. These include clinical trial reports, manufacturing process protocols, quality control standards, and stability study data. Data typically exists as PDFs, Word documents, Excel spreadsheets, and some structured database records. Update frequencies vary; preclinical research data is relatively stable, while manufacturing batch records and quality inspection reports may update per batch or monthly. Document structures are complex, containing numerous technical terms, charts, and data tables.

Common fields and units include dosage units (e.g., TCID50, PFU), purity indicators (e.g., %), potency units (e.g., IU), and chemical concentrations (e.g., mg/mL). Documents also contain extensive biological proper nouns and abbreviations, requiring high accuracy in text parsing.

Deployment and Upgrade Constraints

The complexity of attenuated and inactivated vaccine data imposes specific deployment requirements. Large volumes of PDFs and Word documents necessitate efficient text extraction and chunking strategies to ensure knowledge base completeness. Periodic data updates, especially dynamic updates to manufacturing and quality control data, require flexible incremental update and version management capabilities. This avoids duplicate indexing and data redundancy.

Specialized terminology and units within documents, such as TCID50 or PFU/mL, require models to accurately identify and match them during vectorization and retrieval. This influences the selection of Embedding models and retrieval algorithms. Furthermore, data sensitivity often mandates on-premise deployment, requiring high standards for deployment environment stability and resource allocation. The ability to parse chart and table content directly impacts knowledge base construction quality.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Clinical trial reports and manufacturing protocols are generally large.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Complex PDF parsing is time-consuming; allow sufficient time to avoid timeouts.
`maxContext`	`32000 tokens`	Ensures coverage of lengthy discussions in vaccine development.
`Chunk size` (Chunk Length)	`800–1200 characters`	Balances technical terms and contextual relevance, avoiding over-fragmentation or overly long chunks.
`Recall count` (Retrieval Count)	`Top 8`	Ensures retrieval of sufficient detail from numerous relevant documents, increasing coverage.
`Similarity threshold` (Similarity Threshold)	Calibrate `0.75-0.85` based on actual measurements	Vaccine domain has many technical terms; balance precise recall with generalization.
`Rerank result count` (Reranked Return Count)	`Top 3`	Filters for the most relevant key information, reducing model processing load.

Common Pitfalls

Ollama model calls return empty: Logs often show successful connection but no data. This typically indicates the Ollama container did not load model weights correctly, or the model ran out of memory after loading, leading to an abnormal response.
Browser access to ip:3000 shows a blank page: This may be due to incorrect Docker container port mapping, or the service inside the container failed to start and listen on the port.
Model testing fails after an update: Logs may show Model not found or Invalid API key. This usually means environment variables like API_KEY or MODEL_NAME were not correctly persisted or reconfigured in the new container environment.

Verification Steps

Upload a typical attenuated and inactivated vaccine clinical trial report PDF file. Check if the file parsing progress bar completes normally and if the text content is viewable via the knowledge base preview function.
Query the knowledge base about key steps in vaccine manufacturing processes, such as "virus culture" or "purification process." Verify that the retrieved document snippets are accurate and highly relevant.
Simulate updates to quality inspection data for different batches. Verify that the knowledge base's incremental update mechanism is effective, and new data is correctly indexed and used for queries.
Check system logs for ERROR or WARNING level messages, especially those related to file parsing and model calls, to ensure the system operates without anomalies.

Note: 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.