Deployment and Upgrade for Structured Analysis of Rational

Data Characteristics

Rational drug use data originates from clinical guidelines, drug inserts, pharmacopeias, medical journal literature, and pharmaceutical monographs. These documents typically exist as PDFs, Word files, or structured databases. Update frequency is high, especially for drug inserts and clinical guidelines, which are often revised quarterly or semi-annually due to new drug approvals, adverse event monitoring, or updated clinical practices. Document internal structures are complex, containing numerous tables, nested lists, charts, and unstructured text such as indications, contraindications, dosage and administration, adverse reactions, and pharmacology/toxicology. Fields and units include dosage (mg, g, IU), concentration (mg/mL, %), time (hours, days, weeks), and frequency (times/day, QID), often with abbreviations and specific terminology.

Constraints Imposed by Data Characteristics on Deployment and Upgrade

High-frequency data updates require efficient file synchronization and incremental update capabilities in the deployment environment to avoid redundant parsing and data staleness. Complex document structures demand advanced parsers that can accurately identify and extract table data, list items, and text paragraphs while maintaining their logical relationships. For drug inserts, precise identification of units and standardization of critical information like dosage and frequency are essential to ensure accurate retrieval and reasoning. In offline deployment scenarios, model and dependency library completeness is crucial, requiring pre-installation of all necessary components and the ability to handle exceptions caused by model version and configuration mismatches. Resource planning during deployment must account for the continuous consumption of computing resources (CPU, memory) by extensive document parsing, as well as vector storage capacity requirements. During upgrades, new model versions or parsing logic must smoothly integrate with existing data and support rollback verification.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Rational drug use documents often contain many images and complex formats, leading to large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Complex document parsing can be time-consuming; this prevents timeouts.
`Chunk size`	`800–1200 characters`	Balances semantic completeness with vector retrieval efficiency.
`Similarity threshold`	`0.75`	Ensures precision of retrieved content, avoiding interference from irrelevant information.
`maxContext`	`4000`	Accommodates long texts, providing more comprehensive context.
`Vector ModelVersion`	`v2`	Improves embedding quality, more accurately capturing medical terminology semantics.

Common Pitfalls

An embedding error when uploading documents to the knowledge base often indicates that the specified vector model was not correctly loaded or configured in the deployment environment, causing model invocation to fail.
Repeated service restarts with failed messages in logs after system startup typically occur in purely offline deployments due to missing dependency packages or incomplete image files.
When parsing dosage and administration fields in drug inserts, extracted dosage values may be separated from units or units may be missing. This happens when the document parser's structured extraction capabilities for tables and lists are insufficient to correctly associate contextual information.

Verification Steps

Upload a multi-page PDF drug insert containing tables and complex lists. Verify that the knowledge base correctly extracts all table data and key fields, confirming structured parsing capability.
Query for drug regimens related to a specific disease. Observe if the model accurately retrieves relevant clinical guidelines or drug insert content and verify that cited dosages, frequencies, and other information match the original text. This confirms the effectiveness of the similarity threshold and chunk length.
Check system logs for the running status of the embedding service and file parsing services. Confirm there are no frequent errors or restarts, ensuring stable operation of core components.

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.