Deployment and Upgrades for Cardiovascular Intervention

Data Characteristics

Cardiovascular intervention medical device regulatory submission data sources primarily include clinical trial reports, biocompatibility test reports, performance test reports, risk management reports, product technical requirements, instructions for use, and labeling. This data often exists in various formats, such as PDF, Word, and Excel, containing extensive specialized terminology, charts, and structured data. Data update frequency is relatively low, concentrating on key points in the product lifecycle, such as clinical trial data releases, regulatory policy adjustments, or product iterations. Document structures typically follow standardized templates from national regulatory bodies (e.g., NMPA) or international medical device regulatory agencies (e.g., FDA, CE). Fields include model specifications, scope of application, contraindications, intended use, material composition, performance indicators, and testing methods. Units involve engineering and biomedical measurements like millimeters (mm), milligrams (mg), Pascals (Pa), and flow rates (mL/min). Some data may be in scanned image format, requiring Optical Character Recognition (OCR) processing.

Constraints on Deployment and Upgrades

The data characteristics of cardiovascular intervention regulatory submission documents impose specific requirements on FastGPT deployment and upgrades. First, diverse document formats, especially PDFs with numerous tables and diagrams, necessitate the integration of efficient and accurate OCR and document parsing capabilities during FastGPT deployment to ensure complete information extraction. Second, the highly specialized and low-update-frequency nature of the data means that knowledge base construction requires importing large volumes of historical data at once, along with support for precise long-text chunking and vectorization to guarantee recall accuracy. This also implies a relatively lower requirement for incremental update efficiency. Furthermore, regulatory compliance and data sensitivity demand a deployment environment with high security and data isolation capabilities, typically favoring on-premise or private cloud deployments. The specialized nature of fields and the strictness of units mean that synonym and abbreviation preprocessing is required during vectorization and retrieval. Customized embedding models may also be necessary to enhance understanding of specialized domain content. For individual documents spanning thousands of pages, file upload size and parsing timeout must have sufficient redundancy.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`2000 MB`	Accommodates single submission documents (e.g., clinical trial reports) that may contain hundreds or even thousands of scanned pages, resulting in large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing complex PDF documents (with many charts, scanned images) takes a long time for parsing and OCR; this prevents parsing timeouts.
`Chunk size`	`800–1200 characters`	Paragraphs in cardiovascular intervention documents often contain extensive specialized terminology and logical connections, ensuring segment integrity.
`Recall count`	`Top 10 entries`	Improves retrieval relevance, preventing critical information loss due to minor differences.
`Similarity threshold`	`0.75–0.85`	The domain is highly specialized; this ensures retrieved results are highly relevant to the query, reducing interference from irrelevant information.
`maxContext`	`8000 Tokens`	Submission documents often require long context understanding, ensuring the model can handle complex logical relationships and data references.

Common Pitfalls

FastGPT container starts normally, but port 3000 is inaccessible, and logs only show basic startup information: This usually indicates incorrect port mapping in docker-compose.yml or firewall rules blocking external access, preventing the frontend service from being properly exposed.
"File too large" or parsing timeout errors when uploading large PDF documents: UPLOAD_FILE_MAX_SIZE or PARSE_FILE_TIMEOUT_SECONDS parameters are set too low, failing to accommodate the actual file size and parsing complexity of cardiovascular intervention submission documents.
Knowledge base query results contain a large amount of irrelevant or low-quality information: Incorrect Chunk size settings lead to semantic fragmentation, or Similarity threshold is too low, failing to effectively filter out general knowledge not highly relevant to the cardiovascular intervention domain.

Verification Steps

Upload a typical cardiovascular intervention submission PDF document containing charts and scanned images (e.g., a 500-page clinical trial report). Confirm that the file uploads and parses successfully, and that key technical parameters and conclusions from the document are retrievable from the knowledge base.
Through the FastGPT interface or API, perform multi-turn questioning on the cardiovascular intervention data in the knowledge base. Observe whether the AI's responses accurately understand and reference specialized terminology, specific model specifications, and clinical data, and check if the cited original snippets are relevant.
Check FastGPT backend logs to confirm that no errors such as File Parsing Timeout, out of memory, or API Call Failed occur when processing long texts or complex queries, ensuring system stability under high load.

Note: The values provided are common starting points. It is recommended to measure 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.