Deployment and Upgrade for Cardiovascular Quality

Data Characteristics

Cardiovascular quality documentation includes clinical trial protocols, investigator brochures, informed consent forms, case report forms (CRFs), ethics approval documents, pharmacovigilance reports, device manuals, and various Standard Operating Procedures (SOPs). These documents originate from diverse sources, such as regulatory bodies, hospital ethics committees, Contract Research Organizations (CROs), medical device manufacturers, and pharmaceutical internal quality management departments. Document update frequency is high, especially for clinical trial-related files, which may undergo multiple revisions as research progresses. Document structure typically includes strict section numbering, figures, tables, appendices, and references. They extensively use medical terminology, drug names, device models, dosage units (e.g., mg/kg, mmol/L), and time units (e.g., days, weeks). Some documents also contain complex decision trees or flowcharts.

Constraints on Deployment and Upgrade

The complexity and specialized nature of cardiovascular quality documentation impose specific requirements on FastGPT's deployment and upgrade processes. The medical terminology and specialized units in these documents demand high accuracy from the model during tokenization and entity recognition. This can influence the choice of embedding models and chunk splitting strategies. The heterogeneous document formats (PDF, Word, scanned images) require the file parsing module to have robust compatibility and resilience. During deployment, ensure the integrity of relevant dependency libraries. High update frequency means the knowledge base must support efficient incremental updates and version management, avoiding redundant indexing and data duplication. Deployment and upgrades in an offline environment require all components and dependencies to be installable offline, and upgrade packages must include all necessary patches and migration scripts. marker errors or file parsing exceptions typically indicate improper parser configuration or missing dependencies.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Cardiovascular documents often contain many images and charts, leading to large file sizes. Increase this limit.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing complex PDFs and scanned documents can be time-consuming. Prevent parsing timeouts that cause task failures.
`Chunk Length`	`800–1200 characters`	Ensure the completeness of medical terminology and context, reducing semantic fragmentation.
`Recall Count`	`Top 10`	Ensure coverage of multiple relevant knowledge points, improving Q&A accuracy.
`Similarity Threshold`	`0.75`	Address the need for precise matching of specialized terminology, balancing recall and accuracy.
`Rerank Return Count`	`Top 5`	After reranking, focus on the most relevant content to improve answer quality.

Common Pitfalls

marker errors or split exceptions in logs: Typically indicate that the file parsing module's Python dependency packages are not correctly installed or are version-incompatible, preventing the text segmenter from initializing properly.
Files remain in a parsing state for a long time or fail to parse after upload: Common causes include PARSE_FILE_TIMEOUT_SECONDS being set too low, or insufficient parsing service resources to handle large or complex documents.
Inaccurate knowledge base Q&A results, with low relevance between recalled content and the question: Potentially due to the embedding model not being fine-tuned for the medical domain, or inappropriate Chunk Length leading to key information being fragmented.

Validation Steps

Upload a PDF document containing complex charts and medical terminology. Verify that its parsing results are complete and that segmentation is logical.
Query the document using key terms to confirm that the model's recalled snippets are accurate and contain core information.
In the FastGPT administration interface, check the File Parsing task status. Ensure there are no timeouts or failures, and verify that log output is normal.
Simulate an offline environment and attempt a minor version upgrade. Confirm that all components and database migration scripts execute successfully and that system functions remain normal.

Note: The values provided are common starting points. Measure them against your own samples for optimal performance.

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.