Deployment and Upgrade for Endocrine and Metabolic

Data Characteristics

Data sources for endocrine and metabolic regulations and SOP documents primarily include diagnostic and treatment guidelines from national health commissions, industry consensus documents, internal hospital regulations, and drug/device instructions. These documents update frequently. For example, diagnostic guidelines typically revise every 2–3 years, and drug instructions may update with post-market study data. Documents are semi-structured, often in PDF, Word, or scanned image formats, containing extensive text descriptions, charts, flowcharts, and medical terminology. Fields and units involve specific values like blood glucose (mmol/L), blood pressure (mmHg), hormone levels (ng/mL, pmol/L), as well as normative descriptions for disease diagnostic criteria, treatment pathways, and drug dosages.

Constraints on Deployment and Upgrade

The high update frequency of endocrine and metabolic regulation data requires a deployment solution with rapid iteration and incremental update capabilities to avoid full reprocessing. The semi-structured document format and presence of charts necessitate robust file parsing, especially accurate extraction of tables and flowcharts from PDFs, and high-quality Optical Character Recognition (OCR) for scanned documents. The widespread use of medical terminology and specialized units demands higher accuracy in text segmentation and vector embedding to ensure precise semantic understanding. Additionally, regulations may have cross-references and version dependencies, requiring the knowledge base to identify and maintain these relationships during updates to prevent knowledge silos or misleading outdated information.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Handles large guidelines or multi-chart PDF files, preventing parsing timeouts.
`Segment Length`	`800–1200 characters`	Accommodates medical text paragraph length while maintaining contextual completeness.
`Recall Count`	`Top 8`	Covers more potentially relevant regulatory clauses, improving recall rate.
`Similarity Threshold`	`0.78`	Precisely matches specialized terminology, reducing interference from irrelevant information.
`Rerank Return Count`	`Top 3`	Refines the final answer, prioritizing the most relevant core regulations.
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	Addresses regulation documents containing numerous images or scanned pages.

Common Pitfalls

permission denied error when uploading large files to the knowledge base. This may be due to improper FE_DOMAIN or storage volume permission configurations, preventing FastGPT from accessing or writing to the upload directory.
ERRO errors and service failure after container startup. This often results from port conflicts or incorrect environment variable configurations in the Docker Compose file, especially database connection information.
Incomplete drug dosages or diagnostic criteria in question-answering results. This occurs when the text segmentation strategy is too aggressive, truncating critical information and failing to form complete semantic units.

Verification Steps

Upload a PDF of an endocrine disease diagnostic and treatment guideline containing charts and tables. Verify that it is fully parsed and generates retrievable text content.
Query a specific drug dosage clause. Verify that the returned answer is accurate, complete, and includes all key values and units.
Simulate a query about the latest diagnostic consensus for an endocrine disease. Check if the answer references the most recent version of the guideline content in the knowledge base.
Test multiple complex questions. Observe whether the knowledge base provides logically clear regulatory basis with clear sources when facing cross-references and multi-condition queries.

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.