Deployment and Upgrade for Metabolism and Endocrinology

Data Characteristics for this Category

Metabolism and endocrinology data primarily originates from clinical trial reports, drug development documents, disease guidelines, and research papers. This data updates frequently due to new drug development, clinical result releases, and treatment plan adjustments. Document structures typically include standardized report formats (e.g., ICH E3 clinical study report structure), medical journal articles (with abstract, introduction, methods, results, discussion sections), and specific disease diagnosis and treatment guidelines. Fields and units are highly specialized. Examples include blood glucose values in mmol/L or mg/dL, hormone levels in pmol/L or ng/mL, and drug dosages in mg or IU. Complex medical terminology and abbreviations are common. Data also contains numerous charts and tables, requiring image recognition and structured extraction.

Constraints on Deployment and Upgrade from these Characteristics

High-frequency data updates demand real-time knowledge base capabilities, requiring the deployment environment to support efficient data synchronization and index rebuilding. Complex document structures and specialized fields necessitate robust parsing capabilities during data ingestion, especially for extracting key information from unstructured text and recognizing chart content. Examples include interpreting dose-response curves in clinical trial reports or analyzing hormone level fluctuation graphs. Specialized fields and units require FastGPT's entity recognition and unit conversion features to accurately identify and process them, preventing result discrepancies due to unit confusion. Furthermore, the presence of multilingual medical terminology challenges the model's language processing capabilities and the integration of multilingual knowledge bases. The deployment environment must provide sufficient computing resources to support large-scale, high-dimensional medical knowledge vector embedding and retrieval.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Accommodates large clinical research reports or merged documents.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Ensures sufficient time for parsing complex PDF documents and image content.
`Chunk size` (Chunk Length)	`800–1200 characters`	Balances specialized terminology context and retrieval efficiency.
`Recall count` (Recall Count)	`Top 10 entries`	Covers a broader range of potentially relevant medical knowledge points.
`Similarity threshold` (Similarity Threshold)	`0.75`	Ensures high relevance of recall results to medical queries.
`Rerank result count` (Rerank Return Count)	`Top 5 entries`	Refines final results, focusing on the most critical medical information.

Three Common Mistakes

Query results are inconsistent or missing after a knowledge base update. This occurs when the incremental update strategy is incorrectly configured, leading to some old data not being effectively replaced or new data not being indexed.
After a system upgrade, the accuracy of specialized terminology recognition decreases. This typically happens when the new model version or configuration is not optimized or retrained for specific vocabulary in the metabolism and endocrinology domain.
External services cannot access FastGPT interfaces after Docker container startup, and Connection refused errors appear in logs. Common causes are incorrect container port mapping configuration or firewall rules not being open.

How to Confirm Correct Configuration

Upload a recent clinical trial report containing complex charts and specialized terminology. Check if the knowledge base can correctly parse and extract key data points, such as drug dosages and patient indicators.
Query core metabolic indicators like blood glucose and insulin resistance using different units (mmol/L and mg/dL). Confirm the system correctly understands and provides consistent answers.
Simulate a system upgrade process. After the upgrade, perform random sample queries on the knowledge base. Compare the consistency and accuracy of query results before and after the upgrade, paying special attention to whether newly added data is retrievable.
Check FastGPT container logs to ensure no index-related error messages, such as text index required for $text query, appear during data ingestion and querying.

Note: 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.