Deployment and Upgrade for Real-World Study R&D Document

Data Characteristics for This Category

Real-World Study (RWS) R&D documents primarily originate from clinical practice data. This includes Electronic Health Records (EHR), medical insurance claims databases, disease registries, and patient-reported outcomes (PRO). Data update frequencies vary from real-time to daily, quarterly, or annually. Document structures are diverse, encompassing both structured tabular data and extensive unstructured text such as treatment records, imaging report interpretations, and follow-up notes. Field and unit specificities arise from the standardization and complexity of medical terminology, laboratory indicator units (e.g., mmol/L, ng/mL), dosage units (mg, IU), and timestamp formats (YYYY-MM-DD HH:MM:SS). The data often contains numerous abbreviations, synonyms, and irregular medical descriptions.

Constraints Imposed by These Characteristics on "Deployment and Upgrade"

The diversity and update frequency of RWS data demand specific storage and computational resources from the deployment environment. Unstructured text parsing, in particular, requires higher processing power. Medical terminology, abbreviations, and specific units within documents necessitate that FastGPT's parsing model possesses a high degree of domain expertise. This requires customized dictionaries or model fine-tuning to improve parsing accuracy. The complexity of integrating multi-source heterogeneous data increases the difficulty of data preprocessing and cleaning, requiring a longer PARSE_FILE_TIMEOUT_SECONDS. Frequent data updates mean that the knowledge base's incremental update mechanism must be efficient and stable. The settings for Chunk size (segment length) and Recall count (recall count) directly affect the recall effectiveness of new data. Data sensitivity also requires strict adherence to data security and privacy protection regulations during deployment.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	RWS documents, especially those containing imaging reports or detailed treatment records, can be large. Sufficient upload limits are necessary.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing complex and lengthy medical texts is time-consuming. This prevents parsing failures due to timeouts.
`Chunk size`	`800–1200 characters`	Medical texts have strong contextual relevance. Maintaining a certain segment length helps preserve semantic integrity while avoiding redundancy from excessive length.
`Recall count`	`Top 10 entries`	RWS queries often require more comprehensive background information. Increasing the recall count appropriately improves the accuracy and completeness of answers.
`Similarity threshold`	`0.75`	The domain is highly specialized. Increasing the threshold allows for more precise matching of relevant medical concepts, reducing interference from irrelevant information.
`maxContext`	`4096 tokens`	Complex clinical questions often require a longer context for understanding and reasoning, ensuring the model has sufficient information processing capability.

Three Common Mistakes

Workflow code execution components fail with an Error: Script execution failed. This usually indicates missing necessary dependency libraries or insufficient permissions in the deployment environment.
Team edition features are unavailable after local deployment, displaying Feature unauthorized. This occurs when TEAM_VERSION_KEY is not configured correctly or the license file is not placed in the specified path.
Knowledge base document upload parsing progress stalls for an extended period, showing Parsing. This likely means PARSE_FILE_TIMEOUT_SECONDS is set too short, causing large or complex file parsing to be interrupted.

How to Confirm Correct Configuration

Upload a real-world study report containing medical terminology and laboratory indicators. Check if key fields (e.g., diagnosis, drug dosage, laboratory values, and units) are correctly identified and structured in the parsing results.
Run a test case with a multi-step workflow that simulates an RWS data analysis process. Verify that the output of each component meets expectations, especially that code execution components run normally.
Perform an incremental update on the knowledge base by uploading a batch of new clinical follow-up records. Verify that the updated knowledge base can recall key information from the new records and evaluate the accuracy of the recall results.

The values provided are common starting points and should be measured against your 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.