Data Characteristics of This Category
Phase I clinical trial quality documents include research protocols, informed consent forms, ethics approvals, subject screening records, enrollment records, adverse event reports, laboratory test reports, and data management plans. These documents are typically stored in formats such as PDF, Word, and Excel. Some data may originate from export files from Clinical Trial Management Systems (CTMS) or Electronic Data Capture (EDC) systems. Data update frequency is high during the trial, especially for subject follow-up and adverse event reports, with daily or weekly additions. Document structure is relatively fixed, but content varies based on specific trial designs and sponsor requirements. Fields include subject ID, visit date, dosage, vital signs, and laboratory indicators. Units typically adhere to international units or common clinical units.
Constraints Imposed by These Characteristics on "Workflow Orchestration"
The data characteristics of Phase I clinical quality documents impose specific requirements on workflow orchestration. Document sources are diverse and heterogeneous in format, requiring the workflow to have robust file parsing and content extraction capabilities to standardize data formats. Data updates are frequent during trials, so the workflow needs to support incremental updates and real-time synchronization to ensure knowledge base timeliness. Document content is sensitive and involves multi-party review, requiring the workflow to incorporate strict access control and version management mechanisms during data processing. Standardization of fields and units means the workflow must perform precise entity recognition and unit conversion during data ingestion to avoid inconsistencies. Furthermore, the need for multi-turn conversations and complex queries demands higher capabilities in context management and logical judgment from the workflow to support engineers in tracing specific subjects or events.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Phase I clinical documents often contain detailed records. A moderate chunk size helps maintain contextual completeness and improves recall quality. |
overlapSize | 100–200 characters | Appropriate overlap ensures semantic coherence across chunks, especially when processing descriptive text. |
maxContext | 16000 tokens | To handle multi-turn conversations and complex queries, a sufficient context window is needed to cover multiple key document segments. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | When processing large PDFs or Excel files with complex tables, sufficient parsing time must be allocated to prevent timeouts. |
Recall count (Number of Retrieved Items) | Top 5 | Initially retrieve more relevant chunks to provide a comprehensive information basis for subsequent re-ranking. |
Similarity threshold (Similarity Threshold) | 0.75 | Ensures strong relevance of retrieved content, reduces interference from irrelevant information, and improves answer accuracy. |
Three Common Mistakes
- When executing a workflow, a "connect ETIMEDOUT" error when connecting to an external database typically indicates network policy restrictions on outbound access from the FastGPT deployment environment, or that the target database port is not open for external connections.
- When processing large documents, file parsing steps in the workflow often fail due to memory overflow or timeouts. This is usually because
PARSE_FILE_TIMEOUT_SECONDSis set too short, or the file size significantly exceeds the system's default processing capacity. - Unexpected output from multiple AI question-and-answer sessions, returning only partial content, may be due to incorrect configuration of the AI node's output strategy in the workflow, or failure to aggregate all AI node outputs into the final return node.
How to Confirm Proper Configuration
- Upload and parse typical Phase I clinical research protocols, informed consent forms, and adverse event reports. Verify that all key information fields (e.g., subject ID, visit date, drug dosage) are correctly extracted without garbled characters.
- Use test cases with specific keywords and multi-turn follow-up questions to simulate actual engineer query scenarios. Check if the knowledge base can accurately retrieve relevant document snippets and generate clear, complete, and logically sound answers.
- Monitor workflow execution logs to ensure no
ETIMEDOUTorMemoryErrormessages occur when handling a large number of concurrent requests, and that processing times for each node are within acceptable limits. - Evaluate the accuracy and completeness of AI question-and-answer results by comparing them against known correct answers, paying particular attention to the understanding and expression of complex medical terminology and data units.
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.