Deviation and CAPA Clinical Trial Pre-screening: Tool

Data Characteristics

Deviation and Corrective and Preventive Action (CAPA) data in biopharmaceutical clinical trials primarily originates from internal quality management systems and external audit reports. This data typically exists as unstructured text, such as Word documents, PDF reports, internal database records, or LIMS (Laboratory Information Management System) export files. Update frequency varies from daily to monthly, depending on deviation occurrence and CAPA implementation cycles. Deviation reports usually contain fields like deviation number, date, description, root cause analysis, impact assessment, corrective actions, preventive actions, responsible person, and completion status. CAPA reports detail CAPA number, creation date, approval date, implementation plan, verification results, and closure date. Field content often includes free-text descriptions, but also structured information like dates and numbers.

Constraints on Tool Calling and Plugins

The semi-structured and multi-source nature of deviation and CAPA data imposes specific requirements on tool calling and plugin configuration. For example, extensive free-text descriptions necessitate robust natural language processing capabilities to extract key information, influencing model selection and the maxContext parameter. Frequent updates require tools to regularly fetch or receive new data and promptly update indexes, affecting PARSE_FILE_TIMEOUT_SECONDS and data synchronization mechanisms. Specific terminology (e.g., drug names, trial phases, adverse event codes) and regulatory requirements (e.g., GCP regulations) within fields demand customized entity recognition and knowledge graph integration, directly impacting plugin development and tool prompt design. Inconsistent data formats exported from different systems also require flexible data preprocessing capabilities to ensure information consistency.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`tool_code_timeout`	`60 seconds`	Deviation analysis and CAPA plan generation may involve complex logic and multi-step reasoning, requiring sufficient execution time.
`maxContext`	`8000 tokens`	Deviation reports and CAPA records often contain detailed descriptions and analyses, requiring a larger context window to capture complete information.
`temperature`	`0.3`	Clinical trial pre-screening demands high accuracy and consistency. A lower `temperature` helps generate more stable and reliable output.
`Knowledge base recall count` (Knowledge Base Retrieval Count)	`Top 5`	The pre-screening stage requires quick access to relevant regulations, SOPs, or historical cases related to the current deviation/CAPA. A moderate retrieval count balances efficiency and accuracy.
`Similarity threshold` (Similarity Threshold)	`0.78`	Ensures retrieved knowledge snippets are highly relevant to the query, avoiding excessive noise and improving pre-screening precision.
`PARSE_FILE_MAX_SIZE`	`50 MB`	Deviation and CAPA reports may include charts and attachments. The file size limit must accommodate the typical scale of such documents.

Common Pitfalls

Two thought process outputs during tool calling node debugging: This manifests as duplicate thought fields in the logs. The reason is often unnecessary print statements or logging within tool_code, causing the FastGPT engine to misinterpret them as two separate thoughts.
Knowledge base queries fail after enabling tool calling: The model does not retrieve relevant information from the knowledge base, even when matching content exists. This happens because tool calling logic takes precedence over knowledge base retrieval. If a tool returns a result, the model may not proceed with a knowledge base query, or the tool's prompt design may not effectively guide the model to query the knowledge base in specific situations.
Inconsistent results between local large models and online APIs with identical parameters: The local model's output quality or format does not meet expectations. This is due to differences in fine-tuning data, internal architecture, and parameter sensitivity across models (even within the same series), leading to varying effects of parameters like temperature and top_p on different models.

Validation Steps

For typical deviation descriptions, compare the CAPA suggestions returned by the tool call with approved CAPA plans. Confirm the matching degree of key information (e.g., root cause, corrective actions).
Select a batch of deviation reports containing specific regulatory clauses or SOP numbers. Test whether the tool call can accurately identify and cite the corresponding regulations/SOP documents. Confirm the effectiveness of Knowledge base recall count (Knowledge Base Retrieval Count) and Similarity threshold (Similarity Threshold).
Simulate scenarios that trigger both tool calling and knowledge base queries. Observe if the model's output integrates tool execution results with knowledge base retrieval content. Confirm if the knowledge base query guidance logic in the tool prompt is effective.

Note: The values provided are common starting points. Always measure against specific samples and adjust as needed.

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.