Tool Calling and Plugins for Regulatory Affairs

Data Characteristics

Regulatory affairs data in the biopharmaceutical sector originates from regulatory bodies such as the National Medical Products Administration (NMPA), European Medicines Agency (EMA), and U.S. Food and Drug Administration (FDA). This data updates frequently with new regulations, guidelines, announcements, and review reports. Documents are typically in PDF, Word, or HTML format. They feature complex structures, specialized terminology, tables, figures, and cross-references. Fields include drug classification, indications, clinical trial data, manufacturing processes, quality standards, adverse event monitoring, approval procedures, and timelines. Units cover dosage (mg, g), concentration (%), time (days, months, years), and temperature (°C). Different regions may use varying terminology for the same concepts.

Constraints Imposed by These Characteristics on Tool Calling and Plugins

The complexity and dynamic nature of regulatory affairs data impose specific requirements on tool calling and plugins. First, diverse document formats and complex structures demand file parsing tools with robust multi-format processing and deep structured information extraction capabilities. Second, frequent regulatory updates require a knowledge base update mechanism that supports high-frequency, automated synchronization of newly published documents, along with the ability to identify and flag differences between regulatory revisions. Third, extensive specialized terminology and cross-references mean that standard keyword matching is insufficient for accuracy. Tool calling must support contextual understanding and semantic association queries. Finally, regulatory systems vary by region. Tool calling needs to dynamically adjust query scope and matching rules based on the user-specified regulatory region (e.g., China, EU, US) to prevent information confusion.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Regulatory documents are often lengthy and require more parsing time to avoid interruptions.
`Chunk size` (Segment Length)	`800 characters`	Ensures each text segment contains sufficient context for understanding complex regulations and related information.
`Recall count` (Recall Count)	`Top 10`	Regulatory affairs Q&A demands high accuracy; increasing the recall count improves the probability of hitting relevant regulations.
`Similarity threshold` (Similarity Threshold)	`0.75`	Detailed regulatory clauses require a higher similarity match to distinguish subtle semantic differences.
`Rerank result count` (Reranked Return Count)	`Top 5`	After high recall, reranking selects the most relevant clauses, improving the quality and precision of the final answer.
`TOOL_MAX_RETRIES`	`3 times`	Increases stability against potential temporary network fluctuations from external services (e.g., NMPA official website API).

Common Pitfalls

File parsing tools report timeouts or parsing failures. Logs may show HTTP 504 Gateway Timeout or parser returned empty content. This often occurs because regulatory files are large, structurally complex, or contain many images, causing parsing time to exceed the PARSE_FILE_TIMEOUT_SECONDS setting.
Tool calls within a conversation return inaccurate results or omit key information. Generated responses may lack specific regulatory clauses or cite incorrect information. This happens when Chunk size (Segment Length) is too short or Similarity threshold (Similarity Threshold) is too high, filtering out relevant but not precisely matching knowledge points.
Custom tool variables are not correctly passed or are missing in a conversation. Tool call logs may show parameter missing or variable is empty. This usually means the custom tool's parameter definition does not correctly map to entities in the user's query, or the variable name does not match the actual incoming field.

Verification

Upload and parse a typical regulatory guideline document (e.g., "Measures for the Administration of Drug Registration"). Check the file parsing logs to ensure no errors occurred and that segment count and average segment length align with expectations.
Simulate a user query, such as "What are the clinical trial requirements for new drug registration?". Observe if the tool call is correctly triggered and verify that the returned raw knowledge snippets contain key regulatory clauses, ensuring consistency with actual regulatory content.
Ask specific questions related to different regulatory bodies (e.g., China, USA). Confirm that tool calling correctly invokes the appropriate regional regulatory query tool based on the geographical information in the question and returns the relevant regulatory content for that region.

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.