Data Characteristics in This Category
Regulatory submission documents in medical affairs involve diverse and fragmented data sources. These primarily include clinical trial reports, investigator brochures, medical literature, drug labels, and regulatory documents. Data often exists in unstructured or semi-structured formats such as PDF, Word, and Excel. Update frequencies vary; clinical trial data updates in real-time with trial progress, while regulatory documents change according to agency release cycles, typically quarterly or annually. Document structures are complex, containing numerous specialized terms, abbreviations, figures, and tables. Fields often consist of free-text descriptions with diverse units, such as dosage units (mg, g, IU), time units (days, weeks, months), and statistical indicators (P-value, confidence interval), lacking uniform standardization.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
Fragmented data sources require tool calling to flexibly connect with multiple data sources and process different file formats. The uncertain update frequency of documents, especially the periodic changes in regulatory files, means the knowledge base needs to support incremental update mechanisms, either regular or on-demand, to ensure information timeliness. The complexity of unstructured documents demands high text parsing capabilities, requiring accurate identification and extraction of key information, such as adverse events from clinical trial reports or submission requirements from regulatory documents. The diversity of fields and lack of standardization require tool calling to perform unit conversions or conceptual mapping during data processing to prevent call failures due to data format mismatches.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 800–1200 characters | Regulatory submission documents are text-dense, requiring a sufficiently long context window to understand complex logic. |
chunkOverlapSize | 100–200 characters | Ensures connectivity of specialized terms and concepts across paragraphs, preventing information truncation. |
recallThreshold | 0.75–0.85 | Medical affairs demands high accuracy. Increasing the threshold reduces irrelevant recalls and improves matching precision. |
toolCallTimeout | 600 seconds | External interfaces (e.g., databases, regulatory query systems) have uncertain response times; sufficient call time should be reserved. |
maxIterations | 3 | Complex regulatory submission questions may require multiple tool calls and logical inferences to reach a conclusion. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Processing large PDF or Word documents can be time-consuming; this prevents parsing failures due to timeouts. |
Three Common Pitfalls
- Calling a third-party API results in
Error: write EPROTO. This typically occurs due to SSL/TLS certificate verification failure or protocol incompatibility. - A tool call node does not return the expected AI response. Possible reasons include incorrect parsing of tool output or an output format that does not match the model's expectations.
- Database connection failure or empty query results often stem from incorrect
oracledatabase connection string configuration or insufficient permissions.
How to Verify Configuration
- Execute a simulated submission process. Check that all tool call steps successfully return data and verify that the returned data content matches expectations.
- Randomly select multiple medical documents of different formats. Upload and index them through the knowledge base. Then, verify that key information within the documents can be accurately retrieved via tool calls.
- For critical regulatory update cycles, manually trigger a knowledge base update. Verify the differences in retrieval results between new and old versions of regulatory information to ensure the update mechanism is effective.
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.