Batch Record Review for Pharmacovigilance: Tool Calling and

Data Characteristics

Batch record review data primarily originates from pharmaceutical manufacturing. This includes batch production records, batch inspection records, deviation records, and change control records. These records are typically PDF documents, scanned images, or structured text. The update frequency aligns with production batches; a new set of records is generated for each drug batch produced. Document structures usually contain predefined tables, text descriptions, signature fields, and attachments. Key fields include batch number, production date, expiration date, operator, equipment ID, material batch number, inspection results, abnormal event descriptions, and corrective actions. Units involve dosage (mg, g), volume (mL, L), time (hours, minutes), and temperature (°C). Precision requirements are very high, often including decimal places.

Constraints Imposed by These Characteristics on Tool Calling and Plugins

The diverse and semi-structured nature of batch record data requires tool calling and plugins to have robust document parsing capabilities. These capabilities must accurately extract key information from PDFs and scanned images. High update frequency means knowledge base content needs rapid synchronization, potentially requiring integration with automated data ingestion processes. The numerous specialized terms and abbreviations in records challenge model understanding and contextualization, necessitating support from a specialized domain dictionary or glossary. Strict requirements for precision and units mean that when extracting numerical data, the integrity of values and correctness of units must be ensured to avoid misjudgments due to parsing errors. Complex descriptions of abnormal events require the model to possess generalization and reasoning capabilities to identify potential adverse reaction signals.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`4096`	Batch record documents are often long; a sufficiently long context window is needed to understand complete batch information.
`PARSE_FILE_TIMEOUT_SECONDS`	`300` seconds	Processing large PDFs or scanned images can be time-consuming; this avoids parsing failures due to timeouts.
`Chunk size` (Segment Length)	`800` characters	Balances semantic completeness and retrieval efficiency, ensuring each segment contains enough information.
`Recall count` (Recall Count)	`Top 8`	Improves recall rate of relevant information, covering potentially dispersed key points in batch records.
`Similarity threshold` (Similarity Threshold)	`0.75`	Recalls more potentially related information while ensuring relevance. This value requires adjustment based on actual data.
`Max Tool Concurrency`	`5`	Balances system resource usage and response speed, preventing a single tool call from becoming a bottleneck.

Three Common Pitfalls

Tool calls return an Invalid JSON error. This often happens when an external API's return format does not meet expectations, or when the model generates tool call parameters with special characters.
Retrieving from the batch record knowledge base yields an empty or inaccurate response. This may be due to poor document parsing quality, leading to incomplete key field extraction or imprecise index terms.
When processing specific batch records, the model cannot correctly identify drug dosages or time units. This typically results from a lack of pre-training or fine-tuning for specific units and notations in the biomedical field.

Validation Steps

Select multiple typical batch record documents for parsing. Verify the extraction accuracy of key fields such as batch number, production date, and inspection results. Ensure no omissions or errors.
Simulate various adverse reaction query scenarios. Test whether tool calls can accurately retrieve abnormal event descriptions and treatment records from relevant batch records and correctly identify associated drug information.
Check the knowledge base update mechanism. Ensure that after new batch records are uploaded, they are indexed and retrievable within the preset time, and that retrieval results reflect the latest data.

Note: 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.