Knowledge Base Retrieval and Recall for Pharmacovigilance in

Data Characteristics in This Category

Laboratory service data in pharmacovigilance primarily originates from preclinical study reports, toxicology reports, pharmacokinetic reports, batch analysis records, and stability study data. This data typically exists in a mixed format of structured (e.g., experimental results in clinical trial databases, numerical values in analysis reports) and unstructured (e.g., experimental logs, observation records, pathology slide descriptions). Update frequency varies from weekly to quarterly, depending on the experimental cycle and report release schedule. Document formats are diverse, including PDF reports, Word experimental protocols, Excel raw data sheets, and XML or JSON files exported from proprietary Laboratory Information Management Systems (LIMS). Fields and units are highly specialized, such as dosage (mg/kg), concentration (µM), toxicity indicators (LD50), pharmacokinetic parameters (AUC, Cmax, Tmax), and various biomarker measurements.

Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall

The highly specialized nature and diverse document formats of laboratory service data pose challenges for knowledge base preprocessing and indexing. Structured data requires precise field mapping to ensure accurate retrieval of specific metrics. Specialized terminology and abbreviations in unstructured text demand that lexical analyzers possess domain knowledge to prevent recall degradation due to improper tokenization. Multiple file formats necessitate robust file parsing capabilities to ensure effective data extraction from various sources. Since data update frequency is relatively fixed but each update can be substantial, the knowledge base must support incremental updates and version management to avoid duplicate indexing and ensure data timeliness. Furthermore, the correlation between data from different experimental batches requires the retrieval model to identify and integrate information from various documents describing the same drug or experimental process to provide comprehensive adverse reaction clues.

Configuration Settings

Configuration Item	Recommended Value	Rationale for Recommendation
`Chunk Size`	`800–1200 characters`	Laboratory reports often contain lengthy experimental descriptions and results analysis. Chunks that are too short can break context, affecting semantic completeness.
`Chunk Overlap Size`	`100–200 characters`	Ensures contextual continuity at chunk boundaries, helping the model understand cross-chunk related information, especially when involving experimental steps or result discussions.
`Recall Count`	`Top 5–8 items`	Pharmacovigilance analysis requires multi-faceted information. Appropriately increasing the recall count can capture more potentially relevant details, improving analytical comprehensiveness.
`Similarity Threshold`	`0.75–0.85`	Laboratory data is highly specialized. A high similarity threshold helps filter out generic, irrelevant results, focusing on precisely matched experimental data or adverse reaction descriptions.
`Rerank Count`	`3–5 items`	After reranking, more precise and important information is prioritized, reducing the effort for engineers to filter irrelevant information and improving efficiency.
`Max File Size`	`200 MB`	Considering large toxicology reports or PDF files with embedded charts, a relaxed file size limit supports uploading complete documents.

Three Common Pitfalls

Retrieval results contain a large amount of non-experimental data or irrelevant medical terminology. This is due to a lack of pre-training or domain dictionaries for biomedical vocabulary, leading to inaccurate tokenization and vectorization.
Key data from some experimental reports are not recalled. This manifests as missing specific experimental indicators or dosage information in the retrieval results, possibly because file parsing failed to correctly extract data from tables or specific formats.
New data is not reflected in retrieval results promptly after a knowledge base update. This appears as queries for recently published reports still returning old information, potentially due to incorrect incremental indexing configuration or an excessively long index rebuilding cycle.

How to Verify Configuration

Select a batch of typical laboratory service documents (PDF, Word, Excel). Import them into FastGPT's knowledge base using the file upload function and check if the Parsing Status is successful.
For specific drug adverse events, construct query statements containing specialized terminology and experimental indicators. Observe if the Recall Count and Similarity meet expectations, and check if the recalled results include key laboratory data points.
Regularly add new experimental reports to the knowledge base. Query immediately after updating to confirm that new data can be accurately retrieved, and assess if data timeliness meets business requirements.

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.