Preclinical Safety Assessment Pharmacovigilance Deployment

Data Characteristics in this Category

Preclinical safety assessment data primarily originates from animal experiment reports, toxicology studies, pharmacokinetics (PK), and pharmacodynamics (PD) data. This data often exists in a hybrid format, combining structured (e.g., preclinical study databases, electronic lab notebooks) and unstructured (e.g., PDF experiment reports, Word documents, images, raw data files) elements. Data update frequency can be weekly or even daily during early project phases, then transition to monthly or quarterly during stable phases. Document structures vary, including study protocols, raw data, data analysis results, and summary reports. Common content includes toxicological pathology reports, histological images, and clinical biochemical indicator curves. Fields and units are highly specialized, such as dose units mg/kg, time points h (hours) and d (days), and various biomarker concentrations like ng/mL and μg/L. These data characteristics dictate the RAG system's processing approach.

Constraints on Deployment and Upgrade

The highly specialized nature, mixed structure, and high update frequency of preclinical safety assessment data impose specific requirements on FastGPT's deployment and upgrade. First, a large volume of unstructured documents requires efficient text extraction and parsing capabilities, ensuring that tables and captions within images are accurately recognized. The frequent data updates necessitate that the RAG system supports incremental indexing and rapid re-indexing to capture the latest safety signals. Second, the accuracy of specialized field and unit recognition directly impacts retrieval quality, requiring the model to have a deep understanding of biomedical terminology. Additionally, raw data files can be large, demanding significant storage and processing capacity. During deployment, consider data source connection methods (API, direct database connection, file system monitoring) and ensure the system can stably handle data streams of varying formats and sizes. During upgrades, pay close attention to the impact of model updates on the understanding and parsing accuracy of specialized terminology, and allocate sufficient resources for retrospective validation.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Preclinical reports often contain numerous images and raw data, leading to large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDF or Word documents can be time-consuming; this prevents timeout interruptions.
`maxContext`	`3000 Tokens`	Toxicology descriptions and pathology reports are rich in detail, requiring a longer context window.
`Chunk size`	`800–1200 characters`	Maintains contextual coherence and prevents critical information from being split.
`Recall count`	`Top 10 entries`	Ensures coverage of safety information from different experimental stages and perspectives.
`Similarity threshold`	Calibrate by actual measurement	Balances sensitivity and specificity, avoiding false negatives or false positives.

Three Common Mistakes

Symptom: After a system upgrade, the AI response directly outputs <think></think> tags in the main text. Reason: The model configuration did not correctly enable or recognize FastGPT's thought process visualization feature, or related configuration items were reset during the upgrade.
Symptom: Table data in some experiment reports are not correctly indexed, leading to retrieval failures during queries. Reason: The file parser has insufficient capability to recognize nested tables or tables within images of specific formats, or PARSE_FILE_TIMEOUT_SECONDS is set too low, causing incomplete parsing.
Symptom: When querying for adverse reactions of a specific drug at a certain dose group, the returned results are inconsistent with the actual report or are empty. Reason: Data indexing failed to accurately identify and associate specialized fields like dose unit mg/kg or time point h, leading to semantic matching failure.

How to Verify Correct Configuration

Upload a preclinical toxicology report PDF containing complex tables and captions. Then, use keywords to retrieve and verify whether the table and caption content is accurately recalled.
Simulate a data update scenario by uploading a revised version of an existing report. Check if the system can identify and index the latest information and prioritize recalling the updated content.
For a specific drug, use query statements that include dose, time points, and biomarker units. Verify if the AI response accurately identifies and references the context of these specialized fields, and compare if the Similarity threshold of the recalled results is within a reasonable range.

Note: The values provided are common starting points. Measure them against your 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.