Deployment and Upgrade for Lead Optimization

Data Characteristics

Lead optimization pharmacovigilance data originates from preclinical study reports, early clinical trial (Phase I) safety data, toxicology study reports, and off-target effect records from compound screening. This data combines structured formats, such as database records, and unstructured formats, such as PDF research reports, lab logs, and SOP documents. The update frequency is relatively slow, aligning with experimental batches and research progress, typically weekly or monthly. Document structures vary; for example, toxicology reports include histopathological descriptions, biomarker data, and dose-response curves. Fields and units are highly specialized, covering compound IDs, dosages (mg/kg), administration routes, observation indicators (e.g., ALT, AST, heart rate), adverse event descriptions, severity grades (e.g., CTCAE v5.0), and frequency. Units must be precise, such as μg/mL and mmHg.

Constraints on Deployment and Upgrade

The mixed structure of lead optimization data challenges data preprocessing and knowledge base construction. Unstructured reports require advanced text parsing to accurately extract critical safety information. The relatively low update frequency means full knowledge base rebuilds are not often necessary. However, the incremental update mechanism must be robust to handle new experimental data. The specialized nature of fields and strict unit requirements demand rigorous validation and standardization during data ingestion. This prevents misinterpreting safety signals due to inconsistent units. Examples include uniform handling of different dosage units and normalization of various toxicity grade descriptions. Data involves sensitive compound information, so the deployment environment requires high data security and permission management, ensuring data isolation and access control.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Accommodates large PDF research reports, ensuring single file uploads succeed.
`Chunk size`	`800–1200 characters`	Balances the integrity of detailed paragraphs in toxicology reports with RAG retrieval efficiency.
`Recall count`	`Top 8 entries`	Ensures sufficient relevant context coverage for complex safety signal analysis.
`Similarity threshold`	`0.75`	Reduces false positive recall, focusing on highly relevant safety information.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Allows sufficient time for parsing complex PDF documents, such as scanned files or multi-page tables.
`Knowledge Base Update Strategy`	`Incremental Update`	Most data is appended, avoiding resource consumption from full rebuilds.

Common Pitfalls

Missing API Keys or Base URLs when configuring models leads to model call failures, with "Unauthorized" or "Connection refused" error messages.
Uploading large PDF documents results in file upload failures or parsing timeouts. This occurs when UPLOAD_FILE_MAX_SIZE or PARSE_FILE_TIMEOUT_SECONDS are set too low.
Knowledge base retrieval results contain many irrelevant or duplicate text blocks. This usually happens when Chunk size is too short or Similarity threshold is set too low, causing semantic units to be incorrectly segmented or recall to be too broad.

Verification Steps

Upload a PDF report containing key toxicology data. Check if the knowledge base correctly parses and creates text blocks. Verify that text block content is complete and not missing.
Use a query with a specific compound ID and adverse event. Verify that relevant toxicology report snippets and preclinical safety data are accurately recalled. Check the number of recalled items.
Simulate a model call by inputting a typical pharmacovigilance question. Observe if the model's answer is based on knowledge base content. Verify that the cited original text snippets are accurate.

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.