Target Discovery Pharmacovigilance Deployment and Upgrade

Data Characteristics for This Category

Target discovery pharmacovigilance data originates from preclinical studies, clinical trial reports, literature analysis, patent information, and public databases. Update frequencies vary; clinical trial data typically updates after phase report releases, while literature and patent data flow continuously. Document types are diverse, including structured tables (e.g., gene-disease associations, compound activity data), unstructured text (e.g., research papers, experimental records), and semi-structured reports (e.g., toxicology assessment reports). Key fields include target name, mechanism of action, potential side effects, toxicity data, related pathways, compound ID, dosage units (e.g., mg/kg, nM), and effect indicators (e.g., IC50, LD50). The data volume is large, and associations are complex, requiring processing of multi-source heterogeneous information.

Constraints Imposed by These Characteristics on "Deployment and Upgrade"

The multi-source and heterogeneous nature of target discovery data requires FastGPT to have robust data ingestion and cleansing capabilities during deployment to integrate data from different systems and formats. Irregular update frequencies mean the system must support flexible data synchronization strategies, handling both bulk historical data imports and continuous incremental updates. Large volumes of unstructured text data, such as research papers and toxicology reports, demand high performance from text parsing and entity recognition modules. Additionally, the complex associations between targets and side effects make knowledge graph construction and reasoning critical. Deployment must ensure the accuracy of knowledge base indexing and recall efficiency. For numerical fields with units, such as dosage and effect indicators, the data processing pipeline must standardize units to prevent calculation errors.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Target discovery literature and reports are often large, requiring support for uploading large PDF or TXT files.
`Chunk size`	`800–1200 characters`	Balances semantic completeness of text and retrieval granularity, preventing context loss.
`maxContext`	`4000 characters`	Ensures the model receives sufficient context to understand complex medical text.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large literature reports can be time-consuming; this prevents failures due to timeouts.
`Similarity threshold`	`0.75–0.85`	Improves the precision of target and adverse reaction association retrieval, reducing false positives.
`Rerank result count`	`Top 5 entries`	Focuses on the most relevant knowledge snippets, reducing the model's processing of irrelevant information.

Common Mistakes

Model test errors Invalid API Key or Authentication Failed: This typically indicates incorrect or expired Alibaba Cloud Bailian API Key or Ollama model service authentication information. Verify LLM_API_KEY and other environment variables.
Logs show OCR Error: When processing scanned PDFs or image-format pharmacovigilance reports, the OCR module failed to correctly recognize text. Check OCR service availability and image quality.
Knowledge base retrieval results are empty or inaccurate: This can be due to incorrect field mapping during data import, leading to key information like targets, side effects, or dosages not being properly indexed, or insufficient knowledge base update frequency.

How to Confirm Correct Configuration

Upload typical target discovery reports and literature to verify FastGPT correctly parses text content and extracts key fields such as target names, potential side effects, and dosages.
Execute a series of retrieval tests with complex queries, such as "cardiac toxicity of target A at X dose," to check the relevance and completeness of returned results against original data.
Monitor data synchronization tasks to ensure newly published clinical trial data and literature are imported into the knowledge base at the expected frequency, either automatically or manually, and verify the accuracy of the imported data.

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.