Data Characteristics
Target discovery involves diverse data sources. These include public databases (DrugBank, KEGG, PDB, PubMed), internal experimental reports, research papers, clinical trial data, and patent documents. Update frequencies vary: public databases update quarterly or annually, while internal reports generate in real-time as projects progress. Document structures are complex, encompassing text, tabular data, chemical structures, biological sequences, and graphs. Fields and units often include gene IDs, protein sequences, compound CAS numbers, IC50 values (nM), Ki values (nM), binding affinity (µM), cell activity data (% inhibition), and various biological pathway and disease association details.
Deployment and Upgrade Constraints from Data Characteristics
Target discovery data diversity and update frequency impose specific deployment and upgrade requirements. The broad range of data sources demands support for multiple file formats, such as PDF, DOCX, SDF, and FASTA. The real-time nature of internal reports requires rapid incremental updates to the knowledge base, supporting frequent small-scale data ingestion to avoid full index rebuilds. For complex structured documents, FastGPT must preserve semantic relationships during parsing, especially for tables and chemical structures, to prevent loss of structured information. Standardization of fields and units is critical. Inconsistent units, particularly when integrating data from different sources, can lead to misinterpretation. Define these strictly during data preprocessing or parsing configuration to ensure accurate extraction and comparison of numerical data.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Internal experimental reports and patent documents can contain numerous charts, graphs, and complex structures, leading to large file sizes. |
PARSE_FILE_TIMEOUT_SECONDS | 600 | Parsing large PDFs and files with deeply embedded tables and images can be time-consuming. |
Chunk size (Chunk Length) | 800-1200 characters (characters) | Ensures the integrity of complex biomedical concepts like target descriptions and mechanisms of action, minimizing context loss. |
Recall count (Recall Count) | Top 10 entries (top 10) | Target discovery involves multi-dimensional information; increasing recall helps cover a broader range of potential associations. |
Similarity threshold (Similarity Threshold) | 0.75-0.85 | Balances recall and precision, preventing the introduction of irrelevant biological pathways or compound information. |
Rerank result count (Reranked Return Count) | Top 5 entries (top 5) | Focuses on the most directly relevant key information for target discovery, improving the accuracy of the final output. |
Three Common Pitfalls
- Knowledge base query results do not match expectations, and model output lacks coherence. This can occur if document parsing fails to correctly identify and extract critical target-disease associations or compound-target binding data, leading to incomplete semantic storage in the knowledge base.
- After an upgrade, some historical data cannot be retrieved correctly or appears empty. This typically happens when a new version adjusts data models or field definitions, and old data is not handled for compatibility during migration, especially for specific fields like
tmbId. - A locally deployed FastGPT container is accessible, but connecting to models via OneAPI results in connection issues. This might stem from Docker network configuration or firewall rules restricting OneAPI's access to FastGPT's internal services.
How to Verify Configuration
- Upload representative target discovery documents. Check if the chunked content in the knowledge base is complete, especially if tabular data and key biological entities (e.g., gene names, compound CAS numbers) are correctly identified and extracted.
- For complex queries related to target mechanisms of action or drug activity data, verify that FastGPT's answers accurately cite original text from the knowledge base and that cited data sources are reliable.
- Simulate high-concurrency data ingestion scenarios. Monitor the incremental update speed and index rebuilding efficiency of the knowledge base to ensure new internal experimental reports are promptly incorporated and searchable.
- Check system logs to confirm no significant timeout or parsing failure errors occur during document parsing and knowledge base retrieval.
The values provided are common starting points and should be measured 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.