Data Characteristics in Target Discovery
Target discovery data originates from public databases (e.g., DrugBank, ChEMBL, PubChem, GenBank), patent literature, scientific papers, clinical trial registries, and internal experimental data. Update frequencies vary; public databases typically update monthly or quarterly, while papers and patents are continuously published. The data structure is complex, including molecular structures, protein sequences, gene expression profiles, disease pathway information, mechanism of action descriptions, pharmacokinetic parameters, toxicology data, and preclinical study results. Document formats are diverse, ranging from structured CSV and JSON to large volumes of unstructured text like PDF papers and patent specifications. Fields include molecular weight, affinity constant Ki, half-life t1/2, and IC50 values. Units are commonly nM, μM, kDa, h, and often accompanied by experimental condition descriptions.
Constraints on Deployment and Upgrades from Data Characteristics
The multi-source and heterogeneous nature of target discovery data requires FastGPT to have robust data integration capabilities during deployment, supporting various data import formats. The presence of numerous unstructured documents challenges the robustness of text parsing and vectorization models, necessitating the configuration of high-performance text embedding models. Varying data update frequencies, especially the periodic updates of public databases, mean the system needs to support incremental update mechanisms to avoid resource consumption from full re-indexing. Specific data types like molecular structures and protein sequences may require customized preprocessing plugins or data connectors. Additionally, the presence of extensive specialized terminology and abbreviations requires FastGPT's tokenizer and stop word lists to adapt to biomedical language characteristics. These factors directly influence the memory, storage, and computational resource configuration of FastGPT instances, as well as the formulation of data synchronization strategies.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Patent and paper PDF files in target discovery can be large, especially when containing diagrams and complex structural information. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Processing complex PDF documents (e.g., full patents, detailed experimental reports) may require significant time for parsing and content extraction. |
Chunk size | 800–1200 characters | Ensures each text segment contains sufficient contextual information to understand biomolecular interactions and disease pathways, while avoiding excessive length that leads to information redundancy. |
Similarity threshold | 0.78–0.85 | Target discovery demands high precision for specialized terms and concepts. A lower threshold may introduce irrelevant information, while a higher one might miss weakly related but potentially valuable literature. |
maxContext | 4096 Tokens | Target discovery contexts often need to include complete experimental methods, results, and discussions for accurate model reasoning and judgment. |
MYSQL_PORT | 3306 | Database connection port, typically a standard port. If the deployment environment has special configurations, it must match the database service. |
Common Pitfalls
- Vector index error
400 status code no bodyafter upgrade: This typically results from vector database version incompatibility or API interface changes during the upgrade. Check FastGPT and vector database integration version compatibility and potentially rebuild or migrate the vector index. - Missing key fields or incomplete content after PDF document parsing: This may indicate an outdated version of
pdf-markeror other file parsing services, which cannot correctly process the structure of the latest PDF document formats, leading to partial content extraction failures. - Successful database connection but inability to read data: This is often due to
MYSQL_PORTorMYSQL_HOSTconfigurations in the database connection string not matching the actual environment, or insufficient database user permissions to access specific tables.
Verification Steps
- Upload a PDF document containing complex molecular structure diagrams and experimental data tables. Check its parsing results to ensure key information like molecular formulas and IC50 values are accurately extracted and vectorized.
- Perform a target-related query. Check the number of retrieved results and their relevance ranking to ensure the expected information retrieval precision is met. Observe if the results include information from multiple data sources.
- Simulate a periodic update of a public database. Use FastGPT's data import function to verify that the incremental update mechanism works correctly. Check if new data is successfully incorporated into the existing knowledge base.
- Review system logs to ensure no error messages related to database connection failures, file parsing timeouts, or vectorization model loading exceptions appear.
The values provided are common starting points and should be measured against specific 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.