Data Characteristics in This Category
Target discovery data typically originates from research literature, patent databases, clinical trial reports, genomics and proteomics data platforms, and various bioinformatics tools. Data update frequencies vary; literature and patents might update monthly or quarterly, while experimental data and clinical reports show irregular incremental updates. Document structures are diverse, including unstructured text descriptions (e.g., experimental methods, results discussions), semi-structured tables (e.g., gene expression profiles, compound activity data), and structured database records. Field and unit specificities include biomacromolecule nomenclature (e.g., gene IDs, protein sequences), chemical structures (e.g., SMILES codes), biological activity indicators (e.g., IC50, Ki values, typically in nanomolar range), and statistical significance (e.g., P-values).
Constraints Imposed by These Characteristics on "Deployment and Upgrade"
The heterogeneous nature of target discovery data requires high flexibility in the RAG system's document parsing stage. Extensive unstructured text necessitates text segmentation strategies that effectively preserve contextual semantics, preventing key information from being fragmented. Semi-structured and structured data require customized parsers to ensure accurate extraction of fields and values, for example, identifying and correctly processing gene IDs and their corresponding annotations. Irregular data updates mean the knowledge base synchronization mechanism must support incremental updates and effectively handle version conflicts and data deduplication. The specialized nature of biological activity indicators demands higher accuracy from the model's understanding and Q&A, requiring appropriate embedding models and retrieval strategies to improve relevance. Parsing and retrieving special fields like chemical structures may require integration with external tools or preprocessing workflows.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Ensures the ability to upload literature attachments containing large amounts of experimental data or complex structural diagrams. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Balances contextual completeness with retrieval efficiency, accommodating typical paragraph lengths in biomedical literature. |
maxContext | 8192 | Accommodates the longer context window required for complex biological questions, reducing information loss. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Addresses potentially long parsing times for large PDF documents or complex data files. |
Similarity threshold (Similarity Threshold) | 0.78 | Ensures retrieved results are highly relevant to professional terminology and concepts in target discovery, reducing generalization. |
Rerank result count (Reranked Return Count) | Top 5 entries (top 5) | Focuses on the most relevant core information, improving efficiency for engineers to obtain valid information. |
Three Common Mistakes
- After uploading knowledge base documents, information related to specific genes or compounds is not retrievable in conversations. This can occur if the document parser fails to correctly identify or extract key entities such as gene IDs or compound names.
- After a system upgrade, existing knowledge base Q&A responses become slow or even time out. This usually happens if the index rebuilding process is not optimized, or if the new version has increased hardware resource demands, leading to insufficient existing deployment resources.
- Numerical errors or unit confusion appear in Q&A results regarding biological activity indicators (e.g., IC50). This occurs when values and their corresponding units are separated during text segmentation, or if the embedding model inadequately understands specialized units.
How to Confirm Correct Configuration
- Upload a typical document containing gene IDs, chemical structures, and biological activity data. Verify accurate parsing and embedding generation.
- Formulate test questions with specialized terminology for a specific target or disease. Verify that Q&A results recall relevant document snippets and provide precise numerical values and units.
- Conduct multi-turn Q&A tests via API or frontend interface under simulated high-concurrency scenarios. Monitor
response_timeto ensure it remains within acceptable limits. - Regularly check
fastgpt-container logs for abnormal errors, especially those related to document parsing and vectorization.
Note: 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.