Data Characteristics in This Category
Target discovery involves diverse data sources. These include research papers, patent literature, clinical trial reports, genomic and proteomic databases (e.g., UniProt, PDB, OMIM), and internal experimental records. Document formats are complex, covering PDF, Word, Excel, and Markdown. PDF and scanned documents are common. Data update frequencies vary; public databases may update weekly or monthly, while internal experimental data generates in real-time. Document structures typically include standard scientific sections like abstract, introduction, materials and methods, results, and discussion. Internal reports often have more flexible structures. Fields and units involve gene names, protein IDs, pathways, disease names, compound structures, and pharmacological parameters like IC50 and EC50. Concentration units (nM, μM) and time units (h, day) are also present. Accurate recognition of numerical values and units is critical.
Constraints from These Characteristics on "Multiturn Conversation and Prompts"
The complexity of target discovery documents imposes specific requirements on multiturn conversation and prompt design. First, multimodal data input is often necessary, such as processing PDF documents containing charts, graphs, and chemical structures. Second, asynchronous data updates require the system to support version management and incremental updates. This ensures the timeliness and accuracy of retrieval results. Diverse document structures necessitate flexible parsing strategies. These strategies must adapt to varying section divisions and information extraction needs across formats. For example, accurately identifying an IC50 value and its nM unit from experimental results, or distinguishing the same metric under different experimental conditions. Furthermore, the highly specialized domain vocabulary, such as CRISPR-Cas9 and GPCR, demands strong domain knowledge understanding from the model. This prevents conversational drift due to terminology misunderstandings. Accurate tracking of historical conversation context is essential for understanding complex causal relationships and experimental designs. An example is tracking data for multiple inhibitors of a specific protein across multiple turns.
Configuration Settings
| Configuration Item | Suggested Value | Rationale for This Value |
|---|---|---|
maxContext | 8 | Ensures coverage of context across multiple experimental steps or results in complex queries |
Chunk size | 800–1200 characters | Balances semantic completeness of long paragraphs with retrieval granularity of short paragraphs, suiting scientific document characteristics |
Recall count | Top 5 entries | Increases initial retrieval coverage, providing richer candidate information for reranking |
Similarity threshold | 0.78 | Filters out low-relevance document segments, reducing interference from irrelevant information in subsequent reasoning |
Rerank result count | 3 | Refines the final returned results, focusing on the most relevant and information-dense paragraphs |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses the time required to parse large scientific literature and multi-page PDFs, preventing file parsing timeouts |
Three Common Mistakes
- Model output in Markdown tables is truncated, displaying
...[hide 38432 char]: This occurs when the model output length exceeds themaxTokenlimit or the system's default maximum message body size. - System unresponsiveness or errors after a user uploads a voice file: This indicates the current system configuration does not support voice input or lacks integrated Automatic Speech Recognition (ASR) capabilities.
- In multiturn conversations, the model fails to accurately link a specific gene mentioned in earlier turns to a compound queried in the current turn: This happens when
maxContextis set too low, causing the model to lose critical historical conversation information.
How to Confirm Proper Configuration
- Conduct multiturn conversation tests. Verify the model correctly references targets, compounds, or experimental conditions mentioned in previous turns.
- Upload PDF documents containing complex tables and chemical structure diagrams. Check if text content is correctly extracted and if structured information (like tables) is queryable.
- Input specialized terminology for a specific target or disease. Cross-reference if the returned document segments are accurate and semantically relevant. Check if numerical values and units within them are correctly identified.
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.