Data Characteristics
Peptide drug data originates from drug discovery databases, clinical trial reports, patent literature, and academic papers. This data updates infrequently, typically with new drug development or clinical trial results, ranging from months to years. Document structures are complex, often including chemical structures, sequence information, pharmacological activity data, toxicology reports, pharmacokinetic parameters, synthesis process descriptions, and stability test results. Field types vary, encompassing standardized numerical values (e.g., IC50, Ki values, molecular weight, purity) and unstructured text descriptions (e.g., mechanism of action, side effects, indications). Some fields involve specific biological or chemical units (e.g., nM, μg/mL, Da).
Constraints on Multi-Turn Conversations and Prompts
The complexity of peptide drug data imposes specific requirements on multi-turn conversation accuracy and prompt design. Unstructured text and specialized terminology necessitate prompts that guide the model to precisely understand context and avoid semantic drift. Infrequent updates mean knowledge base construction must prioritize data authority and timeliness, ensuring the inclusion of the latest research. Diverse fields and units require the model to correctly identify and convert units in responses, preventing misinterpretation. Additionally, special data types like chemical structures and sequence information may require specific parsers or embedding methods for effective citation and explanation in conversations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 3000 Tokens | Balances peptide drug information density with model processing capacity, retaining sufficient context. |
Recall count (Recall Count) | 8 entries | Ensures coverage of relevant peptide drug knowledge points while avoiding excessive redundancy. |
Similarity threshold (Similarity Threshold) | 0.75 | Precisely matches peptide drug professional terminology and concepts, reducing irrelevant information. |
Chunk size (Segment Length) | 400 characters | Accommodates the mixed nature of structured and unstructured information in peptide drug documents. |
Rerank result count (Rerank Return Count) | 4 entries | Prioritizes displaying peptide drug information most relevant to the user's query. |
promptTemplate | Calibrated by testing | Guides the model to focus on key parameters and mechanisms of action, specific to peptide drug characteristics. |
Common Mistakes
- The model has a long response time or returns empty content after a user query. This may be due to a mismatch between the embedding model and the recall model for peptide drug documents in the knowledge base, leading to inefficient retrieval.
- The model provides contradictory pharmacological activity data for peptide drugs in multi-turn conversations. This occurs when prompts do not explicitly instruct the model to cite specific values from the knowledge base, allowing the model to generate information freely.
- The system continues to generate responses after a user requests to cancel the conversation, and chat history is not fully saved. This usually happens because the client does not close the SSE connection promptly or the backend service does not correctly handle interruption signals.
Verification Steps
- Conduct multi-turn conversation tests for common peptide drug inquiries (e.g., mechanism of action, side effects). Confirm the model consistently provides accurate and coherent information.
- Randomly select detailed peptide drug information from the knowledge base. Verify the model's ability to accurately cite chemical structures, sequence information, and specific efficacy data through questioning.
- Simulate a user interrupting a request during a conversation. Check if the model immediately stops generating and if the conversation record is completely saved up to the interruption point.
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.