Data Characteristics in this Category
Pharmacovigilance data during the lead optimization phase originates primarily from preclinical research reports, toxicology studies, early clinical trials (Phase I/II), and related in vitro/in vivo activity screening data. This data typically exists as structured tables (e.g., compound activity data, toxicity indicators), unstructured text (e.g., experimental records, animal observation reports, investigator brochures), and semi-structured data (e.g., adverse event report forms). Data update frequency is high, especially during ongoing experiments, with new compound screening results and toxicity test data continuously being entered. Document structures vary, containing numerous specialized terms, compound codes, and dosage units. Data format discrepancies may exist across different experimental platforms. Common fields include compound ID, dose, administration route, toxicity endpoints (e.g., LD50, NOAEL), adverse reaction descriptions, observation indicators (e.g., organ weight, complete blood count, biochemical indicators), and their units (e.g., mg/kg, mM, U/L).
Constraints Imposed by these Characteristics on Multi-Turn Conversations and Prompts
The high update frequency of lead optimization data requires multi-turn dialogue systems to quickly index and process new data, preventing decision bias due to outdated information. Diverse document structures and specialized terminology necessitate prompt design that considers precise context matching and accurate term recognition to handle complex queries from users about specific compounds or toxic effects. The mix of large amounts of structured and unstructured data means that during multi-turn conversations, the system must simultaneously extract quantitative information from tabular data and understand the qualitative characteristics of adverse reactions from text descriptions. Furthermore, data format discrepancies across different experimental platforms pose challenges for RAG (Retrieval Augmented Generation) system knowledge base construction and data preprocessing. This requires more refined data cleaning and standardization processes to ensure that multi-turn conversations can consistently retrieve relevant and coherent information.
Configuration Settings
| Configuration Item | Suggested Value | Rationale for this Value |
|---|---|---|
maxContext | 6000 characters | Ensures the capacity to carry multi-turn conversation context, especially when discussing complex compound structures or multiple toxicological indicators. |
Chunk size (Segment Length) | 500 characters | Balances semantic completeness and retrieval efficiency, preventing individual segments from being too long and diluting core information or too short and causing semantic fragmentation. |
Recall count (Recall Count) | 8 items | Balances retrieval accuracy and response speed, covering more potentially relevant information and reducing the risk of missing critical data. |
Similarity threshold (Similarity Threshold) | 0.75 | For specialized domain text, this improves the precision of recall, filtering out irrelevant general information. |
Rerank result count (Reranked Return Count) | 3 items | Focuses on the most relevant core information, enhancing the focus of multi-turn conversations and reducing the burden on the model to process irrelevant information. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Handles the parsing of large experimental reports or compound library files, preventing file processing failures due to timeouts. |
Three Common Mistakes
- The AI frequently displays "unable to find relevant information" during conversations. This occurs because the knowledge base index does not adequately cover the latest toxicology data for lead compounds or specific experimental reports.
- When users inquire about the dose-response relationship of a specific compound, the AI's response shows confusion in dosage units or effect values. This is due to a lack of unit standardization from different sources or incorrect field mapping during the data preprocessing stage.
- Multi-turn conversations fail to maintain a continuous discussion about the same compound or adverse reaction, requiring scope redefinition for each new question. This happens because the
maxContextparameter is set too small, leading to historical conversation information being truncated prematurely.
How to Confirm Proper Configuration
- Select a batch of test cases containing new compound toxicology data and early clinical adverse reaction reports. Verify that the multi-turn dialogue system can accurately retrieve and cite the latest data.
- For the same compound, pose questions using different dosage units (e.g.,
mg/kg,nM). Check the consistency of values and units in the AI's responses. - Simulate a user engaging in 5-8 rounds of in-depth discussion on a specific compound or toxicity indicator. Confirm that the conversation context is effectively maintained and that the AI's responses are coherent as expected.
The values provided are common starting points and should be measured against the reader's 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.