Multi-turn Conversation and Prompts for Structured Analysis

Data Characteristics

siRNA nucleic acid drug R&D data primarily originates from experimental reports, patent literature, clinical trial records, and academic papers. These documents are frequently updated, especially during early R&D stages, where experimental data and results continuously iterate. Document structures typically include experimental methods, siRNA sequence information, target genes, in vitro/in vivo activity data, toxicology assessments, pharmacokinetic parameters (e.g., plasma half-life, tissue distribution), formulation recipes, and quality control standards. Fields cover nucleotide sequences, concentrations (e.g., nM, µg/mL), inhibition rates (%), cell viability (%), biomarker expression levels in animal models, and time points of action (h, day). Units are diverse and require high precision. Patent literature and clinical reports also contain complex legal and medical terminology.

Constraints Imposed by These Characteristics on Multi-turn Conversation and Prompts

The specificity and diversity of siRNA sequences require the model to accurately identify and differentiate various sequence variants in multi-turn conversations, linking them to corresponding biological functions and experimental results. High data update frequency means the knowledge base needs frequent synchronization to ensure the timeliness and accuracy of model responses, preventing content generation based on outdated information. The complex structure and specialized terminology in documents, particularly data involving pharmacokinetics and toxicology, demand more refined prompting. This requires guiding the model to focus on key information and perform logical reasoning, such as extracting inhibition effects at specific doses from multiple experimental results. Furthermore, diverse units and numerical formats require the model to identify and convert units, avoiding data misinterpretation due to unit confusion. Managing conversation history is also crucial to ensure the model maintains contextual understanding of specific siRNAs, targets, or experimental conditions during follow-up questions.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`500–800 characters`	siRNA experimental reports often contain closely related numerical and descriptive text; shorter segments can lead to context fragmentation, while excessively long ones introduce noise.
`Recall count` (Recall Count)	`Top 8–12 entries`	Ensures coverage of multiple key data points across different experimental conditions, targets, or siRNAs.
`Similarity threshold` (Similarity Threshold)	`0.78–0.85`	siRNA sequences and biological data have high specificity, requiring a higher similarity to precisely match relevant segments.
`maxContext`	`6000–8000 tokens`	Complex experimental designs and multi-turn follow-up questions require a longer context window to maintain conversational coherence.
`Rerank result count` (Reranked Return Count)	`Top 5 entries`	Reranks recall results to prioritize key experimental data and conclusions most relevant to the current question.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Large patents or clinical reports can contain extensive figures and complex text, requiring longer parsing times.

Three Common Mistakes

The model fails to correctly associate specific siRNA sequences with their corresponding inhibition rate data in conversations. The symptom is the model providing generic biological action descriptions without specific numerical values. This happens because prompts do not effectively guide the model to focus on the correspondence between siRNA sequence and inhibition rate (%) fields.
In multi-turn follow-up questions, the model confuses previously mentioned drug dosages or action time points. The symptom is the model's answer not matching conditions from previous conversation turns. This happens because maxContext is set too low, leading to truncation of conversation history and the model losing context from earlier turns.
After parsing, some critical experimental data in uploaded documents are missing or have abnormal formats. The symptom is corresponding fields in the knowledge base being empty or containing incorrect content. This happens because documents contain non-standard table formats or embedded text within images, preventing the Text Content Extraction node from correctly identifying and processing them.

How to Verify Configuration

For activity data of specific siRNA sequences, ask multi-turn questions to verify if the model can accurately return their IC50 or inhibition rate (%) in different cell lines or animal models, and check if values and units are consistent.
Simulate a user asking in-depth follow-up questions about the pharmacokinetic characteristics of an siRNA, such as its plasma half-life (h) and tissue distribution. Confirm the model can maintain context within the conversation history and provide coherent and accurate answers.
Upload an siRNA patent document containing complex tables and figures. Check if key fields like siRNA sequence, target gene, and experimental conditions are completely and correctly extracted and structured in the knowledge base, validating the performance of the Text Content Extraction plugin.
Randomly select a batch of experimental reports containing different units (e.g., nM, µM, mg/kg). Ask questions to verify if the model can correctly identify and process these units, avoiding confusion or misinterpretation.

Note: The values provided are common starting points. Measure against your own samples for optimal performance.

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.