Multi-Turn Conversations and Prompts for Lead Compound

Data Characteristics

Lead compound screening data originates from high-throughput screening reports, in vitro activity test data, ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) prediction reports, compound structure databases, and relevant literature. Data updates are infrequent, typically occurring after a series of experimental batches complete. Document structures are primarily experimental reports. These reports often include fields such as chemical structures, IC50/EC50 values, solubility, stability, and toxicity prediction indicators. Units commonly include nanomolar (nM), micromolar (µM), moles per liter (mol/L), and micrograms per milliliter (µg/mL). Reports may contain complex charts and spectral data. Some data stores in proprietary formats, requiring specific parsing tools.

Constraints on Multi-Turn Conversations and Prompts

Infrequent data updates for lead compound screening mean knowledge base construction does not require frequent large-scale index rebuilding. Focus on initial index accuracy and completeness. Complex charts and spectra in experimental reports require the RAG system to have multimodal processing capabilities. If not available, extract key information into structured text beforehand. Specific fields and units, such as IC50 values, require prompt design to clearly specify numerical ranges and units to prevent model confusion. In multi-turn conversations, users may frequently trace similar properties of different compounds or compare experimental results from different batches. This requires efficient retrieval and integration of cross-document information from the conversation context management mechanism, supporting horizontal comparisons between compounds. Proprietary data formats may require additional preprocessing steps to convert them into a universal format understandable by AI models.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8` turns	Comparative analysis in lead compound screening often involves multiple attributes. 8 turns of conversation cover most comparison scenarios.
`Chunk size` (Segment Length)	`800–1000` chars	Experimental report paragraphs are long. Retaining sufficient context avoids semantic breaks while controlling the information volume per segment.
`Recall count` (Recall Count)	`Top 7 entries` (Top 7)	Ensures coverage of key data points for different compounds or experiments.
`Similarity threshold` (Similarity Threshold)	`0.78`	Precisely matches compound attributes or experimental results, reducing interference from irrelevant information.
`Rerank result count` (Rerank Return Count)	`Top 3 entries` (Top 3)	Further refines results based on recall count, focusing on the most relevant information.
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Experimental reports may contain numerous charts and spectra. This reserves ample file upload space.

Common Pitfalls

Query results show "insufficient_quota Current Group Upstream Load Saturated" (insufficient_quota current group upstream load saturated). This typically indicates too many concurrent requests exceeding current resource quotas, preventing the model from responding in time.
Model output in Markdown tables truncates content, displaying "...[hide 38432 char". This indicates the model's output length exceeded the set maximum token limit, preventing full display of some information.
The model repeatedly asks for already provided information in a conversation. This occurs when maxContext is set too low, preventing the model from effectively remembering historical conversation content and performing deep logical reasoning.

Validation Steps

Upload an experimental report containing detailed data for multiple compounds. Attempt multi-turn comparative queries for solubility or IC50 values of different compounds. Check if the model accurately identifies and references entities from historical conversations.
Construct a complex query asking the model to filter based on multiple numerical fields in the report. For example, "Find all compounds with IC50 less than 100 nM and solubility greater than 5 µg/mL." Check the accuracy and completeness of the model's returned results.
Simulate high concurrency scenarios, such as initiating multiple query requests simultaneously via a script. Observe if the system response time is within an acceptable range and if resource saturation errors appear. This evaluates the match between maxContext and system load.

The values provided are common starting points. Measure them 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.