Multi-Turn Conversations and Prompts for Rational Drug Use

Data Characteristics in this Domain

Rational drug use data in pharmacovigilance primarily comes from drug inserts, clinical trial reports, real-world studies, adverse event reporting systems, pharmacopoeias, and professional medical literature. Data update frequencies vary. Drug inserts and pharmacopoeias are typically revised periodically based on post-marketing regulatory requirements. Adverse event reporting systems receive and process data in real-time. Document structures often include unstructured text (e.g., narrative descriptions in adverse event reports) and semi-structured tables (e.g., drug dosage, contraindications, precautions). Common fields include generic drug name, brand name, indications, dosage and administration, adverse event name, incidence, severity, management measures, and basic patient information. Units involve dosage (milligrams, milliliters), frequency (times per day), and treatment duration (days, weeks).

Constraints Imposed by these Characteristics on Multi-Turn Conversations and Prompts

The diversity and update frequency of rational drug use data challenge the accuracy and timeliness of multi-turn conversations. Understanding unstructured text requires strong semantic analysis capabilities to accurately extract key information about adverse events. Parsing semi-structured data requires correct mapping of fields and units to avoid misinterpreting dosages or instructions. Frequently updated drug inserts and adverse event reports demand rapid knowledge base synchronization to provide the latest medication advice, preventing erroneous guidance based on outdated information. In multi-turn conversations, users may describe symptoms or drugs using colloquial or non-standard terms. Prompt design must cover various expressions and guide users to provide necessary information. The specialized and rigorous nature of medical terminology requires conversation systems to ensure professionalism and accuracy in generating responses, avoiding vague or misleading statements.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`6` turns	Balances contextual coherence with system resource consumption, preventing model misinterpretation due to excessively long conversation history.
`Chunk size` (Segment Length)	`500` characters	Drug inserts and adverse event reports are often lengthy; this length effectively captures complete semantic information.
`Recall count` (Recall Count)	`10` items	Ensures retrieval of sufficient relevant information from the knowledge base, covering various dimensions of medication advice or adverse event details.
`Similarity threshold` (Similarity Threshold)	Calibrate by actual measurement	Requires adjustment through test sets based on actual data characteristics and recall effectiveness, balancing recall and precision.
`Rerank result count` (Rerank Return Count)	`3` items	Prioritizes displaying the most relevant and concise medication guidance or adverse event information, improving user efficiency in obtaining effective information.
`PARSE_FILE_TIMEOUT_SECONDS`	`300` seconds	Allocates sufficient time for content parsing and structured processing when handling large documents like drug inserts.

Common Pitfalls

Image links in conversations fail to load, showing "Resource inaccessible." This occurs because image URLs are not whitelisted or network proxy settings are incorrect, preventing the FastGPT service from directly accessing external image resources.
After uploading large CSV or XLSX adverse event data tables, the AI conversation function cannot properly reference the data. This happens due to file parsing timeouts or complex file content structures, leading to failed data vectorization or indexing.
In multi-turn conversations, the model fails to accurately identify the generic drug name mentioned by the user, confusing it with brand names or similar drugs. This indicates a lack of mapping between generic and brand names in the knowledge base, or prompts that do not emphasize the priority of generic name recognition.

Verification Steps

Conduct multi-turn conversation tests. Input specific drug names and symptoms, then verify the consistency of the system's medication advice with information in the drug insert, especially regarding dosage, administration, and contraindications.
Upload pharmacovigilance documents in various formats (e.g., PDF drug inserts, CSV adverse event reports). Check if files are parsed correctly and can be effectively referenced in conversations. This verifies the PARSE_FILE_TIMEOUT_SECONDS setting.
Simulate a user describing an adverse event. Observe if the system accurately extracts key fields like adverse event name and severity, and provides corresponding management advice based on the knowledge base. This evaluates the effectiveness of Similarity threshold (Similarity Threshold) and Recall count (Recall Count).

Note: The values provided are common starting points. Measure against your own samples to determine optimal settings.

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.