Multi-turn Conversation and Prompts for Structured Analysis

Data Characteristics for This Category

Respiratory system disease R&D documents are diverse. They include clinical trial protocols, research reports, medical imaging reports, pathology analysis reports, and drug mechanism of action literature. Data sources are complex, spanning hospital electronic medical record systems, research institution databases, pharmaceutical company internal R&D platforms, and international medical journals. Document update frequency varies; clinical trial data might update in phases, while basic research literature sees continuous publication. Document structure is often unstructured or semi-structured text. Clinical reports, for example, contain free-text descriptions, tabular data, and charts. Fields and units frequently involve lung function indicators (e.g., FEV1, FVC, in L or %), imaging features (e.g., nodule size, in mm), pathological diagnostic codes (e.g., ICD-O-3), and drug dosages (e.g., mg/kg). There is also extensive use of medical terminology and abbreviations.

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

The complex data characteristics of respiratory system R&D documents impose specific constraints on multi-turn conversation and prompt design. The high proportion of unstructured text requires the conversation model to have strong text understanding capabilities to extract key information from extensive medical descriptions. Frequently updated literature and data mean the knowledge base needs an efficient synchronization mechanism to ensure the timeliness of conversation results. In multi-turn conversations, users might frequently switch topics, for example, from inquiring about drug mechanisms of action to clinical trial data. This requires the model to maintain contextual coherence and dynamically adjust information retrieval strategies based on user intent. The use of medical terminology and abbreviations demands high accuracy and professionalism in prompts; incorrect term understanding can lead to information deviation. Furthermore, diverse fields and units require precise numerical comparison and unit conversion in multi-turn conversations to avoid confusion, for example, when comparing lung function data from different studies.

Configuration Settings

Configuration Item	Recommended Value	Rationale for Recommendation
`maxContext`	`8`	Respiratory system R&D conversations often involve complex logical chains, requiring a longer context window to maintain coherence in multi-turn conversations and capture user intent shifts.
`Chunk size`	`500–700 characters`	Ensures each text segment contains sufficient information without being excessively long, which could lead to redundancy or semantic drift, especially for paragraphs in clinical reports.
`Recall count`	`10–15 entries`	Given the specialized nature and information density of respiratory system documents, increasing the number of retrieved items helps cover more comprehensive relevant knowledge points and improves recall accuracy.
`Similarity threshold`	Calibrated by measurement	Adjust based on the specific corpus and retrieval effectiveness to ensure retrieved results are both relevant and distinctive, avoiding interference from low-relevance documents.
`Rerank result count`	`5 entries`	Re-ranks retrieved results, prioritizing document snippets most relevant to the current conversation, improving response quality and user experience.
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Considers the file size of medical imaging reports and large research reports, setting a larger file upload limit to support multimodal data uploads.

Three Common Mistakes

The conversation interface fails to correctly parse an uploaded image URL. This occurs because the image URL was not passed via the img_url field, or the image content was not converted to a recognizable Base64 encoding.
AI conversation fails after uploading an XLSX file. This is typically due to the file parser not recognizing the application/vnd.openxmlformats-officedocument.spreadsheetml.sheet MIME type, preventing data extraction.
The model output in a multi-turn conversation does not align with user intent. A common reason is that the prompt does not explicitly specify variable names like userName or user_message, preventing the model from accurately identifying the user's query.

How to Confirm Correct Configuration

Upload a clinical trial report containing lung function indicators (e.g., FVC) and drug dosages (e.g., 吸入剂量). Engage in a multi-turn conversation to confirm the model can accurately extract and compare these values and correctly identify units.
Test the model's responses to the latest research progress on specific respiratory system diseases (e.g., 慢性阻塞性肺疾病 COPD). Check if the knowledge base has synchronized the latest medical literature and can provide timely information.
In a complex multi-turn conversation, test whether the model can maintain contextual coherence and provide relevant information when the user switches from drug mechanism of action (MOA) to clinical symptom description (clinical symptom).
Upload a PDF document containing charts or tables. Check if the model can identify and parse key data points within it and reference them in the conversation.

The values given are common starting points and should be measured 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.