Multi-Turn Conversations and Prompts for Mental Health

Data Characteristics in This Category

Mental health quality document data originates from diverse sources. These include clinical guidelines, diagnostic criteria (e.g., ICD-10, DSM-5), drug inserts, treatment protocols, case reports, and regulatory compliance documents. Document update frequencies vary. Diagnostic criteria and drug inserts are typically revised annually or biennially. Clinical guidelines may update irregularly based on research advancements. Document structures often contain extensive unstructured text, such as medical history descriptions and symptom assessments, alongside semi-structured data like scale scores and drug dosages. Common fields include patient ID, diagnosis code, symptom description, treatment duration, drug name, dosage unit (milligrams mg, milliliters ml), and specific items and scores from various assessment scales.

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

The mixed unstructured and semi-structured nature of mental health quality documents requires multi-turn conversation systems to consider both textual semantics and structural information relationships when understanding context. Examples include mapping patient symptom descriptions to diagnosis codes, and linking drug dosages to patient vital signs. The uncertain document update frequency necessitates an efficient knowledge base update mechanism to ensure information timeliness. This prevents the system from providing outdated diagnostic or treatment advice. In multi-turn conversations, users may frequently mention specialized terminology, abbreviations, or disease aliases. This demands higher standards for entity recognition and terminology standardization in prompt engineering. Furthermore, mental health diagnosis and treatment processes often involve multi-faceted assessments. Conversations must support in-depth, detailed questioning to gather sufficient information for accurate judgment. This directly impacts maxContext and recallWindow configurations.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	800–1200 characters	Balances semantic completeness with recall efficiency, avoiding excessive irrelevant information in long segments.
`Recall count` (Recall Count)	8–12 items	Mental health diagnosis is complex, requiring multi-faceted information to support multi-turn conversation context.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements; 0.75 is a suggested starting point.	Ensures relevance of recall results, filtering out low-quality or irrelevant content.
`maxContext`	4096 tokens	Supports multi-turn conversation context length, handling complex diagnostic and treatment logic.
`PARSE_FILE_TIMEOUT_SECONDS`	600 seconds	Handles parsing of large files (over 10MB Word/PDF), preventing timeouts.
`Rerank result count` (Reranked Return Count)	5 items	Ensures the few most relevant pieces of information are prioritized in multi-turn conversations.

Common Pitfalls

Symptom: A symptom is mentioned multiple times in a conversation, but the system fails to recognize the contextual link and asks about it repeatedly. Reason: maxContext is set too low, preventing the system from effectively maintaining conversation history.
Symptom: A user asks about the latest side effects of a drug, but the system returns information from an older version of the drug insert. Reason: The knowledge base is not updated promptly, or version information was not correctly extracted during document parsing.
Symptom: API calls show no title content in conversation logs, or return an unexpected data structure. Reason: The chat_id or stream parameters in the API call are set incorrectly, or the API's JSON response format is not handled properly.

How to Verify Configuration

Conduct multi-turn simulated conversations covering various mental health diagnoses, treatments, and medication inquiries. Check if the system maintains coherent context.
Upload new clinical guidelines or drug inserts. Immediately ask relevant questions and verify if the system returns the latest version of the information.
Test with prompts containing specialized terminology, abbreviations, and aliases. Confirm the system accurately recognizes them and provides professional answers. Concurrently, check the recall quality corresponding to the Similarity threshold (Similarity Threshold).
Invoke the conversation function via the API. Check if the response field contains complete and correct information and if the status code is 200.

The values provided are common starting points. They 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.