Multi-turn Conversation and Prompts for Mental Health

Data Characteristics for This Category

Mental health regulations and SOP documents originate from various sources. These include diagnostic and treatment guidelines from national health authorities, internal management regulations from medical institutions, clinical pathway documents, and drug inserts. Update frequencies vary; national guidelines might update every few years, while internal SOPs might revise annually. Documents are typically in PDF or Word format, containing extensive unstructured text interspersed with tables and diagrams. Common fields and units include diagnostic criteria (e.g., DSM-5 classification codes), treatment cycles (in weeks or months), drug dosages (milligrams, grams), follow-up frequency (times/month), and risk assessment scale scores (integer values).

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

The unstructured nature of mental health regulation documents makes traditional keyword matching ineffective for precise information retrieval in multi-turn conversations. Varying document update cycles necessitate version management capabilities to ensure the timeliness and accuracy of retrieved information. The abundance of specialized terminology and cross-references requires deep semantic understanding from the model to avoid ambiguity during multi-turn questioning. For example, subtle differences in dosage units can directly impact the accuracy of medical advice. Furthermore, the sensitive nature of these conditions demands that the dialogue system maintain strict medical professionalism when providing information, avoiding overgeneralization or misleading statements.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8` turns	Balances conversational coherence with computational resource consumption, covering common Q&A scenarios.
`Chunk size`	`800` characters	Balances semantic integrity of long documents with efficiency of segment retrieval.
`Recall count`	`5` entries	Ensures coverage of relevant information and reduces interference from irrelevant content.
`Similarity threshold`	`0.78`	Filters out low-relevance content, improving retrieval accuracy.
`Rerank result count`	`2` entries	Focuses on the most critical information, reducing user reading burden.
`temperature`	`0.1-0.3`	Reduces model's freeform generation, ensuring rigor and accuracy in responses.

Three Common Mistakes

The dialogue displays "no relevant regulations found." This occurs when segment length is too long or too short, causing critical information to be truncated or obscured.
When users ask about specific drug dosages, the answer provides a general treatment plan instead of accurately extracting the dosage value. This happens because dosage information in documents often appears in tables or complex sentences, which the model fails to parse effectively.
After connecting an external system via API, fetching corresponding conversation records using customUid results in a 404 error. This is typically due to a mismatch between API request parameters and the backend's expected field names or format.

How to Verify Configuration

Select typical mental health conditions (e.g., depression, anxiety) and their treatment guidelines. Simulate multi-turn questions to check if answers accurately cite original regulatory text.
For critical information like dosages and cycles presented in tables or diagrams within documents, ask targeted questions. Verify if the model extracts consistent numerical values and units.
Submit dialogue requests with customUid via the API interface. Attempt to query historical records to verify that the customUid session isolation function works correctly.

The values provided 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.