mentalsaathi-ai 0.2.1

LangChain-based AI engine for mental health support conversations

🌿 MentalSaathi AI Engine

The AI backbone of Mental Saathi — a mental health platform built for Indian college students. This package handles risk detection, LLM chains, prompt management, and agent orchestration.

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Features

• Risk Detection Agent — analyses user-submitted text and returns a structured risk assessment (risk_score, risk_level, summary)
• LangChain-powered chains — modular, composable chains built on langchain-core
• Groq LLM integration — fast inference via langchain-groq
• Pydantic output schemas — structured, validated responses from every chain
• Pluggable prompt system — prompts and parsers are decoupled and easy to swap

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Project Structure

mentalsaathi.backend/ai/
├── agent/               # High-level agents (entry points)
│   └── risk_detection.py
├── chains/              # LangChain chains
│   └── calculate_risk.py
├── core/                # App settings & config
│   └── settings.py
├── llms/                # LLM instantiation
│   └── llm.py
├── prompts/             # Prompt templates & parsers
│   └── risk.py
└── schemas/             # Pydantic output schemas
    └── risk.py

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Installation

Requires Python 3.11+.

# Clone the repo
git clone https://github.com/your-org/mentalsaathi.git
cd mentalsaathi.backend

# Install the AI package in editable mode
pip install -e ".[dev]"

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Configuration

Create a .env file in mentalsaathi.backend/:

GROQ_API_KEY=your_groq_api_key
GROQ_MODEL=llama3-8b-8192

All settings are loaded via ai.core.Settings (Pydantic BaseSettings).

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Usage

Risk Detection

from ai.agent import risk_agent

result = risk_agent.invoke({"user_input": "I've been feeling really hopeless lately..."})

print(result.risk_level)   # e.g. "HIGH"
print(result.risk_score)   # e.g. 78
print(result.summary)      # e.g. "User expresses signs of hopelessness..."

Risk Levels

Level	Description
MINIMAL	No significant distress detected
LOW	Mild emotional difficulty
MODERATE	Noticeable distress, worth monitoring
HIGH	Significant risk, prompt attention needed
CRITICAL	Immediate intervention recommended

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Dependencies

Key packages used:

Package	Purpose
langchain-core	Chain & prompt abstractions
langchain-groq	Groq LLM integration
pydantic	Output schema validation
python-dotenv	Environment variable loading
rich	Pretty console output
langsmith	Tracing & observability

Full list in pyproject.toml.

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Development

# Run a quick smoke test on the risk agent
python -c "from ai.agent import risk_agent; print(risk_agent.invoke({'user_input': 'test'}))"

Make sure the package is installed from the repo root so absolute imports (from ai.x import ...) resolve correctly.

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License

MIT © 2026 Mental Saathi