Build a RAG-Powered AI Therapist Chatbot with Mood Tracking Dashboard and Next.js
What You'll Learn
In this tutorial, you will build a secure, empathetic AI therapist chatbot using Retrieval-Augmented Generation (RAG). You will integrate a mood tracking dashboard to visualize user sentiment over time. This project combines advanced AI techniques with modern web development.
You will learn how to process unstructured text data effectively. The system retrieves relevant therapeutic context before generating responses. This ensures accurate and safe interactions for users seeking support.
Why This Matters
Mental health resources are often scarce or expensive. An AI assistant can provide immediate, scalable support between sessions. It offers a safe space for users to express feelings anonymously.
However, standard LLMs can hallucinate or give harmful advice. RAG mitigates this by grounding answers in verified therapeutic texts. This makes the application safer and more reliable for sensitive topics.
Prerequisites
Before starting, ensure you have the following tools installed:
- Node.js 18+: Required for running the Next.js framework efficiently.
- Python 3.9+: Needed for the backend vector database and embedding scripts.
- OpenAI API Key: For accessing language models and embedding endpoints.
- Basic React Knowledge: Familiarity with hooks and component structure is essential.
You should also understand basic concepts of Vector Databases. These store numerical representations of text for semantic search. We will use ChromaDB for its simplicity and local deployment capabilities.
Setting Up Your Environment
Initialize your Next.js project with TypeScript support. Open your terminal and run the create-next-app command. This sets up the foundational file structure and dependencies automatically.
npx create-next-app@latest ai-therapist-chatbot --typescript --tailwind --app
Navigate into your new directory and install the required packages. You need libraries for API calls, environment variable management, and UI components.
cd ai-therapist-chatbot
npm install openai axios lucide-react class-variance-authority clsx tailwind-merge
Create a .env.local file in the root directory. Add your OpenAI API key here to keep it secure. Never commit this file to version control systems like GitHub.
OPENAI_API_KEY=your_actual_api_key_here
NEXT_PUBLIC_APP_URL=http://localhost:3000
Designing the Data Pipeline
The core of a RAG system is its data pipeline. You must convert raw therapeutic texts into embeddings. Embeddings are vectors that capture the semantic meaning of words and sentences.
We will use a simple Python script to process our dataset. This dataset contains excerpts from cognitive behavioral therapy manuals. Ensure all data is anonymized and ethically sourced.
Creating Embeddings
Install the necessary Python libraries for handling embeddings an
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