https://github.com/mithoon278/openmind-ai-genai-project

A compassionate mental health chatbot built using Retrieval-Augmented Generation (RAG). This project leverages advanced natural language processing techniques, including SentenceTransformers, Pinecone for vector storage, and fine-tuned LLaMA 3.3, to provide thoughtful, context-aware, and empathetic responses.
https://github.com/mithoon278/openmind-ai-genai-project

chunking genai-chatbot groq langchain mental-health-awareness python rag sentence-transformers support

Last synced: about 2 months ago
JSON representation

A compassionate mental health chatbot built using Retrieval-Augmented Generation (RAG). This project leverages advanced natural language processing techniques, including SentenceTransformers, Pinecone for vector storage, and fine-tuned LLaMA 3.3, to provide thoughtful, context-aware, and empathetic responses.

• Host: GitHub
• URL: https://github.com/mithoon278/openmind-ai-genai-project
• Owner: Mithoon278
• License: mit
• Created: 2025-01-07T16:04:49.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2025-01-08T06:17:48.000Z (4 months ago)
• Last Synced: 2025-01-20T00:39:07.118Z (3 months ago)
• Topics: chunking, genai-chatbot, groq, langchain, mental-health-awareness, python, rag, sentence-transformers, support
• Language: Jupyter Notebook
• Homepage:
• Size: 2.69 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# **OPENMIND_AI-RAG PROJECT**

This project implements a mental support chatbot using a Retrieval-Augmented Generation (RAG) framework. The chatbot is designed to provide compassionate, thoughtful, and context-aware responses by leveraging pre-existing datasets and integrating advanced natural language processing techniques.

---

## **Project Overview**

The chatbot:

- Uses a csv file of psychological contexts and responses to generate insightful answers.

- Embeds data using `SentenceTransformers` and stores it in a Pinecone vector database.

- Retrieves relevant context dynamically during conversation.

- Fine-tunes an LLM model to act as a compassionate, friendly personal assistant.

- Processes queries to provide meaningful, concise, and empathetic responses.

---

## **Workflow**

1. **Data Preprocessing**:

   - The data is taken from [huggingface](https://huggingface.co/datasets/jkhedri/psychology-dataset/viewer/default/train?p=1), removed the third column from it because of irrelevant responses.

   - Converted to csv using pandas.

   - Load the dataset using LangChain's CSVLoader.

   - Format the data into question-answer pairs.

2. **Text Chunking**:

   - Use LangChain's `RecursiveCharacterTextSplitter` for chunking large texts.

3. **Embeddings**:

   - Generate vector embeddings using `SentenceTransformers` (`all-MiniLM-L6-v2`).

4. **Storage**:

   - Store embeddings in Pinecone for fast and scalable retrieval.

5. **Retrieval-Augmented Generation**:

   - Retrieve context based on user queries.

   - Use LLaMA 3.3 for generating responses augmented with retrieved context.

---

## **Technologies Used**

- Language Models: LLaMA 3.3

- Embeddings: SentenceTransformers (all-MiniLM-L6-v2)

- Database: Pinecone

- Frameworks: LangChain, Streamlit

- Programming Language: Python