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Requirements.md

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-NLP Final Task 2026 
-RAG-Based Mental Health Support Chatbot 
-Introduction 
-This project requires designing and implementing a Retrieval-Augmented Generation (RAG) 
-based chatbot for a mental health support system. The chatbot must provide grounded, 
-empathetic, and context-aware responses to queries related to anxiety, depression, stress, 
-and crisis support. 
-The system integrates multiple NLP techniques into a single pipeline, where each component 
-plays a critical role in improving overall system performance. 
-Prerequisites 
-Before starting this project, complete the following course: 
-• Retrieval Augmented Generation (RAG) @ deeplearning.ai 
-ONLY the first 4 modules. 5th module is extra. 
-System Overview 
-The chatbot system is composed of multiple interconnected modules. Each module 
-contributes directly to the performance of the final system, making this a fully integrated, 
-end-to-end project rather than isolated tasks. 
-Project Modules 
-1) Language Detection 
-Build a multi-class classifier using Traditional NLP such as: Count Vectorizer or TF-IDF 
-Vectorizer. Train ML model to classify the language of the user’s question. This module is very 
-important for the entire system for searching right in the knowledgebase in addition to replying in 
-the same language and so on. 
-2) Emotion Classifier 
-Build a multi-class classifier using either Recurrent Neural Networks OR Transformers. Feel 
-free to choose as you like. Train model to classify emotion of the user’s question. This module is 
-very important for optimizing the final chatbot response depending on the user’s emotion to be 
-able to handle his different emotions. This is a key factor in the success of the system. 
-3) Intent Classifier 
-Build a multi-class classifier using either zero shot or few shot LLM prompting to classify the 
-intent of the user’s question. Intent is one of the following:  greeting, goodbye, gratitude, 
-asking_mental_health_question, out_of_scope.  This module is very important for routing the 
-entire system to the best route for answering. For example: - If the user is greeting, there is no need for RAG and answer directly. - If the user is asking a mental health question, you must use RAG to answer. 
-4) Q&A RAG 
-Build RAG pipeline using the mental health counseling dataset to answer the upcoming user’s 
-questions. Feel free to use the suitable framework you prefer or build from scratch, as you like. 
-You need to follow these components: - For vector database, use free cloud qdrant.  - For embeddings, use senetence transformer. - For LLM, use free groq account and gpt-oss-120b or gpt-oss-20b. 
-Datasets 
-1) Language Identification Dataset 
-2) Emotion Dataset 
-3) Mental Health Counseling Conversations 
-Guidelines 
-• You must use python. 
-• Choose the most suitable data pre-processing techniques. 
-• Use Flask or FastAPI or any suitable web framework to deploy the model locally. 
-Deliverables 
-• Four module-specific notebooks. 
-• Deployment scripts. 
-• Any additional files/documentation you need. 
-Notes 
-• In the assessment phase, you’ll be asked to run your models locally, furthermore, you’ll 
-be asked in any technical decision/implementation you’ve made, so be well prepared, 
-and avoid overcomplicated approaches you don’t fully grasp. 
-• Early submission doesn’t affect your grade, take your time.