Context_Aware_Chatbot_Presentationn.pptx

chatbot

1. Context-Aware Conversational Chatbot using RAG Mini Project Presentation Your Name & College Details

2. Introduction What is a Context-Aware Chatbot? Why use RAG (Retrieval-Augmented Generation)? Project Objective: Enhance chatbot with contextual understanding and document retrieval

3. Project Overview Transition from Dialogflow to GPT-4 for better NLP Integration of Resource Centre for knowledge enrichment Use of document retrieval system for accurate responses

4. Key Features GPT-4 Integration for natural conversations Document Retrieval System for multi-source knowledge Conversational Memory for context retention Question Refinement for incomplete queries Text Streaming for real-time responses

5. Tech Stack OpenAI GPT-4 – Response generation LangChain – Orchestration & query handling Azure Document Intelligence – Parsing PDFs & Excel Pinecone Vector DB – Storing embeddings BeautifulSoup – Web scraping

6. System Architecture User → Chatbot → Query Refinement → Embedding Generation → Pinecone → GPT-4 → Response

7. Workflow Section 1: Data Ingestion - Upload documents via Resource Centre - Extract text using Azure Document Intelligence - Convert to embeddings and store in Pinecone Section 2: Chatbot Interaction - User query → Refinement → Embedding → Similarity Search → GPT-4 Response

8. Advantages Context-aware responses Handles incomplete queries Scalable and efficient retrieval Real-time response streaming

9. Future Enhancements Multi-turn conversational memory Support for multimedia documents Integration with voice assistants

10. Conclusion Summarize benefits of RAG-based chatbot Real-world applications in customer support, education, etc.