docs: mark Unit 1 (RAG Fundamentals) as complete ✅

Author: gsinghjay

README.md

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+# Project: Real-Time Streaming RAG Chat
+
+## Goal
+
+This project aims to build a functional real-time chat application enhanced with Retrieval-Augmented Generation (RAG), demonstrating how to combine WebSockets with AI-driven information retrieval and generation for live conversations.
+
+## Project Units
+
+This project is broken down into the following units. See each unit's README for details:
+
+1.  **[Unit 1: RAG Fundamentals & Basic Demo](assignment1_llm_embeddings/README.md)** - ✅ Complete! Basic RAG implementation with document processing, embedding generation, and query answering. ([See demo output](RAG-output.md))
+2.  **[Unit 2: Real-Time WebSocket Chat](assignment2_websocket_chat/README.md)** - Builds the foundational real-time chat server and client.
+3.  **[Unit 3: Integrating RAG with Real-Time Chat](assignment3_rag_chat_integration/README.md)** - (TBA) Focuses on combining RAG (Unit 1) with the chat system (Unit 2).
+4.  **[Unit 4: Advanced RAG Techniques & Optimization](assignment4_advanced_rag/README.md)** - (TBA) Explores improvements to the RAG pipeline.
+5.  **[Unit 5: User Interface Enhancements](assignment5_ui_ux/README.md)** - (TBA) Focuses on improving the chat UI/UX.
+6.  **[Unit 6: Deployment & Scalability](assignment6_deployment/README.md)** - (TBA) Covers preparing the application for deployment.
+
+---
+
+*Initial Setup: Run `npm install` in the root directory and create a `.env` file with your `OPENAI_API_KEY`.*