Memorizing RAG: Next-Generation Retrieval Augmented Generation
Learn about Memor RAG, a next-generation retrieval augmented generation framework that uses long-term memory for enhanced knowledge processing and complex task handling.
Course Timeline
🤔 Introduction to Memor RAG
Overview of traditional RAG limitations and introduction to Memor RAG as a solution for handling complex information needs.
🧠 How Memor RAG Works: Long-Term Memory
Explains the concept of global memory in Memor RAG, comparing it to human long-term memory and its role in compressing and storing information.
🤖 Memor RAG Architecture: Dual System Approach
Details the dual-system architecture of Memor RAG, outlining the functions of the lightweight LLM for context understanding and the large language model for generation.
🚀 Advantages of Memor RAG: Overcoming Limitations
Discusses how Memor RAG addresses the limitations of traditional RAG systems, specifically focusing on its ability to handle long texts and multiple documents.
🛠️ Implementing Memor RAG: Practical Applications
Explores practical applications of Memor RAG in various fields and provides a brief overview of its simple integration into existing AI systems via API.
💻 Building a Chatbot with Memor RAG: Python Implementation
Step-by-step guide on installing and utilizing Memor RAG in Python, covering aspects like using Hugging Face models, tokenization, and data loading.
🎯 Memor RAG in Action: Question Answering and Summarization
Demonstrates Memor RAG's capabilities in question answering and summarization tasks, emphasizing the role of memory in improving accuracy and retrieval.
🏆 Conclusion: Future of Memor RAG
Summarizes the key advantages of Memor RAG and highlights its potential in revolutionizing future AI applications dealing with complex tasks and unstructured data.