Retrieval-Augmented Generation (RAG) has revolutionized how language models provide responses by incorporating external knowledge retrieval. However, traditional RAG pipelines often follow a static approach, retrieving a fixed set of documents and generating responses without…
Retrieval-Augmented Generation (RAG) improves AI responses by integrating relevant documents. LangGraph enhances traditional RAG with a dynamic, graph-based framework, allowing adaptive reasoning and multi-agent collaboration. It supports iterative decision-making, facilitating better context retrieval and…
QLoRA, or Quantized Low-Rank Adaptation, is an innovative technique enhancing AI model training efficiency, speed, and memory usage. By combining quantization and low-rank adaptation, it allows for faster deployment of large language models while…
This blog post discusses the use of large language models (LLMs) through APIs, focusing on both commercial options like ChatGPT, Claude, and Gemini, and open-source models such as Llama. It outlines the straightforward process…
This content outlines the architecture and development process for creating a DIY chatbot using open-source tools like Llama and Streamlit. It covers essential components including user interface design, knowledge base integration through Retrieval Augmented…
