RAG is a popular approach to address the issue of a powerful LLM not being aware of specific content due to said content not being in its training data, or hallucinating even when it has seen it before. This repository showcases a curated collection of advanced techniques designed to supercharge your RAG systems, enabling them to deliver more accurate, contextually relevant, and comprehensive responses. LLM RAG Tutorial This tutorial will give you a simple introduction to how to get started with an LLM to make a simple RAG app. However, traditional RAG methods tend to have increasingly long prompts, sometimes exceeding 40k, which can result in high financial and latency costs. Code in Python and use any LLM or vector database. The entire pipeline is a series of LLM calls with carefully crafted prompt templates. There are four main components in RAG: 📖 Introduction RAG Web UI is an intelligent dialogue system based on RAG (Retrieval-Augmented Generation) technology that helps build intelligent Q&A systems based on your own knowledge base. RAG-LLM enables interactive question answering leveraging RAG architecture and Large Language Models (LLMs) applied to custom dataset regarding Medium articles. 2 as the LLM. The Retrieval-Augmented Generation (RAG) framework addresses this issue by using external documents to improve the LLM's responses through in-context learning. - ray-project/llm-applications Resolve questions around your documents, cross-reference multiple data points or gain insights from existing knowledge bases. A local retrieval augmented generation LLM. The web crawling, scraping, and search API for AI. These prompt templates are the secret sauce that enable advanced RAG pipelines to perform complex tasks. To measure the effectiveness of RAG, compare: - Retrieval Accuracy: How relevant are the retrieved documents? - Response Quality: Does the LLM provide accurate answers based on the retrieval? Oct 16, 2025 · The Retrieval-Augmented Generation (RAG) framework is an advanced AI architecture developed to improve the capabilities of LLMs by integrating external information into the response generation process. Connect any model, extend with code, protect what matters—without compromise. Contribute to langchain-ai/rag-from-scratch development by creating an account on GitHub. . Retrieval augmented generation (RAG) has emerged as a popular and powerful mechanism to expand an LLM's knowledge base, using documents retrieved from an external data source to ground the LLM generation via in-context learning. Additionally, we will provide a practical guide on how to build and implement your own RAG pipeline for LLM-based projects, ensuring your model is equipped to handle both general and domain-specific queries. May 12, 2024 · Let’s explore straight how to build a Large Language Model (LLM) app that can chat with GitHub using Retrieval-Augmented Generation (RAG) in just 10 lines of Python code. Feb 8, 2025 · This list features 17 open-source RAG (Retrieval-Augmented Generation) projects from 2024 with over 1,000 GitHub stars, plus a RAG survey and benchmarks for quick reference. Built for scale. This innovative solution leverages the power of modern AI to combine the strengths of retrieval-based and generation Run AI on your own terms. The secret sauce Our key insight is that each component in an advanced RAG pipeline is powered by a single LLM call. Clean, structured, and ready to reason with. Develop, deploy, and manage autonomous agents, RAG pipelines, and more for teams at any scale, effortlessly. To address this, we build on existing work and adopt an LLM-based multi-dimensional comparison method. Contribute to Varelion/RAG_LLM development by creating an account on GitHub. Dec 19, 2025 · Awesome LLM RAG Application Awesome LLM RAG Application is a curated list of application resources based on LLM with RAG pattern. Contribute to Sankethhhh/RAG-LLM development by creating an account on GitHub. - kottoization/RAG-LLM Experiments Defining ground truth for many RAG queries, particularly those involving complex high-level semantics, poses significant challenges. Llama-3. Jan 29, 2024 · RAG Chatbot using Confluence. This repository features LLM apps that use models from OpenAI , Anthropic, Google, xAI and open-source models like Qwen or Llama that you can run locally on your computer. Documentation LangChain is the platform for agent engineering. Introduction Retrieval-Augmented Generation (RAG) is revolutionizing the way we combine information retrieval with generative AI. Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) is a powerful and popular technique that applies specialized knowledge to large language models (LLMs). May 29, 2024 · RAG (Retrieval-Augmented Generation) primarily solves the challenges posed by LLM (Large Language Models) hallucinations and out-of-date training data by incorporating a retrieval mechanism that Oct 25, 2024 · Retrieval-Augmented Generation (RAG) systems using large language models (LLMs) often generate inaccurate responses due to the retrieval of irrelevant or loosely related information.

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