RetrievalAugmented

Visualize your RAG Data — Evaluate your Retrieval-Augmented Generation System with RagasHow to use UMAP dimensionality reduction for Embeddings to show multiple evaluation Questions and their relationships to source documents with Ragas, OpenAI, Langchain and ChromaDBRetrieval-Augmented Generation (RAG) adds a retrieval step to the workflow of an LLM, enabling it to query relevant data from additional sources like private documents when responding to questions and queries . This workflow does not require costly training…

How to address limitations of naive RAG pipelines by implementing targeted advanced RAG techniques in PythonDifference between Naive and Advanced RAG (Image by the author, inspired by )A recent survey on Retrieval-Augmented Generation (RAG) summarized three recently evolved paradigms:Naive RAG,advanced RAG, andmodular RAG.The advanced RAG paradigm comprises of a set of techniques targeted at addressing known limitations of naive RAG. This article first discusses these techniques, which can be categorized into…

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Building generative AI applications that use retrieval augmented generation (RAG) can pose a host of challenges. Let’s look at troubleshooting RAG implementations that rely on vector databases to retrieve relevant context that’s then included in a prompt to a large language model to provide more relevant results. We will break this process down into two main parts. The first, which we’ll address in this first article in the series, is the embedding pipeline, which populates the vector database with embeddings: Here, we…

Too Long; Didn't ReadThis blog explores the integration of Retrieval-Augmented Generation (RAG) with vector databases, particularly Milvus, to enhance Language Learning Models (LLMs) in enterprise applications. It covers the challenges of LLMs, introduces RAG and vector databases, and provides practical examples and tutorials. The blog details the setup of a local environment for RAG and Milvus, including installation and configuration, and concludes with the significant potential of combining these technologies to…

This article is intended for data scientists, AI researchers, machine learning engineers, and advanced practitioners in the field of artificial intelligence who have a solid grounding in machine learning concepts, natural language processing, and deep learning architectures. It assumes familiarity with neural network optimization, transformer models, and the challenges of integrating real-time data into generative AI systems. Introduction Retrieval-Augmented Generation (RAG) models have emerged as a compelling solution to…

From the theory of the original academic paper to its Python implementation with OpenAI, Weaviate, and LangChainRetrieval-Augmented Generation WorkflowSince the realization that you can supercharge large language models (LLMs) with your proprietary data, there has been some discussion on how to most effectively bridge the gap between the LLM’s general knowledge and your proprietary data. There has been a lot of debate around whether fine-tuning or Retrieval-Augmented Generation (RAG) is more suited for this (spoiler…