Henry Aduhenryadu.hashnode.dev·Dec 16, 2024Step-by-Step Guide to Local RAG with Ollama's Gemma 2, and LangChain.dartIntroduction to Local RAG What is Retrieval-Augmented Generation (RAG)? The core idea is to enhance the generative capabilities of language models by incorporating external knowledge retrieved from a document store or database. This approach is very ...38 readsLangChainRAG
Ekta Guptaharnessingllama.hashnode.dev·Oct 12, 2024Llama 3.1: Your Cold Email AllyIn the fast-paced world of digital communication, sending cold emails has become an essential strategy for businesses and professionals alike. Whether you're a freelancer seeking clients, a startup pitching investors, or a job seeker reaching out to ...2 likesAI
Muhammad Fahad Bashirmfahadbashir.hashnode.dev·Sep 26, 20246. Understanding Retrievers in detail -LangChain Retriever MethodsThis is the 6th article in my Retrieval-Augmented Generation (RAG) series. In previous articles, we explored what RAG is and how to create a vector store. Now, we’ll cover the next critical step—retrieving data from vector stores. This is a core comp...1 like·44 readsImplementing RAG systems from Scratch in-depthRAG
Muhammad Fahad Bashirmfahadbashir.hashnode.dev·Sep 12, 20245.Vector Stores: Efficient Storage and Retrieval for EmbeddingsIn this continuation of our series of Retrieval-Augmented Generation (RAG), we will learn about the final step of the ingestion pipeline—vector stores. Previously, we covered embeddings in detail, from understanding what they are to implementing them...12 likes·57 readsImplementing RAG systems from Scratch in-depthTutorial
Debanjan Chakrabortysubspace.hashnode.dev·Jul 5, 2024Content Engine for PDF Document Comparison using Retrieval Augmented Generation Techniques with Large Language Models locally with StreamlitIntroduction In today's data-driven world, accessing relevant information quickly and accurately from vast collections of documents is crucial. Whether for research, enterprise documentation, legal searches, or knowledge management, the ability to ef...10 likesdocument comparison
Manoharan MRgadzilla.in·May 12, 2024Chroma Vector DB: Unveiling the Power of High-Dimensional Data ManagementIn today's data-driven world, the demand for efficient storage and retrieval of high-dimensional data, such as vectors, is greater than ever. Enter Chroma Vector DB – a cutting-edge solution revolutionizing the way organizations manage and analyze co...AI | ML | DL | Gen AIAI
Manish Singh PariharforFutureSmart AI Blogblog.futuresmart.ai·May 2, 2024Building your own LLM RAG chatbot with Neo4j and LangchainIntroduction Chatbots have become increasingly prevalent, yet traditional models often falter when faced with complex queries or natural language variations, leading to unsatisfactory user experiences. Retrieval-augmented generation (RAG) chatbots of...6 likes·4.5K readsnatural language processing
Siddhesh Agarwalsiddhesh2003.hashnode.dev·Apr 28, 2024RAG Model using Langchain.py and ChromaDBToday, I will discuss creating a Retrieval Augmented Generation (RAG) Model on your custom data using Python, LangChain, and ChromaDB (or any VectorStore you choose). You can find the source code here: Siddhesh-Agarwal/django-rag (github.com) What ...124 readsPython
Pablo Sanchidrianbeabetterswe.com·Apr 20, 2024FeaturedPython RAG with Complex PDFsINTRO If you are here, it means you have the same problem I had. We can help each other. I want to create a RAG capable of understanding complex PDFs. Dealing with complex documents is very hard. If your client does not want to change their documents...30 likes·1.0K readsRAG
Brian Kingsolodev.app·Feb 27, 20241 of 2: Installing AnythingLLM for Linux.TL;DR. This post provides a detailed guide on how I install a Dockerized AnythingLLM on a Debian-based Linux distro called Ubuntu. My process involves setting up various tools including Miniconda, Ollama, ChromaDB, Docker, and an LLM (large language ...1.7K readsThe AI SeriesAnythingLLM
