RAG using Llama3, Langchain and ChromaDB
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Updated
Sep 24, 2024 - Jupyter Notebook
RAG using Llama3, Langchain and ChromaDB
META LLAMA3 GENAI Real World UseCases End To End Implementation Guide
RAG-nificent is a state-of-the-art framework leveraging Retrieval-Augmented Generation (RAG) to provide instant answers and references from a curated directory of PDFs containing information on any given topic. Supports Llama3.1 and OpenAI Models via the Groq API.
Cloning Yourself using your whatsapp chat history and training a model on it.
In this end to end project I have built a RAG app using ObjectBox Vector Databse and LangChain. With Objectbox you can do OnDevice AI, without the data ever needing to leave the device.
A ChatBot designed to assist WhatsAgenda customers in configuring their calendar. This tool streamlines the setup of scheduling, managing appointments, and customizing service hours, ensuring an efficient and user-friendly experience.
📜 Briefly utilizes open-source LLM's with text embeddings and vectorstores to chat with your documents
This project leverages Retrieval Augmented Generation (RAG) to create an LLM model based on the Constitution of Nepal. The model, powered by LLAMA 3 70B and executed using ChatGROQ, enables efficient information retrieval and interaction with the constitutional text.
Experiment using Meta's newly released llama 3 model.
Local RAG using LLaMA3
A Retrieval Augmented Generation (RAG) Chatbot that allows you to interact with your documents using the open source LLM model Llama 3.
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