RAG using Llama3, Langchain and ChromaDB
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Updated
Jun 15, 2024 - Jupyter Notebook
RAG using Llama3, Langchain and ChromaDB
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 and OpenAI Models via the Groq API.
META LLAMA3 GENAI Real World UseCases End To End Implementation Guide
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.
📜 Briefly utilizes open-source LLM's with text embeddings and vectorstores to chat with your documents
Experiment using Meta's newly released llama 3 model.
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.
Local RAG using LLaMA3
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