RAG using Llama3, Langchain and ChromaDB
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Updated
Jun 15, 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 and OpenAI Models via the Groq API.
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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