Llama 3 RAG: How to Create AI App using Ollama?

Показать описание

🚀 Join me as I dive into the world of AI with LLaMA 3! In this video, we'll explore how to create a powerful RAG (retrieval-augmented generation) app using LLaMA 3 to enhance your projects with intelligent data retrieval. 🧠💻

🔍 What You Will Learn:
Downloading and Setting Up LLaMA 3: Get started by installing the necessary libraries and downloading the LLaMA 3 model.
Creating the RAG App: Step-by-step process of building the app, from loading data from a URL to saving it in a vector database.
Designing a User Interface: Implement a UI where users can interact by asking questions to retrieve contextually relevant responses.
Enhancing Performance with Nomic Embeddings: Upgrade your app by integrating specialised embedding models for improved accuracy.

🔗 Components:
Ollama to Download LLaMA 3
Vector Databases: Chroma DB
Gradio: An easy way to build custom UIs for your projects

👍 Why Watch This Video?
Gain hands-on experience in AI application development, from basic setups to advanced data handling techniques, all tailored to empower your software development and data science skills.

🔗 Resources:

📌 Timestamps:
0:00 - Introduction to LLaMA 3 and RAG App
0:35 - Setup and Downloads
1:10 - Building the RAG App Core Functionality
3:00 - Embedding Generation and Storage
4:05 - Creating and Integrating the User Interface
5:25 - Final Testing and Demonstration

Make sure to subscribe and hit the bell icon to get notified about our latest uploads! Smash the like button if you find this tutorial helpful and share it to help others in the tech community. 🌟

#LLaMA3 #RAG #OLLaMA #AIApp #LLaMA3App #LLaMA3AIApp #LLaMA3RAG #LLaMA3RAG #RetrievalAugmentedGeneration #RetrievalAugmentedGenerationLangchain #RetrievalAugmentedGenerationLLaMA3 #LLaMA3 #RetrievalAugmented #LLaMARag #LamaRag #OLLaMARag #LLaMA3OLLaMA #LLaMA3OLLaMARag #OLLaMALLaMA3Rag

Рекомендации по теме

Комментарии

This is my 1st ever comment, but I feel it is necessary. Excellent presentation without resorting to Click Baits and Time wasting segments. Thanks 👍

linkit

Amazing video in only 7 minutes!!! Straight to the points. Great!

kirilkirchev

I like how you break every line down. Subbed. Looking forward to new videos.

Slim

Gradio and Streamlit are both great UIs. I will try this .

ukls

Absolute king, thanks for the great tutorial and code

DerNamenvolle

Excellent content, superb efforts, kudos bro

hrmanager

Great video again, I love to see how can I use llama 3 with TensorRT as well, I believe I can be awesome!

mahdihosseini

Hello there, honestly, I'm so grateful for this video as i was confused so much about RAG, i just started learning about RAG, and i wish i found your channel 2 weeks ago, nevertheless thank you so much!

Krishyt-mp

So amazing! Now something like this setup but with custom tools to automate with agents 😂

nexuslux

Thank You from a New Subscriber. More Ollama vids please ! Also, Codestral under Ollama ...

davidtindell

Hi, Mervin. Thank you for your excellent presentation and tutorial. Could you please do the procedures in docker compose?

zamanganji

You said you wanted to put a link for a video of yours about chunking into the description? I’m especially interested in advanced chunking strategies like semantic and agentic chunking!

ilianos

what about uploading document instead of webpage for retrivial?

MirGlobalAcademy

Great video!
How do you compare LLM to evaluate which LLMs are going to be better for your usecase?

ShishirKumar

Great video! And I am confused by the prompt format in LlaMa 3: 8B instruct (I followed the Meta document), but it always generates errors like keep repeating for some words and some symbols like <|eot_id|> in the generation. Is there an example of prompt engineering? Thanks!

silenthusky

Great Video! Why didn't you use agentic chunking?

MrDenisMurphy

Mervin, what if we do the rag in production having multiple requests at the same time, how chroma db behaves, will it load seperately or mixup?

ukcp

vectorising the document(s) and generating the response based on the prompt takes time for me, either using Chroma or FAISS, but yours went fast, any walkaround to ensure more efficiency and less runtime?

fvxexmh

Thanks you very much. It is working perfectly. Question. concerning : Create Ollama embeddings and vector store. How to save it, and how to load it to avoid again the embedding process. (If I want to have a model for a specific url). It is to win time. Thank for your answer. BR.

Enkumnu

Can you share with us the Llama 3 resource details and which compute instance you were using?

vijayrameshkumar

Llama 3 RAG: How to Create AI App using Ollama?

Llama 3 RAG: How to Create AI App using Ollama?

Ollama Llama 3 - RAG: How to create a local RAG system with LLAMA 3 using OLLAMA

Llama 3 RAG: Create Chat with PDF App using PhiData, Here is how..

Fully local RAG agents with Llama 3.1

Llama 3 8B: BIG Step for Local AI Agents! - Full Tutorial (Build Your Own Tools)

Llama 3 RAG Demo with DSPy Optimization, Ollama, and Weaviate!

How to create a RAG tool with Llama 3, Langchain and Ollama

How-To Run Llama 3 LOCALLY with RAG!!! (GPT4ALL Tutorial)

RAG with Langchain, Ollama Llama3, and HuggingFace Embedding | Complete Guide

Llama 3 Local RAG On Custom Documents Tutorial

How Good is LLAMA-3 for RAG, Routing, and Function Calling

Advanced RAG with Llama 3 in Langchain | Chat with PDF using Free Embeddings, Reranker & LlamaPa...

Unleash the Power of Local Llama 3 RAG with Streamlit & Ollama! 🦙💡

'okay, but I want Llama 3 for my specific use case' - Here's how

Build Anything with Llama 3 Agents, Here’s How

Meta's LLAMA 3 with Hugging Face - Hands-on Guide | Generative AI | LLAMA 3 | LLM

I Built a LLama-3 RAG PDF Chatbot

Fine-Tuning Llama 3 on a Custom Dataset: Training LLM for a RAG Q&A Use Case on a Single GPU

LLAMA-3 RAG BEATS GPT-4 ?!

Superfast RAG with Llama 3 and Groq

Building RAG With Llama 3.1

Meta Llama 3 Fine tuning, RAG, and Prompt Engineering for Drug Discovery

Building RAG with Llama 3 using LlamaIndex

LLAMA-3 🦙: EASIET WAY To FINE-TUNE ON YOUR DATA 🙌