Install and Use Ollama / Llama 3.1 in LangChain and Python+ LLM Chains and Templates in LangChain
#ollama #llama #rag #langchain #machinelearning #LLM #python
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What is covered in this tutorial: In this particular tutorial, we explain how to use Ollama and Llama 3.1 through LangChain, and we will provide an introduction to chaining and prompt templates in LangChain. In particular, we explain how to install Ollama, Llama 3.1, and LangChain in Python and Windows. We will write two test codes explaining how to use Ollama in LangChain. Everything will be done through Python virtual environments.
Motivation and big picture: Our goal is to create a series of tutorials on how to develop Retrieval Augmented Generation (RAG) applications from scratch by using open-source Large Language Models (LLMs). RAG applications combine (augment) LLMs with additional knowledge databases (documents, reports, tables, etc.) to improve the outputs of LLMs or to obtain intelligent conclusions from the provided knowledge database. This means that we are giving to an LLM an additional library that should be used to provide answers to our questions and queries.
The ultimate goal is to develop a personal AI-based assistant that can analyze our documents, tables, images, etc.
In these tutorials, we will use Ollama and LangChain to run and test different LLMs. We selected Ollama since it enable us to easily run a number of powerful LLMs locally. In particular, we will extensively use and test Llama 3.1 LLM. On the other hand, we will use LangChain (as well as some other libraries) to develop the RAG application that is based on Ollama.
Watch video Install and Use Ollama / Llama 3.1 in LangChain and Python+ LLM Chains and Templates in LangChain online, duration hours minute second in high quality that is uploaded to the channel Aleksandar Haber PhD 22 September 2024. Share the link to the video on social media so that your subscribers and friends will also watch this video. This video clip has been viewed 274 times and liked it 9 visitors.
