LangChain: Build 26 LLM Apps with OpenAI, Llama & DeepSeek

In this video tutorial you will learn how to create account on OpenAI and create your own API Key

In this video tutorial you will learn how to create an account on Hugging Face and create your own Access Token.

In this tutorial, we will set up the environment and initialize the OpenAI and Hugging Face model wrappers

In this lecture, we will learn how to create prompt template and how to use LLM Chain to run/execute the prompt template.

In this video tutorial, we will learn how to use Agents in LangChain to get real time information from Google Search Tool and Wikipedia etc.

In this video tutorial we will learn about Simple Sequential Chain and Sequential Chain, we will cover the limitations of Simple Sequential Chain and why we need Sequential Chain

In this video tutorial, we will  learn how to use memory in LangChain so that our Large Language Model can keep track of the previous conversation

In this video tutorial, we will cover how we can upload PDF file, doc files, txt files  and  extract the data from the uploaded files with LangChain

This lectures presents a quick overview of Llama 2 model, how Llama 2 model was trained and how Llama 2 outperforms other benchmark models.

In this lecture, we will cover how to run Llama 2 model in Google Colab

In this lecture, we will cover how to run Llama 2 with LangChain

In this lecture, we will learn how to create prompt template and how to use LLM Chain to run/execute the prompt template.

In this video tutorial, we will  learn how to use memory in LangChain so that our Large Language Model can keep track of the previous conversation

In this video tutorial, we will cover how we can upload PDF file, doc files, txt files  and  extract the data from the uploaded files with LangChain

In this video tutorial, we will learn how we can create a simple chatbot with LangChain and Llama 2

In this video tutorial, we will cover how we can use pandas dataframe agent in LangChain to analyze single or multiple dataframe

In this video tutorial, we will cover how we can use different opensource models from hugging face.

This lecture presents a demo of the Streamlit Application that generates the title and script of your Youtube Video. You will pass the topic of your Youtube video as input and the application will generate the title and script of the Youtube video.

In this lecture, we will create an initial Streamlit application that generates the title of the Youtube video.

In this video tutorial, we will further go ahead and update our Streamlit application and create a Prompt Template to generate the title of the Youtube video  and execute the Prompt Template with LLMChain.

In this video tutorial, we will create Prompt Templates and LLM Chains to generate the title and script of the Youtube video.

To run Multiple LLM Chains, we use Simple Sequential Chain, in this tutorial we will explore how to use Simple Sequential Chain to run multiple LLM Chains, along with this we will also discuss the limitations of Simple Sequential Chain as well.

In this video tutorial, we will see how we can add memory into our Streamlit application so that we can keep track of our previous conversation.

In this video tutorial, we will explore how we can use Agents to get information from other tools like Wikipedia, Google Search etc.

In this video tutorial, we will see how we can chat with our PDF files with LangChain and OpenAI. We will start by discussing the complete process flow of the project in detail. We will use Google Colab  for implementation and the complete project is divided into multiple parts.

In this video tutorial, we will create a Streamlit application to Chat with our  PDF file using LangChain and OpenAI.  We will start by discussing the complete process flow of the project in detail. We will use PyCharm Community Edition 2021   for implementation and the detail explanation of each line of the code is provided in this project.

In this video tutorial, we will learn how we can summarize any Youtube video using LangChain and OpenAI. The video starts by explaining the complete process flow in detail. The complete project is divided into multiple small steps and the explanation of each line of the code is provided in the video.