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Learn LangChain: Build #22 LLM Apps using OpenAI & Llama 2
Build Real World LLM powered applications with LangChain, OpenAI, Llama2, Hugging Face. Create Web Apps with Streamlit.
This Course Includes
udemy
3.9 (338 reviews )
18 total hours 
english
Online - Self Paced
course
Udemy
About Learn LangChain: Build #22 LLM Apps using OpenAI & Llama 2
Master LangChain, OpenAI, Llama 2 and Hugging Face. Learn to Create hands-on generative LLM-powered applications with LangChain.
Create powerful web-based front-ends for your LLM Application using Streamlit.
By the end of this course, you will have a solid understanding of the fundamentals of LangChain OpenAI, Llama 2 and HuggingFace. You'll also be able to create modern front-ends using Streamlit in Python.
Dive into hands-on projects that will shape your expertise, including:
Project 1: Create a Simple Chatbot with Llama 2 and LangChainProject 2: PDF Chat App (GUI) | ChatGPT for Your PDF File - Streamlit Application to chat with your PDF file using LangChain and OpenAI.
Project 3: YouTube Script Writing App - Effortlessly create title and script for the YouTube video using LangChain and OpenAI
Project 4: MCQ Quiz Creator App - Seamlessly create multiple-choice quizzes for your students using LangChain and OpenAI/ Hugging Face
Project 5: Chat with Multiple PDF Documents | Streamlit Application- Chat with your PDF files using LangChain and OpenAI.
Project 6: Support Chat Bot For Your Website - Helps your visitors/customers to find the relevant data or blog links that can be useful to them.
Project 7: YouTube Video Summarizer - YouTube Video Summarizer, powered by the dynamic duo of LangChain and OpenAI! In this groundbreaking tool, we have harnessed the cutting-edge capabilities of language processing technology to transform the way you consume YouTube content.
Project 8: Summarize PDF Using LangChain, OpenAI and Gradio: Summarize PDF files using Lang Chain and OpenAI and create a sharable web interface using Gradio
Project 9: PrivateGPT- Chat with your Files Offline and Free
Project 10: Question a Book with (LangChain + Llama 2 + Pinecone): Create a chatbot to chat with Books or with PDF files. using LangChain, Llama 2 Model and Pinecone as vector store.
Project 11: Chat with Multiple Documents with Llama 2/ OpenAI and ChromaDB: Create a chatbot to chat with multiple documents including pdf, .docs, .txt using LangChain, Llama 2/ OpenAI and ChromaDB as our vector database.
Project 12: Create a Custom Chatbot for any Website with LangChain and Llama 2/ OpenAI: Create a chatbot for your own or for any website using LangChain, Llama 2/ OpenAI and FAISS as the vector store / vector database
Project 13: Creating a Flask API for Automatic Content Summarization using LangChain and Llama 2/ Open AI
Project 14: Introducing 'GPT-LLM-Trainer' — the world's simplest way to train a task-specific model. Just input your idea, and let the AI do the rest.
Project 15: Create a Medical Chatbot with Llama2, Pinecone and LangChain
Project 16: Fine-Tune Llama 2 Model with LangChain on Custom Dataset
Project 17: ChatCSV App - Chat with CSV files using LangChain and Llama 2
Project 18: Chat with Multiple PDFs using Llama 2, Pinecone and LangChain
Project 19: Run Code Llama on CPU and Create a Web App with Gradio
Project 20: Source Code Analysis with LangChain, OpenAI and ChromaDB
Project 21: Chat with Multiple PDFs using PaLM 2, Pinecone and LangChain
Project 22: Streamlit App | Chat with Multiple PDFs using PaLM 2, FAISS and LangChain
Project 23: Chat with Your Documents using Llama-Index and Google PaLM 2
Course Content:
In this course, we will explore the capabilities of LangChain, to build scalable and performant AI applications.
You will gain in-depth knowledge of LangChain components, including LLM wrappers, Prompt Template, Chains, Agents, Memory and Document Loaders. Additionally, we will delve into embeddings and vector databases
What You Will Learn?
- Master the basics of LangChain and the fundamentals of Large Language Models (LLMs).
- How to Use LangChain, OpenAI, Llama 2, Hugging Face to Build LLM-Powered Applications..
- Learn about LangChain components, including LLM wrappers, prompt templates, chains, agents, memory and document loaders.
- Learn to apply LLM techniques to personal documents and projects.
- Learn how to use embeddings and vector data stores..
- Learn about FAISS and Similarity Search..
- Learn about Pinecone and ChromaDB.
- Project: Create a Simple Chatbot with Llama 2 and LangChain.
- Project: Quiz MCQ Creator Application.
- Project: YouTube Script Writing Application.
- Project: PDF Chat App (GUI) | ChatGPT for Your PDF File.
- Project: Chat with Multiple PDF Documents | Streamlit Application.
- Project: Summarize PDF Using LangChain, OpenAI & Gradio.
- Project: YouTube Video Summarizer.
- Project: PrivateGPT- Chat with your Files Offline and Free.
- Project: Support Chat Bot For Your Website.
- Project: Question a Book with (LangChain + Llama 2 + Pinecone).
- Project: Create a chatbot to chat with multiple documents including pdf, .docs, .txt using Llama 2 , LangChain/ OpenAI and ChromaDB.
- Project: Create a Custom Chatbot for any Website with LangChain and Llama 2/ OpenAI.
- Project: Creating a Flask API for Automatic Content Summarization using LangChain and Llama 2/ Open AI.
- Fine-Tune Llama 2 Model with LangChain on Custom Dataset.
- Introducing 'GPT-LLM-Trainer' — the world's simplest way to train a task-specific model. Just input your idea, and let the AI do the rest..
- Project: Create a Medical Chatbot with Llama2, Pinecone and LangChain.
- Project: ChatCSV App - Chat with CSV files using LangChain and Llama 2.
- Project: Chat with Multiple PDFs using Llama 2, Pinecone and LangChain.
- Project: Source Code Analysis with LangChain, OpenAI and ChromaDB.
- Project: Run Code Llama on CPU and Create a Web App with Gradio.
- Run PaLM 2 in Google Colab | How to use Free Google PaLM API.
- Project: Chat with Multiple PDFs using PaLM 2, Pinecone and LangChain.
- Project: Streamlit App | Chat with Multiple PDFs using PaLM 2, FAISS and LangChain.
- Project: Chat with Your Documents using Llama-Index and Google PaLM 2.