LangChain Agents Tutorial: Integrating Google Search into LLMs using Python | HuggingFace Models

In this tutorial, I'll guide you through the process of integrating an agent into your Large Language Model (LLM) using LangChain. We'll explore step-by-step how to seamlessly incorporate the power of a Google Search Engine into your LLM, enhancing its capabilities and providing richer responses. 🔍 What You'll Learn: - Setting up Google and Search Engine credentials - Integrating Google Search Engine API with your LLM - Enhancing LLM responses with real-time search results - Harnessing a chat model from Hugging Face 🚀 Timestamps: 0:00 Introduction 0:45 Create credentials 2:46 Setup environment 04:05 Load Agent to use Google Search 05:55 Test Google Search Engine 06:54 Load LLM from HuggingFace 08:41 Load prompt for the LLM 10:13 Embed Agent to the LLM 12:08 Test Agent with real-time search results 15:52 Conclusion 🚀 Ready to level up your AI game? Watch now and unlock the power of LangChain integration with Google Search Engine! Don't forget to like, share, and subscribe for more AI tutorials and insights! Resources: LangChain Agents: https://python.langchain.com/docs/integrations/tools/google_search/ Google API Key: https://console.cloud.google.com/apis/credentials Google Search Engine: https://programmablesearchengine.google.com/controlpanel/create Links: 💻 GitHub repo for code: https://github.com/Eduardovasquezn/langchain-agent/blob/main/agent-test.ipynb ☕️ Buy me a coffee... or an iced tea: https://www.buymeacoffee.com/eduardov 👔 LinkedIn: https://www.linkedin.com/in/eduardo-vasquez-n/ #LLM #LangChain #GoogleSearch #Agents #Tutorial #AI #GenerativeAI #HuggingFace #MachineLearning

Ollama Tutorial | Run Llama2 locally | 7 billion parameter model | No GPU | LangChain Integration

Welcome to this comprehensive tutorial on Ollama! In this step-by-step guide, I'll walk you through how to use Ollama and everything you need to know to make the most out of it. From downloading and setting up the platform to exploring available models and seamlessly integrating Ollama with LangChain. Stay tuned as we put Ollama to the test with Llama2, boasting a 7 billion parameters, all accomplished locally without the need for a GPU. Unlock the full potential of OLLAMA and revolutionize your language processing journey! Don't forget to like, share, and subscribe for more tutorials on Generative AI. Let's unlock the full potential of AI together! 🚀 Timestamps: 0:00 Introduction 1:02 Download Ollama 1:50 Available LLMs 03:00 Run Llama2 04:22 Download LLM 05:45 Customize prompt 07:48 Create customized LLM 08:26 Test customized LLM 08:48 LangChain integration 12:36 Conclusion ‌Resources: Ollama: https://ollama.com/ Ollama GitHub: https://github.com/ollama/ollama ChatOllama - LangChain 🦜️🔗: https://python.langchain.com/docs/integrations/chat/ollama Links: 💻 GitHub repo for code: https://github.com/Eduardovasquezn/ollama-intro ☕️ Buy me a coffee... or an iced tea: https://www.buymeacoffee.com/eduardov 👔 LinkedIn: https://www.linkedin.com/in/eduardo-vasquez-n/ #Ollama #LLM #AI #MachineLearning #TechTutorial #GenerativeAI #Innovation #LangChain #LLama2 #Meta #tutorial

Running a 7B model locally is easy until you hit VRAM limits. This guide to the best GPUs for local LLM inference covers it in detail. Watch next: Python Advanced AI Agent Tutorial - LlamaIndex, Ollama and Multi-LLM!.

Ollama Tutorial | Run Llama2 locally | 7 billion parameter model | No GPU

Welcome to this comprehensive tutorial on Ollama! In this step-by-step guide, I'll walk you through how to use Ollama and everything you need to know to make the most out of it. From downloading and setting up the platform to exploring available models and seamlessly integrating Ollama with LangChain. Stay tuned as we put Ollama to the test with Llama2, boasting a 7 billion parameters, all accomplished locally without the need for a GPU. Unlock the full potential of OLLAMA and revolutionize your language processing journey! Don't forget to like, share, and subscribe for more tutorials on Generative AI. Let's unlock the full potential of AI together! 🚀 Timestamps: 0:00 Introduction 1:02 Download Ollama 1:50 Available LLMs 03:00 Run Llama2 04:22 Download LLM 05:45 Customize prompt 07:48 Create customized LLM 08:26 Test customized LLM 08:48 LangChain integration 12:36 Conclusion ‌Resources: Ollama: https://ollama.com/ Ollama GitHub: https://github.com/ollama/ollama ChatOllama - LangChain 🦜️🔗: https://python.langchain.com/docs/integrations/chat/ollama Links: 💻 GitHub repo for code: https://github.com/Eduardovasquezn/ollama-intro ☕️ Buy me a coffee... or an iced tea: https://www.buymeacoffee.com/eduardov 👔 LinkedIn: https://www.linkedin.com/in/eduardo-vasquez-n/ #Ollama #LLM #AI #MachineLearning #TechTutorial #GenerativeAI #Innovation #LangChain #LLama2 #Meta #tutorial

Simple Text Summarization App with Open AI, Streamlit and LangChain

How to build an LLM based Text summarization app using OpenAI, LangChain & Streamlit - Full tutorial end-end. Full code : https://www.linkedin.com/pulse/building-text-summarization-app-open-ai-streamlit-langchain-sri-laxmi-keiuc/?trackingId=6851YvmIRFy1YCJ5ShXBqw%3D%3D In this tutorial, you will learn how to build a web application for text summarization using Open AI models, Streamlit and LangChain. Our app will allow users to input a lengthy text, which will then be summarized using OpenAI's language models through the LangChain library. This video also provides langchain tutorial. You will understand. - how to build an llm app - what is langchain - how to use streamlit - what is OpenAI - how to get OpenAI API key - how to build AI app using open AI - How to deploy on streamlit using python langchain tutorial, streamlit tutorial, generative ai, langchain agents, python streamlit, langchain ai, python web app, machine learning, learn streamlit, how to use streamlit, openai, how to use langchain, langchain community tools, langchain tutorial,langchain tutorial chatbot,langchain installation tutorial,langchain tutorial projects,streamlit tutorial,streamlit tutorial 2,streamlit python tutorial,tutorial streamlit,streamlit tutorial python 2,streamlit tutorial playlist,streamlit tutorial openai,streamlit full tutorial,tutorial streamlit python,streamlit llm tutorial,streamlit langchain tutorial,streamlit tutorial for beginners,streamlit web app tutorial,streamlit tutorial python #2,ai tutorial streamlit,basic streamlit tutorial,streamlit complete tutorial,streamlit machine learning tutorial #generativeai #aitutorialforbeginners #aitutorial #langchain #streamlit #openai #llm #aiapplications #nlp

Simple Text Summarization App with Open AI, Streamlit and LangChain

How to build an LLM based Text summarization app using OpenAI, LangChain & Streamlit - Full tutorial end-end. Full code : https://www.linkedin.com/pulse/building-text-summarization-app-open-ai-streamlit-langchain-sri-laxmi-keiuc/?trackingId=6851YvmIRFy1YCJ5ShXBqw%3D%3D In this tutorial, you will learn how to build a web application for text summarization using Open AI models, Streamlit and LangChain. Our app will allow users to input a lengthy text, which will then be summarized using OpenAI's language models through the LangChain library. This video also provides langchain tutorial. You will understand. - how to build an llm app - what is langchain - how to use streamlit - what is OpenAI - how to get OpenAI API key - how to build AI app using open AI - How to deploy on streamlit using python langchain tutorial, streamlit tutorial, generative ai, langchain agents, python streamlit, langchain ai, python web app, machine learning, learn streamlit, how to use streamlit, openai, how to use langchain, langchain community tools, langchain tutorial,langchain tutorial chatbot,langchain installation tutorial,langchain tutorial projects,streamlit tutorial,streamlit tutorial 2,streamlit python tutorial,tutorial streamlit,streamlit tutorial python 2,streamlit tutorial playlist,streamlit tutorial openai,streamlit full tutorial,tutorial streamlit python,streamlit llm tutorial,streamlit langchain tutorial,streamlit tutorial for beginners,streamlit web app tutorial,streamlit tutorial python #2,ai tutorial streamlit,basic streamlit tutorial,streamlit complete tutorial,streamlit machine learning tutorial #generativeai #aitutorialforbeginners #aitutorial #langchain #streamlit #openai #llm #aiapplications #nlp

Tutorial: Bild Chat App with Your Images using Gemini Pro Vision Model

Ever wish you could have a conversation with a photograph? Now you practically can! Learn how to use Google's powerful Gemini Pro Vision AI to ask your images questions, get creative insights, and have fun. In this tutorial, you'll learn how to make your images reveal surprising insights, answer your questions, and inspire creative ideas. Hit that like button and subscribe for more mind-blowing AI adventures! ⚙️ Projects Files used in this tutorial: 1. Streamlit application: 🔗 https://github.com/atef-ataya/GeminiVisionProChatWithAnImage 2. Jupyter Notebook file: 🔗 ⚙️ Related YouTube Tutorials: 1. Unleash Google Gemini: A Comprehensive LangChain Tutorial 🔗 https://youtu.be/LKaHYkLTyTM?si=fnH6_MpIFllEkPYV 2. Building a Question Answering / Chat Application Using OpenAI, Pinecone, & LangChain: 🔗 https://youtu.be/CLOsuciiPL4?si=zxIzXzawoUSLOwYu ⚙️ Github Project for you to follow along in this tutorial: 1. Chat With Wikipedia (Streamlit Project) 🔗 https://github.com/atef-ataya/ChatAppWithWikipedia 2. Build your own Wikipedia Chatbot with Streamlit & LangChain: 🔗https://youtu.be/2j8Ns1UCatI?si=KkZ4xamHj2q1vxq7 3. LangChain Tutorials: 🔗 https://github.com/atef-ataya/LangChain-Tutorial ⚙️ Medium Articles: 1. LangChain Tutorials: 🔗 https://medium.com/ai-advances/langchain-and-googles-gemini-pro-and-pro-vision-models-a4e6e8565000 TIMESTAMPS: 00:06 Intro 01:20 Jupyter Notebook Prototype 08:05 Building the application using Streamlit ⚡️ Social Media: 🔗 Twitter: / atayaatef 🔗 Github: https://github.com/atef-ataya 🔗 Medium: / atef.ataya ⚙️ Links: 🔗 LangChain documentation: https://python.langchain.com/docs/get_started/introduction 🔗 Google API: https://aistudio.google.com/app/apikey #AI #ArtificialIntelligence #ImageAnalysis #ComputerVision #MachineLearning #GoogleAI #Tech #Innovation #GeminiProVision #GoogleGemini #Tutorial #HowTo #LearnAI #imageChat #Streamlit #ConversationalAI #GenerativeAI #LLM #LargeLanguageModel #NLP

LangChain Tutorial: Unlock 3 Powerful Apps with LangChain! (Dall-E, DuckDuckGo, Wikipedia)

Ever thought you could generate stunning images, search the web instantly, and build your own Wikipedia chatbot? With LangChain and this existing tutorial, you can! - This step-by-step guide will teach you how to seamlessly integrate LangChain with Dall-E, DuckDuckGo, and Wikipedia. - Learn how to create captivating images, streamline your search, and build interactive applications without complex coding. - No prior experience with LangChain or these tools? No problem! This tutorial is designed for beginners and will guide you through each step. Outline: - Dive into Dall-E: Discover how to generate images from text descriptions using LangChain's powerful integration. - Boost your search with DuckDuckGo: Learn how to access and process information instantly with LangChain's built-in capabilities. - See LangChain in action: Follow along with clear demonstrations and code examples. - Don't miss out on this opportunity to unlock the potential of LangChain! Watch the tutorial now and start building your own AI-powered applications. - Subscribe for more exciting tech tutorials. - Comment below and share your thoughts on LangChain and these integrations. Don't just watch, create! Join us on this exciting journey and build your AI-powered app! I hope you enjoy the video! ⚙️ Related YouTube Tutorials: 1. Build Your Own Wikipedia Chatbot with Streamlit & Langchain: 🔗 https://youtu.be/2j8Ns1UCatI?si=1WqsS3ZZvh3s4kZj 2. Building a Question Answering / Chat Application Using OpenAI, Pinecone, & LangChain: 🔗 https://youtu.be/CLOsuciiPL4?si=zxIzXzawoUSLOwYu ⚙️ Github Project for you to follow along in this tutorial: 1. Chat With Wikipedia (Streamlit Project) 🔗 https://github.com/atef-ataya/ChatAppWithWikipedia 2. LangChain Tutorials: 🔗 https://github.com/atef-ataya/LangChain-Tutorial ⚙️ Medium Articles: 1. LangChain Tutorials: 🔗 https://medium.com/@atef.ataya/list/langchain-32ed74ab05c8 TIMESTAMPS: 00:06 Intro 01:09 Dall-E Image Generator 06:20 DuckDuckGo Search 08:42 Wikipedia 12:54 Final Thoughts ⚡️ Social Media: 🔗 Twitter: / atayaatef 🔗 Github: https://github.com/atef-ataya 🔗 Medium: / atef.ataya ⚙️ Links: 🔗 Pinecone: https://www.pinecone.io/ 🔗 LangChain documentation: https://python.langchain.com/docs/get... 🔗 OpenAI API: https://platform.openai.com/ #ai #artificialintelligence #machinelearning #chatbot #questionanswering #appdevelopment #coding #programming #langchain #openai #pinecone#langchainjs #openaiapi #pineconedb #vectorsearch #embeddings#buildyourown #diy #beginnerfriendly #tutorial #chatbotapp #searchengine #aiassistant #personalizedsearch#NaturalLanguageProcessing#NLP#TechTutorial#Programming#Chatbot#QuestionAnswering #langchainagent #mapreducechain #refinechain #youtubevideosummarization #videosummarization #textsummarization #langchain #openai #summarization #naturallanguageprocessing #ai #machinelearning #deeplearning #youtube #videotutorial #learnhowto #stuffchain #artificialintelligence #machinelearning #mapreducechain #refinechain #youtubevideosummarization #videosummarization #textsummarization