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