LangChain Mastery: Build GenAI Apps with LangChain &Pinecone
About this course
Fully Updated for the latest versions of LangChain, OpenaAI, and Pinecone.Unlock the Power of LangChain and Pinecone to Build Advanced LLM Applications with Generative AI and Python!This LangChain course is the 2nd part of “OpenAI API with Python Bootcamp”. It is not recommended for complete beginners as it requires some essential Python programming experience.Are you ready to dive into the world of Large Language Models (LLMs) and Generative AI (GenAI)? This comprehensive course will guide you through building cutting-edge LLM applications using OpenAI or Gemini API, LangChain, and Pinecone.By the end of this course, you'll master LangChain and Pinecone to create powerful, production-ready LLM apps in Python. You'll also develop modern web front-ends with Streamlit, bringing your AI applications to life.In this course, you will:Understand the fundamentals of LangChain for simplified LLM app development.Dive into Generative AI with OpenAI and Google's Gemini.Build real-world LLM applications step-by-step with Python.Utilize LangChain Agents and Chains for advanced functionalities.Explore Pinecone for efficient vector embeddings and similarity search.Work with vector databases like Pinecone and Chroma.Implement embeddings and indexing for custom document QA systems.Create RAG (Retrieval-Augemented Generation) Apps with LangChain.Summarize large texts using LLMs.Learn Prompt Engineering best practices.Create engaging front-ends using Streamlit.Become proficient in using AI Coding Assistants (Jupyter AI)
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What you'll learn
- understand LangChain fundamentals
- build LLM applications using OpenAI and Gemini APIs
- utilize LangChain Agents and Chains
- explore Pinecone for vector embeddings
- implement retrieval-augmented generation (RAG) apps
- create engaging web front-ends with Streamlit
- apply prompt engineering best practices
Course objectives
- master LangChain and Pinecone to create production-ready LLM apps
- develop custom document QA systems
- summarize large texts with LLMs
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