What you'll learn:
- How to Use LangChain, Pinecone, and OpenAI to Build LLM-Powered Applications.
- Learn about LangChain components, including LLM wrappers, prompt templates, chains, and agents.
- Learn about using multimodal Google's Gemini Pro Vision
- How to integrate Google's Gemini Pro and Pro Vision AI models with LangChain
- Learn about the different types of chains available in LangChain, such as stuff, map_reduce, refine, and LangChain agents.
- Acquire a solid understanding of embeddings and vector data stores.
- Learn how to use embeddings and vector data stores to improve the performance of your LangChain applications.
- Deep Dive into Pinecone.
- Learn about Pinecone Indexes and Similarity Search.
- Project: Build an LLM-powered question-answering app with a modern web-based front-end for custom or private documents.
- Project: Build a summarization system for large documents using various methods and chains: stuff, map_reduce, refine, or LangChain Agents.
- This will be a Learning-by-Doing Experience. We'll Build Together, Step-by-Step, Line-by-Line, Real-World Applications (including front-ends using Streamlit).
- You'll learn how to create web interfaces (front-ends) for your LLM and generative AI apps using Streamlit.
- Streamlit: main concepts, widgets, session state, callbacks.
- Learn how to use Jupyter AI efficiently.
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)
Create LLM-Based Hands-On Projects with LangChain for the Real-Word: RAG, ChatBot, Summarization
Who should take this course?
Python developers interested in AI, LLMs, LangChain and LangGraph.
Data scientists and AI enthusiasts looking to expand their skill set.
Professionals aiming to leverage Generative AI (GenAI) and LangChain in real-world applications.
Don't miss out on the AI revolution! Equip yourself with the skills to build state-of-the-art LLM applications. Enroll now and stay ahead in the rapidly evolving field of AI.
Join me on this exciting journey to master LangChain, Pinecone, and Generative AI. Let's build the future together!
I look forward to seeing you in the course!
