Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
10 Hour(s) 44 Minute(s)
Language
English
Taught by
Andrei Dumitrescu, Crystal Mind Academy
Rating
2 views
Course Overview
Step-by-Step Approach to LangChain and Pinecone for GenAI with LLMs. Develop Real-World LLM-Powered Apps with Python
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!
Course Content
- 10 section(s)
- 111 lecture(s)
- Section 1 Getting Started
- Section 2 Deep Dive into LangChain
- Section 3 LangChain and Vector Stores (Pinecone)
- Section 4 LangChain and Google's Gemini
- Section 5 Jupyter AI
- Section 6 Project #1: Building a Custom ChatGPT App with LangChain From Scratch
- Section 7 Project #2: RAG - Q&A App on Your Private Documents (Pinecone and Chroma)
- Section 8 Project #3: Building a Front-End for the Question-Answering App Using Streamlit
- Section 9 Project #4: Summarizing With LangChain and OpenAI
- Section 10 Project #5: Building a Custom ChatGTP App with LangChain and Streamlit
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.
Skills covered in this course
Reviews
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SSilvia Espinosa
Hasta este momento entiendo es excelente la información que se nos comparte.
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FFrancisco Tornay
It's being a great experience, with clear and interesting explanations and examples
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JJorge Lopez Tercero
good info about the topic
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GGurpreet Singh Grewal
The world is abuzz with AI and i also wanted to get my feet wet in this journey. I am a decent python programmer but had no idea about LangChain. I have seen a few You tube videos about LangChain but could not find any end to end complete course. This was my first LangChain course and Andrei did a very good job in explaining the details. He teaches the concepts from ground up and by the end you have a good understanding about the topic. I am thankful to Andrei for explaining the concepts in detail. Only critique of this course i have is the annoying "deprecated" warning that you get when you run the code provided by him. Towards the end of the Projects section he has mentioned the change in import statements that you have to make, so keep a note of the change in the import statements. Instructor should have specified the version of the modules in the requirements.txt file so that we would not have run into the "deprecated" warning issue (The code still works, this is just a warning message). Still, this is a 5 star course as it should be in anyone's LangChain journey.
