Udemy Learn LangChain Pinecone OpenAI Build Next Gen LLM Apps

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Download Free Download : Udemy Learn LangChain Pinecone OpenAI Build Next Gen LLM Apps
mp4 | Video: h264,1280X720 | Audio: AAC, 44.1 KHz
Genre:eLearning | Language: English | Size:2.75 GB
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Files Included :

Spoiler

001 How to Get the Most Out of This Course.mp4 (19 MB)
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001 LangChain Demo.mp4 (58.93 MB)
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002 Introduction to LangChain.mp4 (14.73 MB)
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004 Setting Up the Environment LangChain, Pinecone, and Python-dotenv.mp4 (81.1 MB)
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005 ChatModels GPT-3 5-Turbo and GPT-4.mp4 (24.9 MB)
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006 Prompt Templates.mp4 (33.17 MB)
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007 Simple Chains.mp4 (41.23 MB)
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008 Sequential Chains.mp4 (68.13 MB)
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009 Introduction to LangChain Agents.mp4 (16.82 MB)
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010 LangChain Agents in Action.mp4 (33.41 MB)
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011 Short Recap of Embeddings.mp4 (12.57 MB)
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012 Introduction to Vector Databases.mp4 (68.58 MB)
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013 Diving into Pinecone, Part 1.mp4 (61.8 MB)
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014 Diving into Pinecone, Part 2.mp4 (32.05 MB)
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015 Splitting and Embedding Text Using LangChain.mp4 (68.6 MB)
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016 Inserting the Embeddings into a Pinecone Index.mp4 (39.14 MB)
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017 Asking Questions (Similarity Search).mp4 (64.9 MB)
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001 Jupyter AI.mp4 (5.34 MB)
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002 Introduction to Jupyter AI and Other Coding Companions.mp4 (33.5 MB)
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003 Installing Jupyter AI.mp4 (41.76 MB)
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004 Using Jupyter AI in JupyterLab.mp4 (83.89 MB)
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005 Setting Up Jupyter AI in Jupyter Notebook.mp4 (26.49 MB)
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006 Using Jupyter AI in Jupyter Notebook.mp4 (31.29 MB)
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007 Using Interpolation for More Advanced Use Cases.mp4 (29.35 MB)
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008 Using Jupyter AI with Other Providers and Models.mp4 (25.76 MB)
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001 Project Introduction.mp4 (14.63 MB)
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002 Implementing a ChatGPT App with ChatPromptTemplates and Chains.mp4 (72.04 MB)
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003 Adding Chat Memory Using ConversationBufferMemory.mp4 (54.86 MB)
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004 Saving Chat Sessions.mp4 (41.04 MB)
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001 Project Introduction.mp4 (33.18 MB)
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002 Loading Your Custom (Private) PDF Documents.mp4 (27.12 MB)
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003 Loading Different Document Formats.mp4 (41.73 MB)
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004 Public and Private Service Loaders.mp4 (40.53 MB)
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005 Chunking Strategies and Splitting the Documents.mp4 (46.71 MB)
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006 Embedding and Uploading to a Vector Database (Pinecone).mp4 (68.55 MB)
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007 Asking and Getting Answers.mp4 (84.85 MB)
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008 Adding Memory (Chat History).mp4 (77.03 MB)
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001 Project Introduction and Library Installation.mp4 (28.28 MB)
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002 Defining Functions.mp4 (23.55 MB)
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003 Creating the Sidebar.mp4 (43.01 MB)
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004 Reading, Chunking, and Embedding Data.mp4 (47.25 MB)
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005 Asking Questions and Getting Answers.mp4 (18.43 MB)
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006 Saving the Chat History.mp4 (23.4 MB)
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007 Clearing Session State History Using Callback Functions.mp4 (18.95 MB)
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001 Project Introduction.mp4 (9.77 MB)
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002 Summarizing Using a Basic Prompt.mp4 (48.36 MB)
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003 Summarizing using Prompt Templates.mp4 (39.91 MB)
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004 Summarizing Using StuffDocumentsChain.mp4 (47.63 MB)
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005 Summarizing Large Documents Using map reduce.mp4 (37.81 MB)
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006 map reduce With Custom Prompts.mp4 (36.67 MB)
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007 Summarizing Using the refine CombineDocumentChain.mp4 (68.74 MB)
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008 refine With Custom Prompts.mp4 (28.64 MB)
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009 Summarizing Using LangChain Agents.mp4 (37.49 MB)
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001 Project Introduction.mp4 (4.62 MB)
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002 Building the App.mp4 (41.24 MB)
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003 Displaying the Chat History.mp4 (44.58 MB)
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004 Testing the App.mp4 (8.06 MB)
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002 Introduction to Streamlit.mp4 (42.47 MB)
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003 Streamlit Main Concepts.mp4 (27.38 MB)
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004 Displaying Data on the Screen st write() and Magic.mp4 (20.57 MB)
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005 Widgets, Part 1 text input, number input, button.mp4 (10.12 MB)
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006 Widgets, Part 2 checkbox, radio, select.mp4 (43.37 MB)
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007 Widgets, Part 3 slider, file uploader, camera input, image.mp4 (46.37 MB)
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008 Layout Sidebar.mp4 (3.21 MB)
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009 Layout Columns.mp4 (12.69 MB)
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010 Layout Expander.mp4 (10.16 MB)
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011 Displaying a Progress Bar.mp4 (7.12 MB)
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012 Session State.mp4 (19.45 MB)
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013 Callbacks.mp4 (13.61 MB)
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002 While and continue Statements.mp4 (7.43 MB)
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003 While and break Statements.mp4 (14.02 MB)
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004 List Slicing and Iteration.mp4 (16.87 MB)
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005 List Comprehension - Part 1.mp4 (13.14 MB)
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006 List Comprehension - Part 2.mp4 (15.41 MB)
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007 Working with Dictionaries.mp4 (22.46 MB)
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008 JSON Data Serialization.mp4 (21.07 MB)
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009 JSON Data Deserialization.mp4 (17.15 MB)
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010 Assignment JSON and RequestsREST API.mp4 (5.13 MB)
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011 Assignment Answer JSON and RequestsREST API.mp4 (17.68 MB)
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001 Setting Up the Environment Jupyter Notebook.mp4 (80.41 MB)
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002 Setting Up the Environment Google Colab.mp4 (51.64 MB)
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Learn LangChain, Pinecone & OpenAI: Build Next-Gen LLM Apps
Last updated 6/2023
Duration: 5h 57m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 2.05 GB​

Genre: eLearning | Language: English [/center]

Unleash the Power of AI: Hands-On Applications with LangChain, Pinecone, and OpenAI. Join the AI Revolution Today!

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 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 application 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.
You'll learn how to create web interfaces (front-ends) for you LLM and generative AI apps using Streamlit.
Streamlit: main concepts, widgets, session state, callbacks.

Requirements
Basic Python programming experience is required.
You should be able to sign up to OpenAI API with a valid phone number.
Description
Master LangChain, Pinecone, and OpenAI.
Build hands-on generative LLM-powered applications with LangChain.
The AI revolution is here and it will change the world!
In a few years, the entire society will be reshaped by artificial intelligence.
By the end of this course, you will have a solid understanding of the fundamentals of LangChain, Pinecone, and OpenAI.
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.
Currently, the effort, knowledge, and money of major technology corporations worldwide are being invested in AI.
In this course, you'll learn how to build state-of-the-art LLM-powered applications with LangChain.
What is LangChain?
LangChain
is an open-source framework that allows developers working with AI to combine large language models (LLMs) like GPT-4 with external sources of computation and data. It makes it easy to build and deploy AI applications that are both scalable and performant.
It also facilitates entry into the AI field for individuals from diverse backgrounds and enables the deployment of AI as a service.
In this course, we'll go over LangChain components, LLM wrappers, Chains, and Agents. We'll dive deep into embeddings and vector databases such as Pinecone.
This will be a learning-by-doing experience. We'll build together, step-by-step, line-by-line, real-world LLM applications with Python, LangChain, and OpenAI.
We will develop an
LLM-powered question-answering application
using LangChain, Pinecone, and OpenAI for custom or private documents. This opens up an infinite number of practical use cases.
We will also build a
summarization system
, which is a valuable tool for anyone who needs to summarize large amounts of text. This includes students, researchers, and business professionals.
I will continue to add new projects that solve different problems.
This course, and the technologies it covers, will always be under development and continuously updated.
The topics covered in this "LangChain, Pinecone and OpenAI" course are:
LangChain Fundamentals
Setting Up the Environment with Dotenv: LangChain, Pinecone, OpenAI
LLM Models (Wrappers): GPT-3
ChatModels: GPT-3.5-Turbo and GPT-4
LangChain Prompt Templates
Simple Chains
Sequential Chains
Introduction to LangChain Agents
LangChain Agents in Action
Vector Embeddings
Introduction to Vector Databases
Diving into Pinecone
Splitting and Embedding Text Using LangChain
Inserting the Embeddings into a Pinecone Index
Asking Questions (Similarity Search) and Gettings Answers (GPT-4)
Creating front-ends for LLM and generative AI apps using Streamlit
Streamlit: main concepts, widgets, session state, callbacks.
The skills you will acquire will allow you to build and deploy real-world AI applications. I can't tell you how excited I am to teach you all these cutting-edge technologies.
Come on board now, so that you are not left behind.
I will see you in the course!
Who this course is for:
Python programmers who want to build LLM-Powered Applications using LangChain, Pinecone and OpenAI.
Any technical person interested in the most disruptive technology of this decade.
Any programmer interested in AI.

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