Level up LLM applications development with LangChain and OpenAI

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Sandy Ludosky

3:52:26

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01 - Introduction

01 - Level up LLM applications.mp4

00:39

02 - What you should know.mp4

02:48

02 - 1. LangChain Basics Intro to Building LLM-Powered Apps

01 - Setup and installation.mp4

04:03

02 - Create a chain and interface with LLM.mp4

04:12

03 - Define and structure a prompt.mp4

05:19

04 - Create and invoke a chain (LCEL syntax).mp4

02:54

05 - Work with output parsers.mp4

02:37

03 - 2. Adding Similarity Search and Context

01 - Quickstart Installation and setup.mp4

02:28

02 - Create embeddings from text (Faiss).mp4

01:34

03 - Querying the vector store.mp4

01:56

04 - Querying as a retriever.mp4

04:59

04 - 3. Leveraging LLMs with LangChain and RAG

01 - RAG Overview and architecture.mp4

02:12

02 - Breaking down the RAG pipeline.mp4

02:50

03 - Project setup.mp4

03:33

04 - Load and split documents into chunks.mp4

05:06

05 - Initialize a vector store (Chroma) and ingest documents.mp4

05:06

06 - Create the chain Prompt + model + parser.mp4

05:39

07 - Create the chain Add context with a retriever.mp4

04:48

08 - Pass data with RunnablePassthrough and query data.mp4

03:27

09 - Challenge Create a custom agent with history.mp4

03:12

10 - Solution Add a chain with chat history.mp4

05:19

11 - Solution Context- and history-aware chatbot.mp4

05:49

05 - 4. Create an Interactive Web App (Streamlit)

01 - Set up the Streamlit application.mp4

04:16

02 - Build the layout with Streamlit components.mp4

05:53

03 - Adding functionality with Streamlit.mp4

04:50

04 - Challenge Deploy your Streamlit app.mp4

03:37

05 - Solution Add app to GitHub.mp4

03:46

06 - Solution Deploy your app.mp4

05:47

06 - 5. Build a Q&A Agent with Multiple Data Sources and Query Analysis

01 - Retrieval with query analysis.mp4

01:16

02 - Connect to a data source and create an index.mp4

04:23

03 - Set up query analysis to handle multiple data sources.mp4

05:55

04 - Retrieval with query analysis.mp4

05:07

05 - Challenge Retrieval with multiple data sources.mp4

03:11

06 - Solution Q&A with multiple data sources.mp4

07:13

07 - 6. Perform Semantic Search Using MongoDB Atlas Vector Search and OpenAI

01 - Getting started with MongoDB Create an account.mp4

01:35

02 - Build and deploy a free cluster.mp4

01:41

03 - Set up the MongoDB environment and connect to the cluster.mp4

06:23

04 - Create a secured database access (user).mp4

03:27

05 - Load sample data and create the vector store.mp4

04:18

06 - Create the Atlas Vector Search index.mp4

04:04

07 - Run vector search queries.mp4

05:33

08 - 7. Interact with a NoSQL Database (MongoDB)

01 - Create a retrieval chain Define the prompt.mp4

02:51

02 - Create a retrieval chain Define the context.mp4

05:08

03 - Create a retrieval chain Parse and format results.mp4

01:47

04 - Query documents and generate extended responses.mp4

03:33

09 - 8. LLM Fine-Tuning with the OpenAI Tools and Functions

01 - Using agents to perform actions in chains.mp4

01:36

02 - Define tools.mp4

05:37

03 - Select the perfect prompt.mp4

01:12

04 - Bind tools and create agent.mp4

02:19

05 - Create and run the agent executor.mp4

04:41

06 - Challenge Create a multitask agent.mp4

05:31

07 - Solution Define tools and functions.mp4

06:09

10 - 9. Deploy Chains as a RESTful API with LangServe

01 - Introducing LangServe Installation and setup.mp4

03:35

02 - Create a server.mp4

00:49

03 - Create the routes and the endpoints.mp4

05:56

04 - Create a runnable to combine a prompt, a model, and output.mp4

03:35

05 - Challenge Deploy a RESTful API.mp4

01:39

06 - Solution Deploy a RESTful API.mp4

02:51

11 - 10. Finish Line Deploy to the Cloud and Share with the World

01 - Manage and deploy an app on Render.mp4

01:53

02 - Create a GitHub repository and push your project.mp4

04:21

03 - Deploy a new web service on Render.mp4

04:10

12 - Conclusion

01 - Conclusion.mp4

00:28

Description

Dive into the world of large language models (LLMs) with a focus on integrating them into practical applications utilizing OpenAI APIs. Discover how to enhance LLMs with retrieval components, deploy interactive chat applications, and construct multi-retriever agents for advanced data handling. Join instructor Sandy Ludosky to gain the skills to create intelligent agents capable of performing complex tasks, from semantic searches to question-answering chatbots, significantly enhancing user experiences. Whether you're aiming to innovate in your current role or embark on new AI projects, this course provides the foundational knowledge and practical skills needed to harness the power of LLMs effectively.

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Machine Learning

Sandy Ludosky

Instructor's Courses

HelloI am Sandy, freelance web and mobile Developer based out of Toronto, in Ontario, Canada, I specialize in Front-End development with HTML, CSS, CSS3 Animation, Sass, Javascript and JQuery. I love creating beautiful, professional and user-friendly websites using the Adobe Creative Suite: Photoshop, Illustrator and Flash to name a few. I am also keen on Web marketing, Web analytics, Visual Design, Video Editing, Photography and WordPress development. On top of being a Udemy instructor, I am an avid learner of new technologies and digital stuff. *****************************Bonjour,Je suis Sandy, développeur javascript. Je suis passionnée de développement Front (HTML, CSS, CSS3 Animation, Sass, Javascript et ReactJS...).Mes autres intérêts sont le graphisme et motion design. Je suis également passionnée de conception visuelle, montage vidéo, photographie et gaming.Venez rejoindre ma communauté de 20k+ apprenants. Je publie régulièrement pour enrichir mon catalogue de nouveaux contenus. Depuis 2014, je partage mes connaissances, aussi bien en français qu'en anglais, sur les technologies Front et javascript qui ne cessent d'évoluer et d'offrir de nouvelles fonctionnalités pour faciliter notre réussite dans ce beau métier du développement et de la transformation digitale.Salutations !

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