English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 3h 52m | 656 MB
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.
Table of Contents
Introduction
1 Level up LLM applications
2 What you should know
LangChain Basics Intro to Building LLM-Powered Apps
3 Setup and installation
4 Create a chain and interface with LLM
5 Define and structure a prompt
6 Create and invoke a chain (LCEL syntax)
7 Work with output parsers
Adding Similarity Search and Context
8 Quickstart Installation and setup
9 Create embeddings from text (Faiss)
10 Querying the vector store
11 Querying as a retriever
Leveraging LLMs with LangChain and RAG
12 RAG Overview and architecture
13 Breaking down the RAG pipeline
14 Project setup
15 Load and split documents into chunks
16 Initialize a vector store (Chroma) and ingest documents
17 Create the chain Prompt + model + parser
18 Create the chain Add context with a retriever
19 Pass data with RunnablePassthrough and query data
20 Challenge Create a custom agent with history
21 Solution Add a chain with chat history
22 Solution Context- and history-aware chatbot
Create an Interactive Web App (Streamlit)
23 Set up the Streamlit application
24 Build the layout with Streamlit components
25 Adding functionality with Streamlit
26 Challenge Deploy your Streamlit app
27 Solution Add app to GitHub
28 Solution Deploy your app
Build a Q&A Agent with Multiple Data Sources and Query Analysis
29 Retrieval with query analysis
30 Connect to a data source and create an index
31 Set up query analysis to handle multiple data sources
32 Retrieval with query analysis
33 Challenge Retrieval with multiple data sources
34 Solution Q&A with multiple data sources
Perform Semantic Search Using MongoDB Atlas Vector Search and OpenAI
35 Getting started with MongoDB Create an account
36 Build and deploy a free cluster
37 Set up the MongoDB environment and connect to the cluster
38 Create a secured database access (user)
39 Load sample data and create the vector store
40 Create the Atlas Vector Search index
41 Run vector search queries
Interact with a NoSQL Database (MongoDB)
42 Create a retrieval chain Define the prompt
43 Create a retrieval chain Define the context
44 Create a retrieval chain Parse and format results
45 Query documents and generate extended responses
LLM Fine-Tuning with the OpenAI Tools and Functions
46 Using agents to perform actions in chains
47 Define tools
48 Select the perfect prompt
49 Bind tools and create agent
50 Create and run the agent executor
51 Challenge Create a multitask agent
52 Solution Define tools and functions
Deploy Chains as a RESTful API with LangServe
53 Introducing LangServe Installation and setup
54 Create a server
55 Create the routes and the endpoints
56 Create a runnable to combine a prompt, a model, and output
57 Challenge Deploy a RESTful API
58 Solution Deploy a RESTful API
Finish Line Deploy to the Cloud and Share with the World
59 Manage and deploy an app on Render
60 Create a GitHub repository and push your project
61 Deploy a new web service on Render
Conclusion
62 Conclusion
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