Level up LLM applications development with LangChain and OpenAI

سرفصل های دوره

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.

01 - Introduction

01 - Level up LLM applications

02 - What you should know

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

01 - Setup and installation

02 - Create a chain and interface with LLM

03 - Define and structure a prompt

04 - Create and invoke a chain (LCEL syntax)

05 - Work with output parsers

03 - 2. Adding Similarity Search and Context

01 - Quickstart Installation and setup

02 - Create embeddings from text (Faiss)

03 - Querying the vector store

04 - Querying as a retriever

04 - 3. Leveraging LLMs with LangChain and RAG

01 - RAG Overview and architecture

02 - Breaking down the RAG pipeline

03 - Project setup

04 - Load and split documents into chunks

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

06 - Create the chain Prompt + model + parser

07 - Create the chain Add context with a retriever

08 - Pass data with RunnablePassthrough and query data

09 - Challenge Create a custom agent with history

10 - Solution Add a chain with chat history

11 - Solution Context- and history-aware chatbot

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

01 - Set up the Streamlit application

02 - Build the layout with Streamlit components

03 - Adding functionality with Streamlit

04 - Challenge Deploy your Streamlit app

05 - Solution Add app to GitHub

06 - Solution Deploy your app

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

01 - Retrieval with query analysis

02 - Connect to a data source and create an index

03 - Set up query analysis to handle multiple data sources

04 - Retrieval with query analysis

05 - Challenge Retrieval with multiple data sources

06 - Solution Q&A with multiple data sources

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

01 - Getting started with MongoDB Create an account

02 - Build and deploy a free cluster

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

04 - Create a secured database access (user)

05 - Load sample data and create the vector store

06 - Create the Atlas Vector Search index

07 - Run vector search queries

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

01 - Create a retrieval chain Define the prompt

02 - Create a retrieval chain Define the context

03 - Create a retrieval chain Parse and format results

04 - Query documents and generate extended responses

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

01 - Using agents to perform actions in chains

02 - Define tools

03 - Select the perfect prompt

04 - Bind tools and create agent

05 - Create and run the agent executor

06 - Challenge Create a multitask agent

07 - Solution Define tools and functions

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

01 - Introducing LangServe Installation and setup

02 - Create a server

03 - Create the routes and the endpoints

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

05 - Challenge Deploy a RESTful API

06 - Solution Deploy a RESTful API

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

01 - Manage and deploy an app on Render

02 - Create a GitHub repository and push your project

03 - Deploy a new web service on Render

12 - Conclusion

01 - Conclusion

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تولید کننده: LinkedIn Learning (Lynda)