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Unleash the Power of Large Language Models Using LangChain

Explore how to leverage LangChain for LLM-powered applications. Gain insights into using prompt templates, chains, memory types, APIs, and retrieval-augmented generation techniques for dynamic app development.

Beginner

15 Lessons

2h

Certificate of Completion

Explore how to leverage LangChain for LLM-powered applications. Gain insights into using prompt templates, chains, memory types, APIs, and retrieval-augmented generation techniques for dynamic app development.
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This course includes

19 Playgrounds
6 Quizzes
Course Overview
What You'll Learn
Course Content
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Course Overview

LangChain is an open-source framework that facilitates the integration of large language models (LLMs) to develop LLM-powered applications. You’ll start with an introduction to LLMs and the LangChain framework. Next, you’ll learn how to utilize prompt templates to perform repetitive tasks and parse the output of an LLM. You’ll also learn about different types of chains with their use cases. Additionally, you’ll cover different memory types in LangChain to store the chat history. You’ll also learn how to c...Show More
LangChain is an open-source framework that facilitates the integration of large language models (LLMs) to develop LLM-powered applications. You’ll start with an introduction to LLMs and the LangChain framework. Next, you’ll learn how to utilize prompt te...Show More

What You'll Learn

An understanding of large language models and why they are preferred in natural language processing tasks
An understanding of the LangChain framework and how it improves over the functionalities of OpenAI
Familiarity with the core components of LangChain, including models, prompt templates, output parsers, chains, and memory
Hands-on experience implementing a chatbot that responds to queries in multiple languages using the router chain in LangChain
The ability to add tools in agents that connect to external information sources through APIs and use the reasoning and action (ReAct) technique
Working knowledge of the retrieval-augmented generation (RAG) technique to implement question answering from a document
An understanding of large language models and why they are preferred in natural language processing tasks

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Course Content

1.

Large Language Models

2 Lessons

Get familiar with leveraging LangChain and large language models for GenAI solutions.

2.

Introduction to LangChain

2 Lessons

Look at how LangChain enhances LLM capabilities, integrating data sources for diverse NLP tasks.

3.

LangChain Basics

6 Lessons

Work your way through LangChain's models, prompts, output parsers, chains, router chain, memory, and language routing.

4.

Advanced Features of LangChain

4 Lessons

Grasp the fundamentals of LangChain's agents, RAG architecture, and question-answering bots.

5.

Wrapping Up

1 Lessons

Take a closer look at integrating LangChain with LLMs, dynamic agents, and future possibilities.

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