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LangChain Unleashed: A Guide To Using Open Source LLM Models

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LangChain Unleashed: A Guide To Using Open Source LLM Models, available at $19.99, has an average rating of 4.5, with 33 lectures, based on 10 reviews, and has 66 subscribers.

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You will learn about Learn the basics of langchain Use Langchain to build LLM powered Applications Connect Langchain with Open Source LLM Models Build A chatbot using Langchain This course is ideal for individuals who are Whoever that want to build applications powered by llms It is particularly useful for Whoever that want to build applications powered by llms.

Enroll now: LangChain Unleashed: A Guide To Using Open Source LLM Models

Summary

Title: LangChain Unleashed: A Guide To Using Open Source LLM Models

Price: $19.99

Average Rating: 4.5

Number of Lectures: 33

Number of Published Lectures: 32

Number of Curriculum Items: 33

Number of Published Curriculum Objects: 32

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

  • Learn the basics of langchain
  • Use Langchain to build LLM powered Applications
  • Connect Langchain with Open Source LLM Models
  • Build A chatbot using Langchain

Who Should Attend

  • Whoever that want to build applications powered by llms

Target Audiences

  • Whoever that want to build applications powered by llms

In this course I will teach you how to use langchain to build LLM powered Applications and I will be using Open source models from hugging face

What is LangChain?

LangChain serves as a framework aimed at streamlining the development of applications utilizing Large language models. Functioning as a language model integration framework, LangChain’s applications align closely with those of language models, spanning document analysis, summarization, chatbots, and code analysis.

What is an LLM?

A Large Language Model (LLM) is a type of artificial intelligence model that is trained on a vast amount of text data. It’s designed to generate human-like text based on the input it receives.

In this course, I will be using LLMs such as Llama 2 7B and Mistral 7B.

What is LCEL?

LangChain Expression Language (LCEL) emerges as a declarative method within the LangChain framework, enabling effortless composition of chains. From its inception, LCEL prioritizes seamless transition from prototypes to production, accommodating a spectrum of complexities, from straightforward “prompt + LLM” sequences to intricate chains comprising hundreds of steps. Noteworthy features encompass streaming support for optimal time-to-first-token, asynchronous capabilities for versatile API usage, and optimized parallel execution for reduced latency. LCEL further offers configurations for retries, fallbacks, and access to intermediate results, enhancing reliability and debugging.

In this course you learn

– Langchain Basics

– Langchain Expression Language

– Chains

– Memory

– Agents and Tools

– RAG etc

Disclaimer:

In this course I won’t be using Open Ai API instead I would be using Open source models from hugging face and i will be using windows, kaggle

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: What is LangChain?

Lecture 3: Prerequisites – Setup For Windows

Lecture 4: How To Generate Text Using An LLM

Lecture 5: Generating Text Using An LLM

Lecture 6: Code – Generating Text Using An LLM

Chapter 2: Prompts And Chains

Lecture 1: Prompts

Lecture 2: Chains

Lecture 3: Langchain Expression Language

Lecture 4: One Shot Prompting To Get Better Output

Lecture 5: Notes – Prompts And Chains

Chapter 3: Memory

Lecture 1: Creating Chatbot

Lecture 2: Adding Memory To Chatbot

Lecture 3: Notes – Memory

Chapter 4: Retrieval Augmented Generation(RAG)

Lecture 1: What is Retrieval Augmented Generation(RAG)?

Lecture 2: Generating Embeddings And Storing in ChromaDB

Lecture 3: Initializing Our Database

Lecture 4: Retrieval Augmented Generation – Code

Lecture 5: Notes – Retrieval Augmented Generation

Chapter 5: Agents And Tools

Lecture 1: What is an Agent?

Lecture 2: Loading Mistral 7b Instruct Model In Kaggle

Lecture 3: XML Agent

Lecture 4: ReAct Agent

Lecture 5: Notes – Agents And Tools

Chapter 6: Mini Project

Lecture 1: Project Intro

Lecture 2: Github Codespaces

Lecture 3: Downloading Gemma 7b GGUF

Lecture 4: Generating Text With Gemma 7b

Lecture 5: Creating Server Part 1

Lecture 6: Creating Server Part 2

Lecture 7: Running Our Server

Lecture 8: Notes

Instructors

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