LangChain for LLM Application Developement - DeepLearningAI

Overview

Crash course on LangChain for LLM Application Developement by DeepLearning.AI and lectured by Andrew Ng and Harrison Chase LangChain Founder.

Course Plan

• Lesson0: Introduction
• Lesson1: Models, Prompts and Parsers
• Lesson2: Memory
• Lesson3: Chains
• Lesson4: Question & Answer
• Lesson5: Evaluation
• Lesson6: Agents
• Conclusion

Setup & Dependencies

Setup & import your openai key:

• OpenAI Key

import os
import openai

from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv()) # read local .env file
openai.api_key = os.environ['OPENAI_API_KEY']

• OpenAI API call

def get_completion(prompt, model="gpt-3.5-turbo"):
    messages = [{"role": "user", "content": prompt}]
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=0, 
    )
    return response.choices[0].message["content"]

Lab - Notebooks

Chapter	Exercises
Lesson1: Models, Prompts and Parsers
Lesson2: Memory
Lesson3: Chains
Lesson4: Question & Answer
Lesson5: Evaluation
Lesson6: Agents

References

Main Course :

• https://learn.deeplearning.ai/langchain/lesson/1/introduction

LangChain resources :

• https://learn.deeplearning.ai/langchain/lesson/1/introduction

Others short Free Courses available on DeepLearning.AI :

• https://learn.deeplearning.ai/