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Ingenium Academy

This course is designed to empower developers, this comprehensive guide provides a practical approach to integrating LangcChain with OpenAI and effectively using Large Language Models (LLMs) in Python.

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This course is designed to empower developers, this comprehensive guide provides a practical approach to integrating LangcChain with OpenAI and effectively using Large Language Models (LLMs) in Python.

In the course's initial phase, you'll gain a robust understanding of what Langchain is, its functionalities and components, and how it synergizes with data sources and LLMs. We'll briefly dive into understanding LLMs, their architecture, training process, and various applications. We'll set up your environment with a hands-on installation guide and a 'Hello World' example using Google Colab.

Subsequently, we'll explore the LangChain Models, covering different types such as LLMs, Chat Models, and Embeddings. We'll guide you through loading the OpenAI Chat Model, connecting LangChain to Huggingface Hub models, and leveraging OpenAI's Text Embeddings.

The course advances to the essential aspect of Prompting & Parsing in LangChain, focusing on best practices, delimiters, structured formats, and effective use of examples and Chain of Though Reasoning (CoT).

The following sections focus on the concepts of Memory, Chaining, and Indexes in LangChain, enabling you to handle complex interactions with ease. We will study how you can adjust the memory of a chatbot, the significance of Chaining, and the utility of Document Loaders & Vector Stores.

Finally, you'll delve into the practical implementation of LangChain Agents, with a demonstration of a simple agent and a walkthrough of building an Arxiv Summarizer Agent.

By the end of this course, you'll have become proficient in using LangChain with OpenAI LLMs in Python, marking a significant leap in your developer journey. Ready to power up your LLM applications? Join us in this comprehensive course.

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What's inside

Learning objectives

  • Learn how to work with langchain in python
  • Learn how to build langchain agents
  • Learn how embeddings work and how to work with a vector store in langchain
  • Understand how large language models (llms) & embeddings work
  • Learn how to connect langchain to openai's api suite

Syllabus

Learn what Langchain is and how LLMs work.
What is Langchain?
Understanding LLMs
Installing Langchain & Hello World Example
Read more
Learn how to work with the different model types Langchain offers in OpenAI.
Different Types of Supported Models
Working with LLMs
Chat Models In Langchain
What Are Embeddings?
Using OpenAI Text Embeddings to Analyze Sentiment
Google Colab Notebook For Langchain Models
Learn prompting techniques, how to work with Langchain prompt templates, and how to parse outputs from LLMs.
Prompting Best Practices - Formatting, Few Shot Prompting, & CoT
Using Langchain's Built-in Prompt Templates
Output Parsers in Langchain
Google Colab Notebook for Prompt Templates & Output Parsers
Learn how to manage chatbot memory, how to chain together LLM calls, and work with vector stores.

Note: A small portion of the notebook was updated as of 02/2024 to use the new `langchain_openai` module.

What is Chaining?

Note: The notebook for this lecture was updated to reflect the newest updates to LangChain as of 02/2024.

Note: The notebook for this lecture was updated to reflect updates to LangChain as of 02/2024 and to fix an import error.

Learn how to build Langchain agents that can use LLMs to access the internet.
What are Langchain Agents?
Working With Langchain Agents
Building An Arxiv Summarizer Agent
Google Colab Notebook for Langchain Agents
You will learn how to load, split, store, and query your data by having a conversation about with GPT.
Document Loaders For Different Data Types & Sources
Document Splitting: Exploring Different Methods
Recap on Vector Stores
Advanced Retrieval Methods
Querying Your Data with Chat Models
Learn how to extract and query data from various sources using LLMs in Langchain.
Scaling Up Our Arxiv Research Bot
Hooking Up Our Chatbot to Wikipedia
Learn how to create tools in LangChain, how to connect them to LLMs to create agents, and get introduced to LCEL.
LangChain Function Calling & LCEL
Building Agents with Tools
Building An Economics Chatbot with Tools
Google Colab Notebook For This Section

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Taught by Ingenium Academy, who are recognized for their work in NLP and language modeling
Uses LangChain, which is used in industry for working with large language models
Provides a comprehensive study of integrating LangChain with OpenAI's LLMs
Builds a strong foundation for beginners to understand large language models and their applications
Develops professional skills and deep expertise in working with LLMs for various applications
Requires some background knowledge in Python programming and NLP concepts

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in LangChain For Generative AI: Using OpenAI LLMs in Python with these activities:
Course Materials Note Compilation
Improve your understanding and retention of course materials by compiling your notes, assignments, and other resources into a cohesive document.
Show steps
  • Gather all relevant course materials, including lecture notes, slides, and assignments.
  • Review and organize the materials to create a comprehensive study guide.
  • Annotate and highlight key concepts and insights.
Large Language Model Fundamentals
Review the foundational concepts of Large Language Models and their capabilities, enabling you to better understand and utilize them within LangChain.
Browse courses on Large Language Models
Show steps
  • Revisit the characteristics and architectures of LLMs.
  • Explore the different types of LLMs and their strengths.
  • Review best practices for using LLMs effectively.
LangChain Community Workshop
Connect with fellow LangChain enthusiasts, share experiences, and learn from experts in the field.
Browse courses on Networking
Show steps
  • Attend a local or virtual LangChain community workshop.
  • Engage in discussions, presentations, and hands-on activities.
  • Expand your network and build connections with like-minded individuals.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Deep Learning with Python
Understand the fundamental concepts of deep learning and its implementation in Python, providing a strong foundation for utilizing LangChain effectively.
Show steps
  • Review the basics of neural networks and deep learning.
  • Learn about different deep learning architectures and their applications.
  • Get hands-on experience with Python libraries for deep learning.
Prompt Engineering Exercises
Solidify your understanding of prompt engineering principles and best practices through hands-on exercises.
Show steps
  • Identify different prompt templates provided by LangChain.
  • Practice writing structured prompts using delimiters and formatting.
  • Experiment with few-shot prompting and Chain of Thought (CoT) reasoning.
LangChain Discussion Group
Engage in discussions with peers to share knowledge, ask questions, and explore different perspectives on LangChain, LLMs, and their applications.
Browse courses on LangChain
Show steps
  • Join a LangChain discussion group online or in person.
  • Participate in discussions by sharing your experiences and insights.
  • Ask questions to clarify concepts and explore new ideas.
Vector Store Exploration
Enhance your understanding of vector stores and their applications by working through practical examples.
Browse courses on Vector Stores
Show steps
  • Set up a local vector store instance.
  • Load and index different types of data into the vector store.
  • Experiment with different querying techniques to retrieve relevant information.
Chain-Of-Thought Exercises
Practice generating effective Chain of Thought (CoT) prompts to improve your ability to guide LLM responses and extract meaningful insights.
Browse courses on LangChain
Show steps
  • Analyze examples of successful CoT prompts.
  • Develop your own CoT prompts for a variety of scenarios.
  • Review and refine your prompts based on the results.
Advanced OpenAI API Integration
Expand your knowledge by exploring advanced techniques for integrating OpenAI's API with LangChain.
Browse courses on OpenAI API
Show steps
  • Explore available OpenAI models and their capabilities.
  • Set up authentication and manage API keys for secure access.
  • Optimize API usage to minimize latency and costs.
Advanced Prompt Engineering
Explore advanced techniques for crafting effective prompts that elicit specific and informative responses from LLMs, enhancing your ability to harness their capabilities.
Browse courses on LangChain
Show steps
  • Learn about different prompt formats and their applications.
  • Practice writing prompts that incorporate context, instructions, and examples.
  • Analyze the impact of prompt variations on LLM outputs.
LangChain Tool Integration
Develop a custom tool that integrates with LangChain and OpenAI's LLMs, expanding the capabilities of your toolkit and empowering you to solve complex problems.
Browse courses on LangChain
Show steps
  • Identify a need or opportunity for a custom LangChain tool.
  • Design and develop the tool using your preferred programming language.
  • Integrate the tool with LangChain and OpenAI's LLMs.
Arxiv Summarizer Agent
Develop a practical application of LangChain by building an agent that can summarize research papers from Arxiv.
Show steps
  • Set up the LangChain environment and install necessary dependencies.
  • Create a custom LangChain agent for summarizing Arxiv papers.
  • Test and evaluate the performance of your agent.
Conversational AI Project
Engage in a hands-on project to build a conversational agent using LangChain and OpenAI's LLMs, deepening your understanding of these technologies.
Browse courses on LangChain
Show steps
  • Gather and prepare the training data.
  • Design and train the conversational model using LangChain.
  • Integrate the model into a user interface.
  • Evaluate the performance of your conversational agent.
LangChain AI Challenge
Participate in a competition to build innovative applications using LangChain and OpenAI's LLMs, fostering your creativity and problem-solving skills.
Browse courses on LangChain
Show steps
  • Identify a problem or opportunity that can be addressed with LangChain.
  • Design and build a solution using LangChain and OpenAI's LLMs.
  • Submit your solution to the competition.
LangChain Tutorial Blog Post
Share your expertise and contribute to the LangChain community by creating a comprehensive blog post on a specific topic.
Browse courses on LangChain
Show steps
  • Identify a specific aspect of LangChain to focus on.
  • Research and gather relevant information from documentation and other sources.
  • Write a well-structured and engaging blog post that provides insights and value to readers.

Career center

Learners who complete LangChain For Generative AI: Using OpenAI LLMs in Python will develop knowledge and skills that may be useful to these careers:
Natural Language Processing Engineer
Natural Language Processing Engineers bridge the gap between humans and computers by developing systems that can understand and generate human language. This course provides a comprehensive foundation in LangChain, a framework tailored for NLP tasks. By leveraging its capabilities, you can enhance your NLP models, improve communication between machines and humans, and explore cutting-edge applications.
NLP Researcher
NLP Researchers focus on developing novel methods and algorithms for natural language processing tasks, advancing the field of computational linguistics. This course introduces LangChain, a framework that empowers you to harness the power of large language models (LLMs) for NLP research. By leveraging its capabilities, you can explore new research directions, enhance your models, and contribute to the advancement of NLP.
Machine Learning Researcher
Machine Learning Researchers push the boundaries of machine learning theory and develop novel algorithms and techniques. This course introduces LangChain, a framework that empowers you to harness the power of large language models (LLMs) for machine learning research. By leveraging its capabilities, you can explore new research directions, enhance your models, and contribute to the advancement of machine learning.
Computational Linguist
Computational Linguists apply computational techniques to study human language, bridging the fields of linguistics and computer science. This course introduces LangChain, a framework that empowers you to harness the power of large language models for linguistic analysis. By leveraging its capabilities, you can advance your research, gain deeper insights into language structure and usage, and contribute to the field of computational linguistics.
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and maintain AI systems, leveraging their expertise in machine learning, deep learning, and natural language processing. This course introduces LangChain, a powerful framework that seamlessly integrates OpenAI's LLMs into your AI projects. By leveraging its capabilities, you can enhance your AI systems, explore novel applications of LLMs, and contribute to the advancement of artificial intelligence.
Data Analyst
Data Analysts transform raw data into meaningful insights, driving informed decision-making. This course introduces LangChain, a powerful tool for integrating large language models (LLMs) into your data analysis workflow. By mastering these techniques, you can enhance your data analysis capabilities, automate tasks, and uncover hidden patterns and trends in your data.
Machine Learning Engineer
Machine Learning Engineers develop and maintain machine learning models, leveraging their expertise in algorithm design and software engineering. This course introduces LangChain, a powerful tool that simplifies the integration of large language models (LLMs) into Python applications. By mastering these techniques, you can enhance your model performance and contribute to the advancement of machine learning.
Data Scientist
Data Scientists harness their analytical skills to extract insights from vast data sets, driving decision-making and innovation. This course empowers you with LangChain, a versatile framework for connecting OpenAI's LLMs to Python. Through hands-on practice, you'll gain proficiency in prompt engineering, output parsing, and memory management, equipping you to excel as a Data Scientist.
Research Scientist
Research Scientists conduct scientific research to advance knowledge and develop new technologies. This course empowers you with LangChain, a framework that seamlessly integrates OpenAI's LLMs into Python-based research projects. By leveraging its capabilities, you can enhance your research, explore novel applications of LLMs, and contribute to scientific breakthroughs.
Software Engineer
Software Engineers design, develop, and maintain software systems. This course introduces LangChain, a powerful Python-based framework that seamlessly integrates OpenAI's LLMs into your applications. By leveraging its capabilities, you can enhance your software development process, automate tasks, and create more intelligent and responsive systems.
Data Engineer
Data Engineers design, build, and maintain data pipelines, ensuring reliable and efficient access to data for various stakeholders. This course introduces LangChain, a powerful tool for integrating large language models (LLMs) into your data engineering workflow. By mastering these techniques, you can enhance your data management capabilities, automate tasks, and gain deeper insights into your data infrastructure.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course introduces LangChain, a powerful tool for incorporating large language models (LLMs) into your quantitative analysis workflow. By mastering these techniques, you can enhance your financial modeling capabilities, automate tasks, and gain deeper insights into market trends and patterns.
Product Manager
Product Managers are responsible for the development and launch of new products and features, ensuring they meet customer needs and market demands. This course introduces LangChain, a powerful tool for integrating large language models (LLMs) into your product development process. By mastering these techniques, you can enhance your product planning and decision-making, gather customer feedback, and create more innovative and user-friendly products.
Business Analyst
Business Analysts bridge the gap between business and technology, translating business requirements into technical solutions. This course introduces LangChain, a powerful tool for integrating large language models (LLMs) into your business analysis workflow. By mastering these techniques, you can enhance your analytical capabilities, automate tasks, and gain deeper insights into business processes and stakeholder needs.
DevOps Engineer
DevOps Engineers oversee the software development lifecycle, ensuring efficient collaboration between development and operations teams. This course introduces LangChain, a powerful tool for integrating large language models (LLMs) into your DevOps pipeline. By mastering these techniques, you can enhance your automation capabilities, streamline your workflow, and improve the quality and reliability of your software products.

Reading list

We've selected eight books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in LangChain For Generative AI: Using OpenAI LLMs in Python.
Provides a comprehensive overview of NLP concepts and techniques, with a focus on building LLM-based applications from scratch. It serves as a useful reference for understanding the underlying principles.
Provides a cognitive science perspective on language and its acquisition, offering insights into how humans process and understand language. It serves as background reading for the course's focus on prompt engineering and LLM output analysis.
This comprehensive handbook covers the field of psycholinguistics, providing insights into the cognitive processes involved in language comprehension and production. It serves as a valuable reference for understanding the human-computer interaction aspects of LLM-based applications.
Provides a theoretical and practical understanding of information retrieval techniques, which are essential for building LLM-based applications that can effectively search and retrieve information.
Provides a practical introduction to NLP with a focus on building LLM-based applications. It covers essential concepts and techniques for working with LLMs.
Provides a foundational understanding of NLP concepts and techniques. It serves as a useful reference for understanding the theoretical underpinnings of LLM-based applications.
This comprehensive book covers a wide range of topics in speech and language processing, including natural language understanding and generation. It serves as a valuable reference for understanding the broader context of LLM-based applications.
Provides a comprehensive overview of neural network methods for NLP, including deep learning techniques. It serves as a valuable reference for understanding the theoretical foundations of LLM-based applications.

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