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963 Students
Course Highlights
Developers must become proficient in LangChain and LLM (Large Language Models) in the rapidly evolving tech industry of today. This course is a great method to advance your career because of the abundance of chances in the field of LLM applications brought about by the growing demand for AI-driven solutions. This course will walk you through the nuances of LangChain and show you how to use LLM to create creative applications, regardless of your level of experience as a developer. You can stay ahead in a subject that is changing quickly if you comprehend the fundamental ideas of LangChain.
Professionals with experience with LLM-based apps and LangChain are in more demand on the UK employment market. Because AI is being used so widely in sectors like e-commerce, healthcare, and finance, businesses are looking for specialists who can incorporate state-of-the-art language models into their systems. The need for LangChain specialists is anticipated to increase as more companies use these technologies, providing better pay and intriguing employment opportunities. You will be prepared to close this gap and seize the many job chances after completing this course.
After finishing the course, you will have the ability to create complex, scalable applications using LLM technologies. You’ll learn how to build and implement chains, manage memory, optimise data retrieval, and integrate APIs, among other essential skills. The hands-on experience you earn will position you as a top candidate in the competitive job market, preparing you for future challenges and professional advancement.
Learning outcome
- Understand the fundamentals of LangChain and its application in LLM-powered systems.
- Develop basic and advanced chains for building LLM applications.
- Implement memory management to enhance LLM-powered applications.
- Utilize OpenAI Function Calling for seamless integration with language models.
- Leverage Retrieval Augmented Generation (RAG) for dynamic content generation.
- Integrate LangChain Expression Language (LCEL) to streamline LLM operations.
Course media
Why should I take this course?
- Learn how to develop LLM-powered applications using LangChain, a highly sought-after skill in AI.
- Gain proficiency in advanced concepts such as RAG, memory management, and LangChain Expression Language.
- Enhance your career prospects by mastering tools and techniques used in cutting-edge AI applications.
- Increase your earning potential with skills that are in high demand across tech industries in the UK.
Requirements
- A basic understanding of programming languages like Python.
- Familiarity with machine learning and AI concepts.
- No prior knowledge of LangChain required, but an interest in AI is beneficial.
Career Path
- Junior LLM Developer: £40,000 - £55,000
- LangChain Developer: £55,000 - £75,000
- Senior LLM Engineer: £75,000 - £90,000
- AI Application Architect: £90,000 - £110,000
Curriculum
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Why this course is different
00:01:00 -
Prerequisites
00:01:00
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Essential topics and terms (theory)
00:04:00
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Why this course does not cover Open Source models like LLama2
00:01:00
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Optional: Install Visual Studio Code
00:02:00
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Get the source files with Git from Github
00:02:00
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Create OpenAI Account and create API Key
00:02:00
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Setup of a virtual environment
00:03:00
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Setup OpenAI Api-Key as environment variable
00:03:00
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Exploring the vanilla OpenAI package
00:03:00
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LLM Basics
00:07:00
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Prompting Basics
00:02:00
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Theory: Prompt Engineering Basics
00:02:00
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Few Shot Prompting
00:05:00
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Chain of thought prompting
00:02:00
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Pipeline-Prompts
00:04:00
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Prompt Serialisation
00:03:00
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Introduction to chains
00:01:00
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Basic chains – the LLMChain
00:03:00
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Response Schemas and OutputParsers
00:06:00
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LLMChain with multiple inputs
00:02:00
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SequentialChains
00:04:00
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RouterChains
00:04:00
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Callbacks
00:05:00
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Memory basics – ConversationBufferMemory
00:04:00
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ConversationSummaryMemory
00:03:00
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EXERCISE: Use Memory to build a streamlit Chatbot
00:01:00
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SOLUTION: Chatbot with Streamlit
00:03:00
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OpenAI Function Calling – Vanilla OpenAI Package
00:08:00
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Function Calling with LangChain
00:04:00
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Limits and issues of the langchain Implementation
00:03:00
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RAG – Theory and building blocks
00:03:00
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Loaders and Splitters
00:04:00
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Embeddings – Theory and practice
00:04:00
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VectorStores and Retrievers
00:07:00
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RAG Service with FastAPI
00:05:00
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Agents Basics – LLMs learn to use tools
00:06:00
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Agents with a custom RAG-Tool
00:07:00
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ChatAgents
00:03:00
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Indexing API – keep your documents in sync
00:02:00
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PREREQUISITE: Docker Installation
00:01:00
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Setup of PgVector and RecordManager
00:04:00
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Indexing Documents in practice
00:06:00
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Document Retrieval with PgVector
00:03:00
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Introduction to LangSmith (User Interface and Hub)
00:02:00
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LangSmith Projects
00:07:00
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LangSmith Datasets and Evaluation
00:13:00
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Introduction to Microservice Architecture
00:04:00
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How our Chatbot works in a Microservice Architecture
00:02:00
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Introduction to Docker
00:05:00
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Introduction to Kubernetes
00:02:00
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Deployment of the LLM Microservices to Kubernetes
00:13:00
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Intro to LangChain Expression Language
00:01:00
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LCEL Part 1 – Pipes and OpenAI Function Calling
00:02:00
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LCEL – Part 2 – VectorStores, ItemGetter, Tools
00:06:00
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LCEL – Part 3 – Arbitrary Functions, Runnable Interface, Fallbacks
00:07:00
Offer Ends in
All
Courses for £49-
Duration:3 hours, 37 minutes
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Access:1 Year
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Units:56
