Overview
Step into the world of advanced language processing with our course, “LangChain in Action: Develop LLM-Powered Applications.” This dynamic programme offers a unique approach to mastering the LangChain framework, empowering you to harness the power of Large Language Models (LLMs) for developing cutting-edge applications. From understanding the fundamentals to exploring advanced concepts like Prompt Engineering and Retrieval Augmented Generation (RAG), this course provides a comprehensive journey through theory and practical implementation. Dive deep into topics such as prompt chaining, memory integration, and microservice architecture, equipping you with the skills to build sophisticated LLM-powered applications with confidence and proficiency.
How will I get my certificate?
You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.
Who is This course for?
- Developers aspiring to leverage Large Language Models (LLMs) for advanced application development.
- Data scientists and AI enthusiasts interested in exploring cutting-edge language processing techniques.
- Students studying computer science or related fields seeking practical experience in LLM-powered application development.
- Professionals aiming to enhance their skills in microservice architecture and language processing technologies.
- Anyone intrigued by the intersection of artificial intelligence and software engineering, eager to dive into the realm of LLM applications.
Requirements
Our LangChain in Action: Develop LLM-Powered Applications has been designed to be fully compatible with tablets and smartphones. Here are some common requirements you may need:
- Computer, smartphone, or tablet with internet access.
- English language proficiency.
- Required software/tools. (if needed)
- Commitment to study and participate.
There is no time limit for completing this course; it can be studied at your own pace.
Career Path
Popular Career Paths for a LangChain in Action: Develop LLM-Powered Applications Course:
- AI Application Developer: £40,000 – £60,000
- Machine Learning Engineer: £45,000 – £70,000
- Natural Language Processing (NLP) Specialist: £50,000 – £80,000
- Software Architect: £55,000 – £90,000
- AI Research Scientist: £60,000 – £100,000
- Technical Lead in AI Development: £65,000 – £110,000
Salary ranges can vary by location and experience.
Course Curriculum
- 13 sections
- 56 lectures
- 3 hours, 36 minutes total length
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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 00:08:00
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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
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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
Frequently Bought Together
Big Data Analytics with PySpark Tableau Desktop and MongoDB
4.7
(1 student)4.7 course rating - 7 reviews
£19.99
£419
This course includes:
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Duration:3 hours, 36 minutes
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Access:1 year access
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Units:56
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Level:Intermediate
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CPD Points:10
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Certificate:Yes
