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LangChain in Action: Develop LLM-Powered Applications
LangChain in Action: Develop LLM-Powered Applications
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Intermediate
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3 hours, 42 minutes
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Overview
Empower your career journey with our in-demand course: LangChain in Action: Develop LLM-Powered Applications
Boost your proficiency and propel your career forward with our meticulously crafted course, designed to be your ultimate guide to professional development. Our super-accessible modules break down complex topics into bite-sized, easy-to-understand lessons, filling your knowledge gaps and equipping you with real-world, practical skills.
Seeking career advancement and application of your skills? You’ve found the right place. This LangChain in Action: Develop LLM-Powered Applications is your exclusive passport to unlocking your full potential.
Enroll today and enjoy:
- 100% online flexibility: Learn at your own pace, from anywhere in the world.
- Expert support: We're by your side throughout your journey, providing guidance and assistance.
- Comprehensive curriculum: Dive deep into relevant topics aligned with industry standards.
- Practical focus: Apply your learnings to real-world scenarios and situations.
This sought-after course is your key to a successful and lucrative career. Don’t miss out on this transformative opportunity. Enroll now and take your professional life to the next level!
Course design
The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace.
You are taught through a combination of
- Video lessons
- Online study materials
Exam & Retakes
It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable.
Certification
Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery).
Course Curriculum
| Before we start | |||
| Why this course is different | 00:01:00 | ||
| Prerequisites | 00:01:00 | ||
| Essential topics and terms (theory) | 00:04:00 | ||
| Why this course does not cover Open Source models like LLama2 | 00:01:00 | ||
| Optional: Install Visual Studio Code | 00:02:00 | ||
| Get the source files with Git from Github | 00:02:00 | ||
| Create OpenAI Account and create API Key | 00:02:00 | ||
| Preparation | |||
| Setup of a virtual environment | 00:03:00 | ||
| Setup OpenAI Api-Key as environment variable | 00:03:00 | ||
| Exploring the vanilla OpenAI package | 00:03:00 | ||
| LangChain Basics | |||
| LLM Basics | 00:07:00 | ||
| Prompting Basics | 00:02:00 | ||
| Theory: Prompt Engineering Basics | 00:02:00 | ||
| Few Shot Prompting | 00:05:00 | ||
| Chain of thought prompting | 00:02:00 | ||
| Pipeline-Prompts | 00:04:00 | ||
| Prompt Serialisation | 00:03:00 | ||
| Chains - From basic to advanced chains | |||
| Introduction to chains | 00:01:00 | ||
| Basic chains – the LLMChain | 00:03:00 | ||
| Response Schemas and Output Parsers | 00:06:00 | ||
| LLMChain with multiple inputs | 00:02:00 | ||
| Sequential Chains | 00:04:00 | ||
| Router Chains | 00:04:00 | ||
| Callbacks | |||
| Call backs | 00:05:00 | ||
| Memory | |||
| Memory basics – Conversation Buffer Memory | 00:04:00 | ||
| Conversation Summary Memory | 00:03:00 | ||
| EXERCISE: Use Memory to build a streamlit Chatbot | 00:01:00 | ||
| SOLUTION: Chatbot with Streamlit | 00:03:00 | ||
| OpenAI Function Calling | |||
| OpenAI Function Calling – Vanilla OpenAI Package | 00:08:00 | ||
| Function Calling with LangChain | 00:04:00 | ||
| Limits and issues of the langchain Implementation | 00:03:00 | ||
| Retrieval Augmented Generation (RAG) | |||
| RAG – Theory and building blocks | 00:03:00 | ||
| Loaders and Splitters | 00:04:00 | ||
| Embeddings – Theory and practice | 00:04:00 | ||
| Vector Stores and Retrievers | 00:07:00 | ||
| RAG Service with Fast API | 00:05:00 | ||
| Agents | |||
| Agents Basics – LLMs learn to use tools | 00:06:00 | ||
| Agents with a custom RAG-Tool | 00:07:00 | ||
| Chat Agents | 00:03:00 | ||
| Indexing API | |||
| Indexing API – keep your documents in sync | 00:02:00 | ||
| PREREQUISITE: Docker Installation | 00:01:00 | ||
| Setup of PgVector and Record Manager | 00:04:00 | ||
| Indexing Documents in practice | 00:06:00 | ||
| Document Retrieval with PgVector | 00:03:00 | ||
| LangSmith | |||
| Introduction to Lang Smith (User Interface and Hub) | 00:02:00 | ||
| Lang Smith Projects | 00:07:00 | ||
| Lang Smith Datasets and Evaluation | 00:13:00 | ||
| Microservice Architecture for LLM Applications | |||
| Introduction to Microservice Architecture | 00:04:00 | ||
| How our Chatbot works in a Microservice Architecture | 00:02:00 | ||
| Introduction to Docker | 00:05:00 | ||
| Introduction to Kubernetes | 00:02:00 | ||
| Deployment of the LLM Microservices to Kubernetes | 00:13:00 | ||
| LangChain Expression Language (LCEL) | |||
| Intro to Lang Chain Expression Language | 00:01:00 | ||
| LCEL Part 1 – Pipes and OpenAI Function Calling | 00:07:00 | ||
| LCEL – Part 2 – Vector Stores, Item Getter, Tools | 00:06:00 | ||
| LCEL – Part 3 – Arbitrary Functions, Runnable Interface, Fallbacks | 00:07:00 | ||
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