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 |