About This Course
Course Curriculum
-
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
-
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
-
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
-
Introduction to chains
00:01:00 -
Basic chains – the LLMChain
00:03:00 -
Response Schemas and OutputParsers
00:06:00 -
LLMChain with multiple inputs
00:02:00 -
SequentialChains
00:04:00 -
RouterChains
00:04:00
-
Callbacks
00:05:00
-
Memory basics – ConversationBufferMemory
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 – Vanilla OpenAI Package
00:08:00 -
Function Calling with LangChain
00:04:00 -
Limits and issues of the langchain Implementation
00:03:00
-
RAG – Theory and building blocks
00:03:00 -
Loaders and Splitters
00:04:00 -
Embeddings – Theory and practice
00:04:00 -
VectorStores and Retrievers
00:07:00 -
RAG Service with FastAPI
00:05:00
-
Agents Basics – LLMs learn to use tools
00:06:00 -
Agents with a custom RAG-Tool
00:08:00 -
ChatAgents
00:03:00
-
Indexing API – keep your documents in sync
00:02:00 -
PREREQUISITE: Docker Installation
00:01:00 -
Setup of PgVector and RecordManager
00:04:00 -
Indexing Documents in practice
00:06:00 -
Document Retrieval with PgVector
00:03:00
-
Introduction to LangSmith (User Interface and Hub)
00:02:00 -
LangSmith Projects
00:07:00 -
LangSmith Datasets and Evaluation
00:13:00
-
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
-
Intro to LangChain Expression Language
00:01:00 -
LCEL Part 1 – Pipes and OpenAI Function Calling
00:07:00 -
LCEL – Part 2 – VectorStores, ItemGetter, Tools
00:06:00 -
LCEL – Part 3 – Arbitrary Functions, Runnable Interface, Fallbacks
00:07:00