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Markus Lang

What to Expect from This Course

Welcome to our course on Advanced Retrieval-Augmented Generation (RAG) with the LangChain Framework.

In this course, we dive into advanced techniques for Retrieval-Augmented Generation, leveraging the powerful LangChain framework to enhance your AI-powered language tasks. LangChain is an open-source tool that connects large language models (LLMs) with other components, making it an essential resource for developers and data scientists working with AI.

Course Highlights

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What to Expect from This Course

Welcome to our course on Advanced Retrieval-Augmented Generation (RAG) with the LangChain Framework.

In this course, we dive into advanced techniques for Retrieval-Augmented Generation, leveraging the powerful LangChain framework to enhance your AI-powered language tasks. LangChain is an open-source tool that connects large language models (LLMs) with other components, making it an essential resource for developers and data scientists working with AI.

Course Highlights

Focus on RAG Techniques: This course provides a deep understanding of Retrieval-Augmented Generation, guiding you through the intricacies of the LangChain framework. We cover a range of topics from basic concepts to advanced implementations, ensuring you gain comprehensive knowledge.

Comprehensive Content: The course is designed for developers, software engineers, and data scientists with some experience in the world of LLMs and LangChain. Throughout the course, you'll explore:

  • LCEL Deepdive and Runnables

  • Chat with History

  • Indexing API

  • RAG Evaluation Tools

  • Advanced Chunking Techniques

  • Other Embedding Models

  • Query Formulation and Retrieval

  • Cross-Encoder Reranking

  • Routing

  • Agents

  • Tool Calling

  • NeMo Guardrails

  • Langfuse Integration

Additional Resources

  • Helper Scripts: Scripts for data ingestion, inspection, and cleanup to streamline your workflow.

  • Full-Stack App and Docker: A comprehensive chatbot application with a React frontend and FastAPI backend, complete with Docker support for easy setup and deployment.

  • Additional resources are available to support your learning.

Happy Learning.  :-)

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What's inside

Learning objectives

  • Learn langchain expression language (lcel)
  • Master advanced rag techniques using the langchain framework
  • Evaluate rag pipelines using the ragas framework
  • Apply nemo guardrails for safe and reliable ai interactions

Syllabus

Before we start...
How to get started
Why should you take THIS course?
Requirements for this course
Read more
IMPORTANT! - Quick note about the installation of the packages
Clone the repository and set up the virtual environment
Repository Walkthrough
Full Stack App Walkthrough
Why NOT to take this course
LCEL - Basics
LCEL - The Runnable Interface
Building Our Own Small Version of LCEL
The most important Runnables of LangChain
LCEL - Real World Examples
LCEL - Pipelines with Chat History
Indexing API
Indexing API - keep your raw data in sync with your vectorstore
RAGAS - Framework for Evaluating RAG Performance
Creating an AI Augmented Testset
Evaluating RAG Performance with an LLM
Chunking Techniques
From CharacterTextSplitter to custom LLM based Splitter
Embedding models
Open Source vs Proprietary models (incl. Gen-3 models of OpenAI)
Improving queries for better retrieval
MultiQuery Retrieval
HyDE
Parent Document Retriever - Two-Stage-Retrieval
Parent Document Retrieve with InMemory DocStore
Custom Postgres DocStore built with BaseStore Interface
Agentic RAG
RAG with Agents (LLM + Tools)
Retrieval - Postprocessing Documents
Reranking with a Cross Encoder Model
LLM based Document Compression / Filtering
Routing
Routing with Embeddings vs. LLM based Routing
SQL Chain - Write SQL queries with an LLM
Prevent SQL Injection
Routing between Table & Vectorstore
NeMo Guardrails
Introduction to Guardrails & Colang
Guardrails & LangChain - Register Actions
Integrate Guardrails into LangChain with RunnableRails
Chat History with LangChain & Guardrails
LangFuse
Trace chains with LangFuse
Tool Calling
Introduction to Tool Calling
Use Tool Calling to get data from an API
Congratulations!
You did it! - what´s next?

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Combines many facets of retrieval-augmented generation techniques in one course
Emphasizes practical application and hands-on experience with RAG
Incorporates industry-standard tools and techniques, such as LangChain
Provides in-depth explanations of complex retrieval-augmented generation concepts
Suitable for learners with some prior experience in natural language processing and AI
Covers a broad range of topics, providing learners with a comprehensive understanding of RAG

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Advanced LangChain Techniques: Mastering RAG Applications with these activities:
Review LCEL Syntax
Ensure a strong understanding of LCEL to effectively leverage the LangChain framework.
Show steps
  • Revisit the course materials on LCEL basics.
  • Practice writing simple LCEL expressions.
  • Review real-world examples of LCEL usage.
RAG Pipeline Evaluation with RAGAS
Develop proficiency in evaluating RAG pipelines using the RAGAS framework.
Show steps
  • Set up the RAGAS evaluation environment.
  • Create an AI-augmented testset.
  • Evaluate a RAG pipeline with an LLM.
Build a Simple RAG Chatbot
Reinforce concepts of RAG by building a functional chatbot application.
Browse courses on Chatbot Development
Show steps
  • Design the chatbot's functionality.
  • Implement the LCEL-based RAG retrieval and generation logic.
  • Create a user interface for interacting with the chatbot.
  • Test and refine the chatbot's performance.
One other activity
Expand to see all activities and additional details
Show all four activities
Develop a RAG-based Document Search Engine
Challenge your understanding of RAG by building a project that leverages it for document search.
Show steps
  • Define the requirements and design of the search engine.
  • Implement the RAG indexing and retrieval components.
  • Develop a relevance ranking mechanism for the search results.
  • Incorporate user feedback to refine the search engine's performance.

Career center

Learners who complete Advanced LangChain Techniques: Mastering RAG Applications will develop knowledge and skills that may be useful to these careers:

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