What to Expect from This Course
Welcome to our course on Advanced Retrieval-Augmented Generation (RAG) with the LangChain Framework.
Read more
What to Expect from This Course
Welcome to our course on Advanced Retrieval-Augmented Generation (RAG) with the LangChain Framework.
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
- 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
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Requirements for this course
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Syllabus
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Activities
Review LCEL Syntax
Show steps
Ensure a strong understanding of LCEL to effectively leverage the LangChain framework.
Show steps
-
Revisit the course materials on LCEL basics.
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Practice writing simple LCEL expressions.
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Review real-world examples of LCEL usage.
RAG Pipeline Evaluation with RAGAS
Show steps
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
Show steps
Reinforce concepts of RAG by building a functional chatbot application.
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Chatbot Development
Show steps
-
Design the chatbot's functionality.
-
Implement the LCEL-based RAG retrieval and generation logic.
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Create a user interface for interacting with the chatbot.
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Test and refine the chatbot's performance.
One other activity
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Show all four activities
Develop a RAG-based Document Search Engine
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Challenge your understanding of RAG by building a project that leverages it for document search.
Browse courses on
Retrieval-Augmented Generation
Show steps
-
Define the requirements and design of the search engine.
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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.
Review LCEL Syntax
Show steps
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
Show steps
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
Show steps
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.
Develop a RAG-based Document Search Engine
Show steps
Challenge your understanding of RAG by building a project that leverages it for document search.
Browse courses on
Retrieval-Augmented Generation
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.
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