Build AI Search Engine: Multi-Modal RAG, GraphRAG, and More

Building AI Search: Multi-Modal RAG, PaperQA2, RAFT, & GraphRAG

Build multi-modal intelligence using state-of-the-art techniques and open-source models like ColBERT, ColPali, and Meta Llama

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Course Reviews

Hear from Gen AI 360 Certification takers

“Probably one of the most complete end to end LLM resources I have seen so far. Even if you are more advanced in GenAI and LLMs, there are still many things you might be interested in learning and are likely new for you. Very much recommended.”

Xavier (Xavi) Amatriain, VP of Engineering - Product AI Strategy, LinkedIn

“@activeloopai @LangChainAI I signed up Wednesday and having been working through it.. insane quality for $0. 🤝”

Andrew Riley

“Two of the things that make this certification different from every other LangChain course I've seen: 1. It's very extensive! It could be a book. 2. You'll work on industry-specific projects.”

Santiago @svpino

“Yesterday I completed enough of @activeloopai‘s course to build something useful with #langchain Today a company owner liked the customer service bot I built for him and asked me to put it on his website. ZERO TO ONE BABY. Next: 1 to 10.”

Alex North Rule

“Another great resource to stay ahead in the game - The Gen AI 360 Foundational model extensive course by @activeloopai Check it out : https://learn.activeloop.ai/courses/langchain 🚀🚀✨”

Abhishek Ranjan

Course Curriculum

1. Course Introduction and Logistics
1. From Lexical to Multimodal Search with Deep Lake
1. Module 2 Introduction - Introduction to Advanced RAG
2. Rerankers: Supercharging Your Retrieval Results with ColBERT
3. Hybrid search: Combining the Strengths of Dense and Sparse Vectors
4. Restaurant Reviews: Using Hybrid search with Deep Lake
5. Advanced Chunking: Moving Beyond Arbitrary Token Chunking
6. Embedding Fine-Tuning: Adapting Embeddings to Your Specific Domain
7. Using Deep Memory to Boost Retrieval Accuracy up to 22% on Average
8. Multimodal RAG: Beyond Vectors
9. Restaurant Insights: Multimodal Burger Search
10. Notable Techniques: ColBERT & Contextual Retrieval
11. Using PaperQA2 for Scientific Discovery
1. Module 3 Introduction - Hello, to Graphs! And Using them for Retrieval
2. Distill-SynthKG by Intel Labs and Salesforce Research: Distilling Knowledge Graph Synthesis Workflow for Improved Coverage and Efficiency
3. Graph RAG vs Vector Search for AI Recipe Discovery
4. Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering by LinkedIn Corporation
1. Module 4 Introduction - RAFT: Best of Both Worlds?
2. RAFT Review by LlamaIndex Team: Adapting Language Model to Domain Specific RAG
3. Introduction: Using TorchTune and Deep Lake for RAFT
4. PatentPT: How We Finetuned a Large Language Model and Built a Retrieval Engine to Search & Generate Patents
1. Module 4 Introduction - Evaluating RAG Systems
2. Building an Evaluation Dataset: Setting Up the Target Correctly
3. Evaluation Approaches & Metrics: From ROUGE to LLMs
4. RAG Evaluation Tools: an Overview of the Open-Source Eval Landscape
5. Evaluation in Practice: Optimizing RAG on ai-arxiv Dataset
6. Concluding Remarks on Evaluation

Course Highlights

Free
27 lessons
5+ practical, industry-specific projects using advanced techniques like PaperQA2, RAFT or GraphRAG and models like ColPali and ColBERT
30 hours of learning content

Brought to you in collaboration with

Activeloop, LlamaIndex, & Intel Disruptor Initiative

Activeloop and Intel Disruptor Initiative collaborate to bring Foundational Model Certification to tomorrow’s Gen AI professionals, executives and enthusiasts. The Foundational Model Certification is your essential gateway to mastering Large Language Models (LLMs) - from training to putting them in production. In the third installment, jam-packed with 25+ theoretical lessons & 5+ practical projects, you will learn how to build a production-grade AI search system to harness the multi-modal data at your organization.

Explore the Collaboration

With Great Support From

Who Is This Course For?

Whether you're a developer looking to build sophisticated AI search for your enterprise or a professional seeking to understand cutting-edge RAG implementations, this course is your comprehensive guide to mastering advanced, truly mutli-modal RAG. The curriculum covers crucial aspects from multi-modal RAG, PaperQA, GraphRAG and RAFT, addressing fundamental challenges in AI development such as retrieval accuracy, hallucination prevention, and knowledge graph integration. Through practical projects using industry-standard tools like Deep Lake and hands-on experience with hybrid search systems, you'll learn to build robust, production-ready AI solutions. This course is particularly valuable for those who need to work with complex data types, including images and scientific documents, or want to implement state-of-the-art techniques like ColBERT and contextual retrieval in their applications.

Enroll for free

Industry Leaders on the Course

“In the rapidly evolving business landscape, leveraging Retrieval Augmented Generation (RAG) tools like LlamaIndex & Deep Lake by Activeloop is essential for enterprises seeking a competitive edge in GenAI. This course is tailored to quickly upskill your team in GenAI workflows, emphasizing the integration of Activeloop's advanced features like Deep Memory with LlamaIndex for unmatched retrieval accuracy. It's a strategic investment to enhance your team's capabilities, ensuring your enterprise stays at the forefront of AI innovation.”

Jerry Liu, CEO & Co-Founder, LlamaIndex

“I believe that engineers and technology executives could benefit greatly from this course to stay at the forefront of AI. Intel continues to be at the vanguard of AI and new technology adoption. This Foundational Model Certification could help better equip the next generation of innovators with what they need to succeed with Generative AI and Large Language Models. It could also contribute to the broader adoption of AI applications and solutions across various industries.”

Arijit Bandyopadhyay, CTO – Enterprise Analytics & AI, Head of Strategy – Cloud & Enterprise, DCAI Group, Intel Corporation

“Enterprises are investing in LLM applications across industries. However, there's a huge knowledge gap in how to train and fine-tune LLMs, and what software and hardware stack is needed for it. Gen AI 360-certified developers will be able to apply state-of-the-art methods and construct cost-effective and efficient solutions that won't break the bank while delivering full performance with advanced AI technologies like Deep Lake and performant hardware like 4th Generation Intel® Xeon® Scalable Processors.”

Davit Buniatyan, CEO Activeloop

Why Should You Take This Course?

Master Cutting-Edge RAG Techniques

Vanilla RAG is so 2023. Move beyond basic understanding with state-of-the-art techniques like GraphRAG, RAFT, PaperQA2, and models like ColPali and ColBERT for multi-modality and re-ranking.
Best Papers on Advanced RAG Inside

We sifted through a lot of research done this year to understand what matters, tested if it works, and distilled this for you and your team to learn!
Industry-specific Projects with The Database for AI

Learn how to create a medical pill recognizer app or multi-modal financial analyst. Discover how to improve retrieval performance across healthcare, scientific research, and e-commerce. Or build a fun project like AI recipe search.

More Course Takers' Reviews

Incredible Overview

Lee Pang

As all of these technologies enter from bleeding edge to leading edge, it can be frustrating not having consistent examples or documentation to help carve away at the features Im desiring to implement both personally and for my employers. This was an absolutely incredible overview of LangChain, coupled with fantastic real world examples (with reasoning and failed attempts nonetheless). This course gave me everything I needed to iterate step by step on a few product features I've been exploring for work, as well as additional capabilities of both AI and LangChain that will be hard for my colleagues to believe are possible. I can't recommend this course enough and I've been eagerly awaiting completing it so I can show off my certificate and urge people to get their own. I'm super grateful to activeloop and team for organizing all these wonderful documents and examples and explaining it clearly and intentionally. This has supercharged my career and skills for the foreseeable future!

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I'm Surprised

Brian Barnes

I didn't expect much. There are some tutorials out there covering most of the stuff found here. I mean, how hard can writing a prompt really be? As it turns out, you can get pretty darn advanced with it. This is literally the only course I've found regarding Langchain that is as serious as a heart attack. Most are only high level overviews. This is the real deal. And it's free...

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Interactive and full of real world applications. Incredible!!

Elle Neal

This course is FANTASTIC! I have tried to navigate Langchain for a while now, due to their being so much functionality, I found it hard to grasp all the concepts. Not only did this course give me a great overview, it was packed with exciting projects. I made my own course material chatbot half way through, it was made so simple for me. Can't wait for more!! Thank you so much for making this accessible!

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really good insights.

Ramesh Nandipalli

course is really good. Course is very detailed with very good examples.

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Great Course

raja nouman

Langchain is a framework to create chatbots and other AI apps on given data stored in vector databases. Deep Lake is great as it is a database used to store data of all types audio, videos, text etc. for LLMs. Highly recommend

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Complete course

Sharan Harsoor

Very nice course and amazing projects.

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Best fit for beginners to kickstart their AI journey

Tanmay Sarkar

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this is a good course

Kim Tỵ

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Excellent Course!

Rakesh Arumalla

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Awesome course!

Sanjay Singh

Found it really helpful. It covers theory and projects, both. The way codes are explained by guiding on developing amazing applications is quite compelling. Strongly recommend it to every AI developer. Thanks a lot!

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Very through approach to learn about LLMs & LangChain

Jorge Mario Salazar Rios

The course has a lot of valuable information and many great examples to understand in deep the concepts presented.

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Exceeds the expectations

Ravikumar Malipeddi

Great content and use cases are well crafted. enjoyed the whole learning.

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Build Multimodal AI Search with Deep Lake

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Frequently Asked Questions

Is the course free?

The course is absolutely free to take. You do need a paid OpenAI account or alternatively, build the example projects with Open-Source LLM models. As a result, you can still complete the course without running these projects or paying anything.
Will I get a certificate upon completion?

Yes, you will get Gen AI 360 Certified upon completion.
How long does the course take to complete?

Our quickest learners have completed the course as quickly as in 15 hours, but the average course completion time is 30+ hours of learning. You can also use the first module to go from zero to hearo in under 1 hour!
What are prerequisites for the course?

Please know that prior knowledge of coding and Python is a prerequisite. You are not expected to have prior LlamaIndex knowledge but certain familiarity would be useful.

Course Prerequisites

We've designed the course in a way that would be valuable for non-technical executives as well. To make the most out of it, however, you do need to be technical as it involves a lot of hands-on projects.

Intermediate Python Knowledge
Basic Knowledge of Jupyter & Gradio
Basic Knowledge of GitHub

Introduction to the Course and Course Logistics

In a Rush? Go From Hero to Zero in Building Multimodal AI Search

Building AI Search: Key Components of Advanced RAG

Introduction to Graphs and Using them for Retrieval

Combining RAG and Fine-Tuning with RAFT

Evaluating AI Search and RAG Systems in Production

Course Highlights

Master Cutting-Edge RAG Techniques

Best Papers on Advanced RAG Inside

Industry-specific Projects with The Database for AI