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DOC-5315 Add AI videos page #1672
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| Title: Redis AI video collection | ||||||
| alwaysopen: false | ||||||
| categories: | ||||||
| - docs | ||||||
| - develop | ||||||
| - ai | ||||||
| description: Watch video tutorials and demos showcasing Redis in AI applications, from vector search to RAG implementations. | ||||||
| linkTitle: AI videos | ||||||
| weight: 60 | ||||||
| --- | ||||||
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| Explore our collection of video tutorials and demonstrations showcasing how Redis powers AI applications. From vector search fundamentals to advanced RAG implementations, these videos provide practical insights and hands-on examples. | ||||||
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| | [**Long-Term Memory with LangGraph**](https://www.youtube.com/watch?v=fsENEq4F55Q) | [**Short-Term Memory with LangGraph**](https://www.youtube.com/watch?v=k3FUWWEwgfc) | [**What is semantic search?**](https://www.youtube.com/watch?v=o3XN4dImESE) | | ||||||
| | Learn how to implement long-term memory capabilities in AI agents using LangGraph. This video shows you how to build AI systems that can retain and recall information across extended interactions. | Want your AI agents to remember what users tell them? Short-term memory is the key to natural conversations, and in this tutorial, you'll learn how to implement it with LangGraph. | Traditional search matches words — but what if your AI app could match meaning instead? This video explains how semantic search works and why it's essential for modern AI applications. | | ||||||
| | [**What is a semantic cache?**](https://www.youtube.com/watch?v=AtVTT_s8AGc) | [**Building a RAG Pipeline from Scratch with RedisVL**](https://www.youtube.com/watch?v=cCTKmmGO4CY) | [**What is a vector database?**](https://www.youtube.com/watch?v=Yhv19le0sBw) | | ||||||
| | What if you could skip redundant LLM calls — and make your AI app faster, cheaper, and smarter? This video breaks down semantic caching and shows how it can transform your AI applications. | Unlock the Power of Retrieval-Augmented Generation (RAG) with RedisVL! This tutorial will show you how to build a complete RAG pipeline from scratch using Redis as your vector database. | Vector databases have been trending recently as they power modern search, recommendations, and AI-driven applications. Learn what vector databases are and how they work. | | ||||||
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| | [**Building the future Architecting AI Agents with AWS, LlamaIndex and Redis**](https://www.youtube.com/watch?v=SFWroqAbBM4) | [**Building AI Apps using LangChain**](https://www.youtube.com/watch?v=YhxksXfgsp0) | [**Resources to Learn AI with Redis**](https://www.youtube.com/watch?v=M_WU_fN_lrs) | | ||||||
| | Key topics: The ins & outs of AI agents: Understand their role in breaking down tasks into manageable components for better performance. Learn how to architect AI agents using AWS, LlamaIndex, and Redis. | In this series, we dive into the integration between LangChain and Redis to power AI applications that need runtime speed, scalability, and intelligent data management. | This video shows which resources you can use to learn AI with Redis and build powerful AI applications. | | ||||||
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| ### Additional Resources | ||||||
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Contributor
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Should be an h2, otherwise the sidebar is indented strangely.
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. H2 adds another vertical line, so that is the reason I made this h3 |
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| | [**LLM Session Management with Redis**](https://www.youtube.com/watch?v=2jHtSLVUu0w) | [**A Semantic Cache using LangChain**](https://www.youtube.com/watch?v=LRswXEc5chE) | [**Similarity Search using Vector Store**](https://www.youtube.com/watch?v=BtFJdSiFh00) | | ||||||
| | Developers building AI applications require a way to store the conversation history between an LLM and a user. This is important to provide context and maintain coherent conversations across sessions. | One common concern of developers building AI applications is how fast answers from LLMs will be served to their end users, as well as how much it will cost. Learn how to implement semantic caching using LangChain and Redis. | Similarity search is one of the most popular use cases for developers building AI applications. It allows users to perform searches that can find semantically similar content using vector embeddings. | | ||||||
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| | [**Create a New Database on Redis Cloud**](https://www.youtube.com/watch?v=jF89DiC5RqM) | [**Redis Insight: A Developer's Deep Dive**](https://www.youtube.com/watch?v=dINUz_XOZ0M) | [**Redis + Amazon SageMaker for real-time fraud detection demo**](https://www.youtube.com/watch?v=kQKfXi7NfWs) | | ||||||
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Contributor
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Are these ones relevant? I know they are part of the AI tutorial series, but I'm not sure they've got anything to do with AI specifically. |
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| | Learn how to create a new database on Redis Cloud in this step-by-step tutorial. Perfect for developers getting started with Redis Cloud for their AI and data applications. | This video breaks down Redis Insight and shows developers how to use this powerful tool for database management and development. | See how Redis integrates with Amazon SageMaker to build real-time fraud detection systems. This demo shows practical applications of Redis in machine learning and AI-powered fraud prevention. | | ||||||
| | [**Redis + Amazon Bedrock in two minutes**](https://www.youtube.com/watch?v=1e2tM5kIJ5Y) | | | | ||||||
| | AWS has announced Redis Cloud as one of the few supported vector databases supported for Amazon Bedrock. Learn how to integrate Redis with Amazon Bedrock for your generative AI applications. | | | | ||||||
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| ## Getting Started | ||||||
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| Ready to start building AI applications with Redis? Check out our: | ||||||
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| - [Quickstart guides]({{< relref "/develop/get-started" >}}) | ||||||
| - [Vector search documentation]({{< relref "/develop/interact/search-and-query/advanced-concepts/vectors" >}}) | ||||||
| - [AI ecosystem integrations]({{< relref "/develop/ai/ecosystem-integrations" >}}) | ||||||
| - [Notebook collection]({{< relref "/develop/ai/notebook-collection" >}}) | ||||||
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Might be purely a subjective thing on my part, but I think the lines in the table between the linked headings and the following content makes it look like they are separate. The suggestion below reformats the top row to put the link and the content together, separated with just a
<br/>(I've just done one row so you can compare the two styles). Another option might be to format the table with two columns, one for the heading and the second for the content. A third option is just to ignore me altogether and leave it as it is :-)