Short-term vs. long-term memory in agents; How RAG extends memory through retrieval; Integrating vector databases and embeddings; Demo: augmenting agent responses with external knowledge.

Dive into the theory of multimodal model architecture. Train a Contrastive Pretraining model and create a vector database.

Hands-on workshop building a CLI chatbot with Retrieval-Augmented Generation using Couchbase Shell and Nushell.

Unlock the power of AI agents—even if you’re just starting out. In this hands-on, beginner-friendly workshop, you'll go from understanding how Large Language Models (LLMs) work to building a real AI agent using Python, LangChain, and LangGraph. Live Demo: Your First AI Agent — follow along as we build an AI agent that retrieves, reasons, and responds using LangChain and LangGraph.

Dive into the world of vector databases and Retrieval Augmented Generation (RAG) as we explore how we built a practical application and the challenges we faced. Discover how semantic search can enrich data, enabling recommendation engines, fraud detection, and more. Learn how these technologies can fit into your current applications and data, sparking new ideas for innovation.

In this session, I will discuss the unstructured data and the world of vector databases, we will see how they different from traditional databases. In which cases you need one and in which you probably don’t. I will also go over Similarity Search, where do you get vectors from and an example of a Vector Database Architecture. Wrapping up with an overview of Milvus.

For today’s Gen-AI apps, fast performance, instant scalability, and cost-effectiveness are more critical than ever. This session will delve into the importance of these factors when building RAG pattern apps while maintaining low costs. We will explore the capabilities of Azure Cosmos DB and its new vector database capabilities using DiskANN, a technology developed by Microsoft Research. With DiskANN, users can achieve low latency, high-recall vector search at any scale. Combined with Azure Cosmos DB’s unique scale-out architecture and instant autoscale, it provides enormous value with a cost profile unmatched by any vector database in the market today. This allows for the development of large-scale applications that are not only powerful and reliable but also economical. Join us and discover how to architect high accuracy, low latency, and cost-effective RAG pattern applications at any scale using Azure Cosmos DB and DiskANN. Regardless of your role, this session will provide valuable insights into bringing this new generation of applications to your business.

In this session, I will discuss the unstructured data and the world of vector databases, we will see how they different from traditional databases. In which cases you need one and in which you probably don’t. I will also go over Similarity Search, where do you get vectors from and an example of a Vector Database Architecture. Wrapping up with an overview of Milvus.

In this session, we'll discuss how data is stored, retrieved, augmented and isolated for users, and how index types, quantization, multi-tenancy, sharding, and replication affect their behaviour and performance. We will also discuss vector databases' integration with AI models that can generate vectors, or use retrieved data to produce augmented, or transformed outputs. When you emerge from this deep dive, you will have seen the inner workings of a vector database, and the key aspects that make them different to your grandma's database.

In this talk, Zain Hasan will discuss how we can use open-source multimodal embedding models in conjunction with large generative multimodal models that can that can see, hear, read, and feel data(!), to perform cross-modal search (searching audio with images, videos with text etc.) and multimodal retrieval augmented generation (MM-RAG) at the billion-object scale with the help of open source vector databases. I will also demonstrate, with live code demos, how being able to perform this cross-modal retrieval in real-time can enables users to use LLMs that can reason over their enterprise multimodal data. This talk will revolve around how we can scale the usage of multimodal embedding and generative models in production.

In this talk, we are going to explore how vector databases like Weaviate can be used to build the next level of RAG applications. Based on examples we are looking at the possibilities of RAG architectures that can handle multimodal context like images, videos and much more and how vector databases work in such a setting

Exploring agents with frameworks like Langchain and vector databases; learn how to build apps that can perform tasks autonomously.

Synopsis: Embark on an enlightening journey with Noble as he tackles the challenges of integrating Large Language Models (LLMs) into enterprise environments. Understand the inherent unreliability of these models and explore innovative solutions, ranging from vector databases to prompt chaining, that aim to enhance the trustworthiness of LLMs in crucial applications.

Hands-on, beginner-friendly workshop covering LLM basics, Python, LangChain, LangGraph, retrieval-augmented generation (RAG), prompt engineering, LangChain introduction, and workflow automation with LangGraph, including a live demo of building your first AI agent.

Since the release of ChatGPT late last year, the world has finally embraced vector embeddings and many organisations (from hedge funds to giant retailers) have been experimenting with vector databases. This is because vector embeddings, a component at the heart of large language models, open-up the ability to not only compress information but also to drastically transform search and knowledge retrieval. In this session we will put a spotlight on the embedding revolution that has taken over natural language processing, computer vision, network science and explain how enterprises can build better systems to understand, interact with, and sell to their customers.