Building Vector Search Solutions for Text, Images, and Video Content en es

Data exploration has moved beyond text; now data encompasses images and videos. In today's data-driven world, processing and analyzing large volumes of data efficiently is crucial for many applications. Let's explore together the concept of RAG and how to create and manage text and image embeddings for similarity searches in a PostgreSQL database. We'll dive into a practical example using Python to show you how to create vector bases of images and text for smarter searches. Additionally, we'll explore how to search and understand videos

Let's explore together the concept of RAG and how to create and manage text and image embeddings for similarity searches in a PostgreSQL database. We'll dive into a practical example using Python to show you how to create vector bases of images and text for smarter searches. Additionally, we'll explore how to search and understand videos.

Construcción de sistemas RAG avanzados: del texto al análisis de video en es

La exploración de datos dejó de ser solo texto; ahora los datos van más allá de eso, incluyendo imágenes y videos. En el mundo actual basado en datos, procesar y analizar eficientemente grandes volúmenes de información es crucial para muchas aplicaciones. Exploremos juntos el concepto de RAG y cómo crear y administrar embeddings de texto e imágenes para búsquedas de similitudes en una base de datos PostgreSQL. Nos sumergiremos en un ejemplo práctico utilizando Python para demostrar cómo crear bases vectoriales de imágenes y textos para realizar búsquedas más inteligentes. Adicionalmente, exploraremos cómo hacer búsquedas y comprender videos.

Exploremos juntos el concepto de RAG y cómo crear y administrar embeddings de texto e imágenes para búsquedas de similitudes en una base de datos PostgreSQL. Nos sumergiremos en un ejemplo práctico utilizando Python para demostrar cómo crear bases vectoriales de imágenes y textos para realizar búsquedas más inteligentes. Adicionalmente, exploraremos cómo hacer búsquedas y comprender videos.

Building a RAG System for Video Search and Analysis en es

This talk addresses the challenge of making video content searchable and analyzable using modern AI techniques. While text and image RAG systems are common, video presents unique challenges due to its multimodal nature combining visual frames and audio content.

Key Takeaways:
- Implement multimodal RAG architecture for video content
- Master vector embedding techniques for frames and audio
- Build efficient vector search capabilities using PostgreSQL
- Deploy production-ready video content analysis solutions

Construyendo sistema de RAG para busqueda y analis de videos en es

Esta charla aborda el desafío de hacer que el contenido en video sea buscable y analizable utilizando técnicas modernas de IA. Mientras que los sistemas RAG para texto e imágenes son comunes, el video presenta desafíos únicos debido a su naturaleza multimodal que combina frames visuales y contenido de audio.

Beneficios para los Asistentes:
- Implementar arquitectura RAG multimodal para contenido en video
- Dominar técnicas de embedding vectorial para frames y audio
- Construir capacidades eficientes de búsqueda vectorial usando PostgreSQL
- Desplegar soluciones de análisis de video listas para producción

Extending AI agents: Custom tools and Model Context Protocol en

As AI agents become increasingly prevalent in production systems, you face a critical challenge: extending these agents beyond their base capabilities to interact with proprietary systems, custom APIs, and domain-specific tools.

In this session, you'll build a fully functional AI agent using Strands Agents, an open-source framework, with minimal code. You'll learn how to enhance your agent's capabilities by integrating three types of tools: native framework tools, custom implementations, and Model Context Protocol (MCP) servers.

As AI agents become increasingly prevalent in production systems, you face a critical challenge: extending these agents beyond their base capabilities to interact with proprietary systems, custom APIs, and domain-specific tools.

In this session, you'll build a fully functional AI agent using Strands Agents, an open-source framework, with minimal code. You'll learn how to enhance your agent's capabilities by integrating three types of tools: native framework tools, custom implementations, and Model Context Protocol (MCP) servers

Build multi-modal AI agents with scalable chat memory en

The open-source Strands Agents SDK enables building production-ready, multi-agent AI systems in a few lines of code. The session demonstrates creating custom tools for video content analysis and converting them into Model Context Protocol (MCP) servers. Live code examples show how to implement scalable chat memory with Amazon S3 Vectors, from local development to enterprise production. Developers will learn how agents maintain context across conversations and provide personalized responses through efficient memory storage and retrieval.

Developers will learn how multimodal agents maintain context across conversations and provide personalized responses through efficient memory storage and retrieval

Building Agentic Video RAG: Transform Containers into AI Agents en

Modern video processing systems excel at technical workflows but require deep technical knowledge to operate effectively. This talk addresses the challenge of making sophisticated video analysis accessible through natural language interfaces, specifically targeting AI/ML engineers, cloud architects, and developer advocates working with multi-modal AI systems

Building intelligent research agents for production environments en

Modern applications increasingly need to access and synthesize real-time information from the web, but building intelligent research capabilities from scratch presents significant challenges. Developers struggle with API management, credential security, conversation context, and integrating multiple AI services into cohesive workflows.

This talk demonstrates how to build production-ready research agents in minutes, that provides secure runtime, identity management, and API gateway capabilities for AI agents. You'll learn to create agents that can conduct iterative web research, maintain conversation context, and provide structured responses with proper source attribution.

This talk demonstrates how to build production-ready research agents. You'll learn to create agents that can conduct iterative web research, maintain conversation context, and provide structured responses with proper source attribution.

From Prototype to Production in Minutes: Deploying AI Agents That Scale en

The gap between building an AI agent prototype and deploying it to production costs companies months of engineering time and thousands of dollars. This talk bridges that gap, showing how to deploy production-grade AI agents in minutes instead of months.

Using modern agentic frameworks and cloud infrastructure, you can deploy enterprise-ready agents with:

Cross-session memory for personalized experiences
Zero-code monitoring for observability
Auto-scaling infrastructure that grows with demand
Cost optimization patterns from real production workloads

Reduce AI Agents Costs and Mistakes with Semantic tool Selection en

AI agents with many tools face a dual problem: they pick the wrong tool and waste tokens because tool descriptions get serialized into the context on every call. As agents scale to 50+ tools, errors increase and costs explode.

Semantic tool selection filters tools before they reach the LLM context using vector search, reducing errors and token costs. Join me as I build a live travel agent demo showing how to implement this with minimal Ptython code and production patterns for multi-turn conversations.

AI agents waste tokens sending all tool descriptions on every call and pick wrong tools as they scale. Learn how semantic tool selection reduces errors 75% and token costs 89% using vector search. Live demo with production-ready code you can use today.

Graph-RAG vs RAG: Stop Inaccurate AI Agent Responses en

Traditional RAG has a fundamental limitation: vector search retrieves text, not structured data. When you need precise answers, LLMs guess aggregations from text chunks
instead of executing calculations. This causes wrong averages, fabricated counts, and approximate results.

Graph-RAG solves this by storing data as entities and relationships, enabling structured queries that execute calculations instead of estimating from text. Join me as I build
a travel agent demo that compares both on 515K hotel reviews proving where RAG fails and how graphs deliver accurate answers.

Traditional RAG has a fundamental limitation: vector search retrieves text, not structured data. When you need precise answers, LLMs guess aggregations from text chunks
instead of executing calculations. This causes wrong averages, fabricated counts, and approximate results.