Semantic
A Coding Implementation of an Advanced Tool-Using AI Agent with Semantic Kernel and Gemini
In this tutorial, we build an advanced AI agent using Semantic Kernel combined with Google’s Gemini free model, and we run it seamlessly on Google Colab. We start by wiring Semantic Kernel plugins as tools, like web search, math evaluation, file I/O, and note-taking, and then let Gemini orchestrate them through structured JSON outputs. We…
Getting Started with Mirascope: Removing Semantic Duplicates using an LLM
Mirascope is a powerful and user-friendly library that provides a unified interface for working with a wide range of Large Language Model (LLM) providers, including OpenAI, Anthropic, Mistral, Google (Gemini and Vertex AI), Groq, Cohere, LiteLLM, Azure AI, and Amazon Bedrock. It simplifies everything from text generation and structured data extraction to building complex AI-powered…
Building Advanced Multi-Agent AI Workflows by Leveraging AutoGen and Semantic Kernel
In this tutorial, we walk you through the seamless integration of AutoGen and Semantic Kernel with Google’s Gemini Flash model. We begin by setting up our GeminiWrapper and SemanticKernelGeminiPlugin classes to bridge the generative power of Gemini with AutoGen’s multi-agent orchestration. From there, we configure specialist agents, ranging from code reviewers to creative analysts, demonstrating…
LightOn AI Released GTE-ModernColBERT-v1: A Scalable Token-Level Semantic Search Model for Long-Document Retrieval and Benchmark-Leading Performance
Semantic retrieval focuses on understanding the meaning behind text rather than matching keywords, allowing systems to provide results that align with user intent. This ability is essential across domains that depend on large-scale information retrieval, such as scientific research, legal analysis, and digital assistants. Traditional keyword-based methods fail to capture the nuance of human language,…
A Step-by-Step Guide to Building a Semantic Search Engine with Sentence Transformers, FAISS, and all-MiniLM-L6-v2
Semantic search goes beyond traditional keyword matching by understanding the contextual meaning of search queries. Instead of simply matching exact words, semantic search systems capture the intent and contextual definition of the query and return relevant results even when they don’t contain the same keywords. In this tutorial, we’ll implement a semantic search system using…
Semantic understanding, not just vectors: How Intuit’s data architecture powers agentic AI with measurable ROI
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Intuit — the financial software giant behind products like TurboTax and QuickBooks — is making significant strides using generative AI to enhance its offerings for small business customers. In a tech landscape flooded with AI promises,…
Meet LOTUS 1.0.0: An Advanced Open Source Query Engine with a DataFrame API and Semantic Operators
Modern data programming involves working with large-scale datasets, both structured and unstructured, to derive actionable insights. Traditional data processing tools often struggle with the demands of advanced analytics, particularly when tasks extend beyond simple queries to include semantic understanding, ranking, and clustering. While systems like Pandas or SQL-based tools handle relational data well, they face…