In this tutorial, we build a pipeline on Phi-4-mini to explore how a compact yet highly capable language model can handle a full range of modern LLM workflows within a single notebook. We begin by ...

We launch the OpenAI-compatible llama-server to interact with Bonsai via the OpenAI Python client. We then build a lightweight Mini-RAG example by injecting relevant context into prompts, compare the ...

Microsoft says Agent Framework 1.0 is the production-ready release, with stable APIs and long-term support for both .NET and Python. The framework is presented as a unified successor path that builds ...

Index any document into a navigable tree structure, then retrieve relevant sections using any LLM. No vector databases, no embeddings — just structured tree retrieval. Available for both Python and ...

Abstract: The paper presents a novel Retrieval-Augmented Generation (RAG) framework for intelligent banking assistants, integrating structured financial and regulatory data to improve accuracy and ...

What if you could build an AI system that not only retrieves information with pinpoint accuracy but also adapts dynamically to complex tasks? Below, The AI Automators breaks down how to create a ...

What if your AI agent could not only answer your questions but also truly understand them, navigating complex queries with precision and speed? While the rise of vector search has transformed how AI ...

Lightweight, cost-effective, and easy to deploy Supports document collection management, insertion, querying, and maintenance Modular API design for flexible integration ...