Accessing the Internet From Local LLM
Last Updated on September 29, 2025 by Editorial Team
Author(s): Deepak Krishnamurthy
Originally published on Towards AI.
Building an Internet-Enabled Local LLM with Tavily and Phi-3
Large language models (LLMs) are everywhere, and their need for up-to-date information is constant. While APIs from companies like Perplexity, Google, and OpenAI offer a convenient way to give these models real-time web access, they come with a major drawback, i.e. the lack of data privacy.
The article explores the construction of an internet-enabled AI assistant that functions locally, detailing the use of the Tavily API for real-time web access while addressing privacy concerns associated with traditional cloud APIs. It covers the steps to integrate Tavily with a local LLM, specifically the Phi-3 model, emphasizing the advantages of maintaining data privacy, enhancing query performance, and ensuring that AI capabilities remain within organizational control, ultimately providing a comprehensive solution for developers aiming to implement effective, secure AI systems.
Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.
Related posts
Recent Posts
