Create a Versatile AI Agent with Nebius, Llama 3, and Real-Time Tools
Learn how to build a versatile AI agent using Nebius and Llama 3 that integrates Wikipedia search and calculation tools for enhanced real-time reasoning capabilities.
Building an Advanced AI Agent with Nebius and Llama 3
This tutorial presents an advanced AI agent leveraging the Nebius ecosystem, including ChatNebius, NebiusEmbeddings, and NebiusRetriever. The agent runs on the Llama-3.3-70B-Instruct-fast model, delivering high-quality responses while integrating external tools like Wikipedia search, contextual document retrieval, and safe mathematical calculations.
Key Components and Setup
Essential libraries such as langchain-nebius, langchain-core, langchain-community, and Wikipedia are installed to provide a robust foundation. Core modules like os, getpass, and datetime assist in environment management and data handling.
Core Class: AdvancedNebiusAgent
The AdvancedNebiusAgent class orchestrates the AI assistant's capabilities. It initializes the Llama 3 model via ChatNebius, sets up embeddings and a knowledge base, and configures a semantic retriever for relevant document searches.
The knowledge base includes curated documents covering topics like artificial intelligence, quantum computing, climate change, biotechnology, blockchain, space exploration, renewable energy, and 5G technology.
External Tools Integration
Two main tools enhance functionality:
- Wikipedia Search: Provides additional context by searching and summarizing Wikipedia pages.
- Mathematical Calculation: Performs safe arithmetic computations on user-provided expressions.
Query Processing and Prompting
The agent processes queries by retrieving relevant knowledge base documents, optionally invoking Wikipedia search or calculation tools. A detailed prompt template guides the response generation, including context, external tool results, current date, and user query.
Interactive Session
Users can interact with the agent in real-time, issuing commands prefixed with wiki: to trigger Wikipedia searches or calc: for calculations. The session supports natural conversation flow and terminates on the quit command.
Demonstrations
Sample queries showcase the agent’s ability to answer questions on AI, quantum computing, climate change, space exploration (with Wikipedia data), and mathematical computations related to solar panel efficiency.
This approach highlights how combining Nebius’ LLM capabilities with structured retrieval and external tools creates a powerful, multi-functional AI assistant suitable for diverse knowledge and reasoning tasks.
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