Multi-Agent Collaboration
This notebook implements a multi-agent collaboration system that combines historical research with data analysis to answer complex historical questions. It leverages the power of large language models to simulate specialized agents working together to provide comprehensive answers.
Historical analysis often requires both deep contextual understanding and quantitative data interpretation. By creating a system that combines these two aspects, we aim to provide more robust and insightful answers to complex historical questions. This approach mimics real-world collaboration between historians and data analysts, potentially leading to more nuanced and data-driven historical insights.
What you'll learn
- 1Agent Class: A base class for creating specialized AI agents.
- 2HistoryResearchAgent: Specialized in historical context and trends.
- 3DataAnalysisAgent: Focused on interpreting numerical data and statistics.
- 4HistoryDataCollaborationSystem: Orchestrates the collaboration between agents.
About this tutorial
This hands-on Jupyter notebook is part of GenAI Agents, a free open-source repository by Nir Diamant covering ai agents techniques with runnable code examples and detailed explanations.
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