Build an AI Agent from Scratch in Raw Python
SMRTR summary
A simple chatbot becomes a sophisticated digital assistant in this exploration of ReAct agents. By combining reasoning and action loops, these AI helpers can now browse Wikipedia, search academic papers, or crunch numbers before responding to queries.
"It's a huge advantage and a leap in intelligence," explains the developer, who built a ReAct agent from scratch using only basic Python and OpenAI's GPT-4 model.
The key? A carefully crafted prompt that teaches the AI to "run in a loop of Thought, Action, PAUSE, Observation." This allows the agent to pause, gather information, and reason before replying.
In a test run, the agent seamlessly searched arXiv for details on a research paper without being explicitly told to do so. It's a glimpse into a future where AI assistants are more knowledgeable, resourceful, and eerily intuitive.
SMRTR provides this summary for quick context. The original article belongs to GitConnected.
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