A minimal implementation of an AI agent that retrieves knowledge and learns from its responses has been developed. This system uses OpenAI models for reasoning and NumPy for semantic similarity searches to build a small evolving knowledge base over interactions. Developers can leverage this approach to create domain-specific agents capable of incremental learning without complex orchestration frameworks, enhancing the utility of language models in practical applications.
Read the full article at Towards AI - Medium
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