The article discusses the use of large language models (LLMs) as reasoning engines for powering agent workflows, focusing specifically on ReAct agents. It explains how these agents combine reasoning and action capabilities and provides examples of how they function. Challenges faced while implementing such agents are also mentioned, along with ways to overcome them. Additionally, the integration of open-source models within LangChain is highlighted.
Leverage LLM-enhanced natural language processing and traditional machine learning techniques are used to extract structure and to build a knowledge graph from unstructured corpus.
Introduction of HuggingFace’s New Fine-tuned Model Zephyr-7B-α
