News TL;DR
This project demonstrates the creation of a news summarization agent uses large language models (LLMs) for decision making and summarization as well as a news API calls. The integration of LangGraph to coordinate sequential and cyclical processes, open-ai to choose and condense articles, newsAPI to retrieve relevant article metadata, and BeautifulSoup for web scraping allows for the generation of relevant current event article TL;DRs from a single query.
Although LLMs demonstrate excellent conversational and educational ability, they lack access to knowledge of current events. This project allow users to ask about a news topic they are interested and receive a TL;DR of relevant articles. The goal is to allow users to conveniently follow their interest and stay current with their connection to world events.
What you'll learn
- 1LangGraph: Orchestrates the overall workflow, managing the flow of data between different stages of the process.
- 2GPT-4o-mini (via LangChain): Generates search terms, selects relevant articles, parses html, provides article summaries
- 3NewsAPI: Retrieves article metadata from keyword search
- 4BeautifulSoup: Retrieves html from page
- 5Asyncio: Allows separate LLM calls to be made concurrently for speed efficiency.
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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