The video demonstrates how to build local agents using LangGraph with the Ollama framework, specifically leveraging the Llama 3.1 model to gather pizza recommendations from Reddit. The presenter guides viewers through the installation process, API integration, and showcases the agent’s functionality, highlighting its impressive performance despite some limitations.
In this video, the presenter demonstrates how to build local agents using LangGraph with the Ollama framework, specifically utilizing the Llama 3.1 model with 8 billion parameters. LangGraph is an open-source library from LangChain that allows users to create agents in a graph-like structure, making it a preferred choice for building agents, whether they operate on OpenAI or locally. Ollama is another open-source project that simplifies running large language models (LLMs) locally, and the presenter highlights the efficiency of running the Llama model on a Mac due to its unified memory capabilities.
The video begins with the installation process for Ollama on macOS, guiding viewers through downloading the necessary files and setting up a Python environment. The presenter emphasizes the importance of using a virtual environment for better organization and management of dependencies. After setting up the environment, the presenter pulls the Llama 3.1 model and prepares to run a notebook that will facilitate the agent’s functionality. The notebook will utilize the Reddit API to gather pizza recommendations in Rome, showcasing how the agent can interact with external data sources.
Next, the presenter explains the process of integrating the Reddit API into the agent. Viewers are guided through signing up for the Reddit API to obtain the necessary credentials for making requests. The agent is designed to search for pizza recommendations by querying Reddit, and the presenter outlines the structure of the code that will handle the API interactions. The agent will gather data from Reddit submissions, including titles, descriptions, and comments, to provide users with well-rounded recommendations based on community feedback.
The video then delves into the architecture of the agent, explaining how it utilizes a structured output format to manage responses. The presenter describes the roles of different components within the agent, such as the Oracle (the decision-maker) and the search tool. The agent is designed to decide whether to answer a query directly or to perform a search based on the user’s input. The presenter emphasizes the importance of guiding the agent’s decision-making process to ensure it remains focused on the original query and provides relevant information.
Finally, the presenter showcases the agent in action, testing various queries related to pizza recommendations in Rome. The agent successfully utilizes the Reddit search tool to gather information and returns structured responses based on the data collected. While the presenter notes some limitations, such as occasional inaccuracies in recommendations, the overall performance of the agent is deemed impressive, especially considering the small model size. The video concludes with a reflection on the effectiveness of using JSON mode for agent interactions and the potential for future improvements in the Ollama framework.