The Ultimate Guide to Using LangGraph with State
In this tutorial, the video from business24_ai demonstrates how to use LangGraph with State to create a shared data structure representing the current snapshot of an application. This advanced tutorial builds on previous lessons and showcases how to pass data from the first node to the last node in a graph, rather than just to the next node.
The tutorial begins by recommending viewers join the Business24 AI Skool Community and navigate to the LangGraph course. The process is demonstrated using a shared Google Colab notebook, which eliminates the need to create a Python environment manually.
The video guides viewers through the installation of the required Python packages and the creation of a folder structure. It then defines the LangGraph State as a TypedDict, which includes workflow steps, source folder, and new files. The state schema will be the input to all nodes and edges, allowing all nodes in the graph to update the state.
Several functions representing the nodes in the graph are defined, including `function_init`, `check_new_files`, `router_is_new_file`, `watchdog_wait`, and `process_new_files`. These functions manipulate the state and control the flow of the graph based on certain conditions.
The tutorial explains how to add nodes, edges, and conditional edges to the LangGraph. It emphasizes good practices such as initializing the state at the start and cleaning up at the end. The graph is set up with an entry point and a finish point, and the initial state is passed to the app.
The video demonstrates running the app, showing how the state is updated as the graph processes nodes and edges. It also covers handling cyclic graphs, where the flow continuously cycles in a loop until a condition changes.
Finally, the tutorial provides links to further resources and documentation for more detailed information about LangGraph and its state concept.