Building a Basic Chatbot with LangGraph

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Overview

In this document, we explore how to build a simple chatbot using LangGraph, a framework that allows the creation of state machine-based chatbots. The process is broken down into manageable steps, covering key concepts like defining states, creating nodes, adding edges, compiling the graph, visualizing the chatbot structure, and finally executing the chatbot.

Table of Contents

References


Environment Setup

Setting up your environment is the first step. See the Environment Setup guide for more details.

[Note]

The langchain-opentutorial is a package of easy-to-use environment setup guidance, useful functions and utilities for tutorials. Check out the langchain-opentutorial for more details.

You can set API keys in a .env file or set them manually.

[Note] If you’re not using the .env file, no worries! Just enter the keys directly in the cell below, and you’re good to go.

Step-by-Step: Understanding the Concepts

By following these steps, you will have a functional and clearly structured chatbot built with LangGraph!

STEP 1. Defining the State

  • We start by defining the state of the chatbot using Python's TypedDict . The state keeps track of messages, which are updated as the conversation progresses.

STEP 2. Defining the Nodes

  • Add a "chatbot" node.

  • Nodes represent individual units of work and are typically implemented as regular Python functions.

STEP 3. Defining the Graph and Add Nodes

  • Create a graph and include the defined nodes.

  • The "chatbot" node function takes the current State as input and returns a dictionary (TypedDict ) containing the updated list of messages under the key "messages".

  • The add_messages function in the State appends the LLM's response to the existing messages in the state.

STEP 4. Adding Graph Edges

  • Define the flow of the graph by adding edges:

    • START: Specifies the entry point where the graph execution begins.

    • END: Marks the termination point of the graph's flow.

STEP 5. Compiling the Graph

  • To execute the graph, call the compile() method using the graph builder.

  • This generates a CompiledGraph , which can be invoked during runtime to process states.

STEP 6. Visualizing the Graph

  • Visualizing the graph helps you understand its structure and how different nodes and edges are connected.

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STEP 7. Running the Graph

  • Let's run the chatbot with a sample question!

Complete Code

This code provides the full process of creating a simple chatbot using LangGraph, including defining the state, nodes, edges, compiling the graph, visualizing it, and running it with a user query.

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