Build a Wikipedia tool

The school administration is quite happy with your work! They would like to make the chatbot even more powerful by incorporating external resources. You suggest equipping the chatbot with access to Wikipedia. The administration agrees to extend your commission, so you decide to build a tool for the chatbot using the Wikipedia API.

The following modules have been imported for you to get started: WikipediaQueryRun, WikipediaAPIWrapper

This exercise is part of the course

Designing Agentic Systems with LangChain

Exercise instructions

Create an api_wrapper that initializes WikipediaAPIWrapper() with top_k_results=1 to fetch only the top Wikipedia result.
Create a WikipediaQueryRun() tool called wikipedia_tool, passing in the api_wrapper as its input before saving it to a list called tools.
Bind the tools list to the llm by passing it to the .bind_tools() method.
To direct the chatbot to respond with the tool, pass the content of "messages" in state to the .invoke() method and apply it to llm_with_tools.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Initialize Wikipedia API wrapper to fetch top one result
api_wrapper = ____(top_k_results=1)

# Create a Wikipedia query tool using the API wrapper
wikipedia_tool = ____(api_wrapper=____)
tools = [____]

# Bind the Wikipedia tool to the language model
llm_with_tools = ____.____(____)

# Modify chatbot function to respond with Wikipedia
def chatbot(state: State):
    return {"messages": [____.____(____["____"])]}

Edit and Run Code

This exercise is part of the course

Designing Agentic Systems with LangChain

IntermediateSkill Level

4.8+

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Build intelligent agentic systems! Discover the key components of LangChain agents, including how prompts, LLMs, and tools work together for reasoning and action. You'll set up an agent with OpenAI's API, define custom tools, and tackle real-world tasks like math calculations. Plus, explore how LangChain organizes data using graphs, nodes, and edges.

Exercise 1: Agents in LangChain Exercise 2: Creating a ReAct agent Exercise 3: Components of LangChain agents Exercise 4: Building custom tools Exercise 5: Create a tool for math calculations Exercise 6: Integrating custom tools and queries Exercise 7: Conversation with a ReAct agent Exercise 8: Conversation setup Exercise 9: Ask questions about conversation history

Build dynamic, tool-augmented chatbots with LangChain and LangGraph! You’ll explore how to create a chatbot that adapts based on user input by defining states and integrating external APIs for real-time information retrieval. You'll connect these components into a responsive graph structure, enabling smooth transitions between conversation and tool-assisted responses. By the end, you’ll have a visually represented chatbot framework with enhanced reasoning and multi-step workflows.

Exercise 1: Building graphs for chatbots Exercise 2: Graph and agent states Exercise 3: Adding nodes and edges Exercise 4: Generating chatbot responses Exercise 5: Queries and responses Exercise 6: Generate a graph diagram Exercise 7: Adding external tools to a chatbot Exercise 8: Build a Wikipedia tool

Current Exercise

Exercise 9: Add a tool to a graph Exercise 10: Adding memory and conversation Exercise 11: Visualize the graph and return tool outputs Exercise 12: Adding graph memory Exercise 13: Using graph memory for conversation

Expand your chatbot with dynamic tools and memory! Define and integrate multiple tools into flexible workflows, build functions for dynamic tool calling, and configure your chatbot for multiple-tool handling. Organize memory and outputs to enable interleaved, multi-turn conversations. By the end, you'll have created a sophisticated chatbot capable of complex interactions.

Exercise 1: Defining multiple tools Exercise 2: Build a tool that invokes the LLM Exercise 3: Build a tool with Python code Exercise 4: Binding multiple tools Exercise 5: Defining nodes and edges for flexible function calling Exercise 6: Define a function that stops the chatbot Exercise 7: Create a function to return an LLM response Exercise 8: Create the graph workflow for multiple tools Exercise 9: Organize chatbot outputs with memory Exercise 10: Configure outputs for multiple tools Exercise 11: Enable multi-turn conversation with memory Exercise 12: Congratulations!