Using Tools to Build a Chatbot: A Practical Example in a Jupyter Notebook

Using Tools to Build a Chatbot: A Practical Example in a Jupyter Notebook¶

Following the previous article on the concept of MCP, this time we will get hands-on and build a chatbot prototype directly in a Jupyter Notebook environment. This example will demonstrate how to empower a language model to handle more complex queries by defining and executing tools directly, without relying on an external framework. The entire example covers the definition and execution of tools, as well as the construction of the chatbot’s core code.

Two Powerful Core Tools¶

This chatbot relies on two core tools to perform its tasks:

search_papers:

• Function: This tool searches for relevant papers on the arXiv platform based on a given topic and stores their information (including title, authors, summary, paper URL, and publication date) into a JSON file.
• Special Note: These JSON files are organized by topic in the papers directory, but the tool itself does not download the papers.

• Parameters: topic (the topic to search for) and max_results (the maximum number of papers to retrieve, defaulting to 5).
  extract_info:

• Function: This tool looks for detailed information about a specific paper across all topic directories within the papers folder.

• Parameters: paper_id (the ID of the paper to look up).
• Return Value: If the paper is found, it returns a JSON string with the paper’s information; otherwise, it returns an error message.

The Communication Bridge between Tools and the Language Model¶

To enable the language model to use these tools, we need to define a tool “schema” and establish an execution mechanism.

• Tool Schema: In the tools JSON object, we define the name, a description of the function, and the required parameters for each available tool. The language model refers to this schema to determine when and how to call a tool to fulfill a user's request.
• Tool Mapping and Execution: The execute_tool function plays a crucial role. It connects the tool name decided by the language model to its corresponding Python function (search_papers or extract_info) and executes it. Additionally, it formats the tool's execution results so the language model can easily understand them and use them to generate the final response.

The Chatbot’s Logic¶

This chatbot operates in a simple yet effective manner. It processes user queries one by one but does not persist memory across different queries.

• process_query: This is the core function for handling a single query. It sends the user's question to the language model and then enters a loop. If the language model decides to call a tool, process_query will execute it and return the result to the model, allowing it to continue the conversation based on the new information until a final natural language response is produced.
• chat_loop: This function provides an interactive interface, allowing users to continuously input queries until they type quit to exit.

Try it Out¶

https://github.com/1daniel3333/mcp_trial/blob/main/L3.ipynb¶

You are free to interact with this chatbot. For example, you can enter the following query to test it:

• Search for 2 papers on “LLM interpretability”
  To access the papers folder and view the saved paper files, go to the Jupyter Notebook and click on File → Open → L3.