Building the Research Server

Our agent now possesses the power of sight, capable of analyzing an image and articulating what it sees in a detailed description. But a description alone is not knowledge. The true power of our assistant lies in its ability to reason about what it sees. In this lesson, we will complete our system by building its cognitive engine: the WikipediaResearchServer. This server will take the rich description from our vision module and use it to search for and summarize relevant information, transforming a visual observation into a comprehensive research summary.

Building the WikipediaResearchServer

We will now construct the second component of our agent: the WikipediaResearchServer. This server’s sole responsibility is to act as a dedicated research assistant. It will receive a text query, the very description generated by our VisualAnalysisServer, and use it to retrieve relevant articles from a reliable knowledge source. For this task, we will leverage Wikipedia, using a dedicated Python library to search its vast repository and extract concise summaries of the top results.

Setting up the server environment

Our new research server requires the following library to interact with the Wikipedia API. We can install it using pip.

The wikipedia is a simple Python wrapper for the Wikipedia API. We’ll use it to search topics, fetch article metadata, and extract content.

Note: You don’t need to worry about these installations in the course. We have already set up the environment for you. You can focus directly on writing and executing the code.

Implementing the fetch_wikipedia_info tool

We will now implement the single, specialized tool for our WikipediaResearchServer: the fetch_wikipedia_info tool. This function is designed to be a straightforward research utility. It will accept a text query as input and use the wikipedia library to find the most ...