Building Chatbot with MCP Server Using FastAPI and Streamlit
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Welcome back to the channel where we delve into the exciting world of creating a chatbot that can communicate with an MCP server. In this tutorial, we embark on a journey to build the backend using FastAPI and the frontend utilizing Streamlit. The chatbot's functionality involves sending user queries to a language model via the MCP server, which then fetches the latest documentation for crafting responses. It's a symphony of technology coming together to create a seamless interaction experience.
The MCP server acts as a toolbox, executing functions for the language model to provide accurate responses. The process unfolds with the initialization of the connection, followed by retrieving available tools and executing functions based on user queries. This intricate dance of data retrieval and processing forms the backbone of the chatbot's operation, culminating in delivering informative and timely responses to user inquiries. The video also hints at an AI engineer boot camp for those eager to dive deeper into the realm of artificial intelligence.
Project setup involves creating a virtual environment, installing dependencies, and configuring the API and frontend components. The API setup includes a post endpoint designed to handle user queries and facilitate seamless interaction with the MCP server. Each step in the implementation process adds layers of complexity and sophistication to the chatbot's functionality, showcasing the power of integrating different technologies to create a cohesive user experience. So buckle up and get ready to witness the magic of building a chatbot that can tap into the vast resources of an MCP server to provide insightful responses in real-time.
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