Project Overview
The Basketball RAG Agent is a conversational AI designed to answer complex, semantic questions about basketball history and player statistics. Unlike traditional search engines, which rely on keyword matching, this agent uses a Retrieval-Augmented Generation (RAG) architecture to understand the nuances of natural language and provide accurate, context-aware answers.
The Problem
Standard search tools are great for finding simple facts, but they fall short when it comes to complex, conversational queries. For example, a user might ask, "Which player who was primarily a point guard has the most triple-doubles in a single season?", a question that requires a deep understanding of the domain and the relationships between different pieces of information.
The Solution
To address this challenge, I developed a RAG agent using the following technologies:
- LangChain: A framework for developing applications powered by language models.
- Ollama: A tool for running large language models locally.
- Transformers: A library for state-of-the-art machine learning models.
- Gradio: A library for creating simple web interfaces for machine learning models.
The agent works by first retrieving relevant documents from a specialized knowledge base of basketball statistics and articles. These documents are then passed to a large language model, which uses them to generate a comprehensive and accurate answer to the user's question.
The Outcome
The Basketball RAG Agent can successfully answer a wide range of complex queries that traditional search engines cannot. This project demonstrates the power of domain-specific LLMs and the RAG architecture for creating intelligent, conversational AI applications. The agent is a practical tool for sports analysts, journalists, and fans who need to quickly and easily find information about the world of basketball.