Schedule

Building Advanced LLM Applications with Python and Milvus: A Scalable Vector Database Solution
In the age of large language models (LLMs), the ability to store, search, and retrieve massive amounts of high-dimensional data efficiently is critical for building responsive, intelligent applications. Milvus, an open-source vector database, is designed to handle exactly this challenge. Leveraging Milvus in conjunction with Python, developers can seamlessly create high-performance applications powered by LLMs, such as semantic search, recommendation systems, and intelligent chatbots. This talk will explore the integration of Milvus with Python to build advanced LLM applications. We will dive into how Milvus manages and indexes vector embeddings generated by LLMs, enabling scalable and real-time similarity searches. The session will cover practical workflows, including setting up a Milvus cluster, generating embeddings using popular Python libraries (e.g., Hugging Face Transformers), and querying the database for fast retrieval of relevant information. Attendees will gain hands-on insights into using Milvus with Python to handle large-scale LLM-powered systems, unlocking the full potential of vector databases for modern AI applications. Whether you’re developing a question-answering system, a recommendation engine, or any application relying on efficient vector similarity search, this talk will provide the tools and knowledge to take your LLM-powered projects to the next level.