Agent skill that teaches LLMs to operate Milvus vector database using pymilvus — connection, collections, vector CRUD, search, hybrid search, full-text search, indexing, RBAC, and common patterns like RAG
This project builds a semantic search engine specifically designed for video content. It utilizes SBERT, to understand the meaning behind user queries and videos. This allows users to search for specific information within videos, skipping irrelevant parts and saving them valuable time.
This project uses Python, Hugging Face (sentence-transformers), Milvus + Docker (container running Vector DB) to create a vector database, populate it with details of many people (names, ages, salaries, addresses and their introductions) and enable searching and querying on the database contents using Cosine-Similarity distances on IVF Flat index.
Semantic website content search app with React frontend and FastAPI backend. Enter a URL and query to find the most relevant content chunks using transformer embeddings, FAISS, and Milvus vector database. Modern UI, easy setup, and fast semantic search.
This project uses Python, Hugging Face (sentence-transformers), Milvus + Docker (container running Vector DB) to create a vector database, populate it with details of many people (names, ages, salaries, addresses and their introductions) and enable searching and querying on the database contents using Cosine-Similarity distances on IVF Flat index.
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