---
title: "Epsilla"
description: "Connect to Epsilla vector databases for efficient creation, manipulation, and querying of vector data using pyepsilla."
category: "mcp-servers"
tags: ["data", "integration", "database", "ai", "machine-learning", "vector-storage", "semantic-search", "recommendation-systems"]
tech_stack: ["Epsilla", "Vector Databases", "pyepsilla", "Embeddings", "Semantic Search", "AI/ML"]
---

This MCP connects smoothly with Epsilla vector databases using the pyepsilla client library. This setup allows developers to tap into high-performance vector storage and retrieval.

You’ll find support for key vector database operations, like creating collections, inserting embeddings, and carrying out similarity searches. The optimized query performance makes these tasks straightforward.

This integration shines in AI applications that need semantic search, recommendation systems, and Retrieval-Augmented Generation (RAG) implementations. 

Developers can make the most of Epsilla's efficient indexing and query features. This capability helps in building applications that can handle vector embeddings at scale, which is perfect for machine learning pipelines and smart search solutions.