---
title: "Solr Vector Search"
description: "Enables hybrid keyword and semantic search on Apache Solr indexes using vector embeddings for contextual document retrieval."
category: "mcp-servers"
tags: ["server", "data", "integration", "database", "ai", "vector embeddings", "hybrid search", "semantic understanding"]
tech_stack: ["Apache Solr", "Vector Search", "Semantic Search", "Document Retrieval", "Search Indexing", "Recommendation Systems", "Intelligent Search Applications"]
---

This MCP server connects Apache Solr's strong search features with modern vector embedding technology. This combination allows for hybrid search, blending traditional keyword matching with a deeper semantic understanding.

Now, developers can run contextual searches on structured data repositories without needing direct access to the database. They can use both text-based queries and vector similarity, leading to more accurate and relevant search results.

This integration opens the door for advanced applications like semantic document retrieval, recommendation systems, and smart search solutions.

By using Solr's indexing capabilities alongside vector embeddings, it offers a scalable option for applications that require both precise keyword filtering and semantic insights. This makes it a great fit for content discovery platforms, knowledge bases, and intelligent search interfaces.