The 'mcp-server-qdrant' project provides a Model Context Protocol (MCP) server implementation for the Qdrant vector search engine. This server acts as a semantic memory layer, allowing applications to store and retrieve information using vector search capabilities. By integrating with the Qdrant database, this project enables seamless interaction between large language models (LLMs) and external data sources, facilitating the creation of AI-powered applications like IDEs, chat interfaces, and custom workflows. The server offers two main tools: 'qdrant-store' for storing information with optional metadata and 'qdrant-find' for retrieving relevant information based on queries. This project is particularly useful for developers looking to enhance AI applications with efficient data retrieval and storage mechanisms.
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Highlight AIStore some information in the Qdrant database. Input: information (string): Information to store, metadata (JSON): Optional metadata to store, collection_name (string): Name of the collection to store the information in. Returns: Confirmation message.
Retrieve relevant information from the Qdrant database. Input: query (string): Query to use for searching, collection_name (string): Name of the collection to search in. Returns: Information stored in the Qdrant database as separate messages.
