dolthub/dolt
Dolt is a SQL database with Git-like version control capabilities, enabling users to manage, version, and collaborate on data and schemas as easily as code in Git repositories.
qdrant/mcp-server-qdrant
mcp-server-qdrant is an official implementation of a Model Context Protocol (MCP) server designed specifically for Qdrant, a vector search engine. This server facilitates semantic memory management and integration with large language model (LLM) applications by leveraging vector search capabilities. The MCP is an open protocol that standardizes the connection between LLMs and external data sources or tools, enabling seamless integration for various AI-powered applications such as IDEs, chat interfaces, and custom AI workflows. The mcp-server-qdrant acts as a semantic memory layer on top of the Qdrant database, allowing for efficient storage and retrieval of information. It provides two main tools: qdrant-store and qdrant-find. The qdrant-store tool allows users to store information along with optional metadata into a specified collection within the Qdrant database. The qdrant-find tool enables retrieval of relevant information based on a query from the specified collection. These tools facilitate the management of semantic memories that can be used by LLM applications to enhance their contextual understanding. Configuration of the server is managed through environment variables, including settings for the Qdrant server URL, API key, default collection name, local database path, embedding provider, and embedding model. The server supports different transport protocols such as stdio, server-sent events (SSE), and streamable HTTP, catering to both local and remote clients. Installation options include running the server using uvx, Docker, or via Smithery for integration with Claude Desktop. The server is built on FastMCP, inheriting its environment variable configurations for debugging, logging, and network settings. Overall, mcp-server-qdrant provides a robust and standardized solution for integrating Qdrant's vector search capabilities with LLM applications through the Model Context Protocol, enabling advanced semantic memory management and retrieval functionalities.
https://github.com/qdrant/mcp-server-qdrant
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