Why this server?

Enables interaction with a TrueRAG system, which allows access to policies with a Python SDK and GQL library integration.

Why this server?

Enables semantic search and RAG (Retrieval Augmented Generation) over your Apple Notes.

Why this server?

Connects a RAG application to open-webui using Model Context Protocol (MCP), enabling server-to-client communication for context retrieval and tool usage in remote environments through Server-Sent Events (SSE).

Why this server?

Enables AI assistants to enhance their responses with relevant documentation through a semantic vector search, offering tools for managing and processing documentation efficiently.

Why this server?

Implementation of an MCP server for the RAG Web Browser Actor. This Actor serves as a web browser for large language models (LLMs) and RAG pipelines, similar to a web search in ChatGPT.

Why this server?

An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context

Why this server?

Enables LLMs to interact directly the documents that they have on-disk through agentic RAG and hybrid search in LanceDB. Ask LLMs questions about the dataset as a whole or about specific documents.

Why this server?

An improved implementation of persistent memory using a local knowledge graph with a customizable --memory-path. This lets Claude remember information about the user across chats.