关键词 "Semantic Scholar" 的搜索结果， 共 20 条, 只显示前 480 条
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MCP server for semantic search with Qdrant vector database
A MCP server in development for Google Scholar
A long-term memory storage system for LLMs using the Model Context Protocol (MCP) standard. This system helps LLMs remember the context of work done over the entire history of a project, even across m
A MCP Server for pdffigures2: This server processes scholarly PDFs to extract figures, tables, captions, and section titles with high accuracy. It is designed to support researchers and developers in
A MCP Server for Google Scholar: 🔍 Enable AI assistants to search and access Google Scholar papers through a simple MCP interface.
MCP server to search up-to-date elasticsearch docs
An intelligent code memory system that leverages vector embeddings, structured databases, and knowledge graphs to store, retrieve, and analyze code patterns with semantic search capabilities, quality
MCP Server for querying DBT Semantic Layer
MCP Server for Interacting with Cube Semantic Layers
MCP server providing a knowledge graph implementation with semantic search capabilities powered by Qdrant vector database
MCP server for Semantic Scholar to search for papers
Model Context Protocol (MCP) server implementation for semantic vector search and memory management using TxtAI. This server provides a robust API for storing, retrieving, and managing text-based memo
A MCP server to search for accurate academic articles.
A FastMCP server implementation for the Semantic Scholar API, providing comprehensive access to academic paper data, author information, and citation networks.
Knowledge management system that allows you to build a persistent semantic graph from conversations with AI assistants. All knowledge is stored in standard Markdown files on your computer, giving you
MCP server providing semantic memory and persistent storage capabilities for Claude using ChromaDB and sentence transformers.
IFAdapter是一种新型的文本到图像生成模型，由腾讯和新加坡国立大学共同推出。提升生成含有多个实例的图像时的位置和特征准确性。传统模型在处理多实例图像时常常面临定位和特征准确性的挑战，IFAdapter通过引入两个关键组件外观标记（Appearance Tokens）和实例语义图（Instance Semantic Map）解决问题。外观标记用于捕获描述中的详细特征信息，实例语义图则将特征与特
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