关键词 "Semantic caching" 的搜索结果, 共 22 条, 只显示前 480 条
a powerful coding agent toolkit providing semantic retrieval and editing capabilities (MCP server & Agno integration)
🤖 The Semantic Engine for Model Context Protocol(MCP) Clients and AI Agents 🔥
YouTube MCP Server is an AI-powered solution designed to revolutionize your YouTube experience. It empowers users to search for YouTube videos, retrieve detailed transcripts, and perform semantic sear
🔍 This project implements a Model Context Protocol (MCP) server for interacting with the Semantic Scholar API. It provides tools for searching papers, retrieving paper and author details, and fetching
Mirror of
MCP server for semantic search with Qdrant vector database
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
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 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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