"Information about SVG (Scalable Vector Graphics)" matching MCP servers:

A production-grade MCP server for integrating RAG into AI agents, supporting multiple vector databases with enterprise security and dynamic tool selection.

Integrates RAG into AI agents via MCP Server, supporting multiple vector database technologies for collection management and search operations.

Enables integrating RAG into AI agents via MCP server, supporting multiple vector database technologies.

Enables agents to turn documents into vectors, perform approximate nearest neighbor search with HNSW, and filter by metadata via MCP tools for RAG workflows.

MCP server for OpenAI Vector Store API, managing vector stores, files, file batches, and semantic search.

A secure vector-based memory server that provides persistent semantic memory for AI assistants using sqlite-vec and sentence-transformers. It enables semantic search and organization of coding experiences, solutions, and knowledge with features like auto-cleanup and deduplication.

Enables indexing and semantic search of software project files via MCP, with automatic file watching for updates.

Gives multiple AI coding agents a shared, persistent memory by indexing your code and documentation into a vector database with AST-aware chunking and hybrid semantic/keyword search. Agents can search across projects and store, update, or forget decisions, conventions and lessons learned, all self-hosted with local embeddings.

Provides local vector-based semantic memory storage for AI assistants to persist context and decisions across sessions using local embeddings and LanceDB. It enables private semantic search and session handoff capabilities to maintain long-term project context.

Provides AI coding agents with persistent, long-term memory through local semantic search and SQLite storage. It enables agents to save and retrieve architectural decisions or project context across different conversation sessions without requiring cloud services.

Enables local vector database operations with a Pinecone-compatible API, supporting exact and approximate search, metadata filtering, and semantic text queries over MCP.

Enables AI agents to semantically search GitHub repository documentation by automatically fetching, vectorizing, and indexing content into an Upstash Vector database. It provides a standard MCP interface for agents to retrieve relevant documentation snippets through natural language queries.

Enables semantic code search over a local codebase using Qdrant vector embeddings and OpenAI embeddings, allowing natural language queries from MCP-compatible clients like Claude Desktop.

Enables building and querying vector-based knowledge graphs with node and edge management and semantic search.

Turns your Readwise library into a blazing-fast semantic search engine with a streaming MCP server for LLM clients, supporting vector search, nightly syncs, and deployment on serverless platforms like Vercel.

一个基于FastMCP协议的文件向量化服务，支持自动目录监控、多格式文件处理和高效向量搜索，帮助构建个人或团队知识库。

Combines Neo4j graph database with vector search using OpenAI embeddings for intelligent semantic search across knowledge graphs.

Provides comprehensive management of OpenAI Vector Stores, allowing AI assistants to upload files, manage vector databases, and handle batch operations via the OpenAI API. It supports multiple deployment methods, including Cloudflare Workers and local NPM installation, for seamless integration with MCP-compatible clients.

An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.

Enables any MCP client to index files, directories, and arbitrary text into a local SQLite-backed vector database and perform semantic search with language-aware chunking, filters, and context expansion, all offline without network calls.