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"Connecting OpenAI Codex with antigravity concepts or technology" matching MCP servers:

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    Provides retrieval-augmented generation (RAG) capabilities by ingesting various document formats into a persistent ChromaDB vector store. It enables semantic search and retrieval using either OpenAI or Ollama embeddings for processing local files, directories, and URLs.
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    MIT
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    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.
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    Persistent memory MCP server for AI coding agents (Claude Code, Codex, Gemini CLI). Hybrid retrieval (vector + BM25), cross-encoder reranking, knowledge graph, session checkpoint/resume, and multi-scope isolation. Local-first with LanceDB.
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    MIT
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    Enables ingestion and semantic search over text documents using PostgreSQL + pgvector and OpenAI-compatible embeddings, allowing any LLM agent to retrieve relevant chunks for grounded answers.
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    AGPL 3.0
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    Intelligent knowledge base system that enables users to process documents in 25+ formats, perform semantic search and Q\&A through vector retrieval. Supports multiple AI models including OpenAI and DouBao with local processing capabilities.
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    Provides semantic search capabilities by connecting Claude Desktop to a Cloudflare Workers backend powered by Vectorize. It enables natural language querying of knowledge bases using vector similarity and edge-based embedding generation.
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    MIT
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    A composable semantic memory layer that provides cross-project recall and session context using Qdrant and OpenAI embeddings. It enables users to securely store, search, and manage persistent memories with built-in secret scrubbing for privacy.
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    An MCP server that provides AI assistants with persistent, semantic memory using Turso for storage and OpenAI for vector search. It enables natural language operations to store, retrieve, and refine information with automatic duplicate detection and quality validation.
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    MIT
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    Enables semantic search and contextual conversations with your Calibre ebook library using vector-based RAG technology. Supports project-based organization, multi-format book processing, and OCR capabilities for enhanced content extraction and retrieval.
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    A Node.js-based MCP server that enables AI agents to generate embeddings, index documents, and perform semantic vector searches using OpenAI and Chroma. It facilitates the creation of retrieval-augmented generation (RAG) pipelines for internal knowledge assistants and document-based workflows.
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    Enables retrieval-augmented generation by embedding queries with a chosen provider (e.g., OpenAI) and searching supported vector stores (Pinecone, pgvector) to return relevant content.
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    Apache 2.0
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    A server that implements Retrieval-Augmented Generation using GroundX and OpenAI, enabling semantic search and document retrieval with Modern Context Processing for enhanced context handling.
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    Model Context Protocol (MCP) server for TigerGraph that lets AI agents interact with TigerGraph through the MCP standard using pyTigerGraph's async APIs.
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    Apache 2.0