Model Context Protocol (MCP) server for semantic vector search and memory management using TxtAI
This MCP server offers semantic vector search and memory management capabilities with a robust API, supporting AI tools like Claude and Cline AI. It allows storing, retrieving, and organizing text memories using semantic search, with features like persistent storage, tag organization, and health monitoring, built on top of the powerful txtai engine.
Added on:
Created by:
Apr 22 2025
Model Context Protocol (MCP) server for semantic vector search and memory management using TxtAI
This MCP server offers semantic vector search and memory management capabilities with a robust API, supporting AI tools like Claude and Cline AI. It allows storing, retrieving, and organizing text memories using semantic search, with features like persistent storage, tag organization, and health monitoring, built on top of the powerful txtai engine.
Added on:
Created by:
Apr 22 2025
Roger Mendoza
Featured
What is Model Context Protocol (MCP) server for semantic vector search and memory management using TxtAI?
This server implements the Model Context Protocol (MCP) for advanced semantic search and text memory management leveraging txtai. It offers a high-performance API to store, search, and organize text-based memories with capabilities such as neural search, zero-shot classification, and multi-language support. Designed for AI assistants like Claude and Cline AI, it enables efficient context management, memory retrieval, tagging, and health monitoring. Its architecture is suitable for scalable applications requiring robust semantic search, persistence, and integration with AI-powered search engines, enhancing conversational AI context understanding.
Who will use Model Context Protocol (MCP) server for semantic vector search and memory management using TxtAI?
- AI developers
- Enterprise AI solution providers
- Conversational AI developers
- Research institutions using AI
- Companies implementing semantic search
How to use the Model Context Protocol (MCP) server for semantic vector search and memory management using TxtAI?
- Step1: Clone the repository from GitHub
- Step2: Install dependencies and set up environment
- Step3: Configure environment variables in .env file
- Step4: Run the start.sh script to launch the server
- Step5: Integrate with Claude or Cline AI by updating their MCP configs
- Step6: Use provided API endpoints or MCP tools for memory storage, retrieval, and management
Model Context Protocol (MCP) server for semantic vector search and memory management using TxtAI's Core Features & Benefits
The Core Features
- Semantic search across stored memories
- Persistent storage with file-based backend
- Tag-based memory organization and retrieval
- Memory statistics and health monitoring
- Automatic data persistence
- Integration with Claude and Cline AI
- Configurable CORS and logging
The Benefits
- Enhanced context understanding for AI applications
- Robust, scalable memory management
- Easy integration with popular AI tools
- High performance semantic search
- Flexible organization and retrieval of text data
Model Context Protocol (MCP) server for semantic vector search and memory management using TxtAI's Main Use Cases & Applications
- AI assistant context management
- Knowledge bases for chatbots
- Research on semantic search applications
- Enterprise memory organization
- AI-powered search engines
FAQs of Model Context Protocol (MCP) server for semantic vector search and memory management using TxtAI
Developer
- rmtech1
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