
aleph-10
Vector Memory MCP Server - An MCP server with vector-based memory storage capabilities
3 years
Works with Finder
1
Github Watches
0
Github Forks
0
Github Stars
Aleph-10: Vector Memory MCP Server
Aleph-10 is a Model Context Protocol (MCP) server that combines weather data services with vector-based memory storage. This project provides tools for retrieving weather information and managing semantic memory through vector embeddings.
Features
- Weather Information: Get weather alerts and forecasts using the National Weather Service API
- Vector Memory: Store and retrieve information using semantic search
- Multiple Embedding Options: Support for both cloud-based (Google Gemini) and local (Ollama) embedding providers
- Metadata Support: Add and filter by metadata for efficient memory management
Getting Started
Prerequisites
- Node.js 18.x or higher
- pnpm package manager
Installation
- Clone the repository
git clone https://github.com/yourusername/aleph-10.git
cd aleph-10
- Install dependencies
pnpm install
- Configure environment variables (create a
.env
file in the project root)
EMBEDDING_PROVIDER=gemini
GEMINI_API_KEY=your_gemini_api_key
VECTOR_DB_PATH=./data/vector_db
LOG_LEVEL=info
- Build the project
pnpm build
- Run the server
node build/index.js
Usage
The server implements the Model Context Protocol and provides the following tools:
Weather Tools
-
get-alerts: Get weather alerts for a specific US state
- Parameters:
state
(two-letter state code)
- Parameters:
-
get-forecast: Get weather forecast for a location
- Parameters:
latitude
andlongitude
- Parameters:
Memory Tools
-
memory-store: Store information in the vector database
- Parameters:
text
(content to store),metadata
(optional associated data)
- Parameters:
-
memory-retrieve: Find semantically similar information
- Parameters:
query
(search text),limit
(max results),filters
(metadata filters)
- Parameters:
-
memory-update: Update existing memory entries
- Parameters:
id
(memory ID),text
(new content),metadata
(updated metadata)
- Parameters:
-
memory-delete: Remove entries from the database
- Parameters:
id
(memory ID to delete)
- Parameters:
-
memory-stats: Get statistics about the memory store
- Parameters: none
Configuration
The following environment variables can be configured:
Variable | Description | Default |
---|---|---|
EMBEDDING_PROVIDER |
Provider for vector embeddings (gemini or ollama ) |
gemini |
GEMINI_API_KEY |
API key for Google Gemini | - |
OLLAMA_BASE_URL |
Base URL for Ollama API | http://localhost:11434 |
VECTOR_DB_PATH |
Storage location for vector database | ./data/vector_db |
LOG_LEVEL |
Logging verbosity | info |
Development
Project Structure
The project follows a modular structure:
aleph-10/
├── src/ # Source code
│ ├── index.ts # Main application entry point
│ ├── weather/ # Weather service module
│ ├── memory/ # Memory management module
│ ├── utils/ # Shared utilities
│ └── types/ # TypeScript type definitions
├── tests/ # Test files
└── vitest.config.ts # Vitest configuration
Running Tests
The project uses Vitest for testing. Run tests with:
# Run tests once
pnpm test
# Run tests in watch mode during development
pnpm test:watch
# Run tests with UI (optional)
pnpm test:ui
Building
pnpm build
License
This project is licensed under the ISC License.
Acknowledgments
- Model Context Protocol
- National Weather Service API
- Vitest - Next generation testing framework
相关推荐
Converts Figma frames into front-end code for various mobile frameworks.
I find academic articles and books for research and literature reviews.
Confidential guide on numerology and astrology, based of GG33 Public information
Embark on a thrilling diplomatic quest across a galaxy on the brink of war. Navigate complex politics and alien cultures to forge peace and avert catastrophe in this immersive interstellar adventure.
Advanced software engineer GPT that excels through nailing the basics.
Delivers concise Python code and interprets non-English comments
💬 MaxKB is a ready-to-use AI chatbot that integrates Retrieval-Augmented Generation (RAG) pipelines, supports robust workflows, and provides advanced MCP tool-use capabilities.
Micropython I2C-based manipulation of the MCP series GPIO expander, derived from Adafruit_MCP230xx
The all-in-one Desktop & Docker AI application with built-in RAG, AI agents, No-code agent builder, MCP compatibility, and more.
MCP server to provide Figma layout information to AI coding agents like Cursor
Reviews

user_bPxALz6H
As a loyal user of MCP applications, I highly recommend Aleph-10 by bjkemp. This tool is incredibly efficient and user-friendly, significantly streamlining my workflow. The comprehensive documentation available on GitHub ensures a smooth start and helps you unlock the full potential of Aleph-10 effortlessly. Whether you're a newbie or a pro, this application is a game-changer in the MCP space. Check it out at https://github.com/bjkemp/aleph-10.