BookmarkMemory
БесплатноНе проверенEnables semantic search and retrieval of bookmarked URLs content using vector embeddings, with support for multiple backends and AI assistant integration.
Описание
Enables semantic search and retrieval of bookmarked URLs content using vector embeddings, with support for multiple backends and AI assistant integration.
README
A Python-based semantic search system for bookmarks that enables intelligent querying of URL contents through vector embeddings and semantic chunking.
Features
- 🔍 Semantic Search: Find bookmarks based on meaning, not just keywords
- 🧩 Smart Chunking: Intelligently splits content into meaningful segments
- 🚀 Multiple Backends: Support for Qdrant Cloud, local containers, or auto-start
- 🌐 FastAPI Server: RESTful API with auto-generated documentation
- 🤖 MCP Integration: FastMCP server for AI assistant integration
- 📊 Flexible Embeddings: Support for multiple embedding models
Quick Start
Installation
# Clone the repository
git clone file:///c:/temp/BookmarkMemory
cd BookmarkMemory
# Install dependencies
pip install -r requirements.txt
pip install -e .
Basic Usage
from bookmark_memory import BookmarkMemory
# Initialize
bm = BookmarkMemory()
# Add bookmarks
bm.add_bookmarks([
"https://example.com/article1",
"https://example.com/article2"
])
# Search
results = bm.find_related_bookmarks("machine learning")
for result in results:
print(f"{result['url']} - Score: {result['relevance_score']:.3f}")
API Server
# Start the FastAPI server
uvicorn bookmark_memory.api.fastapi_app:app --reload
# Visit http://localhost:8000/docs for API documentation
MCP Server
Add to your Claude Desktop configuration:
{
"mcpServers": {
"bookmark-memory": {
"command": "python",
"args": ["-m", "bookmark_memory.mcp.mcp_server"],
"env": {
"QDRANT_MODE": "auto"
}
}
}
}
Configuration
Environment Variables
QDRANT_MODE: Connection mode (auto, cloud, local)QDRANT_HOST: Qdrant host addressQDRANT_PORT: Qdrant port (default: 6333)EMBEDDING_MODEL: Model for embeddings (default: sentence-transformers/all-MiniLM-L6-v2)
See config/settings.py for all configuration options.
Documentation
- API Documentation (when server is running)
- Project Requirements
- Examples
Testing
# Run all tests
pytest
# Run with coverage
pytest --cov=bookmark_memory
License
MIT License - See LICENSE file for details.
Установка BookmarkMemory
У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.
▸ github.com/DeeNihl/BookmarkContextFAQ
BookmarkMemory MCP бесплатный?
Да, BookmarkMemory MCP бесплатный — установка в пару кликов через Unyly без оплаты.
Нужен ли API-ключ для BookmarkMemory?
Нет, BookmarkMemory работает без API-ключей и переменных окружения.
BookmarkMemory — hosted или self-hosted?
Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.
Как установить BookmarkMemory в Claude Desktop, Claude Code или Cursor?
Открой BookmarkMemory на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.
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