BookmarkMemory

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

Related MCP server: Local Search MCP Server

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 address

• QDRANT_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.