BookmarkMemory

DeeNihl/BookmarkContext
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Install to Claude Code

This server doesn't publish a one-line install command. Follow the setup in the source repository.

Summary

Enables semantic search and retrieval of bookmarked URLs content using vector embeddings, with support for multiple backends and AI assistant integration.

README.md

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

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

Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=bookmark_memory

License

MIT License - See LICENSE file for details.

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