BookmarkContext

a vector database with simple api and mcp server to find relevant documents to a given context

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README
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.