What is Wisdomforge

WisdomForge is a powerful knowledge management system designed to forge wisdom from experiences, insights, and best practices. It utilizes the Qdrant vector database for efficient knowledge storage and retrieval.

Use cases

Use cases for WisdomForge include creating a centralized knowledge repository for organizations, facilitating learning and development programs, archiving lessons learned from projects, and enhancing decision-making processes through accessible insights.

How to use

To use WisdomForge, clone the repository from GitHub, install the necessary dependencies using npm, configure the environment variables in a .env file, and then build the project. You can deploy it on the Smithery.ai platform.

Key features

Key features include intelligent knowledge management and retrieval, support for multiple knowledge types (best practices, lessons learned, insights, experiences), configurable database selection, efficient embedding generation with Qdrant’s FastEmbed, and domain-specific knowledge storage.

Where to use

WisdomForge can be used in various fields such as education, corporate training, research, and any domain that requires effective knowledge management and sharing.

WisdomForge

A powerful knowledge management system that forges wisdom from experiences, insights, and best practices. Built with Qdrant vector database for efficient knowledge storage and retrieval.

Features

• Intelligent knowledge management and retrieval
• Support for multiple knowledge types (best practices, lessons learned, insights, experiences)
• Configurable database selection via environment variables
• Uses Qdrant’s built-in FastEmbed for efficient embedding generation
• Domain knowledge storage and retrieval
• Deployable to Smithery.ai platform

Prerequisites

• Node.js 20.x or later (LTS recommended)
• npm 10.x or later
• Qdrant or Chroma vector database

Installation

1. Clone the repository:

git clone https://github.com/hadv/wisdomforge
cd wisdomforge

2. Install dependencies:

npm install

3. Create a .env file in the root directory based on the .env.example template:

cp .env.example .env

4. Configure your environment variables in the .env file:

Required Environment Variables

Database Configuration

• DATABASE_TYPE: Choose your vector database (qdrant or chroma)
• COLLECTION_NAME: Name of your vector collection
• QDRANT_URL: URL of your Qdrant instance (required if using Qdrant)
• QDRANT_API_KEY: API key for Qdrant (required if using Qdrant)
• CHROMA_URL: URL of your Chroma instance (required if using Chroma)

Server Configuration

• HTTP_SERVER: Set to true to enable HTTP server mode
• PORT: Port number for local development only (default: 3000). Not used in Smithery cloud deployment.

Example .env configuration for Qdrant:

DATABASE_TYPE=qdrant
COLLECTION_NAME=wisdom_collection
QDRANT_URL=https://your-qdrant-instance.example.com:6333
QDRANT_API_KEY=your_api_key
HTTP_SERVER=true
PORT=3000  # Only needed for local development

5. Build the project:

npm run build

AI IDE Integration

Cursor AI IDE

Add this configuration to your ~/.cursor/mcp.json or .cursor/mcp.json file:

{
  "mcpServers": {
    "wisdomforge": {
      "command": "/bin/zsh",
      "args": [
        "/path/to/wisdomforge/run-wisdomforge-mcp.sh"
      ]
    }
  }
}

Replace the following placeholders in the configuration:

• YOUR_API_KEY: Your Smithery API key
• YOUR_COLLECTION_NAME: Your Qdrant collection name
• YOUR_QDRANT_URL: Your Qdrant instance URL
• YOUR_QDRANT_API_KEY: Your Qdrant API key

Note: Make sure you have Node.js installed and npx available in your PATH. If you’re using nvm, ensure you’re using the correct Node.js version by running nvm use --lts before starting Cursor.

Claude Desktop

Add this configuration in Claude’s settings:

{
  "processes": {
    "knowledge_server": {
      "command": "/path/to/your/project/run-mcp.sh",
      "args": []
    }
  },
  "tools": [
    {
      "name": "store_knowledge",
      "description": "Store domain-specific knowledge in a vector database",
      "provider": "process",
      "process": "knowledge_server"
    },
    {
      "name": "retrieve_knowledge_context",
      "description": "Retrieve relevant domain knowledge from a vector database",
      "provider": "process",
      "process": "knowledge_server"
    }
  ]
}