-
security-
license-
qualityFacilitates knowledge graph representation with semantic search using Qdrant, supporting OpenAI embeddings for semantic similarity and robust HTTPS integration with file-based graph persistence.
Last updated -
7
16
Utilizes .env files for environment variable configuration
Supports cloning the WisdomForge repository from GitHub for installation and setup
Requires Node.js 20.x or later as a prerequisite for running the server
Uses npm for dependency management and build processes
Mentions nvm as an option for ensuring the correct Node.js version when using Cursor AI IDE
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
Clone the repository:
git clone https://github.com/hadv/wisdomforge
cd wisdomforge
Install dependencies:
npm install
Create a
.envfile in the root directory based on the.env.exampletemplate:
cp .env.example .env
Configure your environment variables in the
.envfile:
Required Environment Variables
Database Configuration
DATABASE_TYPE: Choose your vector database (qdrantorchroma)COLLECTION_NAME: Name of your vector collectionQDRANT_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 totrueto enable HTTP server modePORT: 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
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": "npx",
"args": [
"-y",
"@smithery/cli@latest",
"run",
"@hadv/wisdomforge",
"--key",
"YOUR_API_KEY",
"--config",
"{\"database\":{\"type\":\"qdrant\",\"collectionName\":\"YOUR_COLLECTION_NAME\",\"url\":\"YOUR_QDRANT_URL\",\"apiKey\":\"YOUR_QDRANT_API_KEY\"}}",
"--transport",
"ws"
]
}
}
}
Replace the following placeholders in the configuration:
YOUR_API_KEY: Your Smithery API keyYOUR_COLLECTION_NAME: Your Qdrant collection nameYOUR_QDRANT_URL: Your Qdrant instance URLYOUR_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"
}
]
}
This server cannot be installed
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