
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.
What is WisdomForge?
WisdomForge is a powerful knowledge management system designed to forge wisdom from experiences, insights, and best practices, utilizing the Qdrant vector database for efficient knowledge storage and retrieval.
How to use WisdomForge?
To use WisdomForge, clone the repository from GitHub, install the necessary dependencies, configure your environment variables in a .env
file, and then build and start the server. You can deploy it locally or on the Smithery.ai cloud platform.
Key features of WisdomForge?
- 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 FastEmbed for efficient embedding generation
- Domain knowledge storage and retrieval
- Deployable to Smithery.ai platform
Use cases of WisdomForge?
- Storing and retrieving domain-specific knowledge.
- Managing best practices and lessons learned in organizations.
- Integrating with AI IDEs for enhanced knowledge management.
FAQ from WisdomForge?
- What databases does WisdomForge support?
WisdomForge supports Qdrant and Chroma vector databases.
- Is there a cloud deployment option?
Yes, WisdomForge can be deployed on the Smithery.ai cloud platform.
- How do I configure the environment variables?
You can configure the environment variables in the
.env
file based on the provided template.
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
.env
file in the root directory based on the.env.example
template:
cp .env.example .env
- Configure your environment variables in the
.env
file:
Required Environment Variables
Database Configuration
DATABASE_TYPE
: Choose your vector database (qdrant
orchroma
)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 totrue
to 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"
}
]
}
