https://github.com/hadv/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.
https://github.com/hadv/wisdomforge
mcp mcp-server modelcontextprotocol qdrant qdrant-vector-database rag
Last synced: 2 months ago
JSON representation
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.
- Host: GitHub
- URL: https://github.com/hadv/wisdomforge
- Owner: hadv
- License: mit
- Created: 2025-03-27T04:51:04.000Z (6 months ago)
- Default Branch: master
- Last Pushed: 2025-06-07T10:07:08.000Z (4 months ago)
- Last Synced: 2025-06-17T13:09:40.251Z (4 months ago)
- Topics: mcp, mcp-server, modelcontextprotocol, qdrant, qdrant-vector-database, rag
- Language: TypeScript
- Homepage:
- Size: 197 KB
- Stars: 3
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
- mcp-index - WisdomForge - Manage and retrieve knowledge efficiently using a vector database. Capable of intelligent storage and retrieval of various knowledge types, including best practices, lessons learned, and insights. (Task and Project Management)
README
# 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:
```bash
git clone https://github.com/hadv/wisdomforge
cd wisdomforge
```
2. Install dependencies:
```bash
npm install
```
3. Create a `.env` file in the root directory based on the `.env.example` template:
```bash
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:
```env
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:
```bash
npm run build
```
## AI IDE Integration
### Cursor AI IDE
Add this configuration to your `~/.cursor/mcp.json` or `.cursor/mcp.json` file:
```json
{
"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:
```json
{
"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"
}
]
}
```