{"id":28768493,"url":"https://github.com/hadv/wisdomforge","last_synced_at":"2025-08-07T07:57:01.430Z","repository":{"id":284684685,"uuid":"955718585","full_name":"hadv/wisdomforge","owner":"hadv","description":"A powerful knowledge management system that forges wisdom from experiences, insights, and best practices. 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Built with Qdrant vector database for efficient knowledge storage and retrieval.\n\n## Features\n\n- Intelligent knowledge management and retrieval\n- Support for multiple knowledge types (best practices, lessons learned, insights, experiences)\n- Configurable database selection via environment variables\n- Uses Qdrant's built-in FastEmbed for efficient embedding generation\n- Domain knowledge storage and retrieval\n- Deployable to Smithery.ai platform\n\n## Prerequisites\n\n- Node.js 20.x or later (LTS recommended)\n- npm 10.x or later\n- Qdrant or Chroma vector database\n\n## Installation\n\n1. Clone the repository:\n```bash\ngit clone https://github.com/hadv/wisdomforge\ncd wisdomforge\n```\n\n2. Install dependencies:\n```bash\nnpm install\n```\n\n3. Create a `.env` file in the root directory based on the `.env.example` template:\n```bash\ncp .env.example .env\n```\n\n4. Configure your environment variables in the `.env` file:\n\n### Required Environment Variables\n\n#### Database Configuration\n- `DATABASE_TYPE`: Choose your vector database (`qdrant` or `chroma`)\n- `COLLECTION_NAME`: Name of your vector collection\n- `QDRANT_URL`: URL of your Qdrant instance (required if using Qdrant)\n- `QDRANT_API_KEY`: API key for Qdrant (required if using Qdrant)\n- `CHROMA_URL`: URL of your Chroma instance (required if using Chroma)\n\n#### Server Configuration\n- `HTTP_SERVER`: Set to `true` to enable HTTP server mode\n- `PORT`: Port number for local development only (default: 3000). Not used in Smithery cloud deployment.\n\nExample `.env` configuration for Qdrant:\n```env\nDATABASE_TYPE=qdrant\nCOLLECTION_NAME=wisdom_collection\nQDRANT_URL=https://your-qdrant-instance.example.com:6333\nQDRANT_API_KEY=your_api_key\nHTTP_SERVER=true\nPORT=3000  # Only needed for local development\n```\n\n5. Build the project:\n```bash\nnpm run build\n```\n\n## AI IDE Integration\n\n### Cursor AI IDE\nAdd this configuration to your `~/.cursor/mcp.json` or `.cursor/mcp.json` file:\n```json\n{\n  \"mcpServers\": { \n    \"wisdomforge\": {\n      \"command\": \"/bin/zsh\",\n      \"args\": [\n        \"/path/to/wisdomforge/run-wisdomforge-mcp.sh\"\n      ]\n    }\n  }\n}\n```\n\nReplace the following placeholders in the configuration:\n- `YOUR_API_KEY`: Your Smithery API key\n- `YOUR_COLLECTION_NAME`: Your Qdrant collection name\n- `YOUR_QDRANT_URL`: Your Qdrant instance URL\n- `YOUR_QDRANT_API_KEY`: Your Qdrant API key\n\nNote: 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.\n\n### Claude Desktop\nAdd this configuration in Claude's settings:\n```json\n{\n  \"processes\": {\n    \"knowledge_server\": {\n      \"command\": \"/path/to/your/project/run-mcp.sh\",\n      \"args\": []\n    }\n  },\n  \"tools\": [\n    {\n      \"name\": \"store_knowledge\",\n      \"description\": \"Store domain-specific knowledge in a vector database\",\n      \"provider\": \"process\",\n      \"process\": \"knowledge_server\"\n    },\n    {\n      \"name\": \"retrieve_knowledge_context\",\n      \"description\": \"Retrieve relevant domain knowledge from a vector database\",\n      \"provider\": \"process\",\n      \"process\": \"knowledge_server\"\n    }\n  ]\n}\n```\n","funding_links":["https://github.com/sponsors/hadv"],"categories":["Task and Project Management"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhadv%2Fwisdomforge","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhadv%2Fwisdomforge","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhadv%2Fwisdomforge/lists"}