docs: add Smithery badge to README (#19)

Author: prpeh

README.md

@@ -1,5 +1,7 @@
 # WisdomForge
 
+[![smithery badge](https://smithery.ai/badge/@hadv/wisdomforge)](https://smithery.ai/server/@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.
 
 ## Features
@@ -124,292 +126,4 @@ Add this configuration in Claude's settings:
     }
   ]
 }
-```
-
-## Deployment Options
-
-### Option 1: Local Deployment
-Run the MCP server on your local machine or your own infrastructure:
-
-1. Configure your environment variables in `.env` file
-2. Build the project:
-```bash
-npm run build
-```
-3. Start the server:
-```bash
-npm start
-```
-
-The server will run locally and be accessible at `http://localhost:3000` (or your configured PORT).
-
-### Option 2: Smithery.ai Cloud Deployment
-Deploy the MCP server to Smithery's cloud infrastructure:
-
-1. Create an account on [Smithery.ai](https://smithery.ai)
-2. Install the Smithery CLI:
-```bash
-npm install -g @smithery/cli
-```
-
-3. Login to Smithery:
-```bash
-smithery login
-```
-
-4. Create a new instance for your organization:
-```bash
-smithery create instance wisdomforge
-```
-
-5. Configure your environment variables in Smithery dashboard for your instance:
-   - `DATABASE_TYPE` (default: "qdrant")
-   - `COLLECTION_NAME` (required)
-   - `HTTP_SERVER` (default: "true")
-   - `QDRANT_URL` (required if using Qdrant)
-   - `QDRANT_API_KEY` (required if using Qdrant)
-   - `CHROMA_URL` (required if using Chroma)
-   - Note: `PORT` is not needed for cloud deployment as Smithery handles networking
-
-6. Deploy your server:
-```bash
-npm run deploy
-```
-
-### Choosing Between Local and Cloud Deployment
-
-#### Local Deployment is recommended when:
-- You need full control over the infrastructure
-- You have specific security requirements
-- You want to minimize costs
-- You need to run the server behind a firewall
-- You're in development or testing phase
-
-#### Smithery Cloud Deployment is recommended when:
-- You want managed infrastructure
-- You need automatic scaling
-- You want to avoid server maintenance
-- You need high availability
-- You're in production environment
-
-### Instance Management (Cloud Deployment)
-- Each organization/user needs their own Smithery.ai instance
-- Instances are isolated and have their own configuration
-- You can create multiple instances for different environments (dev, staging, prod)
-- Instance URLs follow the pattern: `https://<instance-name>.smithery.ai`
-
-### Post-Deployment
-- Local: Server runs on your machine at `http://localhost:3000`
-- Cloud: Server runs on Smithery at `https://<instance-name>.smithery.ai`
-- Monitor your deployment in the Smithery dashboard (cloud only)
-- Use the health check endpoint at `/health` to verify server status
-
-### Multi-User Setup
-For cloud deployment, if you need to share access with team members:
-1. Add team members to your Smithery.ai organization
-2. Grant them appropriate permissions for your instance
-3. Each team member can use the same instance URL but should configure their own:
-   - Database credentials
-   - Collection names
-   - Other environment-specific settings
-
-For local deployment:
-- Each user runs their own instance
-- Users need to configure their own environment variables
-- No shared access is possible unless you set up your own infrastructure
-
-## MCP Client Connection
-
-### Local Deployment Connection
-When running the server locally, MCP clients (like Cursor or Claude) can connect using the following configuration:
-
-#### Cursor AI IDE
-Update your `~/.cursor/mcp.json` or `.cursor/mcp.json`:
-```json
-{
-  "mcpServers": {
-    "wisdomforge": {
-      "url": "http://localhost:3000",
-      "type": "http"
-    }
-  }
-}
-```
-
-#### Claude Desktop
-Update your Claude settings:
-```json
-{
-  "processes": {
-    "knowledge_server": {
-      "url": "http://localhost:3000",
-      "type": "http"
-    }
-  },
-  "tools": [
-    {
-      "name": "store_knowledge",
-      "description": "Store domain-specific knowledge in a vector database",
-      "provider": "http",
-      "url": "http://localhost:3000"
-    },
-    {
-      "name": "retrieve_knowledge_context",
-      "description": "Retrieve relevant domain knowledge from a vector database",
-      "provider": "http",
-      "url": "http://localhost:3000"
-    }
-  ]
-}
-```
-
-### Connection Notes
-1. For local deployment:
-   - No authentication required
-   - Server must be running before clients can connect
-   - Use `http://localhost:3000` or your configured PORT
-   - HTTP server is enabled by default
-
-2. For cloud deployment:
-   - Authentication required via Smithery API key
-   - Server is always available
-   - Use `https://<instance-name>.smithery.ai`
-   - HTTP server is enabled by default
-   - Replace `<instance-name>` with your actual instance name
-   - Replace `<your-smithery-api-key>` with your actual Smithery API key
-
-3. Testing the Connection:
-   - Use the health check endpoint: `GET /health`
-   - Local: `http://localhost:3000/health`
-   - Cloud: `https://<instance-name>.smithery.ai/health`
-
-## Usage
-
-### Starting the Server
-
-```bash
-npm start
-```
-
-For development with auto-reload:
-```bash
-npm run dev
-```
-
-### Storing Documentation
-
-The server includes a script to store documentation files (PDF and TXT) with metadata:
-
-```bash
-npm run store-doc <path-to-your-file>
-```
-
-Example:
-```bash
-# Store a PDF file
-npm run store-doc docs/manual.pdf
-
-# Store a text file
-npm run store-doc docs/readme.txt
-```
-
-The script will:
-- Extract content from the file (text from PDF or plain text)
-- Store the content with metadata including:
-  - Source: "documentation"
-  - File name and extension
-  - File size
-  - Last modified date
-  - Creation date
-  - Content type
-
-### API Endpoints
-
-#### Store Domain Knowledge
-
-```http
-POST /api/store
-Content-Type: application/json
-
-{
-  "content": "Your domain knowledge content here",
-  "source": "your-source",
-  "metadata": {
-    "key": "value"
-  }
-}
-```
-
-#### Query Domain Knowledge
-
-```http
-POST /api/query
-Content-Type: application/json
-
-{
-  "query": "Your search query here",
-  "limit": 5
-}
-```
-
-## Development
-
-### Running Tests
-
-```bash
-npm test
-```
-
-### Building the Project
-
-```bash
-npm run build
-```
-
-### Linting
-
-```bash
-npm run lint
-```
-
-
-## Using with Remote Qdrant
-
-When using with a remote Qdrant instance (like Qdrant Cloud):
-
-1. Ensure your `.env` has the correct URL with port number:
-```
-QDRANT_URL=https://your-instance-id.region.gcp.cloud.qdrant.io:6333
-```
-
-2. Set your API key:
-```
-QDRANT_API_KEY=your_qdrant_api_key
-```
-
-## FastEmbed Integration
-
-This project uses Qdrant's built-in FastEmbed for efficient embedding generation:
-
-### Benefits
-- Lightweight and fast embedding generation
-- Uses quantized model weights and ONNX Runtime for inference
-- Better accuracy than OpenAI Ada-002 according to Qdrant
-- No need for external embedding API keys
-
-### How It Works
-1. The system connects to your Qdrant instance
-2. When generating embeddings, it uses Qdrant's server-side embedding endpoint
-3. This eliminates the need for external embedding APIs and simplifies the architecture
-
-### Configuration
-No additional configuration is needed as FastEmbed is built into Qdrant. Just ensure your Qdrant URL and API key are correctly set in your `.env` file.
-
-## Troubleshooting
-
-If you encounter issues:
-
-1. Make sure you're using Node.js LTS version (`nvm use --lts`)
-2. Verify your environment variables are correct
-3. Check Qdrant/Chroma connectivity
-4. Ensure your Qdrant instance is properly configured
+```