# d-vecDB Server Python Package
[](https://badge.fury.io/py/d-vecdb-server)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
A Python package that provides the d-vecDB server with embedded pre-built binaries. This package allows you to run the high-performance d-vecDB vector database server directly from Python with a single `pip install` command.
## Prerequisites
- **Python**: 3.8 or higher
- **Operating System**: Linux (x86_64), macOS (Intel/Apple Silicon), or Windows (x86_64)
- **Memory**: Minimum 512MB RAM available
- **Disk Space**: At least 100MB for binaries and data storage
- **Network**: Available ports for REST API (default: 8080) and gRPC (default: 9090)
## Installation Options
### Option 1: Standard Installation (Recommended)
```bash
# Install the server package
pip install d-vecdb-server
# Verify installation
d-vecdb-server version
```
> **Note**: The package includes embedded binaries and is ready to use immediately after installation.
### Option 2: Development Installation
```bash
# Clone the repository
git clone https://github.com/rdmurugan/d-vecDB.git
cd d-vecDB/d-vecdb-server-python
# Install in development mode
pip install -e .
# Verify installation
d-vecdb-server version
```
### Option 3: Virtual Environment Installation (Recommended for Development)
```bash
# Create virtual environment
python -m venv d-vecdb-env
# Activate virtual environment
# On Linux/macOS:
source d-vecdb-env/bin/activate
# On Windows:
d-vecdb-env\Scripts\activate
# Install package
pip install d-vecdb-server
# Verify installation
d-vecdb-server version
```
### Option 4: Install with Python Client
```bash
# Install server with Python client for complete functionality
pip install 'd-vecdb-server[client]'
# Or install separately
pip install d-vecdb-server
pip install d-vecdb # Python client library
```
## Quick Start
### Command Line Usage
```bash
# Show version (includes binary status)
d-vecdb-server version
# Start the server (foreground)
d-vecdb-server start
# Start in background
d-vecdb-server start --daemon
# Start with custom settings
d-vecdb-server start --host 0.0.0.0 --port 8081 --data-dir ./my-data
# Stop the server
d-vecdb-server stop
# Check server status
d-vecdb-server status
```
### Python API
```python
from d_vecdb_server import DVecDBServer
# Create and start server
server = DVecDBServer(
host="127.0.0.1",
port=8080,
data_dir="./vector-data"
)
# Start the server
server.start()
print(f"Server running: {server.is_running()}")
print(f"REST API: http://{server.host}:{server.port}")
print(f"gRPC API: {server.host}:{server.grpc_port}")
# Stop the server
server.stop()
```
### Context Manager
```python
from d_vecdb_server import DVecDBServer
# Automatically start and stop server
with DVecDBServer(port=8080) as server:
print(f"Server is running on port {server.port}")
# Server will be automatically stopped when exiting the context
```
## Configuration
### Step-by-Step Server Configuration
#### Step 1: Choose Your Configuration Method
You can configure the server in three ways:
1. **Command line arguments** (quick setup)
2. **Python API parameters** (programmatic setup)
3. **Configuration file** (persistent setup)
#### Step 2: Basic Configuration
**Default Configuration:**
- **Host**: 127.0.0.1 (localhost only)
- **REST Port**: 8080
- **gRPC Port**: 9090
- **Data Directory**: Temporary directory (auto-generated)
- **Log Level**: info
#### Step 3: Create Data Directory (Optional)
```bash
# Create persistent data directory
mkdir -p /path/to/your/vector-data
chmod 755 /path/to/your/vector-data
```
#### Step 4: Configuration Options
**Option A: Command Line Configuration**
```bash
# Basic configuration
d-vecdb-server start --host 0.0.0.0 --port 8081 --data-dir ./data
# Advanced configuration
d-vecdb-server start \
--host 0.0.0.0 \
--port 8081 \
--grpc-port 9091 \
--data-dir /path/to/data \
--log-level debug \
--daemon
```
**Option B: Python API Configuration**
```python
from d_vecdb_server import DVecDBServer
# Basic configuration
server = DVecDBServer(
host="0.0.0.0", # Listen on all interfaces
port=8081, # Custom REST port
grpc_port=9091, # Custom gRPC port
data_dir="/path/to/data", # Persistent data directory
log_level="debug" # Verbose logging
)
server.start()
```
**Option C: Configuration File Setup**
1. Create a configuration file:
```bash
# Create config directory
mkdir -p ~/.config/d-vecdb
# Create configuration file
cat > ~/.config/d-vecdb/server.toml << EOF
[server]
host = "0.0.0.0"
port = 8080
grpc_port = 9090
workers = 8
[storage]
data_dir = "/path/to/your/data"
wal_sync_interval = "1s"
memory_map_size = "1GB"
[index]
hnsw_max_connections = 32
hnsw_ef_construction = 400
hnsw_max_layer = 16
[monitoring]
enable_metrics = true
prometheus_port = 9091
log_level = "info"
EOF
```
2. Use the configuration file:
```bash
# Start with configuration file
d-vecdb-server start --config ~/.config/d-vecdb/server.toml
```
#### Step 5: Verify Configuration
```bash
# Check server status
d-vecdb-server status
# Test REST API endpoint
curl http://localhost:8080/health
# Check metrics (if enabled)
curl http://localhost:9091/metrics
```
#### Step 6: Production Configuration Tips
```toml
# Production-ready configuration example
[server]
host = "0.0.0.0" # Accept connections from any IP
port = 8080 # Standard HTTP port
grpc_port = 9090 # Standard gRPC port
workers = 16 # Match CPU cores
[storage]
data_dir = "/var/lib/d-vecdb" # Persistent storage location
wal_sync_interval = "5s" # Longer interval for better performance
memory_map_size = "8GB" # More memory for larger datasets
[index]
hnsw_max_connections = 64 # Higher connections for better recall
hnsw_ef_construction = 800 # Higher construction for better quality
hnsw_max_layer = 16 # Default is usually fine
[monitoring]
enable_metrics = true
prometheus_port = 9091
log_level = "warn" # Less verbose for production
```
## API Reference
### DVecDBServer Class
#### Constructor
```python
DVecDBServer(
host: str = "127.0.0.1",
port: int = 8080,
grpc_port: int = 9090,
data_dir: Optional[str] = None,
log_level: str = "info",
config_file: Optional[str] = None
)
```
#### Methods
- **`start(background: bool = True, timeout: int = 30) -> bool`**
- Start the server process
- Returns `True` if successful
- **`stop(timeout: int = 10) -> bool`**
- Stop the server process
- Returns `True` if successful
- **`restart(timeout: int = 30) -> bool`**
- Restart the server
- Returns `True` if successful
- **`is_running() -> bool`**
- Check if server is running
- Returns `True` if running
- **`get_status() -> Dict[str, Any]`**
- Get detailed server status
- Returns status dictionary
### Command Line Interface
```bash
d-vecdb-server [OPTIONS] COMMAND
Commands:
start Start the server
stop Stop the server
status Check server status
version Show version information
Options:
--host HOST Server host (default: 127.0.0.1)
--port PORT REST API port (default: 8080)
--grpc-port PORT gRPC port (default: 9090)
--data-dir DIR Data directory
--config FILE Configuration file
--log-level LEVEL Log level (debug/info/warn/error)
Start Options:
--daemon Run in background
```
## Platform Support
- **Linux**: x86_64 (with musl for better compatibility)
- **macOS**: Intel (x86_64) and Apple Silicon (ARM64)
- **Windows**: x86_64
## Advanced Setup
### Environment Variables
You can also configure the server using environment variables:
```bash
# Set environment variables
export DVECDB_HOST="0.0.0.0"
export DVECDB_PORT="8080"
export DVECDB_GRPC_PORT="9090"
export DVECDB_DATA_DIR="/var/lib/d-vecdb"
export DVECDB_LOG_LEVEL="info"
export RUST_LOG="info"
# Start server (will use environment variables)
d-vecdb-server start
```
### Docker Setup (Alternative)
If you prefer Docker deployment:
```bash
# Pull the Docker image
docker pull rdmurugan/d-vecdb:latest
# Run with custom configuration
docker run -d \
--name d-vecdb-server \
-p 8080:8080 \
-p 9090:9090 \
-v /path/to/data:/data \
rdmurugan/d-vecdb:latest
```
### Service Configuration (Linux)
Create a systemd service for automatic startup:
```bash
# Create service file
sudo cat > /etc/systemd/system/d-vecdb.service << EOF
[Unit]
Description=d-vecDB Vector Database Server
After=network.target
[Service]
Type=simple
User=d-vecdb
Group=d-vecdb
WorkingDirectory=/var/lib/d-vecdb
ExecStart=/usr/local/bin/d-vecdb-server start --config /etc/d-vecdb/server.toml
Restart=always
RestartSec=5
[Install]
WantedBy=multi-user.target
EOF
# Enable and start service
sudo systemctl enable d-vecdb
sudo systemctl start d-vecdb
sudo systemctl status d-vecdb
```
## Troubleshooting
### Installation Issues
**Binary Not Found (should not happen with pip install):**
```bash
# Check if binary is available
d-vecdb-server version
# If you still get binary not found errors:
# 1. Reinstall the package
pip uninstall d-vecdb-server
pip install d-vecdb-server
# 2. For development installations:
pip install -e . --force-reinstall
```
**Permission Denied:**
```bash
# Install in user directory
pip install --user d-vecdb-server
# Or use virtual environment
python -m venv venv && source venv/bin/activate
pip install d-vecdb-server
```
### Runtime Issues
**Port Already in Use:**
```bash
# Check what's using the port
lsof -i :8080 # On Linux/macOS
netstat -ano | findstr :8080 # On Windows
# Use different port
d-vecdb-server start --port 8081
```
**Server Won't Start:**
```bash
# Check server logs
d-vecdb-server start # Run in foreground to see errors
# Check disk space
df -h # On Linux/macOS
# Check permissions on data directory
ls -la /path/to/data/directory
```
**Connection Refused:**
```bash
# Verify server is running
d-vecdb-server status
# Check if ports are accessible
telnet localhost 8080
# For remote connections, ensure host is set to 0.0.0.0
d-vecdb-server start --host 0.0.0.0
```
### Performance Issues
**Slow Performance:**
```toml
# Optimize configuration for better performance
[storage]
memory_map_size = "4GB" # Increase based on available RAM
[index]
hnsw_max_connections = 64
hnsw_ef_construction = 800
[server]
workers = 16 # Match your CPU cores
```
**High Memory Usage:**
```toml
# Reduce memory usage
[storage]
memory_map_size = "512MB" # Reduce if needed
[index]
hnsw_max_connections = 16
hnsw_ef_construction = 200
```
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Using with Python Client
After installing the server, you can use it with the Python client:
```bash
# Install the Python client
pip install d-vecdb
```
```python
from d_vecdb_server import DVecDBServer
from d_vecdb import VectorDBClient
import numpy as np
# Start the server
with DVecDBServer(port=8080) as server:
# Connect client
client = VectorDBClient(host=server.host, port=server.port)
# Create collection
client.create_collection_simple("documents", 128, "cosine")
# Insert vectors
vector = np.random.random(128)
client.insert_simple("documents", "doc1", vector)
# Search
query = np.random.random(128)
results = client.search_simple("documents", query, limit=5)
print(f"Found {len(results)} similar vectors")
```
## Next Steps
After installation and configuration:
1. **Start using the REST API**: Visit `http://localhost:8080/docs` for API documentation
2. **Use Python client**: See example above for Python integration
3. **Check examples**: See the main repository for usage examples
4. **Join community**: Report issues and get support
## Links
- **Main Repository**: https://github.com/rdmurugan/d-vecDB
- **Python Client**: https://pypi.org/project/d-vecdb/
- **Docker Hub**: https://hub.docker.com/r/rdmurugan/d-vecdb
- **Issues & Support**: https://github.com/rdmurugan/d-vecDB/issues
- **Documentation**: https://github.com/rdmurugan/d-vecDB#readme
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"description": "# d-vecDB Server Python Package\n\n[](https://badge.fury.io/py/d-vecdb-server)\n[](https://www.python.org/downloads/)\n[](https://opensource.org/licenses/MIT)\n\nA Python package that provides the d-vecDB server with embedded pre-built binaries. This package allows you to run the high-performance d-vecDB vector database server directly from Python with a single `pip install` command.\n\n## Prerequisites\n\n- **Python**: 3.8 or higher\n- **Operating System**: Linux (x86_64), macOS (Intel/Apple Silicon), or Windows (x86_64)\n- **Memory**: Minimum 512MB RAM available\n- **Disk Space**: At least 100MB for binaries and data storage\n- **Network**: Available ports for REST API (default: 8080) and gRPC (default: 9090)\n\n## Installation Options\n\n### Option 1: Standard Installation (Recommended)\n\n```bash\n# Install the server package\npip install d-vecdb-server\n\n# Verify installation\nd-vecdb-server version\n```\n\n> **Note**: The package includes embedded binaries and is ready to use immediately after installation.\n\n### Option 2: Development Installation\n\n```bash\n# Clone the repository\ngit clone https://github.com/rdmurugan/d-vecDB.git\ncd d-vecDB/d-vecdb-server-python\n\n# Install in development mode\npip install -e .\n\n# Verify installation\nd-vecdb-server version\n```\n\n### Option 3: Virtual Environment Installation (Recommended for Development)\n\n```bash\n# Create virtual environment\npython -m venv d-vecdb-env\n\n# Activate virtual environment\n# On Linux/macOS:\nsource d-vecdb-env/bin/activate\n# On Windows:\nd-vecdb-env\\Scripts\\activate\n\n# Install package\npip install d-vecdb-server\n\n# Verify installation\nd-vecdb-server version\n```\n\n### Option 4: Install with Python Client\n\n```bash\n# Install server with Python client for complete functionality \npip install 'd-vecdb-server[client]'\n\n# Or install separately\npip install d-vecdb-server\npip install d-vecdb # Python client library\n```\n\n## Quick Start\n\n### Command Line Usage\n\n```bash\n# Show version (includes binary status)\nd-vecdb-server version\n\n# Start the server (foreground)\nd-vecdb-server start\n\n# Start in background\nd-vecdb-server start --daemon\n\n# Start with custom settings\nd-vecdb-server start --host 0.0.0.0 --port 8081 --data-dir ./my-data\n\n# Stop the server\nd-vecdb-server stop\n\n# Check server status\nd-vecdb-server status\n```\n\n### Python API\n\n```python\nfrom d_vecdb_server import DVecDBServer\n\n# Create and start server\nserver = DVecDBServer(\n host=\"127.0.0.1\",\n port=8080,\n data_dir=\"./vector-data\"\n)\n\n# Start the server\nserver.start()\n\nprint(f\"Server running: {server.is_running()}\")\nprint(f\"REST API: http://{server.host}:{server.port}\")\nprint(f\"gRPC API: {server.host}:{server.grpc_port}\")\n\n# Stop the server\nserver.stop()\n```\n\n### Context Manager\n\n```python\nfrom d_vecdb_server import DVecDBServer\n\n# Automatically start and stop server\nwith DVecDBServer(port=8080) as server:\n print(f\"Server is running on port {server.port}\")\n # Server will be automatically stopped when exiting the context\n```\n\n## Configuration\n\n### Step-by-Step Server Configuration\n\n#### Step 1: Choose Your Configuration Method\n\nYou can configure the server in three ways:\n1. **Command line arguments** (quick setup)\n2. **Python API parameters** (programmatic setup)\n3. **Configuration file** (persistent setup)\n\n#### Step 2: Basic Configuration\n\n**Default Configuration:**\n- **Host**: 127.0.0.1 (localhost only)\n- **REST Port**: 8080\n- **gRPC Port**: 9090 \n- **Data Directory**: Temporary directory (auto-generated)\n- **Log Level**: info\n\n#### Step 3: Create Data Directory (Optional)\n\n```bash\n# Create persistent data directory\nmkdir -p /path/to/your/vector-data\nchmod 755 /path/to/your/vector-data\n```\n\n#### Step 4: Configuration Options\n\n**Option A: Command Line Configuration**\n\n```bash\n# Basic configuration\nd-vecdb-server start --host 0.0.0.0 --port 8081 --data-dir ./data\n\n# Advanced configuration\nd-vecdb-server start \\\n --host 0.0.0.0 \\\n --port 8081 \\\n --grpc-port 9091 \\\n --data-dir /path/to/data \\\n --log-level debug \\\n --daemon\n```\n\n**Option B: Python API Configuration**\n\n```python\nfrom d_vecdb_server import DVecDBServer\n\n# Basic configuration\nserver = DVecDBServer(\n host=\"0.0.0.0\", # Listen on all interfaces\n port=8081, # Custom REST port\n grpc_port=9091, # Custom gRPC port\n data_dir=\"/path/to/data\", # Persistent data directory\n log_level=\"debug\" # Verbose logging\n)\n\nserver.start()\n```\n\n**Option C: Configuration File Setup**\n\n1. Create a configuration file:\n\n```bash\n# Create config directory\nmkdir -p ~/.config/d-vecdb\n\n# Create configuration file\ncat > ~/.config/d-vecdb/server.toml << EOF\n[server]\nhost = \"0.0.0.0\"\nport = 8080\ngrpc_port = 9090\nworkers = 8\n\n[storage]\ndata_dir = \"/path/to/your/data\"\nwal_sync_interval = \"1s\"\nmemory_map_size = \"1GB\"\n\n[index]\nhnsw_max_connections = 32\nhnsw_ef_construction = 400\nhnsw_max_layer = 16\n\n[monitoring]\nenable_metrics = true\nprometheus_port = 9091\nlog_level = \"info\"\nEOF\n```\n\n2. Use the configuration file:\n\n```bash\n# Start with configuration file\nd-vecdb-server start --config ~/.config/d-vecdb/server.toml\n```\n\n#### Step 5: Verify Configuration\n\n```bash\n# Check server status\nd-vecdb-server status\n\n# Test REST API endpoint\ncurl http://localhost:8080/health\n\n# Check metrics (if enabled)\ncurl http://localhost:9091/metrics\n```\n\n#### Step 6: Production Configuration Tips\n\n```toml\n# Production-ready configuration example\n[server]\nhost = \"0.0.0.0\" # Accept connections from any IP\nport = 8080 # Standard HTTP port\ngrpc_port = 9090 # Standard gRPC port\nworkers = 16 # Match CPU cores\n\n[storage]\ndata_dir = \"/var/lib/d-vecdb\" # Persistent storage location\nwal_sync_interval = \"5s\" # Longer interval for better performance\nmemory_map_size = \"8GB\" # More memory for larger datasets\n\n[index]\nhnsw_max_connections = 64 # Higher connections for better recall\nhnsw_ef_construction = 800 # Higher construction for better quality\nhnsw_max_layer = 16 # Default is usually fine\n\n[monitoring]\nenable_metrics = true\nprometheus_port = 9091\nlog_level = \"warn\" # Less verbose for production\n```\n\n## API Reference\n\n### DVecDBServer Class\n\n#### Constructor\n\n```python\nDVecDBServer(\n host: str = \"127.0.0.1\",\n port: int = 8080,\n grpc_port: int = 9090,\n data_dir: Optional[str] = None,\n log_level: str = \"info\",\n config_file: Optional[str] = None\n)\n```\n\n#### Methods\n\n- **`start(background: bool = True, timeout: int = 30) -> bool`**\n - Start the server process\n - Returns `True` if successful\n\n- **`stop(timeout: int = 10) -> bool`**\n - Stop the server process\n - Returns `True` if successful\n\n- **`restart(timeout: int = 30) -> bool`**\n - Restart the server\n - Returns `True` if successful\n\n- **`is_running() -> bool`**\n - Check if server is running\n - Returns `True` if running\n\n- **`get_status() -> Dict[str, Any]`**\n - Get detailed server status\n - Returns status dictionary\n\n### Command Line Interface\n\n```bash\nd-vecdb-server [OPTIONS] COMMAND\n\nCommands:\n start Start the server\n stop Stop the server \n status Check server status\n version Show version information\n\nOptions:\n --host HOST Server host (default: 127.0.0.1)\n --port PORT REST API port (default: 8080)\n --grpc-port PORT gRPC port (default: 9090)\n --data-dir DIR Data directory\n --config FILE Configuration file\n --log-level LEVEL Log level (debug/info/warn/error)\n\nStart Options:\n --daemon Run in background\n```\n\n## Platform Support\n\n- **Linux**: x86_64 (with musl for better compatibility)\n- **macOS**: Intel (x86_64) and Apple Silicon (ARM64)\n- **Windows**: x86_64\n\n## Advanced Setup\n\n### Environment Variables\n\nYou can also configure the server using environment variables:\n\n```bash\n# Set environment variables\nexport DVECDB_HOST=\"0.0.0.0\"\nexport DVECDB_PORT=\"8080\"\nexport DVECDB_GRPC_PORT=\"9090\"\nexport DVECDB_DATA_DIR=\"/var/lib/d-vecdb\"\nexport DVECDB_LOG_LEVEL=\"info\"\nexport RUST_LOG=\"info\"\n\n# Start server (will use environment variables)\nd-vecdb-server start\n```\n\n### Docker Setup (Alternative)\n\nIf you prefer Docker deployment:\n\n```bash\n# Pull the Docker image\ndocker pull rdmurugan/d-vecdb:latest\n\n# Run with custom configuration\ndocker run -d \\\n --name d-vecdb-server \\\n -p 8080:8080 \\\n -p 9090:9090 \\\n -v /path/to/data:/data \\\n rdmurugan/d-vecdb:latest\n```\n\n### Service Configuration (Linux)\n\nCreate a systemd service for automatic startup:\n\n```bash\n# Create service file\nsudo cat > /etc/systemd/system/d-vecdb.service << EOF\n[Unit]\nDescription=d-vecDB Vector Database Server\nAfter=network.target\n\n[Service]\nType=simple\nUser=d-vecdb\nGroup=d-vecdb\nWorkingDirectory=/var/lib/d-vecdb\nExecStart=/usr/local/bin/d-vecdb-server start --config /etc/d-vecdb/server.toml\nRestart=always\nRestartSec=5\n\n[Install]\nWantedBy=multi-user.target\nEOF\n\n# Enable and start service\nsudo systemctl enable d-vecdb\nsudo systemctl start d-vecdb\nsudo systemctl status d-vecdb\n```\n\n## Troubleshooting\n\n### Installation Issues\n\n**Binary Not Found (should not happen with pip install):**\n```bash\n# Check if binary is available\nd-vecdb-server version\n\n# If you still get binary not found errors:\n# 1. Reinstall the package\npip uninstall d-vecdb-server\npip install d-vecdb-server\n\n# 2. For development installations:\npip install -e . --force-reinstall\n```\n\n**Permission Denied:**\n```bash\n# Install in user directory\npip install --user d-vecdb-server\n\n# Or use virtual environment\npython -m venv venv && source venv/bin/activate\npip install d-vecdb-server\n```\n\n### Runtime Issues\n\n**Port Already in Use:**\n```bash\n# Check what's using the port\nlsof -i :8080 # On Linux/macOS\nnetstat -ano | findstr :8080 # On Windows\n\n# Use different port\nd-vecdb-server start --port 8081\n```\n\n**Server Won't Start:**\n```bash\n# Check server logs\nd-vecdb-server start # Run in foreground to see errors\n\n# Check disk space\ndf -h # On Linux/macOS\n\n# Check permissions on data directory\nls -la /path/to/data/directory\n```\n\n**Connection Refused:**\n```bash\n# Verify server is running\nd-vecdb-server status\n\n# Check if ports are accessible\ntelnet localhost 8080\n\n# For remote connections, ensure host is set to 0.0.0.0\nd-vecdb-server start --host 0.0.0.0\n```\n\n### Performance Issues\n\n**Slow Performance:**\n```toml\n# Optimize configuration for better performance\n[storage]\nmemory_map_size = \"4GB\" # Increase based on available RAM\n\n[index]\nhnsw_max_connections = 64\nhnsw_ef_construction = 800\n\n[server]\nworkers = 16 # Match your CPU cores\n```\n\n**High Memory Usage:**\n```toml\n# Reduce memory usage\n[storage]\nmemory_map_size = \"512MB\" # Reduce if needed\n\n[index]\nhnsw_max_connections = 16\nhnsw_ef_construction = 200\n```\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## Using with Python Client\n\nAfter installing the server, you can use it with the Python client:\n\n```bash\n# Install the Python client\npip install d-vecdb\n```\n\n```python\nfrom d_vecdb_server import DVecDBServer\nfrom d_vecdb import VectorDBClient\nimport numpy as np\n\n# Start the server\nwith DVecDBServer(port=8080) as server:\n # Connect client\n client = VectorDBClient(host=server.host, port=server.port)\n \n # Create collection\n client.create_collection_simple(\"documents\", 128, \"cosine\")\n \n # Insert vectors\n vector = np.random.random(128)\n client.insert_simple(\"documents\", \"doc1\", vector)\n \n # Search\n query = np.random.random(128)\n results = client.search_simple(\"documents\", query, limit=5)\n print(f\"Found {len(results)} similar vectors\")\n```\n\n## Next Steps\n\nAfter installation and configuration:\n\n1. **Start using the REST API**: Visit `http://localhost:8080/docs` for API documentation\n2. **Use Python client**: See example above for Python integration\n3. **Check examples**: See the main repository for usage examples\n4. **Join community**: Report issues and get support\n\n## Links\n\n- **Main Repository**: https://github.com/rdmurugan/d-vecDB\n- **Python Client**: https://pypi.org/project/d-vecdb/\n- **Docker Hub**: https://hub.docker.com/r/rdmurugan/d-vecdb\n- **Issues & Support**: https://github.com/rdmurugan/d-vecDB/issues\n- **Documentation**: https://github.com/rdmurugan/d-vecDB#readme\n",
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