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Python Package Installation

Install Sekha components as Python packages for development, testing, or standalone deployments.

Available Packages

sekha-llm-bridge

The universal LLM adapter that enables the Controller to work with any LLM provider.

PyPI Python Versions

Installation:

pip install sekha-llm-bridge

Requirements: - Python 3.12+ - Redis (for Celery task queue) - Ollama or other LLM provider

Quick Start:

# Install
pip install sekha-llm-bridge

# Configure
export OLLAMA_URL=http://localhost:11434
export REDIS_URL=redis://localhost:6379/0

# Run
python -m sekha_llm_bridge.main

sekha-mcp

MCP (Model Context Protocol) server that exposes Sekha memory tools to Claude Desktop, Claude Code, and any MCP-compatible client.

PyPI Python Versions

Installation:

pip install sekha-mcp

Requirements: - Python 3.11+ - Sekha Controller running (localhost:8080) - Valid Controller API key

Quick Start:

# Install
pip install sekha-mcp

# Configure
export CONTROLLER_URL=http://localhost:8080
export CONTROLLER_API_KEY=your-api-key-here

# Run
python -m sekha_mcp

Use Cases

Development

Install packages locally for development and debugging:

# Clone repositories
git clone https://github.com/sekha-ai/sekha-llm-bridge.git
git clone https://github.com/sekha-ai/sekha-mcp.git

# Install in editable mode
cd sekha-llm-bridge
pip install -e ".[dev]"

cd ../sekha-mcp
pip install -e ".[dev]"

# Run tests
pytest

Standalone Deployment

Run Python components outside of Docker:

# Install both packages
pip install sekha-llm-bridge sekha-mcp

# Configure environment
cat > .env << 'EOF'
# LLM Bridge
OLLAMA_URL=http://localhost:11434
EMBEDDING_MODEL=nomic-embed-text:latest
SUMMARIZATION_MODEL=llama3.1:8b
REDIS_URL=redis://localhost:6379/0

# MCP Server
CONTROLLER_URL=http://localhost:8080
CONTROLLER_API_KEY=your-secure-key-here
EOF

# Source environment
export $(cat .env | xargs)

# Run services
python -m sekha_llm_bridge.main &  # LLM Bridge on :5001
python -m sekha_mcp &               # MCP server (stdio)

Custom Integration

Use as libraries in your Python applications:

from sekha_llm_bridge.services.embedding_service import EmbeddingService
from sekha_mcp.server import create_mcp_server

# Use embedding service
embedding_service = EmbeddingService()
embedding = await embedding_service.generate_embedding(
    text="Hello, world!",
    model="nomic-embed-text:latest"
)

# Create custom MCP server
server = create_mcp_server(
    controller_url="http://localhost:8080",
    api_key="your-key"
)

Testing

Install for integration testing:

# test_integration.py
import pytest
from sekha_llm_bridge.services import EmbeddingService

@pytest.mark.asyncio
async def test_embedding_generation():
    service = EmbeddingService()
    result = await service.generate_embedding(
        text="Test message",
        model="nomic-embed-text:latest"
    )
    assert len(result["embedding"]) == 768
    assert result["model"] == "nomic-embed-text:latest"

Installation Options

Standard Installation

# Base package
pip install sekha-llm-bridge
pip install sekha-mcp

With Development Tools

# Include pytest, ruff, black, mypy
pip install sekha-llm-bridge[dev]
pip install sekha-mcp[dev]

From Source (Latest)

# Install directly from GitHub
pip install git+https://github.com/sekha-ai/sekha-llm-bridge.git
pip install git+https://github.com/sekha-ai/sekha-mcp.git

# Or specific branch/tag
pip install git+https://github.com/sekha-ai/sekha-llm-bridge.git@main
pip install git+https://github.com/sekha-ai/[email protected]

Poetry

# Add to pyproject.toml
poetry add sekha-llm-bridge
poetry add sekha-mcp

# Or with groups
poetry add --group dev sekha-llm-bridge[dev]
poetry add --group dev sekha-mcp[dev]

Configuration

LLM Bridge Environment Variables

# Server
HOST=0.0.0.0
PORT=5001

# Ollama (local LLMs)
OLLAMA_URL=http://localhost:11434
EMBEDDING_MODEL=nomic-embed-text:latest
SUMMARIZATION_MODEL=llama3.1:8b

# Redis (Celery task queue)
REDIS_URL=redis://localhost:6379/0

# Cloud Providers (optional)
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...

# Logging
LOG_LEVEL=INFO

MCP Server Environment Variables

# Controller connection
CONTROLLER_URL=http://localhost:8080
CONTROLLER_API_KEY=your-secure-key-here

# Logging
LOG_LEVEL=INFO

Systemd Service Example

For production Linux deployments:

LLM Bridge Service (/etc/systemd/system/sekha-llm-bridge.service):

[Unit]
Description=Sekha LLM Bridge
After=network.target redis.service
Requires=redis.service

[Service]
Type=simple
User=sekha
Group=sekha
WorkingDirectory=/opt/sekha
EnvironmentFile=/opt/sekha/.env
ExecStart=/opt/sekha/venv/bin/python -m sekha_llm_bridge.main
Restart=on-failure
RestartSec=10

[Install]
WantedBy=multi-user.target

MCP Server Service (/etc/systemd/system/sekha-mcp.service):

[Unit]
Description=Sekha MCP Server
After=network.target sekha-controller.service
Requires=sekha-controller.service

[Service]
Type=simple
User=sekha
Group=sekha
WorkingDirectory=/opt/sekha
EnvironmentFile=/opt/sekha/.env
ExecStart=/opt/sekha/venv/bin/python -m sekha_mcp
Restart=on-failure
RestartSec=10

[Install]
WantedBy=multi-user.target

Enable and start:

sudo systemctl enable sekha-llm-bridge sekha-mcp
sudo systemctl start sekha-llm-bridge sekha-mcp
sudo systemctl status sekha-llm-bridge sekha-mcp

Troubleshooting

Import Errors

# Verify installation
pip list | grep sekha

# Should show:
sekha-llm-bridge    0.2.0
sekha-mcp           0.2.0

# If missing, reinstall
pip install --force-reinstall sekha-llm-bridge sekha-mcp

Module Not Found

# Verify Python path
import sys
print(sys.path)

# Check package location
import sekha_llm_bridge
print(sekha_llm_bridge.__file__)

Version Conflicts

# Check dependencies
pip check

# Update all dependencies
pip install --upgrade sekha-llm-bridge sekha-mcp

# Use virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # Linux/macOS
venv\Scripts\activate     # Windows
pip install sekha-llm-bridge sekha-mcp

Next Steps

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