ollama-local-serve 0.2.1

Local LLM infrastructure with monitoring dashboard for distributed AI applications

Ollama Local Serve

[](htfor Repotps://pypi.org/project/ollama-local-serve/)

Local LLM infrastructure with a professional monitoring dashboard for distributed AI applications. Serve Ollama-powered models across your network with seamless LangChain integration, OpenTelemetry instrumentation, and real-time metrics visualization.

Features

• Service Management: Easy start/stop control of Ollama server instances
• Network Accessible: Configure host/port for LAN accessibility
• LangChain Integration: Seamless integration with LangChain for remote LLM clients
• OpenTelemetry Instrumentation: Built-in metrics collection with OTEL support
• Real-time Monitoring Dashboard: Professional React dashboard with live metrics
• In-App Chat Interface: Floating chat bubble with streaming responses and markdown support
• Model Management: Pull, delete, and manage models directly from the dashboard
• Model Repository: Track favorites, usage stats, and preferences per model
• Multiple Database Backends: Export metrics to ClickHouse or PostgreSQL/TimescaleDB
• Enhanced Request Logging: Capture prompt/response text, client info, and token counts
• Data Management: Clear metrics/logs and view data summaries via API
• Health Checks: Built-in health check endpoints to monitor service status
• Docker Ready: Complete Docker Compose stack for production deployment
• Async/Await: Production-ready async patterns throughout
• Type Hints: Full type annotations with Pydantic configuration

Quick Start

Installation

# Basic installation
pip install ollama-local-serve

# With LangChain integration
pip install ollama-local-serve[langchain]

# With full monitoring stack
pip install ollama-local-serve[monitoring]

# All features
pip install ollama-local-serve[all]

Prerequisites

• Python 3.12 or higher
• Ollama installed on your system (Download Ollama)

Basic Usage

import asyncio
from ollama_local_serve import OllamaService, NetworkConfig

async def main():
    # Create configuration for LAN accessibility
    config = NetworkConfig(
        host="0.0.0.0",  # Accessible from any network interface
        port=11434,      # Default Ollama port
        timeout=30,
        max_retries=3
    )

    # Create and start the service
    service = OllamaService(config)
    await service.start()

    # Check service health
    is_healthy = await service.health_check()
    print(f"Service is healthy: {is_healthy}")

    # Stop the service
    await service.stop()

asyncio.run(main())

Using Context Manager

import asyncio
from ollama_local_serve import OllamaService, NetworkConfig

async def main():
    config = NetworkConfig(host="0.0.0.0", port=11434)

    # Automatic cleanup with context manager
    async with OllamaService(config) as service:
        print(f"Service running at {service.base_url}")
        await service.health_check()
        # Service automatically stops when exiting context

asyncio.run(main())

Docker Deployment

Quick Start with Docker Compose

# Clone the repository
git clone https://github.com/AbhinaavRamesh/ollama-local-serve.git
cd ollama-local-serve

# Initialize environment
make init

# Start all services
make up

# View the dashboard
open http://localhost:3000

Available Services

Service	Port	Description
Ollama	11434	LLM inference service
ClickHouse	8123, 9000	Time-series database
PostgreSQL	5432	TimescaleDB for relational storage
API Server	8000	FastAPI monitoring API
Dashboard	3000	React monitoring dashboard

Note: The API Server is named ollama-monitor in docker-compose files and commands. This naming reflects the service's primary purpose as a monitoring API for Ollama. When you see references to ollama-monitor in docker commands or compose files, this refers to the API Server listed above.

Make Commands

Core Commands

make help          # Show all available commands
make init          # Initialize environment
make up            # Start all services
make down          # Stop all services
make logs          # View logs
make health        # Check service health
make dev           # Start development environment
make clean         # Remove all containers and volumes

Dependency Installation

make install                  # Install all dependencies (Python + Frontend)
make install-python           # Install Python dependencies
make install-python-venv      # Install Python deps in virtual environment
make install-frontend         # Install frontend (Node.js) dependencies
make install-db-clients       # Install database CLI clients (ClickHouse + PostgreSQL)
make install-clickhouse-client # Install ClickHouse client only
make install-postgres-client  # Install PostgreSQL client only
make check-deps               # Check if required dependencies are installed

Selective Service Startup (Toggle Databases)

make up-minimal      # Start only Ollama + API (no databases, no frontend)
make up-clickhouse   # Start full stack with ClickHouse (includes frontend)
make up-postgres     # Start full stack with PostgreSQL (includes frontend)

# Or use environment variables to toggle services (with up-selective target):
make up-selective ENABLE_CLICKHOUSE=false    # Disable ClickHouse
make up-selective ENABLE_POSTGRES=false      # Disable PostgreSQL
make up-selective ENABLE_FRONTEND=false      # Disable frontend dashboard

Local Development (without Docker)

make run-api        # Run API server locally (port 8000)
make run-frontend   # Run frontend dev server locally (port 5173)
make run-local      # Run both API and frontend in parallel

Configuration

Environment Variables

Copy .env.example to .env and configure:

# Ollama Configuration
OLLAMA_HOST=http://ollama:11434
OLLAMA_MODEL=llama3.2
OLLAMA_TIMEOUT=120

# Instrumentation
ENABLE_INSTRUMENTATION=true
EXPORTER_TYPE=clickhouse  # clickhouse, postgres, both, none

# ClickHouse
CLICKHOUSE_HOST=clickhouse
CLICKHOUSE_PORT=9000
CLICKHOUSE_DATABASE=ollama_metrics

# PostgreSQL
POSTGRES_HOST=postgres
POSTGRES_PORT=5432
POSTGRES_DATABASE=ollama_metrics
POSTGRES_USER=ollama
POSTGRES_PASSWORD=your_secure_password

Pydantic Configuration

All configuration classes support environment variable loading:

from ollama_local_serve.config import (
    NetworkConfig,
    InstrumentationConfig,
    ClickHouseConfig,
    PostgresConfig,
    APIConfig,
    AppConfig,
)

# Load from environment variables
config = AppConfig()

# Access nested configs
print(config.network.base_url)
print(config.clickhouse.connection_url)
print(config.api.cors_origins_list)

Monitoring & Instrumentation

Enable Instrumentation

from ollama_local_serve import OllamaService, NetworkConfig

config = NetworkConfig(
    host="0.0.0.0",
    port=11434,
    enable_instrumentation=True,
    exporter_type="clickhouse",  # or "postgres", "both"
    metrics_export_interval=5,
)

async with OllamaService(config) as service:
    # Metrics are automatically collected
    response = await service.generate("llama2", "Hello, world!")

Available Metrics

• ollama_requests_total - Total number of requests
• ollama_tokens_generated_total - Total tokens generated
• ollama_errors_total - Total errors
• ollama_request_latency_ms - Request latency histogram

API Endpoints

The monitoring API provides:

# Health & Stats
GET  /api/health           - Health check
GET  /api/stats/current    - Current statistics
GET  /api/stats/history    - Historical metrics
GET  /api/stats/logs       - Request logs (with prompt/response text)
GET  /api/models           - Model statistics

# Chat
POST /api/chat             - Stream chat with Ollama (SSE)

# Ollama Proxy
GET  /api/ollama/models    - List installed Ollama models
POST /api/ollama/pull      - Pull a model (streaming progress)
DELETE /api/ollama/models/{name} - Delete a model
GET  /api/ollama/library   - Search model library

# Model Repository (PostgreSQL)
GET  /api/models/repository         - Get all models with preferences
GET  /api/models/repository/{name}  - Get model details
POST /api/models/repository         - Add model to repository
PUT  /api/models/repository/{name}  - Update model (favorite, default)
POST /api/models/repository/sync    - Sync with installed models

# Data Management
GET    /api/data/summary   - Get data summary
DELETE /api/data/metrics   - Clear all metrics
DELETE /api/data/logs      - Clear all request logs
DELETE /api/data/all       - Clear all data

LangChain Integration

from ollama_local_serve import create_langchain_client, NetworkConfig

# Connect to a local or remote Ollama service
llm = create_langchain_client(
    base_url="http://192.168.1.100:11434",
    model="llama2",
    temperature=0.7
)

response = llm.invoke("What is the meaning of life?")
print(response)

Project Structure

ollama-local-serve/
├── ollama_local_serve/          # Python package
│   ├── __init__.py
│   ├── config.py                # Pydantic configuration
│   ├── service.py               # OllamaService class
│   ├── client.py                # LangChain client
│   ├── exceptions.py            # Custom exceptions
│   ├── api/                     # FastAPI server
│   │   ├── server.py
│   │   ├── models.py
│   │   └── dependencies.py
│   ├── instrumentation/         # OTEL instrumentation
│   │   ├── metrics_provider.py
│   │   └── tracer.py
│   └── exporters/               # Database exporters
│       ├── base.py
│       ├── clickhouse_exporter.py
│       └── postgres_exporter.py
├── frontend/                    # React dashboard
│   ├── src/
│   │   ├── components/
│   │   │   ├── chat/            # Chat bubble with streaming
│   │   │   ├── charts/          # Visualization components
│   │   │   └── ...              # Other UI components
│   │   ├── pages/
│   │   ├── hooks/
│   │   ├── context/             # App and Theme context
│   │   └── utils/
│   ├── package.json
│   └── Dockerfile
├── schemas/                     # Database schemas
│   ├── clickhouse_init.sql
│   └── postgres_init.sql
├── docker-compose.yml           # Production stack
├── docker-compose.dev.yml       # Development overrides
├── Dockerfile                   # API Dockerfile
├── Makefile                     # Convenience commands
├── pyproject.toml               # Python project config
└── requirements-api.txt         # API dependencies

Installation Options

# Core only
pip install ollama-local-serve

# With specific features
pip install ollama-local-serve[langchain]      # LangChain integration
pip install ollama-local-serve[api]            # FastAPI server
pip install ollama-local-serve[instrumentation] # OpenTelemetry
pip install ollama-local-serve[clickhouse]     # ClickHouse exporter
pip install ollama-local-serve[postgres]       # PostgreSQL exporter
pip install ollama-local-serve[monitoring]     # Full monitoring stack
pip install ollama-local-serve[all]            # Everything

# Development
pip install -e ".[dev]"

Error Handling

from ollama_local_serve import (
    OllamaServiceError,       # Base exception
    ConnectionError,          # Connection failures
    HealthCheckError,         # Health check failures
    ServiceStartError,        # Service start failures
    ServiceStopError,         # Service stop failures
)

try:
    await service.health_check()
except ConnectionError as e:
    print(f"Connection failed: {e}")
except HealthCheckError as e:
    print(f"Health check failed: {e}")

Development

Setup

# Clone repository
git clone https://github.com/AbhinaavRamesh/ollama-local-serve.git
cd ollama-local-serve

# Check what dependencies you have installed
make check-deps

# Option 1: Install all dependencies at once
make install

# Option 2: Use virtual environment for Python
make install-python-venv   # Creates .venv and installs deps
source .venv/bin/activate  # Activate the virtual environment
make install-frontend      # Install frontend deps

# Option 3: Manual setup
python -m venv venv
source venv/bin/activate  # or `venv\Scripts\activate` on Windows
pip install -e ".[dev]"
cd frontend && npm install

# Optional: Install database clients for local debugging
make install-db-clients

Code Quality

# Format code
black ollama_local_serve/

# Lint code
ruff check ollama_local_serve/

# Type checking
mypy ollama_local_serve/

# Run tests
pytest

Development Mode

# Option 1: Use Docker (hot reloading enabled)
make dev

# Or manually:
docker-compose -f docker-compose.yml -f docker-compose.dev.yml up

# Option 2: Run locally without Docker
make run-local   # Runs both API and frontend

# Or run separately:
# Run API locally (update the module path if needed):
python -m ollama_local_serve.api  # API on http://localhost:8000
make run-frontend  # Frontend on http://localhost:5173

API Reference

OllamaService

Main service class for managing Ollama server instances.

Methods:

• async start(startup_delay: float = 2.0) - Start the Ollama server
• async stop(timeout: float = 5.0) - Stop the Ollama server
• async health_check(retries: Optional[int] = None) - Check service health
• async get_models() - Get list of available models
• async generate(model: str, prompt: str) - Generate text

Properties:

• is_running: bool - Check if service is running
• base_url: str - Get the base URL of the service
• uptime_seconds: float - Get service uptime
• metrics_enabled: bool - Check if metrics are enabled

NetworkConfig

Configuration for network settings (Pydantic BaseSettings).

Attributes:

• host: str - Host address (default: "0.0.0.0")
• port: int - Port number (default: 11434)
• timeout: int - Connection timeout in seconds (default: 30)
• max_retries: int - Maximum retry attempts (default: 3)

Computed Properties:

• base_url: str - Get the base URL
• api_url: str - Get the API URL

License

MIT License - see LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request