1534 Docker Compose Guide Adceecdd

Docker Compose Guide

This guide covers Docker Compose setup for Local Deep Research. For the quickest start, see the Quick Start section below.

Quick Start

CPU-Only (All Platforms)

Works on macOS (M1/M2/M3/M4 and Intel), Windows, and Linux:

curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml && docker compose up -d

With NVIDIA GPU (Linux Only)

For hardware-accelerated inference:

curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml && \
curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.gpu.override.yml && \
docker compose -f docker-compose.yml -f docker-compose.gpu.override.yml up -d

Prerequisites for GPU: Install the NVIDIA Container Toolkit first. See the README for detailed instructions.

Open http://localhost:5000 after ~30 seconds.

Using a Different Model

Specify a model with the LDR_LLM_MODEL environment variable:

LDR_LLM_MODEL=gemma3:4b docker compose up -d

The model will be automatically pulled if not already available.

Configuration Options

docker-compose.yml

The base configuration includes:

Key Environment Variables

Most settings can be configured through the web UI at http://localhost:5000/settings. Environment variables override UI settings and lock them.

⚠️ Warning: Setting environment variables causes a hard override—the setting becomes read-only in the UI and cannot be changed until the environment variable is removed. For settings you may want to adjust later, use the web UI instead. Environment variables are best suited for deployment-specific values like LDR_DATA_DIR or API keys.

Changing the External Port

Use Docker's port mapping instead of environment variables:

ports:
  - "8080:5000"  # Expose on port 8080 instead of 5000

Volume Mounts

Local Document Collections

Mount directories to search your own documents:

volumes:
  - ./local_collections/personal_notes:/local_collections/personal_notes/
  - ./local_collections/project_docs:/local_collections/project_docs/
  - /path/to/your/papers:/local_collections/research_papers/:ro

The :ro suffix makes mounts read-only for safety.

Advanced: Cookie Cutter Configuration

For more customization, use Cookie Cutter to generate a tailored docker-compose file:

# Install cookiecutter
pip install --user cookiecutter

# Clone the repository
git clone https://github.com/LearningCircuit/local-deep-research.git
cd local-deep-research

# Generate custom configuration
cookiecutter cookiecutter-docker/

Cookie Cutter will prompt you for:

Then start with:

docker compose -f docker-compose.default.yml up -d

Using External LLM Providers

OpenRouter (100+ Models)

environment:
  - LDR_LLM_PROVIDER=openai_endpoint
  - LDR_LLM_OPENAI_ENDPOINT_URL=https://openrouter.ai/api/v1
  - LDR_LLM_OPENAI_ENDPOINT_API_KEY=<your-api-key>
  - LDR_LLM_MODEL=anthropic/claude-3.5-sonnet

LM Studio (Running on Host)

environment:
  - LDR_LLM_PROVIDER=lmstudio
  - LDR_LLM_LMSTUDIO_URL=http://host.docker.internal:1234/v1
  - LDR_LLM_MODEL=<your-loaded-model>

Common Commands

# Start services
docker compose up -d

# Start with GPU support
docker compose -f docker-compose.yml -f docker-compose.gpu.override.yml up -d

# View logs
docker compose logs -f

# Stop services
docker compose down

# Update to latest version
docker compose pull && docker compose up -d

# Remove all data (fresh start)
docker compose down -v

Troubleshooting

Container won't start

• Check logs: docker compose logs local-deep-research
• Ensure port 5000 is available

Ollama model not loading

• Check Ollama logs: docker compose logs ollama
• Verify model name in LDR_LLM_MODEL environment variable
• Ensure sufficient disk space for model download

GPU not detected

• Verify NVIDIA drivers: nvidia-smi
• Check container toolkit: docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi

Related Documentation

• README - Full Installation Guide
• Environment Configuration
• SearXNG Setup
• Unraid Deployment