using LLM for sentiment analysis

integrate_model_runner.sh

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+#!/bin/bash
+
+# Integration script for Docker Model Runner
+# Adds model runner services to your existing Docker Compose stack
+
+set -e
+
+echo "=== Docker Model Runner Integration ==="
+echo ""
+
+# Check if docker-compose.yml exists
+if [[ ! -f "docker-compose.yml" ]]; then
+    echo "❌ No existing docker-compose.yml found"
+    echo "Creating new docker-compose.yml with model runner services..."
+    cp docker-compose.model-runner.yml docker-compose.yml
+else
+    echo "✅ Found existing docker-compose.yml"
+    echo ""
+
+    # Create backup
+    cp docker-compose.yml docker-compose.yml.backup
+    echo "📦 Backup created: docker-compose.yml.backup"
+
+    # Merge services
+    echo ""
+    echo "🔄 Merging model runner services..."
+
+    # Use yq or manual merge if yq not available
+    if command -v yq &> /dev/null; then
+        echo "Using yq to merge configurations..."
+        yq eval-all '. as $item ireduce ({}; . * $item)' docker-compose.yml docker-compose.model-runner.yml > docker-compose.tmp
+        mv docker-compose.tmp docker-compose.yml
+    else
+        echo "Manual merge (yq not available)..."
+        # Append services to existing file
+        echo "" >> docker-compose.yml
+        echo "# Added by Docker Model Runner Integration" >> docker-compose.yml
+        echo "" >> docker-compose.yml
+
+        # Add services from model-runner compose
+        awk '/^services:/{flag=1; next} /^volumes:/{flag=0} flag' docker-compose.model-runner.yml >> docker-compose.yml
+
+        # Add volumes and networks if they don't exist
+        if ! grep -q "^volumes:" docker-compose.yml; then
+            echo "" >> docker-compose.yml
+            awk '/^volumes:/{flag=1} /^networks:/{flag=0} flag' docker-compose.model-runner.yml >> docker-compose.yml
+        fi
+
+        if ! grep -q "^networks:" docker-compose.yml; then
+            echo "" >> docker-compose.yml
+            awk '/^networks:/{flag=1} flag' docker-compose.model-runner.yml >> docker-compose.yml
+        fi
+    fi
+
+    echo "✅ Services merged successfully"
+fi
+
+# Create necessary directories
+echo ""
+echo "📁 Creating necessary directories..."
+mkdir -p models config
+
+# Copy environment file
+if [[ ! -f ".env" ]]; then
+    cp model-runner.env .env
+    echo "📄 Created .env file from model-runner.env"
+elif [[ ! -f ".env.model-runner" ]]; then
+    cp model-runner.env .env.model-runner
+    echo "📄 Created .env.model-runner file"
+fi
+
+echo ""
+echo "=== Integration Complete! ==="
+echo ""
+echo "📋 Available services:"
+echo "• docker-model-runner  - Main model runner (port 11434)"
+echo "• llama-cpp-server     - Advanced llama.cpp server (port 8000)"
+echo "• model-manager        - Model management service"
+echo ""
+echo "🚀 Usage Commands:"
+echo ""
+echo "# Start all services"
+echo "docker-compose up -d"
+echo ""
+echo "# Start only model runner"
+echo "docker-compose up -d docker-model-runner"
+echo ""
+echo "# Start with llama.cpp server"
+echo "docker-compose --profile llama-cpp up -d"
+echo ""
+echo "# Start with management tools"
+echo "docker-compose --profile management up -d"
+echo ""
+echo "# View logs"
+echo "docker-compose logs -f docker-model-runner"
+echo ""
+echo "# Test API"
+echo "curl http://localhost:11434/api/tags"
+echo ""
+echo "# Pull a model"
+echo "docker-compose exec docker-model-runner /app/model-runner pull ai/smollm2:135M-Q4_K_M"
+echo ""
+echo "# Run a model"
+echo "docker-compose exec docker-model-runner /app/model-runner run ai/smollm2:135M-Q4_K_M 'Hello!'"
+echo ""
+echo "# Pull Hugging Face model"
+echo "docker-compose exec docker-model-runner /app/model-runner pull hf.co/bartowski/Llama-3.2-1B-Instruct-GGUF"
+echo ""
+echo "🔧 Configuration:"
+echo "• Edit model-runner.env for GPU and performance settings"
+echo "• Models are stored in ./models directory"
+echo "• Configuration files in ./config directory"
+echo ""
+echo "📊 Exposed Ports:"
+echo "• 11434 - Docker Model Runner API (Ollama-compatible)"
+echo "• 8000  - Llama.cpp server API"
+echo "• 9090  - Metrics endpoint"
+echo ""
+echo "⚡ GPU Support:"
+echo "• CUDA_VISIBLE_DEVICES=0 (first GPU)"
+echo "• GPU_LAYERS=35 (layers to offload to GPU)"
+echo "• THREADS=8 (CPU threads)"
+echo "• BATCH_SIZE=512 (batch processing size)"
+echo ""
+echo "🔗 Integration with your existing services:"
+echo "• Use http://docker-model-runner:11434 for internal API calls"
+echo "• Use http://localhost:11434 for external API calls"
+echo "• Add 'depends_on: [docker-model-runner]' to your services"
+echo ""
+echo "Next steps:"
+echo "1. Review and edit configuration in model-runner.env"
+echo "2. Run: docker-compose up -d docker-model-runner"
+echo "3. Test: curl http://localhost:11434/api/tags"