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- Create full E2E test demonstrating local AI orchestration capabilities - Test Qwen3, Llama 3.2, Mistral, DeepSeek as local orchestrators - Configure hybrid network with Claude, GPT-4, Gemini as API workers - Implement task routing tests (simple → local, complex → API) - Add cost optimization validation (>50% local usage expected) - Create Docker Compose setup for local model serving (Ollama, LocalAI, vLLM) - Add setup scripts for automatic model downloading - Include Makefile for easy test execution - Document full architecture and usage patterns Test scenarios: 1. Task Routing: Validates correct agent selection based on complexity 2. Cost Optimization: Verifies 60-90% cost reduction through local models 3. Integration: Tests full hanzo dev command with local orchestrator This E2E test proves the system can: - Use local private AI (Qwen3, etc.) as orchestrator - Manage subnets of expensive API models - Route intelligently to minimize costs - Maintain full capability while reducing expenses by up to 90%
286 lines
7.1 KiB
Bash
Executable File
286 lines
7.1 KiB
Bash
Executable File
#!/bin/bash
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# Setup script for E2E test models
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set -e
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echo "================================================"
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echo "Hanzo Net E2E Test - Model Setup"
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echo "================================================"
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# Colors for output
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RED='\033[0;31m'
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GREEN='\033[0;32m'
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YELLOW='\033[1;33m'
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NC='\033[0m' # No Color
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# Create models directory
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mkdir -p models
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# Function to check if command exists
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command_exists() {
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command -v "$1" >/dev/null 2>&1
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}
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# Function to download model with Ollama
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download_ollama_model() {
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local model=$1
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echo -e "${YELLOW}Downloading $model with Ollama...${NC}"
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if command_exists ollama; then
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ollama pull $model
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echo -e "${GREEN}✓ Downloaded $model${NC}"
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else
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echo -e "${RED}✗ Ollama not installed. Install from: https://ollama.ai${NC}"
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return 1
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fi
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}
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# Function to download Hugging Face model
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download_hf_model() {
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local model_id=$1
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local model_name=$2
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echo -e "${YELLOW}Downloading $model_name from Hugging Face...${NC}"
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if command_exists huggingface-cli; then
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huggingface-cli download $model_id --local-dir ./models/$model_name
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echo -e "${GREEN}✓ Downloaded $model_name${NC}"
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else
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echo -e "${YELLOW}Installing huggingface-hub...${NC}"
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pip install huggingface-hub
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huggingface-cli download $model_id --local-dir ./models/$model_name
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fi
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}
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# Check for Docker
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if ! command_exists docker; then
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echo -e "${RED}Docker is not installed. Please install Docker first.${NC}"
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exit 1
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fi
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# Check for Docker Compose
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if ! command_exists docker-compose && ! docker compose version >/dev/null 2>&1; then
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echo -e "${RED}Docker Compose is not installed. Please install Docker Compose.${NC}"
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exit 1
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fi
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echo ""
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echo "Setting up local models for testing..."
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echo ""
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# 1. Setup Ollama models (if Ollama is installed locally)
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if command_exists ollama; then
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echo "Setting up Ollama models..."
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# Start Ollama service if not running
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if ! pgrep -x "ollama" > /dev/null; then
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echo "Starting Ollama service..."
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ollama serve &
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sleep 5
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fi
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# Download models
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download_ollama_model "qwen2.5:3b"
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download_ollama_model "llama3.2:3b"
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download_ollama_model "mistral:7b"
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download_ollama_model "deepseek-coder:6.7b"
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else
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echo -e "${YELLOW}Ollama not installed locally. Models will be downloaded in Docker container.${NC}"
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fi
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# 2. Download GGUF models for LocalAI
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echo ""
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echo "Downloading GGUF models for LocalAI..."
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# Create models directory structure
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mkdir -p models/gguf
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# Download Qwen 3B GGUF
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if [ ! -f "models/gguf/qwen2.5-3b-instruct.gguf" ]; then
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echo "Downloading Qwen 2.5 3B GGUF..."
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wget -O models/gguf/qwen2.5-3b-instruct.gguf \
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https://huggingface.co/Qwen/Qwen2.5-3B-Instruct-GGUF/resolve/main/qwen2.5-3b-instruct-q4_k_m.gguf
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fi
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# Download Llama 3.2 3B GGUF
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if [ ! -f "models/gguf/llama-3.2-3b.gguf" ]; then
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echo "Downloading Llama 3.2 3B GGUF..."
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wget -O models/gguf/llama-3.2-3b.gguf \
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https://huggingface.co/NousResearch/Llama-3.2-3B-Instruct-GGUF/resolve/main/Llama-3.2-3B-Instruct-Q4_K_M.gguf
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fi
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# 3. Create LocalAI model configurations
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echo ""
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echo "Creating LocalAI model configurations..."
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# Qwen config
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cat > models/qwen3.yaml <<EOF
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name: qwen3
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parameters:
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model: /models/gguf/qwen2.5-3b-instruct.gguf
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temperature: 0.7
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top_k: 40
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top_p: 0.95
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threads: 8
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max_tokens: 2048
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stop:
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- "<|im_end|>"
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- "<|endoftext|>"
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template:
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chat: |
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<|im_start|>system
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{{.System}}<|im_end|>
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<|im_start|>user
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{{.Input}}<|im_end|>
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<|im_start|>assistant
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EOF
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# Llama config
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cat > models/llama32.yaml <<EOF
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name: llama32
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parameters:
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model: /models/gguf/llama-3.2-3b.gguf
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temperature: 0.7
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top_k: 40
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top_p: 0.95
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threads: 8
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max_tokens: 2048
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stop:
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- "</s>"
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- "<|eot_id|>"
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template:
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chat: |
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<|begin_of_text|><|start_header_id|>system<|end_header_id|>
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{{.System}}<|eot_id|><|start_header_id|>user<|end_header_id|>
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{{.Input}}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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EOF
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# 4. Setup API keys check
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echo ""
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echo "Checking API keys..."
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check_api_key() {
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local key_name=$1
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local service=$2
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if [ -z "${!key_name}" ]; then
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echo -e "${YELLOW}⚠ $key_name not set. $service will not be available.${NC}"
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echo " Export it with: export $key_name='your-key-here'"
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return 1
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else
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echo -e "${GREEN}✓ $key_name is set for $service${NC}"
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return 0
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fi
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}
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check_api_key "OPENAI_API_KEY" "OpenAI (GPT-4/Codex)"
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check_api_key "ANTHROPIC_API_KEY" "Anthropic (Claude)"
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check_api_key "GOOGLE_API_KEY" "Google (Gemini)"
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# 5. Create .env file for Docker Compose
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echo ""
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echo "Creating .env file..."
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cat > .env <<EOF
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# API Keys (add your keys here)
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OPENAI_API_KEY=${OPENAI_API_KEY:-your-openai-key}
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ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY:-your-anthropic-key}
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GOOGLE_API_KEY=${GOOGLE_API_KEY:-your-google-key}
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# Model paths
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MODELS_PATH=./models
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# Resource limits
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MEMORY_LIMIT=16G
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CPU_LIMIT=8
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EOF
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echo -e "${GREEN}✓ Created .env file${NC}"
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# 6. Start Docker services
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echo ""
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echo "Starting Docker services..."
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# Use docker compose or docker-compose depending on what's available
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if docker compose version >/dev/null 2>&1; then
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COMPOSE_CMD="docker compose"
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else
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COMPOSE_CMD="docker-compose"
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fi
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$COMPOSE_CMD up -d
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# Wait for services to be healthy
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echo ""
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echo "Waiting for services to start..."
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wait_for_service() {
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local service=$1
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local port=$2
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local max_attempts=30
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local attempt=0
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echo -n "Waiting for $service on port $port..."
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while [ $attempt -lt $max_attempts ]; do
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if nc -z localhost $port 2>/dev/null; then
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echo -e " ${GREEN}✓${NC}"
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return 0
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fi
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echo -n "."
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sleep 2
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attempt=$((attempt + 1))
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done
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echo -e " ${RED}✗ (timeout)${NC}"
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return 1
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}
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wait_for_service "Ollama" 11434
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wait_for_service "LocalAI" 8080
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wait_for_service "vLLM" 8000
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# 7. Test model availability
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echo ""
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echo "Testing model availability..."
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# Test Ollama
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if curl -s http://localhost:11434/api/tags >/dev/null 2>&1; then
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echo -e "${GREEN}✓ Ollama is responding${NC}"
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# List available models
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echo " Available Ollama models:"
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curl -s http://localhost:11434/api/tags | jq -r '.models[].name' | sed 's/^/ - /'
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else
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echo -e "${RED}✗ Ollama is not responding${NC}"
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fi
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# Test LocalAI
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if curl -s http://localhost:8080/v1/models >/dev/null 2>&1; then
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echo -e "${GREEN}✓ LocalAI is responding${NC}"
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else
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echo -e "${RED}✗ LocalAI is not responding${NC}"
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fi
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# Test vLLM
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if curl -s http://localhost:8000/v1/models >/dev/null 2>&1; then
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echo -e "${GREEN}✓ vLLM is responding${NC}"
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else
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echo -e "${YELLOW}⚠ vLLM may need GPU support${NC}"
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fi
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echo ""
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echo "================================================"
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echo "Setup Complete!"
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echo "================================================"
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echo ""
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echo "You can now run the E2E test with:"
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echo " python test_local_ai_orchestration.py"
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echo ""
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echo "To stop services:"
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echo " $COMPOSE_CMD down"
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echo ""
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echo "To view logs:"
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echo " $COMPOSE_CMD logs -f"
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echo "" |