代理AI训练营

# Create the virtual environment
python3 -m venv venv

# Activate the virtual environment
source venv/bin/activate

# Create the virtual environment
python -m venv venv

# Activate the virtual environment
venv\Scripts\activate

# Install main packages
pip install -r requirements.txt

# If you're working on AgentCore labs
cd agentcore
pip install -r requirements-agent.txt
cd ..

# Verify that everything is installed correctly
pip list

aws configure

# macOS (with Homebrew)
brew install aws-vault

# Linux
curl https://github.com/99designs/aws-vault/releases/download/v7.2.0/aws-vault-linux-amd64 -o aws-vault
chmod +x aws-vault
sudo mv aws-vault /usr/local/bin

# Windows
choco install aws-vault

# Add your credentials
aws-vault add default

# Verify configuration
aws-vault list

# Execute an AWS command securely
aws-vault exec default -- aws s3 ls

# Or launch Jupyter with credentials
aws-vault exec default -- jupyter notebook

aws-bedrock-workshop/
├── agentcore/                          # AgentCore Labs (1-7)
│   ├── lab-01-create-an-agent.ipynb
│   ├── lab-02-agentcore-memory.ipynb
│   ├── lab-03-agentcore-gateway.ipynb
│   ├── lab-04-agentcore-runtime.ipynb
│   ├── lab-05-agentcore-evals.ipynb
│   ├── lab-06-frontend.ipynb
│   ├── lab-07-agentcore-policy.ipynb
│   ├── lab_helpers/                    # Lab utilities
│   ├── prerequisite/                   # CloudFormation infrastructure
│   └── scripts/                        # Helper scripts
│
├── architecture_patterns/              # GenAI architecture patterns
│   └── 01_text_and_code_generation_w_bedrock.ipynb
│
├── bedrock_data_automation/            # Bedrock automation
│   ├── 01_standard_output_basic_to_advanced.ipynb
│   └── 02_custom_outputs_and_blueprints.ipynb
│
├── guardrails/                         # Chatbot security
│   └── 01-secure_chatbots.ipynb
│
├── image_and_multimodal/               # Image and multimodal processing
│   ├── 01_nova-canvas-notebook.ipynb
│   ├── 02_nova-reel-notebook.ipynb
│   └── 03_bedrock-titan-multimodal-embeddings.ipynb
│
├── knowledgebases_and_rag/             # Knowledge Bases and RAG
│   ├── 01_create_ingest_documents_test_kb.ipynb
│   └── 02_managed-rag-kb-retrieve-generate-api.ipynb
│
├── speech_to_speech/                   # Text-to-Speech and Speech-to-Text
│   ├── 00_introduction_nova_sonic_console.ipynb
│   ├── 01_core_functionality.ipynb
│   ├── 02_repeatable_patterns.ipynb
│   ├── python-server/
│   └── react-client/
│
├── strands_agents/                     # Strands Agents Implementation Examples
│   ├── 01-first-agent/
│   ├── 02-connecting-with-aws-services/
│   ├── 03-using-mcp-tools/
│   ├── 04-agent-as-tool/
│   ├── 05-agent-workflows/
│   ├── 06-graph-agent/
│   └── 07-swarm-agent/
│
├── util/                               # Utility modules and helpers
│   ├── __init__.py
│   ├── model_constants.py              # Model configuration constants
│   ├── model_selector.py               # Model selection utilities
│   ├── strands_retry.py                # Retry logic for agent calls
│   └── tagging.py                      # AWS resource tagging utilities
│
├── cleanup/                            # Cleanup scripts
│   └── 01_cleanup.ipynb
│
├── requirements.txt                    # Main dependencies
├── Agent.md                            # Agents comprehensive guide
└── README.md                           # This file

# Launch Jupyter Notebook
jupyter notebook

# Or launch Jupyter Lab (more modern)
jupyter lab

# Deactivate the virtual environment
deactivate

# Update pip
pip install --upgrade pip

# Reinstall dependencies (if issue)
pip install --force-reinstall -r requirements.txt

# List all installed packages
pip list

# Create a file with current dependencies
pip freeze > requirements.txt

# Use the included cleanup script
cd cleanup
jupyter notebook 01_cleanup.ipynb

# Or run the cleanup script
python cleanup_tagged_resources.py