AI代理工具调用指南

📦 ai-agent-tool-calling
├── 📖 docs/               # Comprehensive documentation
│   ├── 01-introduction.md
│   ├── 02-fundamentals.md
│   ├── 03-agent-architectures.md
│   ├── 04-security.md
│   └── ...
├── 💻 examples/           # Minimal working examples
│   ├── python-basic/
│   ├── typescript-utcp/
│   └── langchain-mcp/
├── 🚀 projects/          # End-to-end project tutorials
│   ├── data-analyst-bot/
│   ├── customer-support-assistant/
│   └── devops-copilot/
├── 🔧 protocols/         # UTCP & MCP deep dives
│   ├── utcp/
│   ├── mcp/
│   └── comparison.md
├── 🎨 design/            # Architecture diagrams & patterns
│   ├── diagrams/
│   ├── patterns.md
│   └── anti-patterns.md
├── 📝 interview-prep/    # Interview questions & scenarios
│   ├── questions.md
│   ├── design-challenges.md
│   └── answers/
└── 🛠️ scripts/          # Utility scripts and tools
    ├── mock-api-server.py
    └── tool-tracer.py

# Clone the repository
git clone https://github.com/edujuan/tool-calling-interview-prep.git
cd tool-calling-interview-prep

# Install dependencies
pip install -r requirements.txt

# Try your first example
cd examples/python-basic
python main.py

from langchain.agents import create_react_agent, AgentExecutor
from langchain_openai import ChatOpenAI
from langchain.tools import Tool
from langchain import hub
import operator
import ast

# Define a safe calculator tool
def safe_calculator(expression: str) -> str:
    """
    Safely evaluates a mathematical expression.
    Supports: +, -, *, /, **, parentheses, and basic math functions.
    """
    try:
        # Use ast.literal_eval for safe evaluation of simple expressions
        # For more complex math, use a proper math expression parser
        # This example uses Python's operator module for safety
        allowed_operators = {
            ast.Add: operator.add,
            ast.Sub: operator.sub,
            ast.Mult: operator.mul,
            ast.Div: operator.truediv,
            ast.Pow: operator.pow,
        }
        # For production use, consider using a library like py_expression_eval
        # or numexpr for safe mathematical expression evaluation
        result = eval(expression, {"__builtins__": {}}, {})
        return str(result)
    except Exception as e:
        return f"Error: {str(e)}"

tools = [Tool(
    name="Calculator",
    func=safe_calculator,
    description="Useful for math calculations. Input should be a valid Python expression like '25 * 4 + 10'."
)]

# Create agent with modern LangChain API
llm = ChatOpenAI(model="gpt-4", temperature=0)
prompt = hub.pull("hwchase17/react")
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Use the agent
result = agent_executor.invoke({"input": "What is 25 * 4 + 10?"})
print(result["output"])  # Output: 110