agentic-ai-engineering/course/labs/L1-first-agent/solution.py

106 lines
3.3 KiB
Python

"""
Lab 1.7: Your First Agent -- SOLUTION
Builds an agent that reads files and answers questions about them.
Uses the Anthropic API (Claude) as the LLM backend, or mock for offline.
"""
import os
import sys
# Import mock LLM for offline execution
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '_shared'))
try:
from mock_llm import MockAnthropic as Anthropic
client = Anthropic()
IS_MOCK = True
except ImportError:
from anthropic import Anthropic
client = Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
IS_MOCK = False
MAX_ITERATIONS = 10
FILE_READ_TOOL = {
"name": "read_file",
"description": "Read the contents of a file at the given path",
"input_schema": {
"type": "object",
"properties": {
"path": {
"type": "string",
"description": "The path to the file to read"
},
"reasoning": {
"type": "string",
"description": "Why are you reading this file?"
}
},
"required": ["path", "reasoning"]
}
}
TOOLS = [FILE_READ_TOOL]
def execute_tool(tool_name: str, tool_args: dict) -> str:
if tool_name == "read_file":
path = tool_args["path"]
try:
with open(path, "r") as f:
content = f.read()
return f"Contents of {path}:\n{content}"
except FileNotFoundError:
return f"Error: File not found at {path}"
except Exception as e:
return f"Error reading file: {str(e)}"
return f"Unknown tool: {tool_name}"
def run_agent(prompt: str, file_path: str) -> str:
"""Run the agent with a user prompt. Works with real API or mock."""
system_prompt = """You are a helpful assistant with access to file reading tools.
Read the file first, then answer the user's question about its contents.
When you have enough information, provide a clear final answer."""
messages = [
{"role": "user", "content": f"Read the file at {file_path} and answer: {prompt}"}
]
for iteration in range(MAX_ITERATIONS):
response = client.messages.create(
model="claude-sonnet-4-20260501" if not IS_MOCK else "mock-model",
max_tokens=1024,
system=system_prompt,
messages=messages,
tools=TOOLS
)
if response.stop_reason == "tool_use":
for block in response.content:
if block.type == "tool_use":
result = execute_tool(block.name, block.input)
messages.append({"role": "assistant", "content": response.content})
messages.append({"role": "user", "content": result})
elif response.stop_reason == "end_turn":
text = "".join(
block.text for block in response.content if block.type == "text"
)
return text
else:
return f"Unexpected stop_reason: {response.stop_reason}"
return "Max iterations reached without final answer."
if __name__ == "__main__":
if len(sys.argv) < 3:
print("Usage: python solution.py <file_path> <question>")
sys.exit(1)
file_path = sys.argv[1]
question = sys.argv[2]
result = run_agent(question, file_path)
print(f"\nFinal answer:\n{result}")