75 lines
1.9 KiB
Python
75 lines
1.9 KiB
Python
"""
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Automated news analysis and sentiment scoring using Bedrock.
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Ensure you have browser-use installed with `examples` extra, i.e. `uv install 'browser-use[examples]'`
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@dev Ensure AWS environment variables are set correctly for Bedrock access.
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"""
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import argparse
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import asyncio
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import os
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import sys
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sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
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from dotenv import load_dotenv
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load_dotenv()
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import boto3
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from botocore.config import Config
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from langchain_aws import ChatBedrockConverse
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from browser_use import Agent
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from browser_use.browser.browser import Browser, BrowserConfig
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from browser_use.controller.service import Controller
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def get_llm():
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config = Config(retries={'max_attempts': 10, 'mode': 'adaptive'})
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bedrock_client = boto3.client('bedrock-runtime', region_name='us-east-1', config=config)
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return ChatBedrockConverse(
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model_id='us.anthropic.claude-3-5-sonnet-20241022-v2:0',
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temperature=0.0,
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max_tokens=None,
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client=bedrock_client,
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)
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# Define the task for the agent
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task = (
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"Visit cnn.com, navigate to the 'World News' section, and identify the latest headline. "
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'Open the first article and summarize its content in 3-4 sentences. '
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'Additionally, analyze the sentiment of the article (positive, neutral, or negative) '
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'and provide a confidence score for the sentiment. Present the result in a tabular format.'
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)
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parser = argparse.ArgumentParser()
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parser.add_argument('--query', type=str, help='The query for the agent to execute', default=task)
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args = parser.parse_args()
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llm = get_llm()
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browser = Browser(
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config=BrowserConfig(
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# browser_binary_path='/Applications/Google Chrome.app/Contents/MacOS/Google Chrome',
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)
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)
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agent = Agent(
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task=args.query,
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llm=llm,
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controller=Controller(),
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browser=browser,
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validate_output=True,
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)
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async def main():
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await agent.run(max_steps=30)
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await browser.close()
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asyncio.run(main())
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