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imnyang
638a3d47ce
- Introduced custom output format instructions with example code. - Detailed connection methods for launching and connecting to browsers, including local and remote options. - Provided guidelines for handling sensitive data securely, including best practices and examples. - Documented supported LangChain chat models with setup instructions and environment variable requirements. - Added instructions for customizing the system prompt to control agent behavior.
1.3 KiB
1.3 KiB
| description | applyTo |
|---|---|
| The default is text. But you can define a structured output format to make post-processing easier. | ** |
Custom output format
With this example you can define what output format the agent should return to you.
from pydantic import BaseModel
# Define the output format as a Pydantic model
class Post(BaseModel):
post_title: str
post_url: str
num_comments: int
hours_since_post: int
class Posts(BaseModel):
posts: List[Post]
controller = Controller(output_model=Posts)
async def main():
task = 'Go to hackernews show hn and give me the first 5 posts'
model = ChatOpenAI(model='gpt-4o')
agent = Agent(task=task, llm=model, controller=controller)
history = await agent.run()
result = history.final_result()
if result:
parsed: Posts = Posts.model_validate_json(result)
for post in parsed.posts:
print('\n--------------------------------')
print(f'Title: {post.post_title}')
print(f'URL: {post.post_url}')
print(f'Comments: {post.num_comments}')
print(f'Hours since post: {post.hours_since_post}')
else:
print('No result')
if __name__ == '__main__':
asyncio.run(main())