AgentOccam/Agent_E/ae/utils/anthropic_llm_helper.py
2025-01-22 11:32:35 -08:00

53 lines
2.2 KiB
Python

import os
import anthropic
from anthropic import AsyncAnthropic
from dotenv import load_dotenv
class AnthropicLLMHelper:
def __init__(self):
load_dotenv()
self.client = AsyncAnthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
async def get_chat_completion_response_async(self, system_msg:str, user_msgs:list[str], model_name:str="claude-3-opus-20240229", temperature:float=0.1, max_tokens:int=256, top_p:int=1, top_k: int=1) -> str:
formatted_user_msgs: list[dict[str, str]] = []
for user_msg in user_msgs:
formatted_user_msgs.append({"type": "text", "text": user_msg})
try:
message = await self.client.messages.create(
model=model_name,
max_tokens=max_tokens,
temperature=temperature,
system=system_msg,
messages=[
{
"role": "user",
"content": formatted_user_msgs # type: ignore
}
]
)
print(message)
return message.content[0].text
except anthropic.APIConnectionError as e:
print("The server could not be reached")
print(e.__cause__) # an underlying Exception, likely raised within httpx.
raise Exception(f"Calling {model_name} LLM failed. The server could not be reached. Error: {e}") # noqa: B904
except anthropic.RateLimitError as e:
print("A 429 status code was received; we should back off a bit.")
raise Exception(f"Calling {model_name} LLM failed. Rate limit error. Error: {e}") # noqa: B904
except anthropic.APIStatusError as e:
print(e.status_code)
print(e.response)
raise Exception(f"Calling {model_name} LLM failed. Error: {e}") # noqa: B904
# async def main():
# from ae.core.prompts import LLM_PROMPTS
# helper = AnthropicLLMHelper()
# response = await helper.get_chat_completion_response_async(LLM_PROMPTS["SKILLS_HARVESTING_PROMPT"], ["What is the weather like today?"], temperature=0, max_tokens=4000)
# print("*******\nResponse: ", response, "\n*******\n")
# asyncio.run(main())