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import os
from models import load_chained_agent
from agents import chatAgent
import langchain
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
# print(chatAgent("why is the sky blue?"))
# try:
# prompt_formatted = prompt.format(query="""
# Who is the president of South Korea? What is his age? What is the digit sum of his age?
# """)
# agent = load_chained_agent(verbose=True)
# response = agent({"input": prompt_formatted})
# print(response["output"])
# except Exception as e:
# print(e)
from langchain.tools import DuckDuckGoSearchRun, GoogleSearchRun
from langchain.utilities import GoogleSearchAPIWrapper
def load_chained_agent(verbose=True, model_name="text-davinci-003"):
llm = OpenAI(model_name=model_name, temperature=0)
toolkit = [GoogleSearchRun(), DuckDuckGoSearchRun()]
toolkit += load_tools(["open-meteo-api", "news-api",
"python_repl", "wolfram-alpha",
"pal-math", "pal-colored-objects"],
llm=llm,
serpapi_api_key=os.getenv('SERPAPI_API_KEY'),
news_api_key=os.getenv('NEWS_API_KEY'),
tmdb_bearer_token=os.getenv('TMDB_BEARER_TOKEN')
)
agent = initialize_agent(toolkit,
llm,
agent="zero-shot-react-description",
verbose=verbose,
return_intermediate_steps=True)
return agent
PROMPT = "Who is the president of South Korea? How old is he? What is the smallest prime greater than his age?"
if __name__ == '__main__':
agent = load_chained_agent()
response = agent(PROMPT)
if response is not None:
"""
print("Steps: ")
for action in response['intermediate_steps']:
print()
print(f"==> Tool: {action[0].tool}")
print(f" Input: {action[0].tool_input}")
print(f" Thought: {action[0].log}")
print(f" Finding: {action[1]}")
"""
print(f"input: {response['input']}")
print(f"output: {response['output']}")
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