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import os
import sys
import typing
from dotenv import load_dotenv
from tools.contacts import get_all_contacts
from tools.vocode import call_phone_number
from tools.get_user_inputs import get_desired_inputs
from tools.email_tool import email_tasks
from langchain.memory import ConversationBufferMemory
from langchain.agents import load_tools
from stdout_filterer import RedactPhoneNumbers
load_dotenv()
from langchain.chat_models import ChatOpenAI
from langchain.chat_models import BedrockChat
from langchain.agents import initialize_agent
from langchain.agents import AgentType
if __name__ == "__main__":
# Redirect stdout to our custom class
sys.stdout = typing.cast(typing.TextIO, RedactPhoneNumbers(sys.stdout))
OBJECTIVE = (
input("Objective: ")
+ "make sure you use the proper tool before calling final action to meet objective, feel free to say you need more information or cannot do something."
or "Find a random person in my contacts and tell them a joke"
)
#llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo") # type: ignore
llm = BedrockChat(model_id="anthropic.claude-instant-v1", model_kwargs={"temperature":0}) # type: ignore
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
# Logging of LLMChains
verbose = True
agent = initialize_agent(
tools=[get_all_contacts, call_phone_number, email_tasks] + load_tools(["serpapi", "human"]),
llm=llm,
agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
verbose=verbose,
memory=memory,
)
agent.run(OBJECTIVE)
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