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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.summarize import summarize
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
from langchain.tools import WikipediaQueryRun
import argparse
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
tools=load_tools(["human", "wikipedia"]) + [get_all_contacts, call_phone_number, email_tasks, summarize]
tools_desc = ""
for tool in tools:
tools_desc += tool.name + " : " + tool.description + "\n"
def rephrase_prompt(objective: str) -> str:
# llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo") # type: ignore
# pred = llm.predict(f"Based on these tools {tools_desc} with the {objective} should be done in the following manner (outputting a single sentence), allowing for failure: ")
# print(pred)
# return pred
return f"{objective}"
with open("info.txt") as f:
my_info = f.read()
memory.chat_memory.add_user_message("User information to us " + my_info + " end of user information.")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Command line argument parser example")
parser.add_argument("--objective", type=str, help="Objective for the program")
parser.add_argument("--verbose", type=bool, help="Verbosity of the program", default=False)
# Parse the arguments
args = parser.parse_args()
# Get the value of --objective
objective_value = args.objective
# Get the value of --verbose
verbose_value = args.verbose
# Redirect stdout to our custom class
sys.stdout = typing.cast(typing.TextIO, RedactPhoneNumbers(sys.stdout))
if objective_value is None:
objective_value = input("What is your objective? ")
OBJECTIVE = (
objective_value
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=tools,
llm=llm,
agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
verbose=verbose_value,
memory=memory,
)
out = agent.run(OBJECTIVE)
print(out)
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