github/LucidLoanMachine
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email field

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hackertron
2024-07-03 17:37:55 +05:30
parent 26cdf58a28
commit 646b02a3bf
1 changed files with 35 additions and 6 deletions
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41
main.py
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@@ -5,7 +5,7 @@ from dotenv import load_dotenv
load_dotenv() # take environment variables from .env.
config_list = [
{
'model': 'gpt-3.5-turbo',
'model': 'gpt-3.5-turbo',
'api_key': os.getenv('OPENAI_API_KEY'),
}
]
@@ -23,13 +23,22 @@ front_desk_assistant = autogen.AssistantAgent(
llm_config=llm_config,
system_message="""You have a personality of monopoly banker. You have to ask questions and collect information from user
Questions that you have to ask : What is your name, How much do you want to borrow, What country do you live in,
What bank do you use, Do you have a job/proof of income, Do you have any history of not paying back loans?
What bank do you use, Do you have a job/proof of income, what's your email?, Do you have any history of not paying back loans?
once you collect all these answers, create a json response with following key
{"first_name" : "", last_name: "", "country" : "", "bank" : "", "income" : "", "history" : "", loan_amount : ""}
{"first_name" : "", last_name: "", "country" : "", "bank" : "", "income" : "", "history" : "", loan_amount : "", email : ""}
Ask user for confirmation that the details are right and want to proceed with it. Show the response in json format and save it to a file
""",
)
email_assistant = autogen.AssistantAgent(
name="email_assistant",
llm_config=llm_config,
system_message="""You will have access to bank.json from front_desk_assistant.
You will guide user to paste their raw email. Assume user has desktop and not on their mobile phone.
guide user to paste their raw email to you. Tell them to paste raw email in chunks, not the complete email in one go.
You will then analyze the email and check if it's valid and details matches with bank.json."""
)
# assistant = autogen.AssistantAgent(
# name="laon_assistant",
# llm_config=llm_config,
@@ -52,8 +61,28 @@ user_proxy = autogen.UserProxyAgent(
)
def main():
# task = """"""
user_proxy.initiate_chat(front_desk_assistant, message="I want to apply for a loan, please help me")
# chat_results = user_proxy.initiate_chats([
# {
# "recipient": front_desk_assistant,
# "message": "I want to apply for a loan, please help me",
# "silent": False,
# "summary_method": "reflection_with_llm"
# },
# {
# "recipient": email_assistant,
# "message": "guide user to paste their raw email and validate with json that you recieved from front_desk_assistant",
# "silent": False,
# "summary_method": "reflection_with_llm"
# }
# ])
groupchat = autogen.GroupChat(agents=[user_proxy, front_desk_assistant, email_assistant], messages=[], max_round=5)
manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)
chat_results = user_proxy.initiate_chat(manager, message="I want to apply for a loan, please help me")
for i, chat_res in enumerate(chat_results):
print(f"*****{i}th chat*******:")
print(chat_res.summary)
print("Human input in the middle:", chat_res.human_input)
print("Conversation cost: ", chat_res.cost)
print("\n\n")
if __name__ == "__main__":
main()