from openai import OpenAI
from eth_account import Account
from mscp import Connector, Chat2Web3
from dotenv import load_dotenv
import os
load_dotenv()
# Create a connector to connect to the component
component_connector = Connector(
"http://localhost:8545", # RPC of the component network
"0x0E2b5cF475D1BAe57C6C41BbDDD3D99ae6Ea59c7", # component address
Account.from_key(os.getenv("EVM_PRIVATE_KEY")),
)
# Create a Chat2Web3 instance
chat2web3 = Chat2Web3([component_connector])
# Create a client for OpenAI
client = OpenAI(api_key=os.getenv("OPENAI_KEY"), base_url=os.getenv("OPENAI_API_BASE"))
# Set up the conversation
messages = [
{
"role": "user",
"content": "What is the user's name and age? 0x8241b5b254e47798E8cD02d13B8eE0C7B5f2a6fA",
}
]
# Add the chat2web3 to the tools
params = {
"model": "gpt-3.5-turbo",
"messages": messages,
"tools": chat2web3.functions,
}
# Start the conversation
response = client.chat.completions.create(**params)
# Get the function message
func_msg = response.choices[0].message
# fliter out chat2web3 function
if func_msg.tool_calls and chat2web3.has(func_msg.tool_calls[0].function.name):
# execute the function from llm
function_result = chat2web3.call(func_msg.tool_calls[0].function)
messages.extend(
[
func_msg,
{
"role": "tool",
"tool_call_id": func_msg.tool_calls[0].id,
"content": function_result,
},
]
)
# Model responds with final answer
response = client.chat.completions.create(model="gpt-3.5-turbo", messages=messages)
print(response.choices[0].message.content)
