llmlearninglecture3-1.py

import os
from dotenv import load_dotenv
from openai import OpenAI
import requests
from langchain_openai import ChatOpenAI
from langchain.schema import HumanMessage
from langchain_core.messages import AIMessage, SystemMessage
from langchain_core.prompts import PromptTemplate

load_dotenv()

def fetch_data():
     
     try:
          api_url = "http://localhost:3000/product"
          response = requests.get(api_url)

          if response.status_code == 200:
               return response.json()
          else:
            print(f"API call failed with status: {response.status_code}")
            return None

     except Exception as e:

        print(f"Error making API call: {e}")
        return None


prompt = PromptTemplate(
input_variables=["data", "user_name"],
template="""
hello {user_name}.
IMPORTANT RULES:
- Only answer questions about data {data}
- Do not answer any other question about any other data
- Always be helpful and provide accurate information
- Greet the user by name: {user_name}
"""
,
validate_template=True
)

model = ChatOpenAI(
            model="gpt-3.5-turbo",
            api_key=os.getenv("OPENAI_API_KEY")
        )


def chat():
    data = fetch_data()
    sytem_prompt = prompt.invoke({"data":str(data),"user_name":"DJ"}).text

    messages = [
     
        SystemMessage(content=sytem_prompt),
        HumanMessage(content="I am DJ"),
        AIMessage(content="Hello DJ, how can I assist you today?"),
    ]

    print('chat started press q to exit.....')
    print('-' *30)

    while True:

        user_input = input("You: ").strip()

        if user_input.lower() in ['q']:
                print("Goodbye!")
                break

        messages.append(HumanMessage(content=user_input))
        response = model.invoke(messages)
        messages.append(response)

        print(f"AI: {response.content}")
        print('-' * 30)

if __name__ == "__main__":
    print(chat())