llmserver.py

""" Using Langchain Going to Generate a Server that can be used to shcedule meetings """
# Importing the required libraries
import os
from langchain.chat_models import ChatOpenAI
from typing import List, Dict, Any
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
# from langchain.llms import OpenAI
from langchain.chains import SimpleSequentialChain

with open("OPEN_AI_KEY.txt", "r") as f:
    OPEN_AI_KEY = f.read()

with open("SERP_API_KEY.txt", "r") as f:
    SERP_API_KEY = f.read()

os.environ["OPENAI_API_KEY"] = OPEN_AI_KEY
os.environ["SERPAPI_API_KEY"] = SERP_API_KEY


class CustomError(Exception):
    pass


class LlmServer:
    """LLm server class"""
    def __init__(self) -> None:
        self.llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")
        self.tools = None
        self.agent = None
        self.chain = None

    def initialize_agent(self, tools: List, agent: str) -> str:
        """Initialize the server"""
        if self.tools is None:
            self.tools = load_tools(tools, llm=self.llm)
        self.agent = initialize_agent(self.tools, self.llm, agent, verbose=True)
        return "Initialized"
    
    def initialize_llm(self):
        template = """You're talking to a friendly agent named Kedar. Context: {output}
                        He will greet according to your query. 
                        
                        User: hi
                        Kedar: hello, how may I help you today?
                        User: how are you?
                        Kedar: I'm good, thanks for asking. How about you?
                        """

        prompt = PromptTemplate(input=['output'], template=template)
        self.chain = LLMChain(llm=self.llm, prompt=prompt)
    
    # def get_response(self, text: str) -> Dict[str, Any]:
    #     if self.agent is None:
    #         raise CustomError('Not Initialized error')
    #     response = self.agent.run(text)
    #     return response

    def get_response(self, text: str, verbose: bool=True):
        response = SimpleSequentialChain(chains=[self.agent, self.chain], verbose=verbose)
        return response.run(text)