helpers/foundation_models.py

import os
from typing import List, Tuple

import openai
import streamlit as st
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.llms import OpenAI as l_OpenAI

OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
SERPAPI_API_KEY = os.environ["SERPAPI_API_KEY"]
openai_client = openai.OpenAI(api_key=OPENAI_API_KEY)


def call_chatgpt(query: str, model: str = "gpt-3.5-turbo") -> str:
    """
    Generates a response to a query using the specified language model.

    Args:
        query (str): The user's query that needs to be processed.
        model (str, optional): The language model to be used. Defaults to "gpt-3.5-turbo".

    Returns:
        str: The generated response to the query.
    """

    # Prepare the conversation context with system and user messages.
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": f"Question: {query}."},
    ]

    # Use the OpenAI client to generate a response based on the model and the conversation context.
    response = openai_client.chat.completions.create(
        model=model,
        messages=messages,
    )

    # Extract the content of the response from the first choice.
    content: str = response.choices[0].message.content

    # Return the generated content.
    return content


def call_langchain(prompt: str) -> str:
    llm = l_OpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)
    tools = load_tools(
        ["serpapi", "llm-math"], llm=llm, serpapi_api_key=SERPAPI_API_KEY
    )
    agent = initialize_agent(
        tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
    )
    output = agent.run(prompt)

    return output