langchain_basic_chat.py

import sys
import os
import re
from pathlib import Path
from datetime import datetime

from langchain.chat_models import ChatOpenAI
from langchain.schema import (
    HumanMessage
)

# Configuration
# Put here your API key
os.environ["OPENAI_API_KEY"] = '<key>'

# Put here your model
# Other options are 'gpt-3.5-turbo-0301'
# model_name = "gpt-3.5-turbo"
model_name = "gpt-3.5-turbo-0301"

# Initialize the chat object.

chat = ChatOpenAI(model_name=model_name, temperature=0)

print("Please enter your input to talk to ChatGPT. Enter 'q' to quit")

def generate_iso_date():
    current_date = datetime.now()
    return re.sub(r"\.\d+$", "", current_date.isoformat().replace(':', '')) 

class ChatFile:
    def __init__(self, current_file: Path, model_name: str) -> None:
        self.current_file = current_file
        self.model_name = model_name
        print(f"Writing to file {current_file}")
        with open(self.current_file, 'w') as f:
            f.write(f"Langchain Session at {generate_iso_date()} with {self.model_name}\n\n")
    
    def store_to_file(self, question: str, answer: str):
        print(f"{answer}")
        with open(self.current_file, 'a') as f:
            f.write(f"{generate_iso_date()}:\nQ: { question}\nA: {answer}\n\n")


chat_file = ChatFile(Path(f"{model_name}_{generate_iso_date()}.txt"), model_name)

for line in sys.stdin:
    print(f"[{model_name}]", end =">> ")
    question = line.strip()
    if 'q' == question:
        break
    # The LLM takes a prompt as an input and outputs a completion
    resp = chat([HumanMessage(content=question)])
    answer = resp.content
    chat_file.store_to_file(question, answer)