app/modules/question_retrieval/models/jd2text.py

from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
from langchain_core.messages import SystemMessage
from langchain_core.output_parsers import JsonOutputParser
import os
from dotenv import load_dotenv

# load the environment variables
load_dotenv()

# Define the google api key
os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY')
GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")

# define the parser object
parser = JsonOutputParser()

def jobdes2text(jobdes):
    # setup the gemini pro
    llm = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3, convert_system_message_to_human=True, api_key=GOOGLE_API_KEY)

    # create the prompt template
    finnal_jd_chat_template = ChatPromptTemplate.from_messages(
        [
            SystemMessage(
                content=(
                    """Return Job title, level(Fresher, Junior, Senior, ...) and Brief summary of required skills about 20 words from the job description. Use the following format: Job Title is {job title}, Level is {level}, and Brief summary of required skills is {brief summary of required skills}."""
                )
            ),
            HumanMessagePromptTemplate.from_template("{text}"),
        ]
    )

    # create the chat message
    chat_message =  finnal_jd_chat_template.format_messages(text=jobdes)

    # create a chain 
    chain =  llm

    result = chain.invoke(chat_message)

    return result.content