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"""CV Screening Agent Module
Run as follows:
>>> docker compose up --build
>>> docker compose run --rm candidates_db_init python -m src.agents.cv_screening.screener
"""
import json
from langchain_openai import ChatOpenAI
from langchain.messages import SystemMessage, HumanMessage
from dotenv import load_dotenv
from src.backend.agents.cv_screening.schemas.output_schema import CVScreeningOutput
from src.backend.agents.cv_screening.utils import read_file
from src.backend.database.candidates import write_cv_results_to_db
from src.backend.prompts import get_prompt
load_dotenv()
SYSTEM_PROMPT = get_prompt(
template_name="CV_Screener",
latest_version=True
)
# --- The evaluator function ---
def screen_cv(cv_text: str, jd_text: str) -> CVScreeningOutput:
"""
Evaluate a candidate's CV against a job description using an LLM.
Args:
cv_text (str): The text content of the candidate's CV.
jd_text (str): The text content of the Job Description.
Returns:
CVScreeningOutput: The structured screening result.
Makes model write feedback before scoring, leading to better calibration
and genuine reasoning that leads to more balanced scores.
**NOTE**:
>>> The model generates feedback first (Chain-of-Thought)
>>> to ensure calibrated scores.
"""
llm = (
ChatOpenAI(
model="gpt-4o-mini",
temperature=0,
max_tokens=1500,
)
.with_structured_output(CVScreeningOutput)
)
# payload
messages = [
# Instruction
SystemMessage(
content=SYSTEM_PROMPT
),
# Payload
HumanMessage(
content=(
f"Job Description:\n{jd_text}\n\n"
f"Candidate CV:\n{cv_text}\n"
)
),
]
return llm.invoke(messages)
# --- Main execution for testing ---
if __name__ == "__main__":
from pathlib import Path
#BASE_PATH = Path("/Users/sebastianwefers/Desktop/projects/recruitment-agent/src/database")
BASE_PATH = Path(__file__).resolve().parents[2] / "database"
cv_text = read_file(BASE_PATH / "cvs/parsed/c762271c-af8f-49db-acbb-e37e5f0f0f98_SWefers_CV-sections.txt")
jd_text = read_file(BASE_PATH / "cvs/job_postings/ai_engineer.txt")
# trigger evaluation
result = screen_cv(cv_text, jd_text)
print(json.dumps(result.model_dump(), indent=2))
# optionally write to DB
write_cv_results_to_db(
candidate_email="sebastianwefersnz@gmail.com",
result=result,
job_title="AI Engineer"
) |