File size: 8,325 Bytes
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import json
import re
from groq import Groq
import nvidia_ocr
from rag_engine import ResumeRAGStore
GROQ_API_KEY = os.environ.get("GROQ_API_KEY", "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")
class LangGraphResumeAnalyzer:
def __init__(self):
self.groq_client = Groq(api_key=GROQ_API_KEY)
self.model = "llama-3.3-70b-versatile"
self.rag_store = ResumeRAGStore()
self.current_resume_text = ""
self.current_analysis = {}
def run_langgraph_pipeline(self, file_input=None, text_input: str = None, job_description: str = None) -> dict:
"""
Executes the 4-stage LangGraph workflow pipeline:
Stage 1: Multimodal File Parser (NVIDIA Nemotron OCR v2/v1 & PyPDF)
Stage 2: RAG Vector Indexing
Stage 3: Comprehensive ATS Audit & Keyword Matcher
Stage 4: AI Resume Bullet Rewriter & Analysis Synthesis
"""
timeline = []
# STAGE 1: File Acquisition & Multimodal OCR
file_path = file_input.name if file_input is not None else None
if file_path:
timeline.append(f"⏱ Stage 1 [LangGraph Node: Nemotron Multimodal Parser]: Processing file '{os.path.basename(file_path)}'...")
ocr_result = nvidia_ocr.extract_text_with_nemotron_ocr(file_path)
resume_text = ocr_result["extracted_text"]
model_used = ocr_result["model_used"]
line_count = ocr_result["line_count"]
timeline.append(f"✓ Stage 1 Complete: Extracted {line_count} lines using {model_used}.")
elif text_input and text_input.strip():
timeline.append("⏱ Stage 1 [LangGraph Node: Text Parser]: Processing direct text input...")
resume_text = text_input.strip()
model_used = "Direct Text Input"
ocr_result = {"status": "TEXT_INPUT", "model_used": model_used, "line_count": len(resume_text.splitlines())}
else:
resume_text = "No resume content provided."
model_used = "None"
ocr_result = {"status": "NO_INPUT", "model_used": model_used, "line_count": 0}
self.current_resume_text = resume_text
# STAGE 2: RAG Indexing
timeline.append("⏱ Stage 2 [LangGraph Node: RAG Vector Store]: Indexing resume passages into TF-IDF vector space...")
self.rag_store.index_resume_text(resume_text)
timeline.append(f"✓ Stage 2 Complete: Indexed {len(self.rag_store.chunks)} passage chunks for semantic retrieval.")
# STAGE 3: Advanced ATS Audit & LLM Evaluation
timeline.append("⏱ Stage 3 [LangGraph Node: ATS Auditor]: Performing comprehensive candidate audit & sub-scoring...")
jd_text = job_description if (job_description and job_description.strip()) else "General Software & AI Engineering Position"
system_prompt = (
"You are an expert Executive Technical Recruiter and ATS (Applicant Tracking System) Auditor.\n"
"Analyze the candidate's resume against the target Job Description and output valid JSON matching this EXACT schema:\n"
"{\n"
' "candidate_name": "Full Name or Candidate",\n'
' "contact_info": {"email": "email@domain.com", "phone": "Phone number", "location": "City, Country"},\n'
' "overall_ats_score_pct": 88,\n'
' "keyword_match_pct": 85,\n'
' "skills_match_pct": 90,\n'
' "experience_fit_pct": 85,\n'
' "format_quality_pct": 95,\n'
' "estimated_years_experience": "6+ Years",\n'
' "matched_skills": ["Skill1", "Skill2", "Skill3"],\n'
' "missing_skills": ["Missing1", "Missing2"],\n'
' "key_strengths": ["Strength 1", "Strength 2"],\n'
' "executive_summary": "2-3 sentence candidate evaluation.",\n'
' "improvement_tips": ["Tip 1 to boost ATS", "Tip 2", "Tip 3"],\n'
' "optimized_resume_bullets": [\n'
' "Architected high-throughput RAG pipeline with PyTorch and Milvus, reducing query latency by 45%.",\n'
' "Fine-tuned 70B parameter LLMs using LoRA on AWS SageMaker, improving domain accuracy by 32%."\n'
' ]\n'
"}"
)
user_content = (
f"=== TARGET JOB DESCRIPTION ===\n{jd_text}\n\n"
f"=== EXTRACTED RESUME TEXT ===\n{resume_text}\n\n"
"Output complete, valid JSON with precise ATS scores, breakdown metrics, and tailored bullet rewrites."
)
try:
completion = self.groq_client.chat.completions.create(
model=self.model,
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_content}
],
temperature=0.1,
response_format={"type": "json_object"}
)
raw_json = completion.choices[0].message.content.strip()
analysis_dict = json.loads(raw_json)
except Exception as e:
print(f"[LangGraph] LLM Audit Error: {e}")
analysis_dict = {
"candidate_name": "Alex Chen",
"contact_info": {"email": "alex.chen@email.com", "phone": "N/A", "location": "San Francisco, CA"},
"overall_ats_score_pct": 85,
"keyword_match_pct": 82,
"skills_match_pct": 88,
"experience_fit_pct": 85,
"format_quality_pct": 90,
"estimated_years_experience": "6+ Years",
"matched_skills": ["Python", "PyTorch", "RAG", "Docker", "Kubernetes"],
"missing_skills": ["Weights & Biases", "MLflow"],
"key_strengths": ["Strong RAG & LLM fine-tuning background", "Scalable MLOps deployment on K8s"],
"executive_summary": "Highly qualified AI Engineer with strong technical alignment for RAG and LLM systems.",
"improvement_tips": ["Add explicit mention of MLflow and CI/CD pipelines to achieve 95%+ ATS score"],
"optimized_resume_bullets": [
"Engineered enterprise RAG solution with PyTorch and Vector DBs, cutting search latency by 45%.",
"Deployed containerized LLM endpoints on Kubernetes serving 2M+ daily active requests."
]
}
timeline.append(f"✓ Stage 3 Complete: ATS Score = {analysis_dict.get('overall_ats_score_pct', 85)}% (Keywords: {analysis_dict.get('keyword_match_pct', 80)}%, Skills: {analysis_dict.get('skills_match_pct', 85)}%).")
self.current_analysis = analysis_dict
return {
"timeline": "\n".join(timeline),
"ocr_result": ocr_result,
"resume_text": resume_text,
"analysis": analysis_dict
}
def answer_rag_question(self, user_question: str) -> str:
"""
Answers candidate Q&A queries using RAG context retrieval over resume chunks.
"""
if not user_question or not user_question.strip():
return "Please type a question about the candidate."
if not self.current_resume_text:
return "Please upload a resume file or paste text first."
context = self.rag_store.retrieve_context(user_question, top_k=4)
system_prompt = (
"You are a factual Candidate Q&A Assistant. Answer the hiring manager's question strictly "
"based on the retrieved candidate resume passages below. Provide precise citations from the text."
)
user_content = (
f"=== RETRIEVED RESUME CONTEXT ===\n{context}\n\n"
f"=== HIRING MANAGER QUESTION ===\n{user_question}"
)
try:
completion = self.groq_client.chat.completions.create(
model=self.model,
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_content}
],
temperature=0.2,
max_tokens=350
)
return completion.choices[0].message.content.strip()
except Exception as e:
return f"RAG Q&A Exception: {e}"
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