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| from sentence_transformers import SentenceTransformer | |
| from faiss import IndexFlatIP | |
| import numpy as np | |
| from huggingface_hub import InferenceClient | |
| import json | |
| import os | |
| class RAGInterviewAgent: | |
| def __init__(self, job_role, cv_summary): | |
| self.job_role = job_role | |
| self.cv_summary = cv_summary | |
| self.current_q = 0 | |
| # Initialize models | |
| self.embedder = SentenceTransformer('all-MiniLM-L6-v2') | |
| self.llm = InferenceClient(model="meta-llama/Meta-Llama-3-8B-Instruct") | |
| # Setup FAISS index | |
| self.index = IndexFlatIP(384) # Embedding dimension | |
| self._setup_knowledge_base() | |
| # Generate questions | |
| self.questions = self._generate_questions() | |
| def _setup_knowledge_base(self): | |
| """Load job requirements into vector store""" | |
| reqs = { | |
| "Software Engineer": [ | |
| "Explain SOLID principles", | |
| "How would you optimize a slow SQL query?", | |
| "Describe your experience with unit testing" | |
| ], | |
| "Data Scientist": [ | |
| "Explain bias-variance tradeoff", | |
| "How would you handle missing data?", | |
| "Describe a machine learning project you worked on" | |
| ] | |
| } | |
| self.knowledge = reqs[self.job_role] | |
| embeddings = self.embedder.encode(self.knowledge) | |
| self.index.add(embeddings) | |
| def _generate_questions(self): | |
| """Generate questions using RAG""" | |
| questions = [] | |
| # Get most relevant requirements | |
| cv_embed = self.embedder.encode(self.cv_summary["text"]) | |
| _, indices = self.index.search(np.array([cv_embed]), 3) | |
| for i in indices[0]: | |
| prompt = f""" | |
| Generate an interview question for a {self.job_role} position | |
| based on this requirement: {self.knowledge[i]} | |
| Candidate's CV summary: {self.cv_summary['text'][:1000]} | |
| """ | |
| response = self.llm.text_generation( | |
| prompt, | |
| max_new_tokens=100, | |
| temperature=0.7 | |
| ) | |
| questions.append({ | |
| "text": response.strip(), | |
| "base_requirement": self.knowledge[i] | |
| }) | |
| return questions | |
| def get_current_question(self): | |
| return self.questions[self.current_q] | |
| def next_question(self): | |
| self.current_q += 1 | |
| def evaluate_answer(self, answer): | |
| """Evaluate answer against knowledge base""" | |
| # Get relevant requirement | |
| req = self.questions[self.current_q]["base_requirement"] | |
| prompt = f""" | |
| Evaluate this interview answer on a scale of 1-10: | |
| Requirement: {req} | |
| Answer: {answer} | |
| Provide JSON output with: score, feedback | |
| """ | |
| response = self.llm.text_generation( | |
| prompt, | |
| max_new_tokens=200, | |
| temperature=0.3 | |
| ) | |
| try: | |
| return json.loads(response.split("{")[1].split("}")[0] + "}") | |
| except: | |
| return {"score": 5, "feedback": "Evaluation failed"} |