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import os
from pathlib import Path
# Path to .env file
ENV_PATH = Path(__file__).parent.parent / '.env'
# Configuration settings for evaluation
# Model and data paths
FINE_TUNED_MODEL_PATH = "svb01/fine-tuned-embedding-model"
TRAINING_DATA_PATH = "../resources/NIST.AI.600-1.pdf" # Adjust this path if needed
# RAG settings
RETRIEVER_K = 6
LLM_MODEL = "gpt-3.5-turbo"
LLM_TEMPERATURE = 0
# Evaluation settings
SAMPLE_QUESTIONS = [
"What are the main objectives of the EU AI Act?",
"How does the Act define high-risk AI systems?",
"What are the transparency requirements for AI systems?",
"How does the Act address AI in the workplace?"
]
# RAGAS metrics
RAGAS_METRICS = ["context_precision", "faithfulness", "answer_relevancy"]
# Chunk size for text splitting
CHUNK_SIZE = 750
CHUNK_OVERLAP = 20
BASE_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
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