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Configuration error
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import os
from dotenv import load_dotenv
load_dotenv()
class Config:
SECRET_KEY = os.getenv('FLASK_SECRET_KEY', 'dev-secret-key-change-in-production')
SQLALCHEMY_DATABASE_URI = os.getenv('DATABASE_URL', 'sqlite:///evalai_new.db')
SQLALCHEMY_TRACK_MODIFICATIONS = False
UPLOAD_FOLDER = 'uploads'
MAX_CONTENT_LENGTH = 5 * 1024 * 1024 * 1024 # 5GB max file size
ALLOWED_EXTENSIONS = {
# Core programming languages
'py', 'js', 'ts', 'jsx', 'tsx', 'java', 'cpp', 'c', 'h', 'hpp', 'cs', 'php', 'rb', 'go', 'rs', 'swift', 'kt', 'scala', 'r', 'matlab', 'm',
# Web technologies
'html', 'htm', 'css', 'scss', 'sass', 'less', 'vue', 'svelte', 'jsx', 'tsx',
# Configuration and data
'json', 'xml', 'yml', 'yaml', 'toml', 'ini', 'cfg', 'conf', 'env', 'properties',
# Documentation
'md', 'txt', 'rst', 'adoc', 'tex',
# Scripts and automation
'sh', 'bash', 'zsh', 'fish', 'ps1', 'bat', 'cmd',
# Database and queries
'sql', 'sqlite', 'db',
# Mobile development
'dart', 'flutter', 'xaml',
# Data science and ML
'ipynb', 'rmd', 'py', 'r',
# Build and deployment
'dockerfile', 'docker-compose', 'makefile', 'cmake', 'gradle', 'maven',
# Archives
'zip', 'tar', 'gz',
# Office documents (for documentation)
'pdf', 'doc', 'docx', 'ppt', 'pptx', 'xls', 'xlsx'
}
# RAG Configuration
# EVALUATION_MODEL = 'opensource' # 'opensource' (Llama-3-8B) or 'openai' (GPT-4) ⭐ USING OPENAI
EVALUATION_MODEL = 'openai' # 'opensource' (Llama-3-8B) or 'openai' (GPT-4) ⭐ USING OPENAI
OPENAI_API_KEY = os.getenv('OPENAI_API_KEY', '') # Set in environment or .env file
EMBEDDING_MODEL = 'all-MiniLM-L6-v2' # Can use 'microsoft/unixcoder-base' for code-specific
CHUNK_SIZE = 512 # Token size for chunks
CHUNK_OVERLAP = 128 # Overlap between chunks
RETRIEVAL_TOP_K = 8 # Number of chunks to retrieve
MAX_CONTEXT_TOKENS = 2000 # Max tokens to send to LLM
# LLM Configuration
LLM_MODEL = 'meta-llama/Meta-Llama-3-8B-Instruct' # Main evaluation model
LLM_TEMPERATURE = 0.3 # Lower = more deterministic
LLM_MAX_TOKENS = 512 # Max tokens in response
USE_QUANTIZATION = True # Use 4-bit quantization (faster & uses ~4GB VRAM instead of 16GB)
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