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plg4-dev-server / backend /config /settings.py

Jesse Johnson

New commit for backend deployment: 2025-09-25_13-24-03

c59d808 about 1 year ago

7.98 kB

# Configuration settings for the Recipe Recommendation Bot
import os
from typing import Optional, List
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

class Settings:
    """Simple settings class that reads environment variables directly"""
    
    def __init__(self):
        # ===========================================
        # Server Configuration
        # ===========================================
        self.PORT = int(os.getenv("PORT", 8000))
        self.HOST = os.getenv("HOST", "0.0.0.0")
        self.ENVIRONMENT = os.getenv("ENVIRONMENT", "development")
        self.DEBUG = os.getenv("DEBUG", "true").lower() == "true"
        
        # ===========================================
        # CORS Configuration
        # ===========================================
        cors_origins = os.getenv("CORS_ORIGINS", '["http://localhost:3000","http://localhost:5173","http://localhost:8080"]')
        self.CORS_ORIGINS = self._parse_list(cors_origins)
        self.CORS_ALLOW_CREDENTIALS = os.getenv("CORS_ALLOW_CREDENTIALS", "true").lower() == "true"
        
        cors_methods = os.getenv("CORS_ALLOW_METHODS", '["GET","POST","PUT","DELETE","OPTIONS"]')
        self.CORS_ALLOW_METHODS = self._parse_list(cors_methods)
        
        cors_headers = os.getenv("CORS_ALLOW_HEADERS", '["*"]')
        self.CORS_ALLOW_HEADERS = self._parse_list(cors_headers)
        
        # ===========================================
        # LLM & Embedding Provider Configuration
        # ===========================================
        self.LLM_PROVIDER = os.getenv("LLM_PROVIDER", "google")
        self.EMBEDDING_PROVIDER = os.getenv("EMBEDDING_PROVIDER", self.LLM_PROVIDER)  # Default to same as LLM
        
        # OpenAI Configuration
        self.OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
        self.OPENAI_MODEL = os.getenv("OPENAI_MODEL", "gpt-5-nano")
        self.OPENAI_TEMPERATURE = float(os.getenv("OPENAI_TEMPERATURE", "0.7"))
        self.OPENAI_MAX_TOKENS = int(os.getenv("OPENAI_MAX_TOKENS", "1000"))
        
        # Google AI Configuration
        self.GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
        self.GOOGLE_MODEL = os.getenv("GOOGLE_MODEL", "gemini-2.5-flash")
        self.GOOGLE_TEMPERATURE = float(os.getenv("GOOGLE_TEMPERATURE", "0.7"))
        self.GOOGLE_MAX_TOKENS = int(os.getenv("GOOGLE_MAX_TOKENS", "1000"))
        
        # Hugging Face Configuration
        self.HUGGINGFACE_API_TOKEN = os.getenv("HUGGINGFACE_API_TOKEN")
        self.HUGGINGFACE_MODEL = os.getenv("HUGGINGFACE_MODEL", "microsoft/DialoGPT-medium")
        self.HUGGINGFACE_API_URL = os.getenv("HUGGINGFACE_API_URL", "https://api-inference.huggingface.co/models/")
        self.HUGGINGFACE_USE_GPU = os.getenv("HUGGINGFACE_USE_GPU", "false").lower() == "true"
        self.HUGGINGFACE_USE_API = os.getenv("HUGGINGFACE_USE_API", "false").lower() == "true"
        
        # Ollama Configuration
        self.OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")
        self.OLLAMA_MODEL = os.getenv("OLLAMA_MODEL", "llama3.1:8b")
        self.OLLAMA_TEMPERATURE = float(os.getenv("OLLAMA_TEMPERATURE", "0.7"))
        
        # ===========================================
        # Embedding Model Configuration 
        # ===========================================
        # Note: Embedding provider is determined by LLM_PROVIDER setting above
        
        # OpenAI Embeddings
        self.OPENAI_EMBEDDING_MODEL = os.getenv("OPENAI_EMBEDDING_MODEL", "text-embedding-ada-002")
        
        # Google Embeddings
        self.GOOGLE_EMBEDDING_MODEL = os.getenv("GOOGLE_EMBEDDING_MODEL", "models/embedding-001")
        
        # Hugging Face Embeddings
        self.HUGGINGFACE_EMBEDDING_MODEL = os.getenv("HUGGINGFACE_EMBEDDING_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
        
        # Ollama Embeddings
        self.OLLAMA_EMBEDDING_MODEL = os.getenv("OLLAMA_EMBEDDING_MODEL", "nomic-embed-text")
        
        # ===========================================
        # Logging Configuration
        # ===========================================
        self.LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO")
        self.LOG_FORMAT = os.getenv("LOG_FORMAT", "%(asctime)s - %(name)s - %(levelname)s - %(message)s")
        self.LOG_FILE = os.getenv("LOG_FILE", "./logs/app.log")

        # ===========================================
        # Langchain Debugging Configuration
        # ===========================================
        # Note: set to "true" to enable detailed Langchain logs
        self.LANGCHAIN_DEBUG = os.getenv("LANGCHAIN_DEBUG", "false").lower() == "true"
    
    def _parse_list(self, value: str) -> List[str]:
        """Parse a string representation of a list into an actual list"""
        try:
            # Remove brackets and quotes, split by comma
            if value.startswith('[') and value.endswith(']'):
                value = value[1:-1]
            items = [item.strip().strip('"').strip("'") for item in value.split(',')]
            return [item for item in items if item]  # Remove empty items
        except:
            return ["*"]  # Fallback to allow all

    def get_llm_config(self):
        """Get LLM configuration based on selected provider"""
        if self.LLM_PROVIDER == "openai":
            return {
                "provider": "openai",
                "api_key": self.OPENAI_API_KEY,
                "model": self.OPENAI_MODEL,
                "temperature": self.OPENAI_TEMPERATURE,
                "max_tokens": self.OPENAI_MAX_TOKENS
            }
        elif self.LLM_PROVIDER == "google":
            return {
                "provider": "google",
                "api_key": self.GOOGLE_API_KEY,
                "model": self.GOOGLE_MODEL,
                "temperature": self.GOOGLE_TEMPERATURE,
                "max_tokens": self.GOOGLE_MAX_TOKENS
            }
        elif self.LLM_PROVIDER == "huggingface":
            return {
                "provider": "huggingface",
                "api_token": self.HUGGINGFACE_API_TOKEN,
                "model": self.HUGGINGFACE_MODEL,
                "api_url": self.HUGGINGFACE_API_URL,
                "use_gpu": self.HUGGINGFACE_USE_GPU,
                "use_api": self.HUGGINGFACE_USE_API
            }
        elif self.LLM_PROVIDER == "ollama":
            return {
                "provider": "ollama",
                "base_url": self.OLLAMA_BASE_URL,
                "model": self.OLLAMA_MODEL,
                "temperature": self.OLLAMA_TEMPERATURE
            }
        else:
            raise ValueError(f"Unsupported LLM provider: {self.LLM_PROVIDER}")
    
    def get_embedding_config(self):
        """Get embedding configuration based on EMBEDDING_PROVIDER setting"""
        provider = self.EMBEDDING_PROVIDER
        
        if provider == "openai":
            return {
                "provider": "openai",
                "api_key": self.OPENAI_API_KEY,
                "model": self.OPENAI_EMBEDDING_MODEL
            }
        elif provider == "google":
            return {
                "provider": "google",
                "api_key": self.GOOGLE_API_KEY,
                "model": self.GOOGLE_EMBEDDING_MODEL
            }
        elif provider == "huggingface":
            return {
                "provider": "huggingface",
                "model": self.HUGGINGFACE_EMBEDDING_MODEL
            }
        elif provider == "ollama":
            return {
                "provider": "ollama",
                "base_url": self.OLLAMA_BASE_URL,
                "model": self.OLLAMA_EMBEDDING_MODEL
            }
        else:
            raise ValueError(f"Unsupported provider: {provider}. Supported providers: openai, google, huggingface, ollama")

# Create global settings instance
settings = Settings()

# Note: Vector store and database configuration is in database.py
# from config.database import db_settings

	# Configuration settings for the Recipe Recommendation Bot
	import os
	from typing import Optional, List
	from dotenv import load_dotenv

	# Load environment variables from .env file
	load_dotenv()

	class Settings:
	"""Simple settings class that reads environment variables directly"""

	def __init__(self):
	# ===========================================
	# Server Configuration
	# ===========================================
	self.PORT = int(os.getenv("PORT", 8000))
	self.HOST = os.getenv("HOST", "0.0.0.0")
	self.ENVIRONMENT = os.getenv("ENVIRONMENT", "development")
	self.DEBUG = os.getenv("DEBUG", "true").lower() == "true"

	# ===========================================
	# CORS Configuration
	# ===========================================
	cors_origins = os.getenv("CORS_ORIGINS", '["http://localhost:3000","http://localhost:5173","http://localhost:8080"]')
	self.CORS_ORIGINS = self._parse_list(cors_origins)
	self.CORS_ALLOW_CREDENTIALS = os.getenv("CORS_ALLOW_CREDENTIALS", "true").lower() == "true"

	cors_methods = os.getenv("CORS_ALLOW_METHODS", '["GET","POST","PUT","DELETE","OPTIONS"]')
	self.CORS_ALLOW_METHODS = self._parse_list(cors_methods)

	cors_headers = os.getenv("CORS_ALLOW_HEADERS", '["*"]')
	self.CORS_ALLOW_HEADERS = self._parse_list(cors_headers)

	# ===========================================
	# LLM & Embedding Provider Configuration
	# ===========================================
	self.LLM_PROVIDER = os.getenv("LLM_PROVIDER", "google")
	self.EMBEDDING_PROVIDER = os.getenv("EMBEDDING_PROVIDER", self.LLM_PROVIDER) # Default to same as LLM

	# OpenAI Configuration
	self.OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
	self.OPENAI_MODEL = os.getenv("OPENAI_MODEL", "gpt-5-nano")
	self.OPENAI_TEMPERATURE = float(os.getenv("OPENAI_TEMPERATURE", "0.7"))
	self.OPENAI_MAX_TOKENS = int(os.getenv("OPENAI_MAX_TOKENS", "1000"))

	# Google AI Configuration
	self.GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
	self.GOOGLE_MODEL = os.getenv("GOOGLE_MODEL", "gemini-2.5-flash")
	self.GOOGLE_TEMPERATURE = float(os.getenv("GOOGLE_TEMPERATURE", "0.7"))
	self.GOOGLE_MAX_TOKENS = int(os.getenv("GOOGLE_MAX_TOKENS", "1000"))

	# Hugging Face Configuration
	self.HUGGINGFACE_API_TOKEN = os.getenv("HUGGINGFACE_API_TOKEN")
	self.HUGGINGFACE_MODEL = os.getenv("HUGGINGFACE_MODEL", "microsoft/DialoGPT-medium")
	self.HUGGINGFACE_API_URL = os.getenv("HUGGINGFACE_API_URL", "https://api-inference.huggingface.co/models/")
	self.HUGGINGFACE_USE_GPU = os.getenv("HUGGINGFACE_USE_GPU", "false").lower() == "true"
	self.HUGGINGFACE_USE_API = os.getenv("HUGGINGFACE_USE_API", "false").lower() == "true"

	# Ollama Configuration
	self.OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")
	self.OLLAMA_MODEL = os.getenv("OLLAMA_MODEL", "llama3.1:8b")
	self.OLLAMA_TEMPERATURE = float(os.getenv("OLLAMA_TEMPERATURE", "0.7"))

	# ===========================================
	# Embedding Model Configuration
	# ===========================================
	# Note: Embedding provider is determined by LLM_PROVIDER setting above

	# OpenAI Embeddings
	self.OPENAI_EMBEDDING_MODEL = os.getenv("OPENAI_EMBEDDING_MODEL", "text-embedding-ada-002")

	# Google Embeddings
	self.GOOGLE_EMBEDDING_MODEL = os.getenv("GOOGLE_EMBEDDING_MODEL", "models/embedding-001")

	# Hugging Face Embeddings
	self.HUGGINGFACE_EMBEDDING_MODEL = os.getenv("HUGGINGFACE_EMBEDDING_MODEL", "sentence-transformers/all-MiniLM-L6-v2")

	# Ollama Embeddings
	self.OLLAMA_EMBEDDING_MODEL = os.getenv("OLLAMA_EMBEDDING_MODEL", "nomic-embed-text")

	# ===========================================
	# Logging Configuration
	# ===========================================
	self.LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO")
	self.LOG_FORMAT = os.getenv("LOG_FORMAT", "%(asctime)s - %(name)s - %(levelname)s - %(message)s")
	self.LOG_FILE = os.getenv("LOG_FILE", "./logs/app.log")

	# ===========================================
	# Langchain Debugging Configuration
	# ===========================================
	# Note: set to "true" to enable detailed Langchain logs
	self.LANGCHAIN_DEBUG = os.getenv("LANGCHAIN_DEBUG", "false").lower() == "true"

	def _parse_list(self, value: str) -> List[str]:
	"""Parse a string representation of a list into an actual list"""
	try:
	# Remove brackets and quotes, split by comma
	if value.startswith('[') and value.endswith(']'):
	value = value[1:-1]
	items = [item.strip().strip('"').strip("'") for item in value.split(',')]
	return [item for item in items if item] # Remove empty items
	except:
	return ["*"] # Fallback to allow all

	def get_llm_config(self):
	"""Get LLM configuration based on selected provider"""
	if self.LLM_PROVIDER == "openai":
	return {
	"provider": "openai",
	"api_key": self.OPENAI_API_KEY,
	"model": self.OPENAI_MODEL,
	"temperature": self.OPENAI_TEMPERATURE,
	"max_tokens": self.OPENAI_MAX_TOKENS
	}
	elif self.LLM_PROVIDER == "google":
	return {
	"provider": "google",
	"api_key": self.GOOGLE_API_KEY,
	"model": self.GOOGLE_MODEL,
	"temperature": self.GOOGLE_TEMPERATURE,
	"max_tokens": self.GOOGLE_MAX_TOKENS
	}
	elif self.LLM_PROVIDER == "huggingface":
	return {
	"provider": "huggingface",
	"api_token": self.HUGGINGFACE_API_TOKEN,
	"model": self.HUGGINGFACE_MODEL,
	"api_url": self.HUGGINGFACE_API_URL,
	"use_gpu": self.HUGGINGFACE_USE_GPU,
	"use_api": self.HUGGINGFACE_USE_API
	}
	elif self.LLM_PROVIDER == "ollama":
	return {
	"provider": "ollama",
	"base_url": self.OLLAMA_BASE_URL,
	"model": self.OLLAMA_MODEL,
	"temperature": self.OLLAMA_TEMPERATURE
	}
	else:
	raise ValueError(f"Unsupported LLM provider: {self.LLM_PROVIDER}")

	def get_embedding_config(self):
	"""Get embedding configuration based on EMBEDDING_PROVIDER setting"""
	provider = self.EMBEDDING_PROVIDER

	if provider == "openai":
	return {
	"provider": "openai",
	"api_key": self.OPENAI_API_KEY,
	"model": self.OPENAI_EMBEDDING_MODEL
	}
	elif provider == "google":
	return {
	"provider": "google",
	"api_key": self.GOOGLE_API_KEY,
	"model": self.GOOGLE_EMBEDDING_MODEL
	}
	elif provider == "huggingface":
	return {
	"provider": "huggingface",
	"model": self.HUGGINGFACE_EMBEDDING_MODEL
	}
	elif provider == "ollama":
	return {
	"provider": "ollama",
	"base_url": self.OLLAMA_BASE_URL,
	"model": self.OLLAMA_EMBEDDING_MODEL
	}
	else:
	raise ValueError(f"Unsupported provider: {provider}. Supported providers: openai, google, huggingface, ollama")

	# Create global settings instance
	settings = Settings()

	# Note: Vector store and database configuration is in database.py
	# from config.database import db_settings