# ===========================================
# Recipe Recommendation Bot - Environment Configuration
# ===========================================

# Server Configuration
PORT=8080
HOST=0.0.0.0
ENVIRONMENT=development
DEBUG=true
LANGCHAIN_DEBUG=true

# CORS Configuration
CORS_ORIGINS=["http://localhost:3000","http://localhost:5173","http://localhost:8000"]
CORS_ALLOW_CREDENTIALS=true
CORS_ALLOW_METHODS=["GET","POST","PUT","DELETE","OPTIONS"]
CORS_ALLOW_HEADERS=["*"]

# ===========================================
# LLM & Embedding Provider Configuration
# ===========================================
# Supported providers: openai, google, huggingface, ollama
# This provider will be used for both LLM and embeddings

LLM_PROVIDER=google
EMBEDDING_PROVIDER=google

# OpenAI Configuration
# Use only if LLM_PROVIDER or EMBEDDING_PROVIDER is set to 'openai'
OPENAI_API_KEY=YOUR_OPENAI_API_KEY_HERE
OPENAI_MODEL=gpt-5-nano
OPENAI_TEMPERATURE=0.7
OPENAI_MAX_TOKENS=1000

# Google AI Configuration (Gemini)
# Use only if LLM_PROVIDER or EMBEDDING_PROVIDER is set to 'google'
GOOGLE_API_KEY=YOUR_GOOGLE_API_KEY_HERE
GOOGLE_MODEL=gemini-2.0-flash
GOOGLE_TEMPERATURE=0.7
GOOGLE_MAX_TOKENS=1000

# Hugging Face Configuration
# Use only if LLM_PROVIDER or EMBEDDING_PROVIDER is set to 'huggingface'
HUGGINGFACE_API_TOKEN=YOUR_HUGGINGFACE_API_TOKEN_HERE
HUGGINGFACE_MODEL=deepseek-ai/DeepSeek-V3.1
HUGGINGFACE_API_URL=https://api-inference.huggingface.co/models/
HUGGINGFACE_USE_API=true
HUGGINGFACE_USE_GPU=false

# Ollama Configuration (local inference)
# Use only if LLM_PROVIDER or EMBEDDING_PROVIDER is set to 'ollama'
OLLAMA_BASE_URL=http://localhost:11434
OLLAMA_MODEL=llama3.1:8b
OLLAMA_TEMPERATURE=0.7


# ===========================================
# Vector Store Configuration
# ===========================================
# Supported stores: chromadb, mongodb
VECTOR_STORE_PROVIDER=mongodb

# ChromaDB Configuration
DB_PATH=./data/chromadb
DB_COLLECTION_NAME=recipes
DB_PERSIST_DIRECTORY=./data/chromadb_persist
# Set to true to delete and recreate DB on startup (useful for adding new recipes)
DB_REFRESH_ON_START=false

# MongoDB Atlas Configuration (for vector search)
# Provide your connection string and collection settings when using MongoDB
MONGODB_URI=mongodb+srv://<username>:<password>@<cluster>.mongodb.net/?retryWrites=true&w=majority&appName=<AppName>
MONGODB_DATABASE=food_recommendation
MONGODB_COLLECTION=AI_DB
MONGODB_INDEX_NAME=foodInstructionIndex
MONGODB_VECTOR_FIELD=ingredients_emb
MONGODB_TEXT_FIELD=title
MONGODB_SIMILARITY_METRIC=dotProduct
MONGODB_NUM_CANDIDATES=100


# ===========================================
# Model Configuration
# ===========================================
# The LLM_PROVIDER setting above controls both LLM and embedding models

# OpenAI Models
OPENAI_EMBEDDING_MODEL=text-embedding-3-small

# Google Models
GOOGLE_EMBEDDING_MODEL=models/embedding-001

# HuggingFace Models
HUGGINGFACE_EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2

# Ollama Models
OLLAMA_EMBEDDING_MODEL=nomic-embed-text:v1.5


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