python-rag-langchain/.env.example at master · zakantonio/python-rag-langchain · GitHub

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# LLM Configuration
OPENROUTER_API_KEY=your_openrouter_key
OLLAMA_BASE_URL=http://localhost:11434

# Vector Store Configuration
VECTOR_STORE_PATH=./vector_stores
VECTOR_STORE_TYPE=chroma

# Document Storage Configuration
DOCUMENTS_STORAGE_PATH=./documents

# API Configuration
API_HOST=0.0.0.0
API_PORT=8000

# UI Configuration
UI_HOST=0.0.0.0
UI_PORT=7860

# Default Models
DEFAULT_LOCAL_MODEL=gemma3:4b
DEFAULT_CLOUD_MODEL=deepseek/deepseek-r1-0528-qwen3-8b:free

# Available Models
AVAILABLE_LOCAL_MODELS=gemma3:4b
AVAILABLE_CLOUD_MODELS=deepseek/deepseek-r1-0528-qwen3-8b:free

# Embedding Configuration (Ollama)
EMBEDDING_MODEL=nomic-embed-text:v1.5
EMBEDDING_MODEL_FALLBACK=nomic-embed-text:latest

# Document Processing
CHUNK_SIZE=1200
CHUNK_OVERLAP=400

# Retrieval Configuration
RETRIEVAL_K=5
SIMILARITY_THRESHOLD=0.7
MAX_CONTEXT_LENGTH=8000

# Logging Configuration
LOG_LEVEL=INFO
ENABLE_DEBUG=false