# Ingestion Configuration
# Set to true to disable Langflow ingestion and use traditional OpenRAG processor
# If unset or false, Langflow pipeline will be used (default: upload -> ingest -> delete)
DISABLE_INGEST_WITH_LANGFLOW=false

# Langflow HTTP timeout configuration (in seconds)
# For large documents (300+ pages), ingestion can take 30+ minutes
# Increase these values if you experience timeouts with very large PDFs
# Default: 2400 seconds (40 minutes) total timeout, 30 seconds connection timeout
# LANGFLOW_TIMEOUT=2400
# LANGFLOW_CONNECT_TIMEOUT=30

# make one like so https://docs.langflow.org/api-keys-and-authentication#langflow-secret-key
LANGFLOW_SECRET_KEY=

# flow ids for chat and ingestion flows
LANGFLOW_CHAT_FLOW_ID=1098eea1-6649-4e1d-aed1-b77249fb8dd0
LANGFLOW_INGEST_FLOW_ID=5488df7c-b93f-4f87-a446-b67028bc0813
LANGFLOW_URL_INGEST_FLOW_ID=72c3d17c-2dac-4a73-b48a-6518473d7830
# Ingest flow using docling
# LANGFLOW_INGEST_FLOW_ID=1402618b-e6d1-4ff2-9a11-d6ce71186915
NUDGES_FLOW_ID=ebc01d31-1976-46ce-a385-b0240327226c

# Set a strong admin password for OpenSearch; a bcrypt hash is generated at
# container startup from this value. Do not commit real secrets.
# must match the hashed password in secureconfig, must change for secure deployment!!!
# NOTE: if you set this by hand, it must be a complex password: 
# The password must contain at least 8 characters, and must contain at least one uppercase letter, one lowercase letter, one digit, and one special character.
OPENSEARCH_PASSWORD=

# Path to persist OpenSearch data (indices, documents, cluster state)
# Default: ./opensearch-data
OPENSEARCH_DATA_PATH=./opensearch-data

# make here https://console.cloud.google.com/apis/credentials
GOOGLE_OAUTH_CLIENT_ID=
GOOGLE_OAUTH_CLIENT_SECRET=

# Azure app registration credentials for SharePoint/OneDrive
MICROSOFT_GRAPH_OAUTH_CLIENT_ID=
MICROSOFT_GRAPH_OAUTH_CLIENT_SECRET=

# AWS Access Key ID and Secret Access Key with access to your S3 instance
AWS_ACCESS_KEY_ID=
AWS_SECRET_ACCESS_KEY=

# OPTIONAL: dns routable from google (etc.) to handle continous ingest (something like ngrok works). This enables continous ingestion
WEBHOOK_BASE_URL=

# Model Provider API Keys
OPENAI_API_KEY=
ANTHROPIC_API_KEY=
OLLAMA_ENDPOINT=
WATSONX_API_KEY=
WATSONX_ENDPOINT=
WATSONX_PROJECT_ID=

# LLM Provider configuration. Providers can be "anthropic", "watsonx", "ibm" or "ollama".
LLM_PROVIDER=
LLM_MODEL=

# Embedding provider configuration. Providers can be "watsonx", "ibm" or "ollama".
EMBEDDING_PROVIDER=
EMBEDDING_MODEL=

# OPTIONAL url for openrag link to langflow in the UI
LANGFLOW_PUBLIC_URL=

# OPTIONAL: Override the full docling-serve URL (e.g., for remote instances)
# If not set, auto-detects host and uses port 5001
# DOCLING_SERVE_URL=http://my-docling-server:5001

# OPTIONAL: Override host for docling service (for special networking setups)
# HOST_DOCKER_INTERNAL=host.containers.internal

# Langflow auth
LANGFLOW_AUTO_LOGIN=False
LANGFLOW_SUPERUSER=
LANGFLOW_SUPERUSER_PASSWORD=
LANGFLOW_NEW_USER_IS_ACTIVE=False
LANGFLOW_ENABLE_SUPERUSER_CLI=False

# Langfuse tracing (optional)
# Get keys from https://cloud.langfuse.com or your self-hosted instance
LANGFUSE_SECRET_KEY=
LANGFUSE_PUBLIC_KEY=
# Leave empty for Langfuse Cloud, or set for self-hosted (e.g., http://localhost:3002)
LANGFUSE_HOST=