anything-llm/docker/.env.example

SERVER_PORT=3001
CACHE_VECTORS="true"
# JWT_SECRET="my-random-string-for-seeding" # Only needed if AUTH_TOKEN is set. Please generate random string at least 12 chars long.

###########################################
######## LLM API SElECTION ################
###########################################
LLM_PROVIDER='openai'
# OPEN_AI_KEY=
OPEN_MODEL_PREF='gpt-3.5-turbo'

# LLM_PROVIDER='azure'
# AZURE_OPENAI_ENDPOINT=
# AZURE_OPENAI_KEY=
# OPEN_MODEL_PREF='my-gpt35-deployment' # This is the "deployment" on Azure you want to use. Not the base model.
# EMBEDDING_MODEL_PREF='embedder-model' # This is the "deployment" on Azure you want to use for embeddings. Not the base model. Valid base model is text-embedding-ada-002


###########################################
######## Vector Database Selection ########
###########################################
# Enable all below if you are using vector database: Chroma.
# VECTOR_DB="chroma"
# CHROMA_ENDPOINT='http://host.docker.internal:8000'

# Enable all below if you are using vector database: Pinecone.
VECTOR_DB="pinecone"
PINECONE_ENVIRONMENT=
PINECONE_API_KEY=
PINECONE_INDEX=

# Enable all below if you are using vector database: LanceDB.
# VECTOR_DB="lancedb"

# Enable all below if you are using vector database: Weaviate.
# VECTOR_DB="weaviate"
# WEAVIATE_ENDPOINT="http://localhost:8080"
# WEAVIATE_API_KEY=

# Enable all below if you are using vector database: Qdrant.
# VECTOR_DB="qdrant"
# QDRANT_ENDPOINT="http://localhost:6333"
# QDRANT_API_KEY=

# CLOUD DEPLOYMENT VARIRABLES ONLY
# AUTH_TOKEN="hunter2" # This is the password to your application if remote hosting.
# NO_DEBUG="true"
STORAGE_DIR="./server/storage"
GOOGLE_APIS_KEY=
UID='1000'
GID='1000'