ragflow/docker/.env

# ------------------------------
# docker env var for specifying vector db type at startup
# (based on the vector db type, the corresponding docker
# compose profile will be used)
# ------------------------------
# The type of doc engine to use.
# Available options:
# - `elasticsearch` (default)
# - `infinity` (https://github.com/infiniflow/infinity)
# - `oceanbase` (https://github.com/oceanbase/oceanbase)
# - `opensearch` (https://github.com/opensearch-project/OpenSearch)
DOC_ENGINE=${DOC_ENGINE:-elasticsearch}

# Device on which deepdoc inference run.
# Available levels:
# - `cpu` (default)
# - `gpu`
DEVICE=${DEVICE:-cpu}

COMPOSE_PROFILES=${DOC_ENGINE},${DEVICE}

# The version of Elasticsearch.
STACK_VERSION=8.11.3

# The hostname where the Elasticsearch service is exposed
ES_HOST=es01

# The port used to expose the Elasticsearch service to the host machine,
# allowing EXTERNAL access to the service running inside the Docker container.
ES_PORT=1200

# The password for Elasticsearch.
ELASTIC_PASSWORD=infini_rag_flow

# the hostname where OpenSearch service is exposed, set it not the same as elasticsearch
OS_PORT=1201

# The hostname where the OpenSearch service is exposed
OS_HOST=opensearch01

# The password for OpenSearch.
# At least one uppercase letter, one lowercase letter, one digit, and one special character
OPENSEARCH_PASSWORD=infini_rag_flow_OS_01

# The port used to expose the Kibana service to the host machine,
# allowing EXTERNAL access to the service running inside the Docker container.
# To enable kibana, you need to:
# 1. Ensure that COMPOSE_PROFILES includes kibana, for example: COMPOSE_PROFILES=${COMPOSE_PROFILES},kibana
# 2. Comment out or delete the following configurations of the es service in docker-compose-base.yml: xpack.security.enabled、xpack.security.http.ssl.enabled、xpack.security.transport.ssl.enabled (for details: https://www.elastic.co/docs/deploy-manage/security/self-auto-setup#stack-existing-settings-detected)
# 3. Adjust the es.hosts in conf/service_config.yaml or docker/service_conf.yaml.template to 'https://localhost:1200'
# 4. After the startup is successful, in the es container, execute the command to generate the kibana token: `bin/elasticsearch-create-enrollment-token -s kibana`, then you can use kibana normally
KIBANA_PORT=6601

# The maximum amount of the memory, in bytes, that a specific Docker container can use while running.
# Update it according to the available memory in the host machine.
MEM_LIMIT=8073741824

# The hostname where the Infinity service is exposed
INFINITY_HOST=infinity

# Port to expose Infinity API to the host
INFINITY_THRIFT_PORT=23817
INFINITY_HTTP_PORT=23820
INFINITY_PSQL_PORT=5432

# The hostname where the OceanBase service is exposed
OCEANBASE_HOST=oceanbase
# The port used to expose the OceanBase service
OCEANBASE_PORT=2881
# The username for OceanBase
OCEANBASE_USER=root@ragflow
# The password for OceanBase
OCEANBASE_PASSWORD=infini_rag_flow
# The doc database of the OceanBase service to use
OCEANBASE_DOC_DBNAME=ragflow_doc

# OceanBase container configuration
OB_CLUSTER_NAME=${OB_CLUSTER_NAME:-ragflow}
OB_TENANT_NAME=${OB_TENANT_NAME:-ragflow}
OB_SYS_PASSWORD=${OCEANBASE_PASSWORD:-infini_rag_flow}
OB_TENANT_PASSWORD=${OCEANBASE_PASSWORD:-infini_rag_flow}
OB_MEMORY_LIMIT=${OB_MEMORY_LIMIT:-10G}
OB_SYSTEM_MEMORY=${OB_SYSTEM_MEMORY:-2G}
OB_DATAFILE_SIZE=${OB_DATAFILE_SIZE:-20G}
OB_LOG_DISK_SIZE=${OB_LOG_DISK_SIZE:-20G}

# The password for MySQL.
MYSQL_PASSWORD=infini_rag_flow
# The hostname where the MySQL service is exposed
MYSQL_HOST=mysql
# The database of the MySQL service to use
MYSQL_DBNAME=rag_flow
# The port used to expose the MySQL service to the host machine,
# allowing EXTERNAL access to the MySQL database running inside the Docker container.
MYSQL_PORT=5455
# The maximum size of communication packets sent to the MySQL server
MYSQL_MAX_PACKET=1073741824

# The hostname where the MinIO service is exposed
MINIO_HOST=minio
# The port used to expose the MinIO console interface to the host machine,
# allowing EXTERNAL access to the web-based console running inside the Docker container.
MINIO_CONSOLE_PORT=9001
# The port used to expose the MinIO API service to the host machine,
# allowing EXTERNAL access to the MinIO object storage service running inside the Docker container.
MINIO_PORT=9000
# The username for MinIO.
# When updated, you must revise the `minio.user` entry in service_conf.yaml accordingly.
MINIO_USER=rag_flow
# The password for MinIO.
# When updated, you must revise the `minio.password` entry in service_conf.yaml accordingly.
MINIO_PASSWORD=infini_rag_flow

# The hostname where the Redis service is exposed
REDIS_HOST=redis
# The port used to expose the Redis service to the host machine,
# allowing EXTERNAL access to the Redis service running inside the Docker container.
REDIS_PORT=6379
# The password for Redis.
REDIS_PASSWORD=infini_rag_flow

# The port used to expose RAGFlow's HTTP API service to the host machine,
# allowing EXTERNAL access to the service running inside the Docker container.
SVR_WEB_HTTP_PORT=80
SVR_WEB_HTTPS_PORT=443
SVR_HTTP_PORT=9380
ADMIN_SVR_HTTP_PORT=9381
SVR_MCP_PORT=9382

# The RAGFlow Docker image to download. v0.22+ doesn't include embedding models.
RAGFLOW_IMAGE=infiniflow/ragflow:v0.22.1

# If you cannot download the RAGFlow Docker image:
# RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:v0.22.1
# RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:v0.22.1
#
# - For the `nightly` edition, uncomment either of the following:
# RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:nightly
# RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:nightly

# The embedding service image, model and port.
# Important: To enable the embedding service, you need to uncomment one of the following two lines:
# COMPOSE_PROFILES=${COMPOSE_PROFILES},tei-cpu
# COMPOSE_PROFILES=${COMPOSE_PROFILES},tei-gpu

# The embedding service image:
TEI_IMAGE_CPU=infiniflow/text-embeddings-inference:cpu-1.8
TEI_IMAGE_GPU=infiniflow/text-embeddings-inference:1.8

# The embedding service model:
# Available options:
# - `Qwen/Qwen3-Embedding-0.6B` (default, requires 25GB RAM/vRAM to load)
# - `BAAI/bge-m3` (requires 21GB RAM/vRAM to load)
# - `BAAI/bge-small-en-v1.5` (requires 1.2GB RAM/vRAM to load)
TEI_MODEL=${TEI_MODEL:-Qwen/Qwen3-Embedding-0.6B}

# The embedding service port:
TEI_HOST=tei
# The port used to expose the TEI service to the host machine,
# allowing EXTERNAL access to the service running inside the Docker container.
TEI_PORT=6380

# The local time zone.
TZ=Asia/Shanghai

# Uncomment the following line if you have limited access to huggingface.co:
# HF_ENDPOINT=https://hf-mirror.com

# Optimizations for MacOS
# Uncomment the following line if your operating system is MacOS:
# MACOS=1

# The maximum file size limit (in bytes) for each upload to your dataset or RAGFlow's File system.
# To change the 1GB file size limit, uncomment the line below and update as needed.
# MAX_CONTENT_LENGTH=1073741824
# After updating, ensure `client_max_body_size` in nginx/nginx.conf is updated accordingly.
# Note that neither `MAX_CONTENT_LENGTH` nor `client_max_body_size` sets the maximum size for files uploaded to an agent.
# See https://ragflow.io/docs/dev/begin_component for details.

# Controls how many documents are processed in a single batch.
# Defaults to 4 if DOC_BULK_SIZE is not explicitly set.
DOC_BULK_SIZE=${DOC_BULK_SIZE:-4}

# Defines the number of items to process per batch when generating embeddings.
# Defaults to 16 if EMBEDDING_BATCH_SIZE is not set in the environment.
EMBEDDING_BATCH_SIZE=${EMBEDDING_BATCH_SIZE:-16}

# Log level for the RAGFlow's own and imported packages.
# Available levels:
# - `DEBUG`
# - `INFO` (default)
# - `WARNING`
# - `ERROR`
# For example, the following line changes the log level of `ragflow.es_conn` to `DEBUG`:
# LOG_LEVELS=ragflow.es_conn=DEBUG

# aliyun OSS configuration
# STORAGE_IMPL=OSS
# ACCESS_KEY=xxx
# SECRET_KEY=eee
# ENDPOINT=http://oss-cn-hangzhou.aliyuncs.com
# REGION=cn-hangzhou
# BUCKET=ragflow65536

# A user registration switch:
# - Enable registration: 1
# - Disable registration: 0
REGISTER_ENABLED=1

# Important: To enable sandbox, you need to uncomment following two lines:
# SANDBOX_ENABLED=1
# COMPOSE_PROFILES=${COMPOSE_PROFILES},sandbox

# Sandbox settings
# Double check if you add `sandbox-executor-manager` to your `/etc/hosts`
# Pull the required base images before running:
#   docker pull infiniflow/sandbox-base-nodejs:latest
#   docker pull infiniflow/sandbox-base-python:latest
# Our default sandbox environments include:
#   - Node.js base image: includes axios
#   - Python base image: includes requests, numpy, and pandas
# Specify custom executor images below if you're using non-default environments.
# SANDBOX_HOST=sandbox-executor-manager
# SANDBOX_EXECUTOR_MANAGER_IMAGE=infiniflow/sandbox-executor-manager:latest
# SANDBOX_EXECUTOR_MANAGER_POOL_SIZE=3
# SANDBOX_BASE_PYTHON_IMAGE=infiniflow/sandbox-base-python:latest
# SANDBOX_BASE_NODEJS_IMAGE=infiniflow/sandbox-base-nodejs:latest
# SANDBOX_EXECUTOR_MANAGER_PORT=9385
# SANDBOX_ENABLE_SECCOMP=false
# SANDBOX_MAX_MEMORY=256m # b, k, m, g
# SANDBOX_TIMEOUT=10s # s, m, 1m30s

# Enable DocLing
USE_DOCLING=false

# Enable Mineru
USE_MINERU=false
MINERU_EXECUTABLE="$HOME/uv_tools/.venv/bin/mineru"
# Uncommenting these lines will automatically add MinerU to the model provider whenever possible.
# MINERU_DELETE_OUTPUT=0   # keep output directory
# MINERU_BACKEND=pipeline  # or another backend you prefer



# pptx support
DOTNET_SYSTEM_GLOBALIZATION_INVARIANT=1