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Don't release full image (#10654)

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### What problem does this PR solve?

Introduced gpu profile in .env
Added Dockerfile_tei
fix datrie
Removed LIGHTEN flag

### Type of change

- [x] Documentation Update
- [x] Refactoring
This commit is contained in:
Zhichang Yu
2025-10-23 23:02:27 +08:00
committed by GitHub
parent 92739ea804
commit 73144e278b
67 changed files with 2792 additions and 3608 deletions
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61
docker/.env
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@ -1,3 +1,8 @@
# ------------------------------
# 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)
@ -5,12 +10,13 @@
# - `opensearch` (https://github.com/opensearch-project/OpenSearch)
DOC_ENGINE=${DOC_ENGINE:-elasticsearch}
# ------------------------------
# docker env var for specifying vector db type at startup
# (based on the vector db type, the corresponding docker
# compose profile will be used)
# ------------------------------
COMPOSE_PROFILES=${DOC_ENGINE}
# Device on which deepdoc inference run.
# Available levels:
# - `cpu` (default)
# - `gpu`
DEVICE=${DEVICE:-cpu}
COMPOSE_PROFILES=${DOC_ENGINE},${DEVICE}
# The version of Elasticsearch.
STACK_VERSION=8.11.3
@ -38,7 +44,7 @@ OPENSEARCH_PASSWORD=infini_rag_flow_OS_01
# The port used to expose the Kibana service to the host machine,
# allowing EXTERNAL access to the service running inside the Docker container.
# To enable kibana, you need to:
# 1. Ensure that COMPOSE_PROFILES includes kibana, for example: COMPOSE_PROFILES=${DOC_ENGINE},kibana
# 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
@ -96,30 +102,47 @@ REDIS_PASSWORD=infini_rag_flow
SVR_HTTP_PORT=9380
ADMIN_SVR_HTTP_PORT=9381
# The RAGFlow Docker image to download.
# The RAGFlow Docker image to download. v0.22+ doesn't include embedding models.
# Defaults to the v0.21.1-slim edition, which is the RAGFlow Docker image without embedding models.
RAGFLOW_IMAGE=infiniflow/ragflow:v0.21.1-slim
#
# To download the RAGFlow Docker image with embedding models, uncomment the following line instead:
# RAGFLOW_IMAGE=infiniflow/ragflow:v0.21.1
#
# The Docker image of the v0.21.1 edition includes built-in embedding models:
# - BAAI/bge-large-zh-v1.5
# - maidalun1020/bce-embedding-base_v1
#
# If you cannot download the RAGFlow Docker image:
#
# - For the `nightly-slim` edition, uncomment either of the following:
# RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:nightly-slim
# RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:nightly-slim
# RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:v0.21.1
# RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:v0.21.1
#
# - 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.
TIMEZONE=Asia/Shanghai
TZ=Asia/Shanghai
# Uncomment the following line if you have limited access to huggingface.co:
# HF_ENDPOINT=https://hf-mirror.com
@ -165,8 +188,11 @@ EMBEDDING_BATCH_SIZE=${EMBEDDING_BATCH_SIZE:-16}
# - Disable registration: 0
REGISTER_ENABLED=1
# Important: To enable sandbox, you need to uncomment following two lines:
# SANDBOX_ENABLED=1
# COMPOSE_PROFILES=${COMPOSE_PROFILES},sandbox
# Sandbox settings
# Important: To enable sandbox, you must re-declare the compose profiles. See hints at the end of file.
# 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
@ -175,7 +201,6 @@ REGISTER_ENABLED=1
# - 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_ENABLED=1
# SANDBOX_HOST=sandbox-executor-manager
# SANDBOX_EXECUTOR_MANAGER_IMAGE=infiniflow/sandbox-executor-manager:latest
# SANDBOX_EXECUTOR_MANAGER_POOL_SIZE=3