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de53498b39
### What problem does this PR solve? Fix: Update env to support PPTX Fix: update README for version changes #11138 ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) - [x] Documentation Update --------- Co-authored-by: writinwaters <93570324+writinwaters@users.noreply.github.com>
222 lines
8.7 KiB
Bash
222 lines
8.7 KiB
Bash
# ------------------------------
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# docker env var for specifying vector db type at startup
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# (based on the vector db type, the corresponding docker
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# compose profile will be used)
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# ------------------------------
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# The type of doc engine to use.
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# Available options:
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# - `elasticsearch` (default)
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# - `infinity` (https://github.com/infiniflow/infinity)
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# - `opensearch` (https://github.com/opensearch-project/OpenSearch)
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DOC_ENGINE=${DOC_ENGINE:-elasticsearch}
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# Device on which deepdoc inference run.
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# Available levels:
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# - `cpu` (default)
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# - `gpu`
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DEVICE=${DEVICE:-cpu}
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COMPOSE_PROFILES=${DOC_ENGINE},${DEVICE}
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# The version of Elasticsearch.
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STACK_VERSION=8.11.3
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# The hostname where the Elasticsearch service is exposed
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ES_HOST=es01
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# The port used to expose the Elasticsearch service to the host machine,
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# allowing EXTERNAL access to the service running inside the Docker container.
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ES_PORT=1200
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# The password for Elasticsearch.
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ELASTIC_PASSWORD=infini_rag_flow
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# the hostname where OpenSearch service is exposed, set it not the same as elasticsearch
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OS_PORT=1201
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# The hostname where the OpenSearch service is exposed
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OS_HOST=opensearch01
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# The password for OpenSearch.
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# At least one uppercase letter, one lowercase letter, one digit, and one special character
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OPENSEARCH_PASSWORD=infini_rag_flow_OS_01
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# The port used to expose the Kibana service to the host machine,
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# allowing EXTERNAL access to the service running inside the Docker container.
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# To enable kibana, you need to:
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# 1. Ensure that COMPOSE_PROFILES includes kibana, for example: COMPOSE_PROFILES=${COMPOSE_PROFILES},kibana
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# 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)
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# 3. Adjust the es.hosts in conf/service_config.yaml or docker/service_conf.yaml.template to 'https://localhost:1200'
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# 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
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KIBANA_PORT=6601
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# The maximum amount of the memory, in bytes, that a specific Docker container can use while running.
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# Update it according to the available memory in the host machine.
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MEM_LIMIT=8073741824
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# The hostname where the Infinity service is exposed
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INFINITY_HOST=infinity
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# Port to expose Infinity API to the host
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INFINITY_THRIFT_PORT=23817
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INFINITY_HTTP_PORT=23820
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INFINITY_PSQL_PORT=5432
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# The password for MySQL.
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MYSQL_PASSWORD=infini_rag_flow
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# The hostname where the MySQL service is exposed
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MYSQL_HOST=mysql
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# The database of the MySQL service to use
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MYSQL_DBNAME=rag_flow
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# The port used to expose the MySQL service to the host machine,
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# allowing EXTERNAL access to the MySQL database running inside the Docker container.
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MYSQL_PORT=5455
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# The maximum size of communication packets sent to the MySQL server
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MYSQL_MAX_PACKET=1073741824
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# The hostname where the MinIO service is exposed
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MINIO_HOST=minio
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# The port used to expose the MinIO console interface to the host machine,
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# allowing EXTERNAL access to the web-based console running inside the Docker container.
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MINIO_CONSOLE_PORT=9001
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# The port used to expose the MinIO API service to the host machine,
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# allowing EXTERNAL access to the MinIO object storage service running inside the Docker container.
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MINIO_PORT=9000
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# The username for MinIO.
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# When updated, you must revise the `minio.user` entry in service_conf.yaml accordingly.
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MINIO_USER=rag_flow
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# The password for MinIO.
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# When updated, you must revise the `minio.password` entry in service_conf.yaml accordingly.
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MINIO_PASSWORD=infini_rag_flow
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# The hostname where the Redis service is exposed
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REDIS_HOST=redis
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# The port used to expose the Redis service to the host machine,
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# allowing EXTERNAL access to the Redis service running inside the Docker container.
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REDIS_PORT=6379
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# The password for Redis.
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REDIS_PASSWORD=infini_rag_flow
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# The port used to expose RAGFlow's HTTP API service to the host machine,
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# allowing EXTERNAL access to the service running inside the Docker container.
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SVR_WEB_HTTP_PORT=80
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SVR_WEB_HTTPS_PORT=443
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SVR_HTTP_PORT=9380
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ADMIN_SVR_HTTP_PORT=9381
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SVR_MCP_PORT=9382
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# The RAGFlow Docker image to download. v0.22+ doesn't include embedding models.
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# Defaults to the v0.21.1-slim edition, which is the RAGFlow Docker image without embedding models.
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RAGFLOW_IMAGE=infiniflow/ragflow:v0.21.1-slim
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# RAGFLOW_IMAGE=infiniflow/ragflow:v0.21.1
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# The Docker image of the v0.21.1 edition includes built-in embedding models:
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# - BAAI/bge-large-zh-v1.5
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# - maidalun1020/bce-embedding-base_v1
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#
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# If you cannot download the RAGFlow Docker image:
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# RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:v0.21.1
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# RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:v0.21.1
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#
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# - For the `nightly` edition, uncomment either of the following:
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# RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:nightly
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# RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:nightly
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# The embedding service image, model and port.
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# Important: To enable the embedding service, you need to uncomment one of the following two lines:
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# COMPOSE_PROFILES=${COMPOSE_PROFILES},tei-cpu
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# COMPOSE_PROFILES=${COMPOSE_PROFILES},tei-gpu
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# The embedding service image:
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TEI_IMAGE_CPU=infiniflow/text-embeddings-inference:cpu-1.8
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TEI_IMAGE_GPU=infiniflow/text-embeddings-inference:1.8
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# The embedding service model:
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# Available options:
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# - `Qwen/Qwen3-Embedding-0.6B` (default, requires 25GB RAM/vRAM to load)
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# - `BAAI/bge-m3` (requires 21GB RAM/vRAM to load)
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# - `BAAI/bge-small-en-v1.5` (requires 1.2GB RAM/vRAM to load)
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TEI_MODEL=${TEI_MODEL:-Qwen/Qwen3-Embedding-0.6B}
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# The embedding service port:
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TEI_HOST=tei
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# The port used to expose the TEI service to the host machine,
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# allowing EXTERNAL access to the service running inside the Docker container.
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TEI_PORT=6380
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# The local time zone.
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TZ=Asia/Shanghai
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# Uncomment the following line if you have limited access to huggingface.co:
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# HF_ENDPOINT=https://hf-mirror.com
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# Optimizations for MacOS
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# Uncomment the following line if your operating system is MacOS:
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# MACOS=1
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# The maximum file size limit (in bytes) for each upload to your knowledge base or File Management.
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# To change the 1GB file size limit, uncomment the line below and update as needed.
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# MAX_CONTENT_LENGTH=1073741824
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# After updating, ensure `client_max_body_size` in nginx/nginx.conf is updated accordingly.
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# Note that neither `MAX_CONTENT_LENGTH` nor `client_max_body_size` sets the maximum size for files uploaded to an agent.
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# See https://ragflow.io/docs/dev/begin_component for details.
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# Controls how many documents are processed in a single batch.
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# Defaults to 4 if DOC_BULK_SIZE is not explicitly set.
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DOC_BULK_SIZE=${DOC_BULK_SIZE:-4}
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# Defines the number of items to process per batch when generating embeddings.
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# Defaults to 16 if EMBEDDING_BATCH_SIZE is not set in the environment.
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EMBEDDING_BATCH_SIZE=${EMBEDDING_BATCH_SIZE:-16}
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# Log level for the RAGFlow's own and imported packages.
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# Available levels:
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# - `DEBUG`
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# - `INFO` (default)
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# - `WARNING`
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# - `ERROR`
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# For example, the following line changes the log level of `ragflow.es_conn` to `DEBUG`:
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# LOG_LEVELS=ragflow.es_conn=DEBUG
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# aliyun OSS configuration
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# STORAGE_IMPL=OSS
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# ACCESS_KEY=xxx
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# SECRET_KEY=eee
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# ENDPOINT=http://oss-cn-hangzhou.aliyuncs.com
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# REGION=cn-hangzhou
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# BUCKET=ragflow65536
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# A user registration switch:
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# - Enable registration: 1
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# - Disable registration: 0
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REGISTER_ENABLED=1
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# Important: To enable sandbox, you need to uncomment following two lines:
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# SANDBOX_ENABLED=1
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# COMPOSE_PROFILES=${COMPOSE_PROFILES},sandbox
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# Sandbox settings
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# Double check if you add `sandbox-executor-manager` to your `/etc/hosts`
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# Pull the required base images before running:
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# docker pull infiniflow/sandbox-base-nodejs:latest
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# docker pull infiniflow/sandbox-base-python:latest
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# Our default sandbox environments include:
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# - Node.js base image: includes axios
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# - Python base image: includes requests, numpy, and pandas
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# Specify custom executor images below if you're using non-default environments.
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# SANDBOX_HOST=sandbox-executor-manager
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# SANDBOX_EXECUTOR_MANAGER_IMAGE=infiniflow/sandbox-executor-manager:latest
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# SANDBOX_EXECUTOR_MANAGER_POOL_SIZE=3
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# SANDBOX_BASE_PYTHON_IMAGE=infiniflow/sandbox-base-python:latest
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# SANDBOX_BASE_NODEJS_IMAGE=infiniflow/sandbox-base-nodejs:latest
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# SANDBOX_EXECUTOR_MANAGER_PORT=9385
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# SANDBOX_ENABLE_SECCOMP=false
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# SANDBOX_MAX_MEMORY=256m # b, k, m, g
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# SANDBOX_TIMEOUT=10s # s, m, 1m30s
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# Enable DocLing and Mineru
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USE_DOCLING=false
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USE_MINERU=false
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# pptx support
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DOTNET_SYSTEM_GLOBALIZATION_INVARIANT=1 |