feat: add .env.example-full and fix .env.example

Author: TangYH321

docker/.env.example

@@ -1,223 +1,49 @@
-# MemOS Environment Variables (core runtime)
-# Legend: [required] needed for default startup; others are optional or conditional per comments.
-
-## Base
-TZ=Asia/Shanghai
-MOS_CUBE_PATH=/tmp/data_test               # local data path
-MEMOS_BASE_PATH=.                          # CLI/SDK cache path
-MOS_ENABLE_DEFAULT_CUBE_CONFIG=true        # enable default cube config
-MOS_ENABLE_REORGANIZE=false                # enable memory reorg
-# MOS Text Memory Type
-MOS_TEXT_MEM_TYPE=general_text             # general_text | tree_text
-ASYNC_MODE=sync                            # async/sync, used in default cube config
-
-## User/session defaults
-# Top-K for LLM in the Product API（old version）
-MOS_TOP_K=50
-
-## Chat LLM (main dialogue)
-# LLM model name for the Product API
-MOS_CHAT_MODEL=gpt-4o-mini
-# Temperature for LLM in the Product API
-MOS_CHAT_TEMPERATURE=0.8
-# Max tokens for LLM in the Product API
-MOS_MAX_TOKENS=2048
-# Top-P for LLM in the Product API
-MOS_TOP_P=0.9
-# LLM for the Product API backend
-MOS_CHAT_MODEL_PROVIDER=openai             # openai | huggingface | vllm | minimax
-OPENAI_API_KEY=sk-xxx                      # [required] when provider=openai
-OPENAI_API_BASE=https://api.openai.com/v1  # [required] base for the key
-# MiniMax LLM (when provider=minimax)
-# MINIMAX_API_KEY=your-minimax-api-key     # [required] when provider=minimax
-# MINIMAX_API_BASE=https://api.minimax.io/v1  # base for MiniMax API
-
-## MemReader / retrieval LLM
-MEMRADER_MODEL=gpt-4o-mini
-MEMRADER_API_KEY=sk-xxx                    # [required] can reuse OPENAI_API_KEY
-MEMRADER_API_BASE=http://localhost:3000/v1 # [required] base for the key
-MEMRADER_MAX_TOKENS=5000
-
-## Embedding & rerank
-# embedding dim
+# Apply through Alibaba Cloud Bailian platform
+# https://bailian.console.aliyun.com/?spm=a2c4g.11186623.0.0.2f2165b08fRk4l&tab=api#/api
+# After successful application, obtain API_KEY and BASE_URL, example configuration below
+
+# OpenAI API Key (use Bailian's API_KEY)
+OPENAI_API_KEY=you_bailian_api_key
+# OpenAI API Base URL
+OPENAI_API_BASE=https://dashscope.aliyuncs.com/compatible-mode/v1
+# Default model name
+MOS_CHAT_MODEL=qwen3-max
+
+# Memory Reader LLM Model
+MEMRADER_MODEL=qwen3-max
+# Memory Reader API Key (use Bailian's API_KEY)
+MEMRADER_API_KEY=you_bailian_api_key
+# Memory Reader API Base URL
+MEMRADER_API_BASE=https://dashscope.aliyuncs.com/compatible-mode/v1
+
+# For Embedder model names, refer to the link below
+# https://bailian.console.aliyun.com/?spm=a2c4g.11186623.0.0.2f2165b08fRk4l&tab=api#/api/?type=model&url=2846066
+MOS_EMBEDDER_MODEL=text-embedding-v4
+# Configure embedding backend, two options: ollama | universal_api
+MOS_EMBEDDER_BACKEND=universal_api
+# Embedder API Base URL
+MOS_EMBEDDER_API_BASE=https://dashscope.aliyuncs.com/compatible-mode/v1
+# Embedder API Key (use Bailian's API_KEY)
+MOS_EMBEDDER_API_KEY=you_bailian_api_key
+# Embedding vector dimension
 EMBEDDING_DIMENSION=1024
-# set default embedding backend
-MOS_EMBEDDER_BACKEND=universal_api        # universal_api | ollama
-# set openai style
-MOS_EMBEDDER_PROVIDER=openai              # required when universal_api
-# embedding model
-MOS_EMBEDDER_MODEL=bge-m3                 # siliconflow → use BAAI/bge-m3
-# embedding url
-MOS_EMBEDDER_API_BASE=http://localhost:8000/v1 # required when universal_api
-# embedding model key
-MOS_EMBEDDER_API_KEY=EMPTY                 # required when universal_api
-OLLAMA_API_BASE=http://localhost:11434     # required when backend=ollama
-# reranker config
-MOS_RERANKER_BACKEND=http_bge             # http_bge | http_bge_strategy | cosine_local
-# reranker url
-MOS_RERANKER_URL=http://localhost:8001     # required when backend=http_bge*
-# reranker model
-MOS_RERANKER_MODEL=bge-reranker-v2-m3     # siliconflow → use BAAI/bge-reranker-v2-m3
-MOS_RERANKER_HEADERS_EXTRA=               # extra headers, JSON string, e.g. {"Authorization":"Bearer your_token"}
-# use source
-MOS_RERANKER_STRATEGY=single_turn
-
-
-# External Services (for evaluation scripts)
-# API key for reproducting Zep(compertitor product)  evaluation
-ZEP_API_KEY=your_zep_api_key_here
-# API key for reproducting Mem0(compertitor product)  evaluation
-MEM0_API_KEY=your_mem0_api_key_here
-# API key for reproducting MemU(compertitor product)  evaluation
-MEMU_API_KEY=your_memu_api_key_here
-# API key for reproducting MEMOBASE(compertitor product)  evaluation
-MEMOBASE_API_KEY=your_memobase_api_key_here
-# Project url for reproducting MEMOBASE(compertitor product)  evaluation
-MEMOBASE_PROJECT_URL=your_memobase_project_url_here
-# LLM for evaluation
-MODEL=gpt-4o-mini
-# embedding model for evaluation
-EMBEDDING_MODEL=nomic-embed-text:latest
-
-
-## Internet search & preference memory
-# Enable web search
-ENABLE_INTERNET=false
-# Internet search backend (bocha | tavily)
-INTERNET_SEARCH_BACKEND=bocha
-# API key for BOCHA Search
-BOCHA_API_KEY=                             # required if ENABLE_INTERNET=true and backend=bocha
-# API key for Tavily Search
-TAVILY_API_KEY=                            # required if ENABLE_INTERNET=true and backend=tavily
-# default search mode
-SEARCH_MODE=fast                          # fast | fine | mixture
-# Slow retrieval strategy configuration, rewrite is the rewrite strategy
-FINE_STRATEGY=rewrite                     # rewrite | recreate | deep_search
-# Whether to enable preference memory
-ENABLE_PREFERENCE_MEMORY=true
-# Preference Memory Add Mode
-PREFERENCE_ADDER_MODE=fast                # fast | safe
-# Whether to deduplicate explicit preferences based on factual memory
-DEDUP_PREF_EXP_BY_TEXTUAL=false
-
-## Reader chunking
-MEM_READER_BACKEND=simple_struct           # simple_struct | strategy_struct
-MEM_READER_CHAT_CHUNK_TYPE=default         # default | content_length
-MEM_READER_CHAT_CHUNK_TOKEN_SIZE=1600      # tokens per chunk (default mode)
-MEM_READER_CHAT_CHUNK_SESS_SIZE=10         # sessions per chunk (default mode)
-MEM_READER_CHAT_CHUNK_OVERLAP=2            # overlap between chunks
-
-## Scheduler (MemScheduler / API)
-# Enable or disable the main switch for configuring the memory scheduler during MemOS class initialization
-MOS_ENABLE_SCHEDULER=false
-# Determine the number of most relevant memory entries that the scheduler retrieves or processes during runtime (such as reordering or updating working memory)
-MOS_SCHEDULER_TOP_K=10
-# The time interval (in seconds) for updating "Activation Memory" (usually referring to caching or short-term memory mechanisms)
-MOS_SCHEDULER_ACT_MEM_UPDATE_INTERVAL=300
-# The size of the context window considered by the scheduler when processing tasks (such as the number of recent messages or conversation rounds)
-MOS_SCHEDULER_CONTEXT_WINDOW_SIZE=5
-# The maximum number of working threads allowed in the scheduler thread pool for concurrent task execution
-MOS_SCHEDULER_THREAD_POOL_MAX_WORKERS=10000
-# The polling interval (in seconds) for the scheduler to consume new messages/tasks from the queue. The smaller the value, the faster the response, but the CPU usage may be higher
-MOS_SCHEDULER_CONSUME_INTERVAL_SECONDS=0.01
-# Whether to enable the parallel distribution function of the scheduler to improve the throughput of concurrent operations
-MOS_SCHEDULER_ENABLE_PARALLEL_DISPATCH=true
-# The specific switch to enable or disable the "Activate Memory" function in the scheduler logic
-MOS_SCHEDULER_ENABLE_ACTIVATION_MEMORY=false
-# Control whether the scheduler instance is actually started during server initialization. If false, the scheduler object may be created but its background loop will not be started
-API_SCHEDULER_ON=true
-# Specifically define the window size for API search operations in OptimizedScheduler. It is passed to the ScherderrAPIModule to control the scope of the search context
-API_SEARCH_WINDOW_SIZE=5
-# Specify how many rounds of previous conversations (history) to retrieve and consider during the 'hybrid search' (fast search+asynchronous fine search). This helps provide context aware search results
-API_SEARCH_HISTORY_TURNS=5
-MEMSCHEDULER_USE_REDIS_QUEUE=false
-
-## Graph / vector stores
-# Neo4j database selection mode
-NEO4J_BACKEND=neo4j-community             # neo4j-community | neo4j | polardb | postgres
-# Neo4j database url
-NEO4J_URI=bolt://localhost:7687           # required when backend=neo4j*
-#  Neo4j database user
-NEO4J_USER=neo4j                          # required when backend=neo4j*
-# Neo4j database password
-NEO4J_PASSWORD=12345678                   # required when backend=neo4j*
-# Neo4j database name
-NEO4J_DB_NAME=neo4j                       # required for shared-db mode
-# Neo4j database data sharing with Memos
+# Reranker Backend (http_bge | etc.)
+MOS_RERANKER_BACKEND=cosine_local
+
+# Neo4j Connection URI
+# Available options: neo4j-community | neo4j | nebular | polardb
+NEO4J_BACKEND=neo4j-community
+# Required when backend=neo4j*
+NEO4J_URI=bolt://localhost:7687
+NEO4J_USER=neo4j
+NEO4J_PASSWORD=12345678
+NEO4J_DB_NAME=neo4j
 MOS_NEO4J_SHARED_DB=false
-QDRANT_HOST=localhost
-QDRANT_PORT=6333
-# For Qdrant Cloud / remote endpoint (takes priority if set):
-QDRANT_URL=your_qdrant_url
-QDRANT_API_KEY=your_qdrant_key
-# milvus server uri
-MILVUS_URI=http://localhost:19530         # required when ENABLE_PREFERENCE_MEMORY=true
-MILVUS_USER_NAME=root                     # same as above
-MILVUS_PASSWORD=12345678                  # same as above
-
-# PolarDB endpoint/host
-POLAR_DB_HOST=localhost
-# PolarDB port
-POLAR_DB_PORT=5432
-# PolarDB username
-POLAR_DB_USER=root
-# PolarDB password
-POLAR_DB_PASSWORD=123456
-# PolarDB database name
-POLAR_DB_DB_NAME=shared_memos_db
-# PolarDB Server Mode:
-# If set to true, use Multi-Database Mode where each user has their own independent database (physical isolation).
-# If set to false (default), use Shared Database Mode where all users share one database with logical isolation via username.
-POLAR_DB_USE_MULTI_DB=false
-# PolarDB connection pool size
-POLARDB_POOL_MAX_CONN=100
-
-## Related configurations of Redis
-# Reddimq sends scheduling information and synchronization information for some variables
-MEMSCHEDULER_REDIS_HOST=                  # fallback keys if not using the global ones
-MEMSCHEDULER_REDIS_PORT=
-MEMSCHEDULER_REDIS_DB=
-MEMSCHEDULER_REDIS_PASSWORD=
-MEMSCHEDULER_REDIS_TIMEOUT=
-MEMSCHEDULER_REDIS_CONNECT_TIMEOUT=
-
 
-## Nacos (optional config center)
-# Nacos turns off long polling listening, defaults to true
-NACOS_ENABLE_WATCH=false
-# The monitoring interval for long rotation training is 60 seconds, and the default 30 seconds can be left unconfigured
-NACOS_WATCH_INTERVAL=60
-# nacos server address
-NACOS_SERVER_ADDR=
-# nacos dataid
-NACOS_DATA_ID=
-# nacos group
-NACOS_GROUP=DEFAULT_GROUP
-# nacos namespace
-NACOS_NAMESPACE=
-# nacos ak
-AK=
-# nacos sk
-SK=
+# Whether to use Redis schedule
+DEFAULT_USE_REDIS_QUEUE=false
 
-# chat model for chat api
-CHAT_MODEL_LIST='[{
-  "backend": "deepseek",
-  "api_base": "http://localhost:1234",
-  "api_key": "your-api-key",
-  "model_name_or_path": "deepseek-r1",
-  "support_models": ["deepseek-r1"]
-}]'
+# Enable Chat API
+ENABLE_CHAT_API=true
 
-# RabbitMQ host name for message-log pipeline
-MEMSCHEDULER_RABBITMQ_HOST_NAME=
-# RabbitMQ user name for message-log pipeline
-MEMSCHEDULER_RABBITMQ_USER_NAME=
-# RabbitMQ password for message-log pipeline
-MEMSCHEDULER_RABBITMQ_PASSWORD=
-# RabbitMQ virtual host for message-log pipeline
-MEMSCHEDULER_RABBITMQ_VIRTUAL_HOST=memos
-# Erase connection state on connect for message-log pipeline
-MEMSCHEDULER_RABBITMQ_ERASE_ON_CONNECT=true
-# RabbitMQ port for message-log pipeline
-MEMSCHEDULER_RABBITMQ_PORT=5672
+CHAT_MODEL_LIST=[{"backend": "qwen", "api_base": "https://dashscope.aliyuncs.com/compatible-mode/v1", "api_key": "you_bailian_api_key", "model_name_or_path": "qwen3-max-preview", "extra_body": {"enable_thinking": true} ,"support_models": ["qwen3-max-preview"]}]