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config.py
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1237 lines (1114 loc) · 51.1 KB
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import base64
import hashlib
import hmac
import json
import logging
import os
import re
import time
from typing import TYPE_CHECKING, Any
import requests
from dotenv import load_dotenv
from memos.context.context import ContextThread
if TYPE_CHECKING:
from memos.configs.mem_cube import GeneralMemCubeConfig
from memos.configs.mem_os import MOSConfig
from memos.mem_cube.general import GeneralMemCube
# Load environment variables
load_dotenv(override=True)
logger = logging.getLogger(__name__)
def _update_env_from_dict(data: dict[str, Any]) -> None:
"""Apply a dict to environment variables, with change logging."""
def _is_sensitive(name: str) -> bool:
n = name.upper()
return any(s in n for s in ["PASSWORD", "SECRET", "AK", "SK", "TOKEN", "KEY"])
for k, v in data.items():
if isinstance(v, dict):
new_val = json.dumps(v, ensure_ascii=False)
elif isinstance(v, bool):
new_val = "true" if v else "false"
elif v is None:
new_val = ""
else:
new_val = str(v)
old_val = os.environ.get(k)
os.environ[k] = new_val
try:
log_old = "***" if _is_sensitive(k) else (old_val if old_val is not None else "<unset>")
log_new = "***" if _is_sensitive(k) else new_val
if old_val != new_val:
logger.info(f"Nacos config update: {k}={log_new} (was {log_old})")
except Exception as e:
# Avoid logging failures blocking config updates
logger.debug(f"Skip logging change for {k}: {e}")
def get_config_json(name: str, default: Any | None = None) -> Any:
"""Read JSON object/array from env and parse. Returns default on missing/invalid."""
raw = os.getenv(name)
if not raw:
return default
try:
return json.loads(raw)
except Exception:
logger.warning(f"Invalid JSON in env '{name}', returning default.")
return default
def get_config_value(path: str, default: Any | None = None) -> Any:
"""Read value from env with optional dot-path for structured configs.
Examples:
- get_config_value("MONGODB_CONFIG.base_uri")
- get_config_value("MONGODB_BASE_URI")
"""
if "." not in path:
val = os.getenv(path)
return val if val is not None else default
root, *subkeys = path.split(".")
data = get_config_json(root, default=None)
if not isinstance(data, dict):
return default
cur: Any = data
for key in subkeys:
if isinstance(cur, dict) and key in cur:
cur = cur[key]
else:
return default
return cur
class NacosConfigManager:
_client = None
_data_id = None
_group = None
_enabled = False
# Pre-compile regex patterns for better performance
_KEY_VALUE_PATTERN = re.compile(r"^([^=]+)=(.*)$")
_INTEGER_PATTERN = re.compile(r"^[+-]?\d+$")
_FLOAT_PATTERN = re.compile(r"^[+-]?(\d+\.?\d*|\.\d+)([eE][+-]?\d+)?$")
@classmethod
def _sign(cls, secret_key: str, data: str) -> str:
"""HMAC-SHA1 sgin"""
signature = hmac.new(secret_key.encode("utf-8"), data.encode("utf-8"), hashlib.sha1)
return base64.b64encode(signature.digest()).decode()
@staticmethod
def _parse_value(value: str) -> Any:
"""Parse string value to appropriate Python type.
Supports: bool, int, float, and string.
"""
if not value:
return value
val_lower = value.lower()
# Boolean
if val_lower in ("true", "false"):
return val_lower == "true"
# Integer
if NacosConfigManager._INTEGER_PATTERN.match(value):
try:
return int(value)
except (ValueError, OverflowError):
return value
# Float
if NacosConfigManager._FLOAT_PATTERN.match(value):
try:
return float(value)
except (ValueError, OverflowError):
return value
# Default to string
return value
@staticmethod
def parse_properties(content: str) -> dict[str, Any]:
"""Parse properties file content to dictionary with type inference.
Supports:
- Comments (lines starting with #)
- Key-value pairs (KEY=VALUE)
- Type inference (bool, int, float, string)
"""
data: dict[str, Any] = {}
for line in content.splitlines():
line = line.strip()
# Skip empty lines and comments
if not line or line.startswith("#"):
continue
# Parse key-value pair
match = NacosConfigManager._KEY_VALUE_PATTERN.match(line)
if match:
key = match.group(1).strip()
value = match.group(2).strip()
data[key] = NacosConfigManager._parse_value(value)
return data
@classmethod
def start_config_watch(cls):
while True:
cls.init()
time.sleep(60)
@classmethod
def start_watch_if_enabled(cls) -> None:
enable = os.getenv("NACOS_ENABLE_WATCH", "false").lower() == "true"
logger.info(f"NACOS_ENABLE_WATCH: {enable}")
if not enable:
return
interval = int(os.getenv("NACOS_WATCH_INTERVAL", "60"))
def _loop() -> None:
while True:
try:
cls.init()
except Exception as e:
logger.error(f"❌ Nacos watch loop error: {e}")
time.sleep(interval)
ContextThread(target=_loop, daemon=True).start()
logger.info(f"Nacos watch thread started (interval={interval}s).")
@classmethod
def init(cls) -> None:
server_addr = os.getenv("NACOS_SERVER_ADDR")
data_id = os.getenv("NACOS_DATA_ID")
group = os.getenv("NACOS_GROUP", "DEFAULT_GROUP")
namespace = os.getenv("NACOS_NAMESPACE", "")
ak = os.getenv("AK")
sk = os.getenv("SK")
if not (server_addr and data_id and ak and sk):
logger.warning("missing NACOS_SERVER_ADDR / AK / SK / DATA_ID")
return
base_url = f"http://{server_addr}/nacos/v1/cs/configs"
def _auth_headers():
ts = str(int(time.time() * 1000))
sign_data = namespace + "+" + group + "+" + ts if namespace else group + "+" + ts
signature = cls._sign(sk, sign_data)
return {
"Spas-AccessKey": ak,
"Spas-Signature": signature,
"timeStamp": ts,
}
try:
params = {
"dataId": data_id,
"group": group,
"tenant": namespace,
}
headers = _auth_headers()
resp = requests.get(base_url, headers=headers, params=params, timeout=10)
if resp.status_code != 200:
logger.error(f"Nacos AK/SK fail: {resp.status_code} {resp.text}")
return
content = resp.text.strip()
if not content:
logger.warning("⚠️ Nacos is empty")
return
try:
data_props = cls.parse_properties(content)
logger.info("nacos config:", data_props)
_update_env_from_dict(data_props)
logger.info("✅ parse Nacos setting is Properties ")
except Exception as e:
logger.error(f"⚠️ Nacos parse fail(not JSON/YAML/Properties): {e}")
raise Exception(f"Nacos configuration parsing failed: {e}") from e
except Exception as e:
logger.error(f"❌ Nacos AK/SK init fail: {e}")
raise Exception(f"❌ Nacos AK/SK init fail: {e}") from e
# init Nacos
NacosConfigManager.init()
NacosConfigManager.start_watch_if_enabled()
class APIConfig:
"""Centralized configuration management for MemOS APIs."""
@staticmethod
def get_openai_config() -> dict[str, Any]:
"""Get OpenAI configuration."""
return {
"model_name_or_path": os.getenv("MOS_CHAT_MODEL", "gpt-4o-mini"),
"temperature": float(os.getenv("MOS_CHAT_TEMPERATURE", "0.8")),
"max_tokens": int(os.getenv("MOS_MAX_TOKENS", "8000")),
"top_p": float(os.getenv("MOS_TOP_P", "0.9")),
"top_k": int(os.getenv("MOS_TOP_K", "50")),
"remove_think_prefix": True,
"api_key": os.getenv("OPENAI_API_KEY", "your-api-key-here"),
"api_base": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
}
@staticmethod
def qwen_config() -> dict[str, Any]:
"""Get Qwen configuration."""
return {
"model_name_or_path": os.getenv("MOS_CHAT_MODEL", "Qwen/Qwen3-1.7B"),
"temperature": float(os.getenv("MOS_CHAT_TEMPERATURE", "0.8")),
"max_tokens": int(os.getenv("MOS_MAX_TOKENS", "4096")),
"remove_think_prefix": True,
}
@staticmethod
def vllm_config() -> dict[str, Any]:
"""Get Qwen configuration."""
return {
"model_name_or_path": os.getenv("MOS_CHAT_MODEL", "Qwen/Qwen3-1.7B"),
"temperature": float(os.getenv("MOS_CHAT_TEMPERATURE", "0.8")),
"max_tokens": int(os.getenv("MOS_MAX_TOKENS", "4096")),
"remove_think_prefix": True,
"api_key": os.getenv("VLLM_API_KEY", ""),
"api_base": os.getenv("VLLM_API_BASE", "http://localhost:8088/v1"),
"model_schema": os.getenv("MOS_MODEL_SCHEMA", "memos.configs.llm.VLLMLLMConfig"),
}
@staticmethod
def get_activation_config() -> dict[str, Any]:
"""Get Ollama configuration."""
return {
"backend": "kv_cache",
"config": {
"memory_filename": "activation_memory.pickle",
"extractor_llm": {
"backend": "huggingface_singleton",
"config": {
"model_name_or_path": os.getenv("MOS_CHAT_MODEL", "Qwen/Qwen3-1.7B"),
"temperature": 0.8,
"max_tokens": 1024,
"top_p": 0.9,
"top_k": 50,
"add_generation_prompt": True,
"remove_think_prefix": False,
},
},
},
}
@staticmethod
def get_memreader_config() -> dict[str, Any]:
"""Get MemReader configuration for chat/doc extraction (fine-tuned 0.6B model)."""
return {
"backend": "openai",
"config": {
"model_name_or_path": os.getenv("MEMRADER_MODEL", "gpt-4o-mini"),
"temperature": 0.6,
"max_tokens": int(os.getenv("MEMRADER_MAX_TOKENS", "8000")),
"top_p": 0.95,
"top_k": 20,
"api_key": os.getenv("MEMRADER_API_KEY", "EMPTY"),
# Default to OpenAI base URL when env var is not provided to satisfy pydantic
# validation requirements during tests/import.
"api_base": os.getenv("MEMRADER_API_BASE", "https://api.openai.com/v1"),
"remove_think_prefix": True,
},
}
@staticmethod
def get_memreader_general_llm_config() -> dict[str, Any]:
"""Get general LLM configuration for non-chat/doc tasks.
Used for: hallucination filter, memory rewrite, memory merge,
tool trajectory extraction, skill memory extraction.
This is the fallback for image_parser_llm and preference_extractor_llm.
Fallback chain: MEMREADER_GENERAL_MODEL -> MEMRADER_MODEL (memreader config)
Note: If you have fine-tuned a custom model for chat/doc extraction only,
you should configure MEMREADER_GENERAL_MODEL to use a general-purpose LLM
for other tasks. Otherwise, all tasks will use the same MEMRADER_MODEL.
"""
# Check if specific general model is configured
general_model = os.getenv("MEMREADER_GENERAL_MODEL")
if general_model:
return {
"backend": os.getenv("MEMREADER_GENERAL_BACKEND", "openai"),
"config": {
"model_name_or_path": general_model,
"temperature": 0.6,
"max_tokens": int(os.getenv("MEMREADER_GENERAL_MAX_TOKENS", "8000")),
"top_p": 0.95,
"top_k": 20,
"api_key": os.getenv(
"MEMREADER_GENERAL_API_KEY", os.getenv("OPENAI_API_KEY", "EMPTY")
),
"api_base": os.getenv(
"MEMREADER_GENERAL_API_BASE",
os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
),
"remove_think_prefix": True,
},
}
# Fallback to memreader config (same behavior as before for users who don't customize)
return APIConfig.get_memreader_config()
@staticmethod
def get_image_parser_llm_config() -> dict[str, Any]:
"""Get LLM configuration for image parsing (requires vision model).
Used for: image content extraction and analysis.
Requires a vision-capable model like GPT-4V, GPT-4o, etc.
Fallback chain: IMAGE_PARSER_MODEL -> general_llm -> OpenAI config
"""
image_model = os.getenv("IMAGE_PARSER_MODEL")
if image_model:
return {
"backend": os.getenv("IMAGE_PARSER_BACKEND", "openai"),
"config": {
"model_name_or_path": image_model,
"temperature": 0.6,
"max_tokens": int(os.getenv("IMAGE_PARSER_MAX_TOKENS", "4096")),
"top_p": 0.95,
"top_k": 20,
"api_key": os.getenv(
"IMAGE_PARSER_API_KEY", os.getenv("OPENAI_API_KEY", "EMPTY")
),
"api_base": os.getenv(
"IMAGE_PARSER_API_BASE",
os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
),
"remove_think_prefix": True,
},
}
# Fallback to general_llm config (which itself falls back to OpenAI)
return APIConfig.get_memreader_general_llm_config()
@staticmethod
def get_preference_extractor_llm_config() -> dict[str, Any]:
"""Get LLM configuration for preference extraction.
Used for: extracting user preferences from conversations.
Fallback chain: PREFERENCE_EXTRACTOR_MODEL -> general_llm -> OpenAI config
"""
pref_model = os.getenv("PREFERENCE_EXTRACTOR_MODEL")
if pref_model:
return {
"backend": os.getenv("PREFERENCE_EXTRACTOR_BACKEND", "openai"),
"config": {
"model_name_or_path": pref_model,
"temperature": 0.6,
"max_tokens": int(os.getenv("PREFERENCE_EXTRACTOR_MAX_TOKENS", "8000")),
"top_p": 0.95,
"top_k": 20,
"api_key": os.getenv(
"PREFERENCE_EXTRACTOR_API_KEY", os.getenv("OPENAI_API_KEY", "EMPTY")
),
"api_base": os.getenv(
"PREFERENCE_EXTRACTOR_API_BASE",
os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
),
"remove_think_prefix": True,
},
}
# Fallback to general_llm config (which itself falls back to OpenAI)
return APIConfig.get_memreader_general_llm_config()
@staticmethod
def get_activation_vllm_config() -> dict[str, Any]:
"""Get Ollama configuration."""
return {
"backend": "vllm_kv_cache",
"config": {
"memory_filename": "activation_memory.pickle",
"extractor_llm": {
"backend": "vllm",
"config": APIConfig.vllm_config(),
},
},
}
@staticmethod
def get_preference_memory_config() -> dict[str, Any]:
"""Get preference memory configuration."""
return {
"backend": "pref_text",
"config": {
"extractor_llm": APIConfig.get_preference_extractor_llm_config(),
"vector_db": {
"backend": "milvus",
"config": APIConfig.get_milvus_config(),
},
"embedder": APIConfig.get_embedder_config(),
"reranker": APIConfig.get_reranker_config(),
"extractor": {"backend": "naive", "config": {}},
"adder": {"backend": "naive", "config": {}},
"retriever": {"backend": "naive", "config": {}},
},
}
@staticmethod
def get_reranker_config() -> dict[str, Any]:
"""Get embedder configuration."""
embedder_backend = os.getenv("MOS_RERANKER_BACKEND", "http_bge")
if embedder_backend in ["http_bge", "http_bge_strategy"]:
return {
"backend": embedder_backend,
"config": {
"url": os.getenv("MOS_RERANKER_URL", "localhost:8000/v1/rerank"),
"model": os.getenv("MOS_RERANKER_MODEL", "bge-reranker-v2-m3"),
"timeout": 10,
"headers_extra": json.loads(os.getenv("MOS_RERANKER_HEADERS_EXTRA", "{}")),
"rerank_source": os.getenv("MOS_RERANK_SOURCE"),
"reranker_strategy": os.getenv("MOS_RERANKER_STRATEGY", "single_turn"),
},
}
else:
return {
"backend": "cosine_local",
"config": {
"level_weights": {"topic": 1.0, "concept": 1.0, "fact": 1.0},
"level_field": "background",
},
}
@staticmethod
def get_feedback_reranker_config() -> dict[str, Any]:
"""Get embedder configuration."""
embedder_backend = os.getenv("MOS_FEEDBACK_RERANKER_BACKEND", "http_bge")
if embedder_backend in ["http_bge", "http_bge_strategy"]:
return {
"backend": embedder_backend,
"config": {
"url": os.getenv("MOS_RERANKER_URL", "localhost:8000/v1/rerank"),
"model": os.getenv("MOS_FEEDBACK_RERANKER_MODEL", "bge-reranker-v2-m3"),
"timeout": 10,
"max_query_tokens": int(os.getenv("MOS_RERANKER_MAX_TOKENS", 8000)),
"concate_len": int(os.getenv("MOS_RERANKER_CONCAT_LEN", 1000)),
"headers_extra": json.loads(os.getenv("MOS_RERANKER_HEADERS_EXTRA", "{}")),
"rerank_source": os.getenv("MOS_RERANK_SOURCE"),
"reranker_strategy": os.getenv("MOS_RERANKER_STRATEGY", "single_turn"),
},
}
else:
return {
"backend": "cosine_local",
"config": {
"level_weights": {"topic": 1.0, "concept": 1.0, "fact": 1.0},
"level_field": "background",
},
}
@staticmethod
def get_embedder_config() -> dict[str, Any]:
"""Get embedder configuration."""
embedder_backend = os.getenv("MOS_EMBEDDER_BACKEND", "ollama")
if embedder_backend == "universal_api":
return {
"backend": "universal_api",
"config": {
"provider": os.getenv("MOS_EMBEDDER_PROVIDER", "openai"),
"api_key": os.getenv("MOS_EMBEDDER_API_KEY", "sk-xxxx"),
"model_name_or_path": os.getenv("MOS_EMBEDDER_MODEL", "text-embedding-3-large"),
"headers_extra": json.loads(os.getenv("MOS_EMBEDDER_HEADERS_EXTRA", "{}")),
"base_url": os.getenv("MOS_EMBEDDER_API_BASE", "http://openai.com"),
"backup_client": os.getenv("MOS_EMBEDDER_BACKUP_CLIENT", "false").lower()
== "true",
"backup_base_url": os.getenv(
"MOS_EMBEDDER_BACKUP_API_BASE", "http://openai.com"
),
"backup_api_key": os.getenv("MOS_EMBEDDER_BACKUP_API_KEY", "sk-xxxx"),
"backup_headers_extra": json.loads(
os.getenv("MOS_EMBEDDER_BACKUP_HEADERS_EXTRA", "{}")
),
"backup_model_name_or_path": os.getenv(
"MOS_EMBEDDER_BACKUP_MODEL", "text-embedding-3-large"
),
},
}
else: # ollama
return {
"backend": "ollama",
"config": {
"model_name_or_path": os.getenv(
"MOS_EMBEDDER_MODEL", "nomic-embed-text:latest"
),
"api_base": os.getenv("OLLAMA_API_BASE", "http://localhost:11434"),
},
}
@staticmethod
def get_reader_config() -> dict[str, Any]:
"""Get reader configuration."""
return {
"backend": os.getenv("MEM_READER_BACKEND", "multimodal_struct"),
"config": {
"chunk_type": os.getenv("MEM_READER_CHAT_CHUNK_TYPE", "default"),
"chunk_length": int(os.getenv("MEM_READER_CHAT_CHUNK_TOKEN_SIZE", 1600)),
"chunk_session": int(os.getenv("MEM_READER_CHAT_CHUNK_SESS_SIZE", 10)),
"chunk_overlap": int(os.getenv("MEM_READER_CHAT_CHUNK_OVERLAP", 2)),
},
}
@staticmethod
def get_oss_config() -> dict[str, Any] | None:
"""Get OSS configuration and validate connection."""
config = {
"endpoint": os.getenv("OSS_ENDPOINT", "http://oss-cn-shanghai.aliyuncs.com"),
"access_key_id": os.getenv("OSS_ACCESS_KEY_ID", ""),
"access_key_secret": os.getenv("OSS_ACCESS_KEY_SECRET", ""),
"region": os.getenv("OSS_REGION", ""),
"bucket_name": os.getenv("OSS_BUCKET_NAME", ""),
}
# Validate that all required fields have values
required_fields = [
"endpoint",
"access_key_id",
"access_key_secret",
"region",
"bucket_name",
]
missing_fields = [field for field in required_fields if not config.get(field)]
if missing_fields:
logger.warning(
f"OSS configuration incomplete. Missing fields: {', '.join(missing_fields)}"
)
return None
return config
def get_internet_config() -> dict[str, Any]:
"""Get embedder configuration."""
reader_config = APIConfig.get_reader_config()
return {
"backend": "bocha",
"config": {
"api_key": os.getenv("BOCHA_API_KEY", "bocha"),
"max_results": 15,
"num_per_request": 10,
"reader": {
"backend": reader_config["backend"],
"config": {
"llm": {
"backend": "openai",
"config": {
"model_name_or_path": os.getenv("MEMRADER_MODEL"),
"temperature": 0.6,
"max_tokens": 5000,
"top_p": 0.95,
"top_k": 20,
"api_key": os.getenv("MEMRADER_API_KEY", "EMPTY"),
"api_base": os.getenv("MEMRADER_API_BASE"),
"remove_think_prefix": True,
},
},
"embedder": APIConfig.get_embedder_config(),
"chunker": {
"backend": "sentence",
"config": {
"save_rawfile": os.getenv(
"MEM_READER_SAVE_RAWFILENODE", "true"
).lower()
== "true",
"tokenizer_or_token_counter": "gpt2",
"chunk_size": 512,
"chunk_overlap": 128,
"min_sentences_per_chunk": 1,
},
},
"chat_chunker": reader_config,
},
},
},
}
@staticmethod
def get_nli_config() -> dict[str, Any]:
"""Get NLI model configuration."""
return {
"base_url": os.getenv("NLI_MODEL_BASE_URL", "http://localhost:32532"),
}
@staticmethod
def get_neo4j_community_config(user_id: str | None = None) -> dict[str, Any]:
"""Get Neo4j community configuration."""
return {
"uri": os.getenv("NEO4J_URI", "bolt://localhost:7687"),
"user": os.getenv("NEO4J_USER", "neo4j"),
"db_name": os.getenv("NEO4J_DB_NAME", "neo4j"),
"password": os.getenv("NEO4J_PASSWORD", "12345678"),
"user_name": f"memos{user_id.replace('-', '')}",
"auto_create": False,
"use_multi_db": False,
"embedding_dimension": int(os.getenv("EMBEDDING_DIMENSION", 1024)),
"vec_config": {
# Pass nested config to initialize external vector DB
# If you use qdrant, please use Server instead of local mode.
"backend": "qdrant",
"config": {
"collection_name": "neo4j_vec_db",
"vector_dimension": int(os.getenv("EMBEDDING_DIMENSION", 1024)),
"distance_metric": "cosine",
"host": os.getenv("QDRANT_HOST", "localhost"),
"port": int(os.getenv("QDRANT_PORT", "6333")),
"path": os.getenv("QDRANT_PATH"),
"url": os.getenv("QDRANT_URL"),
"api_key": os.getenv("QDRANT_API_KEY"),
},
},
}
@staticmethod
def get_neo4j_config(user_id: str | None = None) -> dict[str, Any]:
"""Get Neo4j configuration."""
if os.getenv("MOS_NEO4J_SHARED_DB", "false").lower() == "true":
return APIConfig.get_neo4j_shared_config(user_id)
else:
return APIConfig.get_noshared_neo4j_config(user_id)
@staticmethod
def get_noshared_neo4j_config(user_id) -> dict[str, Any]:
"""Get Neo4j configuration."""
return {
"uri": os.getenv("NEO4J_URI", "bolt://localhost:7687"),
"user": os.getenv("NEO4J_USER", "neo4j"),
"db_name": f"memos{user_id.replace('-', '')}",
"password": os.getenv("NEO4J_PASSWORD", "12345678"),
"auto_create": True,
"use_multi_db": True,
"embedding_dimension": int(os.getenv("EMBEDDING_DIMENSION", 3072)),
}
@staticmethod
def get_neo4j_shared_config(user_id: str | None = None) -> dict[str, Any]:
"""Get Neo4j configuration."""
return {
"uri": os.getenv("NEO4J_URI", "bolt://localhost:7687"),
"user": os.getenv("NEO4J_USER", "neo4j"),
"db_name": os.getenv("NEO4J_DB_NAME", "shared-tree-textual-memory"),
"password": os.getenv("NEO4J_PASSWORD", "12345678"),
"user_name": f"memos{user_id.replace('-', '')}",
"auto_create": True,
"use_multi_db": False,
"embedding_dimension": int(os.getenv("EMBEDDING_DIMENSION", 3072)),
}
@staticmethod
def get_nebular_config(user_id: str | None = None) -> dict[str, Any]:
"""Get Nebular configuration."""
return {
"uri": json.loads(os.getenv("NEBULAR_HOSTS", '["localhost"]')),
"user": os.getenv("NEBULAR_USER", "root"),
"password": os.getenv("NEBULAR_PASSWORD", "xxxxxx"),
"space": os.getenv("NEBULAR_SPACE", "shared-tree-textual-memory"),
"user_name": f"memos{user_id.replace('-', '')}",
"use_multi_db": False,
"auto_create": True,
"embedding_dimension": int(os.getenv("EMBEDDING_DIMENSION", 3072)),
}
@staticmethod
def get_milvus_config():
return {
"collection_name": [
"explicit_preference",
"implicit_preference",
],
"vector_dimension": int(os.getenv("EMBEDDING_DIMENSION", 1024)),
"distance_metric": "cosine",
"uri": os.getenv("MILVUS_URI", "http://localhost:19530"),
"user_name": os.getenv("MILVUS_USER_NAME", "root"),
"password": os.getenv("MILVUS_PASSWORD", "12345678"),
}
@staticmethod
def get_polardb_config(user_id: str | None = None) -> dict[str, Any]:
"""Get PolarDB configuration."""
use_multi_db = os.getenv("POLAR_DB_USE_MULTI_DB", "false").lower() == "true"
if use_multi_db:
# Multi-DB mode: each user gets their own database (physical isolation)
db_name = f"memos{user_id.replace('-', '')}" if user_id else "memos_default"
user_name = None
else:
# Shared-DB mode: all users share one database with user_name tag (logical isolation)
db_name = os.getenv("POLAR_DB_DB_NAME", "shared_memos_db")
user_name = f"memos{user_id.replace('-', '')}" if user_id else "memos_default"
return {
"host": os.getenv("POLAR_DB_HOST", "localhost"),
"port": int(os.getenv("POLAR_DB_PORT", "5432")),
"user": os.getenv("POLAR_DB_USER", "root"),
"password": os.getenv("POLAR_DB_PASSWORD", "123456"),
"db_name": db_name,
"maxconn": int(os.getenv("POLARDB_POOL_MAX_CONN", "100")),
"user_name": user_name,
"use_multi_db": use_multi_db,
"auto_create": True,
"embedding_dimension": int(os.getenv("EMBEDDING_DIMENSION", "1024")),
# .env: CONNECTION_WAIT_TIMEOUT, SKIP_CONNECTION_HEALTH_CHECK, WARM_UP_ON_STARTUP_BY_FULL, WARM_UP_ON_STARTUP_BY_ALL
"connection_wait_timeout": int(os.getenv("CONNECTION_WAIT_TIMEOUT", "60")),
"skip_connection_health_check": os.getenv(
"SKIP_CONNECTION_HEALTH_CHECK", "false"
).lower()
== "true",
"warm_up_on_startup_by_full": os.getenv("WARM_UP_ON_STARTUP_BY_FULL", "false").lower()
== "true",
"warm_up_on_startup_by_all": os.getenv("WARM_UP_ON_STARTUP_BY_ALL", "false").lower()
== "true",
}
@staticmethod
def get_postgres_config(user_id: str | None = None) -> dict[str, Any]:
"""Get PostgreSQL + pgvector configuration for MemOS graph storage.
Uses standard PostgreSQL with pgvector extension.
Schema: memos.memories, memos.edges
"""
user_name = os.getenv("MEMOS_USER_NAME", "default")
if user_id:
user_name = f"memos_{user_id.replace('-', '')}"
return {
"host": os.getenv("POSTGRES_HOST", "postgres"),
"port": int(os.getenv("POSTGRES_PORT", "5432")),
"user": os.getenv("POSTGRES_USER", "n8n"),
"password": os.getenv("POSTGRES_PASSWORD", ""),
"db_name": os.getenv("POSTGRES_DB", "n8n"),
"schema_name": os.getenv("MEMOS_SCHEMA", "memos"),
"user_name": user_name,
"use_multi_db": False,
"embedding_dimension": int(os.getenv("EMBEDDING_DIMENSION", "384")),
"maxconn": int(os.getenv("POSTGRES_MAX_CONN", "20")),
}
@staticmethod
def get_mysql_config() -> dict[str, Any]:
"""Get MySQL configuration."""
return {
"host": os.getenv("MYSQL_HOST", "localhost"),
"port": int(os.getenv("MYSQL_PORT", "3306")),
"username": os.getenv("MYSQL_USERNAME", "root"),
"password": os.getenv("MYSQL_PASSWORD", "12345678"),
"database": os.getenv("MYSQL_DATABASE", "memos_users"),
"charset": os.getenv("MYSQL_CHARSET", "utf8mb4"),
}
@staticmethod
def get_scheduler_config() -> dict[str, Any]:
"""Get scheduler configuration."""
return {
"backend": "optimized_scheduler",
"config": {
"top_k": int(os.getenv("MOS_SCHEDULER_TOP_K", "10")),
"act_mem_update_interval": int(
os.getenv("MOS_SCHEDULER_ACT_MEM_UPDATE_INTERVAL", "300")
),
"context_window_size": int(os.getenv("MOS_SCHEDULER_CONTEXT_WINDOW_SIZE", "5")),
"thread_pool_max_workers": int(
os.getenv("MOS_SCHEDULER_THREAD_POOL_MAX_WORKERS", "10000")
),
"consume_interval_seconds": float(
os.getenv("MOS_SCHEDULER_CONSUME_INTERVAL_SECONDS", "0.01")
),
"enable_parallel_dispatch": os.getenv(
"MOS_SCHEDULER_ENABLE_PARALLEL_DISPATCH", "true"
).lower()
== "true",
"enable_activation_memory": os.getenv(
"MOS_SCHEDULER_ENABLE_ACTIVATION_MEMORY", "false"
).lower()
== "true",
},
}
@staticmethod
def is_scheduler_enabled() -> bool:
"""Check if scheduler is enabled via environment variable."""
return os.getenv("MOS_ENABLE_SCHEDULER", "false").lower() == "true"
@staticmethod
def is_default_cube_config_enabled() -> bool:
"""Check if default cube config is enabled via environment variable."""
return os.getenv("MOS_ENABLE_DEFAULT_CUBE_CONFIG", "true").lower() == "true"
@staticmethod
def is_dingding_bot_enabled() -> bool:
"""Check if DingDing bot is enabled via environment variable."""
return os.getenv("ENABLE_DINGDING_BOT", "false").lower() == "true"
@staticmethod
def get_dingding_bot_config() -> dict[str, Any] | None:
"""Get DingDing bot configuration if enabled."""
if not APIConfig.is_dingding_bot_enabled():
return None
return {
"enabled": True,
"access_token_user": os.getenv("DINGDING_ACCESS_TOKEN_USER", ""),
"secret_user": os.getenv("DINGDING_SECRET_USER", ""),
"access_token_error": os.getenv("DINGDING_ACCESS_TOKEN_ERROR", ""),
"secret_error": os.getenv("DINGDING_SECRET_ERROR", ""),
"robot_code": os.getenv("DINGDING_ROBOT_CODE", ""),
"app_key": os.getenv("DINGDING_APP_KEY", ""),
"app_secret": os.getenv("DINGDING_APP_SECRET", ""),
"oss_endpoint": os.getenv("OSS_ENDPOINT", ""),
"oss_region": os.getenv("OSS_REGION", ""),
"oss_bucket_name": os.getenv("OSS_BUCKET_NAME", ""),
"oss_access_key_id": os.getenv("OSS_ACCESS_KEY_ID", ""),
"oss_access_key_secret": os.getenv("OSS_ACCESS_KEY_SECRET", ""),
"oss_public_base_url": os.getenv("OSS_PUBLIC_BASE_URL", ""),
}
@staticmethod
def get_product_default_config() -> dict[str, Any]:
"""Get default configuration for Product API."""
openai_config = APIConfig.get_openai_config()
qwen_config = APIConfig.qwen_config()
vllm_config = APIConfig.vllm_config()
reader_config = APIConfig.get_reader_config()
backend_model = {
"openai": openai_config,
"huggingface": qwen_config,
"vllm": vllm_config,
}
backend = os.getenv("MOS_CHAT_MODEL_PROVIDER", "openai")
mysql_config = APIConfig.get_mysql_config()
config = {
"user_id": os.getenv("MOS_USER_ID", "root"),
"chat_model": {"backend": backend, "config": backend_model[backend]},
"mem_reader": {
"backend": reader_config["backend"],
"config": {
"llm": APIConfig.get_memreader_config(),
# General LLM for non-chat/doc tasks (hallucination filter, rewrite, merge, etc.)
"general_llm": APIConfig.get_memreader_general_llm_config(),
# Image parser LLM (requires vision model)
"image_parser_llm": APIConfig.get_image_parser_llm_config(),
"embedder": APIConfig.get_embedder_config(),
"chunker": {
"backend": "sentence",
"config": {
"save_rawfile": os.getenv("MEM_READER_SAVE_RAWFILENODE", "true").lower()
== "true",
"tokenizer_or_token_counter": "gpt2",
"chunk_size": 512,
"chunk_overlap": 128,
"min_sentences_per_chunk": 1,
},
},
"chat_chunker": reader_config,
"direct_markdown_hostnames": [
h.strip()
for h in os.getenv(
"FILE_PARSER_DIRECT_MARKDOWN_HOSTNAMES", "139.196.232.20"
).split(",")
if h.strip()
],
"oss_config": APIConfig.get_oss_config(),
"skills_dir_config": {
"skills_oss_dir": os.getenv("SKILLS_OSS_DIR", "skill_memory/"),
"skills_local_tmp_dir": os.getenv(
"SKILLS_LOCAL_TMP_DIR", "/tmp/skill_memory/"
),
"skills_local_dir": os.getenv(
"SKILLS_LOCAL_DIR", "/tmp/upload_skill_memory/"
),
},
},
},
"enable_textual_memory": True,
"enable_activation_memory": os.getenv("ENABLE_ACTIVATION_MEMORY", "false").lower()
== "true",
"enable_preference_memory": os.getenv("ENABLE_PREFERENCE_MEMORY", "false").lower()
== "true",
"top_k": int(os.getenv("MOS_TOP_K", "50")),
"max_turns_window": int(os.getenv("MOS_MAX_TURNS_WINDOW", "20")),
}
# Add scheduler configuration if enabled
if APIConfig.is_scheduler_enabled():
config["mem_scheduler"] = APIConfig.get_scheduler_config()
config["enable_mem_scheduler"] = True
else:
config["enable_mem_scheduler"] = False
# Add user manager configuration if enabled
if os.getenv("MOS_USER_MANAGER_BACKEND", "sqlite").lower() == "mysql":
config["user_manager"] = {
"backend": "mysql",
"config": mysql_config,
}
return config
@staticmethod
def get_start_default_config() -> dict[str, Any]:
"""Get default configuration for Start API."""
config = {
"user_id": os.getenv("MOS_USER_ID", "default_user"),
"session_id": os.getenv("MOS_SESSION_ID", "default_session"),
"enable_textual_memory": True,
"enable_activation_memory": os.getenv("ENABLE_ACTIVATION_MEMORY", "false").lower()
== "true",
"enable_preference_memory": os.getenv("ENABLE_PREFERENCE_MEMORY", "false").lower()
== "true",
"top_k": int(os.getenv("MOS_TOP_K", "5")),
"chat_model": {
"backend": os.getenv("MOS_CHAT_MODEL_PROVIDER", "openai"),
"config": {
"model_name_or_path": os.getenv("MOS_CHAT_MODEL", "gpt-4o-mini"),
"api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxxx"),
"temperature": float(os.getenv("MOS_CHAT_TEMPERATURE", 0.7)),
"api_base": os.getenv("OPENAI_API_BASE", "http://xxxxxx:3000/v1"),
"max_tokens": int(os.getenv("MOS_MAX_TOKENS", 1024)),
"top_p": float(os.getenv("MOS_TOP_P", 0.9)),
"top_k": int(os.getenv("MOS_TOP_K", 50)),
"remove_think_prefix": True,
},