[Misc] Lint code and fix code smells (#1871)

Makefile

@@ -16,15 +16,15 @@ install_all:
 
 # Format code with ruff
 format:
-	poetry run ruff check . --fix $(RUFF_OPTIONS)
+	poetry run ruff format mem0/
 
 # Sort imports with isort
 sort:
-	poetry run isort . $(ISORT_OPTIONS)
+	poetry run isort mem0/
 
 # Lint code with ruff
 lint:
-	poetry run ruff .
+	poetry run ruff check mem0/
 
 docs:
 	cd docs && mintlify dev

cookbooks/add_memory_using_qdrant_cloud.py

@@ -7,27 +7,21 @@
 
 # Loading OpenAI API Key
 load_dotenv()
-OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
+OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
 USER_ID = "test"
-quadrant_host="xx.gcp.cloud.qdrant.io"
+quadrant_host = "xx.gcp.cloud.qdrant.io"
 
 # creating the config attributes
-collection_name="memory" # this is the collection I created in QDRANT cloud
-api_key=os.environ.get("QDRANT_API_KEY") # Getting the QDRANT api KEY
-host=quadrant_host
-port=6333 #Default port for QDRANT cloud
+collection_name = "memory"  # this is the collection I created in QDRANT cloud
+api_key = os.environ.get("QDRANT_API_KEY")  # Getting the QDRANT api KEY
+host = quadrant_host
+port = 6333  # Default port for QDRANT cloud
 
 # Creating the config dict
 config = {
     "vector_store": {
         "provider": "qdrant",
-        "config": {
-            "collection_name": collection_name,
-            "host": host,
-            "port": port,
-            "path": None,
-            "api_key":api_key
-        }
+        "config": {"collection_name": collection_name, "host": host, "port": port, "path": None, "api_key": api_key},
     }
 }
 

mem0/client/main.py

@@ -10,7 +10,11 @@
 from mem0.memory.telemetry import capture_client_event
 
 logger = logging.getLogger(__name__)
-warnings.filterwarnings('always', category=DeprecationWarning, message="The 'session_id' parameter is deprecated. User 'run_id' instead.")
+warnings.filterwarnings(
+    "always",
+    category=DeprecationWarning,
+    message="The 'session_id' parameter is deprecated. User 'run_id' instead.",
+)
 
 # Setup user config
 setup_config()
@@ -82,14 +86,10 @@ def _validate_api_key(self):
             response = self.client.get("/v1/memories/", params={"user_id": "test"})
             response.raise_for_status()
         except httpx.HTTPStatusError:
-            raise ValueError(
-                "Invalid API Key. Please get a valid API Key from https://app.mem0.ai"
-            )
+            raise ValueError("Invalid API Key. Please get a valid API Key from https://app.mem0.ai")
 
     @api_error_handler
-    def add(
-        self, messages: Union[str, List[Dict[str, str]]], **kwargs
-    ) -> Dict[str, Any]:
+    def add(self, messages: Union[str, List[Dict[str, str]]], **kwargs) -> Dict[str, Any]:
         """Add a new memory.
 
         Args:
@@ -253,9 +253,7 @@ def delete_users(self) -> Dict[str, str]:
         """Delete all users, agents, or sessions."""
         entities = self.users()
         for entity in entities["results"]:
-            response = self.client.delete(
-                f"/v1/entities/{entity['type']}/{entity['id']}/"
-            )
+            response = self.client.delete(f"/v1/entities/{entity['type']}/{entity['id']}/")
             response.raise_for_status()
 
         capture_client_event("client.delete_users", self)
@@ -312,7 +310,7 @@ def _prepare_payload(
                 "The 'session_id' parameter is deprecated and will be removed in version 0.1.20. "
                 "Use 'run_id' instead.",
                 DeprecationWarning,
-                stacklevel=2
+                stacklevel=2,
             )
             kwargs["run_id"] = kwargs.pop("session_id")
 
@@ -335,7 +333,7 @@ def _prepare_params(self, kwargs: Dict[str, Any]) -> Dict[str, Any]:
                 "The 'session_id' parameter is deprecated and will be removed in version 0.1.20. "
                 "Use 'run_id' instead.",
                 DeprecationWarning,
-                stacklevel=2
+                stacklevel=2,
             )
             kwargs["run_id"] = kwargs.pop("session_id")
 

mem0/configs/base.py

@@ -17,18 +17,10 @@ class MemoryItem(BaseModel):
     )  # TODO After prompt changes from platform, update this
     hash: Optional[str] = Field(None, description="The hash of the memory")
     # The metadata value can be anything and not just string. Fix it
-    metadata: Optional[Dict[str, Any]] = Field(
-        None, description="Additional metadata for the text data"
-    )
-    score: Optional[float] = Field(
-        None, description="The score associated with the text data"
-    )
-    created_at: Optional[str] = Field(
-        None, description="The timestamp when the memory was created"
-    )
-    updated_at: Optional[str] = Field(
-        None, description="The timestamp when the memory was updated"
-    )
+    metadata: Optional[Dict[str, Any]] = Field(None, description="Additional metadata for the text data")
+    score: Optional[float] = Field(None, description="The score associated with the text data")
+    created_at: Optional[str] = Field(None, description="The timestamp when the memory was created")
+    updated_at: Optional[str] = Field(None, description="The timestamp when the memory was updated")
 
 
 class MemoryConfig(BaseModel):
@@ -60,7 +52,7 @@ class MemoryConfig(BaseModel):
         description="Custom prompt for the memory",
         default=None,
     )
-    
+
 
 class AzureConfig(BaseModel):
     """
@@ -73,7 +65,10 @@ class AzureConfig(BaseModel):
         api_version (str): The version of the Azure API being used.
     """
 
-    api_key: str = Field(description="The API key used for authenticating with the Azure service.", default=None)
-    azure_deployment : str = Field(description="The name of the Azure deployment.", default=None)
-    azure_endpoint : str = Field(description="The endpoint URL for the Azure service.", default=None)
-    api_version : str = Field(description="The version of the Azure API being used.", default=None)
+    api_key: str = Field(
+        description="The API key used for authenticating with the Azure service.",
+        default=None,
+    )
+    azure_deployment: str = Field(description="The name of the Azure deployment.", default=None)
+    azure_endpoint: str = Field(description="The endpoint URL for the Azure service.", default=None)
+    api_version: str = Field(description="The version of the Azure API being used.", default=None)

mem0/configs/embeddings/base.py

@@ -60,6 +60,6 @@ def __init__(
 
         # Huggingface specific
         self.model_kwargs = model_kwargs or {}
-        
+
         # AzureOpenAI specific
         self.azure_kwargs = AzureConfig(**azure_kwargs) or {}

mem0/configs/prompts.py

@@ -59,6 +59,7 @@
 If you do not find anything relevant facts, user memories, and preferences in the below conversation, you can return an empty list corresponding to the "facts" key.
 """
 
+
 def get_update_memory_messages(retrieved_old_memory_dict, response_content):
     return f"""You are a smart memory manager which controls the memory of a system.
     You can perform four operations: (1) add into the memory, (2) update the memory, (3) delete from the memory, and (4) no change.

mem0/configs/vector_stores/chroma.py

@@ -13,9 +13,7 @@ class ChromaDbConfig(BaseModel):
     Client: ClassVar[type] = Client
 
     collection_name: str = Field("mem0", description="Default name for the collection")
-    client: Optional[Client] = Field(
-        None, description="Existing ChromaDB client instance"
-    )
+    client: Optional[Client] = Field(None, description="Existing ChromaDB client instance")
     path: Optional[str] = Field(None, description="Path to the database directory")
     host: Optional[str] = Field(None, description="Database connection remote host")
     port: Optional[int] = Field(None, description="Database connection remote port")

mem0/configs/vector_stores/milvus.py

@@ -1,22 +1,24 @@
 from enum import Enum
-from typing import Dict, Any
-from pydantic import BaseModel, model_validator, Field
+from typing import Any, Dict
+
+from pydantic import BaseModel, Field, model_validator
 
 
 class MetricType(str, Enum):
     """
     Metric Constant for milvus/ zilliz server.
     """
+
     def __str__(self) -> str:
         return str(self.value)
-    
+
     L2 = "L2"
-    IP  = "IP"
-    COSINE  = "COSINE"
-    HAMMING  = "HAMMING"
-    JACCARD  = "JACCARD"
-    
-    
+    IP = "IP"
+    COSINE = "COSINE"
+    HAMMING = "HAMMING"
+    JACCARD = "JACCARD"
+
+
 class MilvusDBConfig(BaseModel):
     url: str = Field("http://localhost:19530", description="Full URL for Milvus/Zilliz server")
     token: str = Field(None, description="Token for Zilliz server / local setup defaults to None.")
@@ -38,4 +40,4 @@ def validate_extra_fields(cls, values: Dict[str, Any]) -> Dict[str, Any]:
 
     model_config = {
         "arbitrary_types_allowed": True,
-    }
+    }

mem0/configs/vector_stores/pgvector.py

@@ -4,12 +4,9 @@
 
 
 class PGVectorConfig(BaseModel):
-
     dbname: str = Field("postgres", description="Default name for the database")
     collection_name: str = Field("mem0", description="Default name for the collection")
-    embedding_model_dims: Optional[int] = Field(
-        1536, description="Dimensions of the embedding model"
-    )
+    embedding_model_dims: Optional[int] = Field(1536, description="Dimensions of the embedding model")
     user: Optional[str] = Field(None, description="Database user")
     password: Optional[str] = Field(None, description="Database password")
     host: Optional[str] = Field(None, description="Database host. Default is localhost")

mem0/configs/vector_stores/qdrant.py

@@ -9,17 +9,11 @@ class QdrantConfig(BaseModel):
     QdrantClient: ClassVar[type] = QdrantClient
 
     collection_name: str = Field("mem0", description="Name of the collection")
-    embedding_model_dims: Optional[int] = Field(
-        1536, description="Dimensions of the embedding model"
-    )
-    client: Optional[QdrantClient] = Field(
-        None, description="Existing Qdrant client instance"
-    )
+    embedding_model_dims: Optional[int] = Field(1536, description="Dimensions of the embedding model")
+    client: Optional[QdrantClient] = Field(None, description="Existing Qdrant client instance")
     host: Optional[str] = Field(None, description="Host address for Qdrant server")
     port: Optional[int] = Field(None, description="Port for Qdrant server")
-    path: Optional[str] = Field(
-        "/tmp/qdrant", description="Path for local Qdrant database"
-    )
+    path: Optional[str] = Field("/tmp/qdrant", description="Path for local Qdrant database")
     url: Optional[str] = Field(None, description="Full URL for Qdrant server")
     api_key: Optional[str] = Field(None, description="API key for Qdrant server")
     on_disk: Optional[bool] = Field(False, description="Enables persistent storage")
@@ -35,9 +29,7 @@ def check_host_port_or_path(cls, values: Dict[str, Any]) -> Dict[str, Any]:
             values.get("api_key"),
         )
         if not path and not (host and port) and not (url and api_key):
-            raise ValueError(
-                "Either 'host' and 'port' or 'url' and 'api_key' or 'path' must be provided."
-            )
+            raise ValueError("Either 'host' and 'port' or 'url' and 'api_key' or 'path' must be provided.")
         return values
 
     @model_validator(mode="before")

mem0/embeddings/azure_openai.py

@@ -15,14 +15,14 @@ def __init__(self, config: Optional[BaseEmbedderConfig] = None):
         azure_deployment = self.config.azure_kwargs.azure_deployment or os.getenv("EMBEDDING_AZURE_DEPLOYMENT")
         azure_endpoint = self.config.azure_kwargs.azure_endpoint or os.getenv("EMBEDDING_AZURE_ENDPOINT")
         api_version = self.config.azure_kwargs.api_version or os.getenv("EMBEDDING_AZURE_API_VERSION")
-        
+
         self.client = AzureOpenAI(
-            azure_deployment=azure_deployment, 
+            azure_deployment=azure_deployment,
             azure_endpoint=azure_endpoint,
             api_version=api_version,
             api_key=api_key,
-            http_client=self.config.http_client
-            )
+            http_client=self.config.http_client,
+        )
 
     def embed(self, text):
         """
@@ -35,8 +35,4 @@ def embed(self, text):
             list: The embedding vector.
         """
         text = text.replace("\n", " ")
-        return (
-            self.client.embeddings.create(input=[text], model=self.config.model)
-            .data[0]
-            .embedding
-        )
+        return self.client.embeddings.create(input=[text], model=self.config.model).data[0].embedding

mem0/embeddings/configs.py

@@ -8,9 +8,7 @@ class EmbedderConfig(BaseModel):
         description="Provider of the embedding model (e.g., 'ollama', 'openai')",
         default="openai",
     )
-    config: Optional[dict] = Field(
-        description="Configuration for the specific embedding model", default={}
-    )
+    config: Optional[dict] = Field(description="Configuration for the specific embedding model", default={})
 
     @field_validator("config")
     def validate_config(cls, v, values):

mem0/embeddings/ollama.py

@@ -9,7 +9,7 @@
     from ollama import Client
 except ImportError:
     user_input = input("The 'ollama' library is required. Install it now? [y/N]: ")
-    if user_input.lower() == 'y':
+    if user_input.lower() == "y":
         try:
             subprocess.check_call([sys.executable, "-m", "pip", "install", "ollama"])
             from ollama import Client

mem0/embeddings/openai.py

@@ -29,8 +29,4 @@ def embed(self, text):
             list: The embedding vector.
         """
         text = text.replace("\n", " ")
-        return (
-            self.client.embeddings.create(input=[text], model=self.config.model)
-            .data[0]
-            .embedding
-        )
+        return self.client.embeddings.create(input=[text], model=self.config.model).data[0].embedding

mem0/embeddings/vertexai.py

@@ -6,6 +6,7 @@
 from mem0.configs.embeddings.base import BaseEmbedderConfig
 from mem0.embeddings.base import EmbeddingBase
 
+
 class VertexAI(EmbeddingBase):
     def __init__(self, config: Optional[BaseEmbedderConfig] = None):
         super().__init__(config)
@@ -34,6 +35,6 @@ def embed(self, text):
         Returns:
             list: The embedding vector.
         """
-        embeddings = self.model.get_embeddings(texts=[text], output_dimensionality= self.config.embedding_dims)
-        
+        embeddings = self.model.get_embeddings(texts=[text], output_dimensionality=self.config.embedding_dims)
+
         return embeddings[0].values

mem0/graphs/configs.py

@@ -18,28 +18,16 @@ def check_host_port_or_path(cls, values):
             values.get("password"),
         )
         if not url or not username or not password:
-            raise ValueError(
-                "Please provide 'url', 'username' and 'password'."
-            )
+            raise ValueError("Please provide 'url', 'username' and 'password'.")
         return values
 
 
 class GraphStoreConfig(BaseModel):
-    provider: str = Field(
-        description="Provider of the data store (e.g., 'neo4j')", 
-        default="neo4j"
-    )
-    config: Neo4jConfig = Field(
-        description="Configuration for the specific data store",
-        default=None
-    )
-    llm: Optional[LlmConfig] = Field(
-        description="LLM configuration for querying the graph store",
-        default=None
-    )
+    provider: str = Field(description="Provider of the data store (e.g., 'neo4j')", default="neo4j")
+    config: Neo4jConfig = Field(description="Configuration for the specific data store", default=None)
+    llm: Optional[LlmConfig] = Field(description="LLM configuration for querying the graph store", default=None)
     custom_prompt: Optional[str] = Field(
-        description="Custom prompt to fetch entities from the given text",
-        default=None
+        description="Custom prompt to fetch entities from the given text", default=None
     )
 
     @field_validator("config")
@@ -49,4 +37,3 @@ def validate_config(cls, v, values):
             return Neo4jConfig(**v.model_dump())
         else:
             raise ValueError(f"Unsupported graph store provider: {provider}")
-