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

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 @@ class BaseEmbedderConfig(ABC):
 
         # 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 @@ You should detect the language of the user input and record the facts in the sam
 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 @@ class MilvusDBConfig(BaseModel):
 
     model_config = {
         "arbitrary_types_allowed": True,
-    }
+    }

mem0/configs/vector_stores/pgvector.py

@@ -4,12 +4,9 @@ from pydantic import BaseModel, Field, model_validator
 
 
 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 @@ class QdrantConfig(BaseModel):
             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")