Fix all lint errors (#2627)

mem0/__init__.py

@@ -3,4 +3,4 @@ import importlib.metadata
 __version__ = importlib.metadata.version("mem0ai")
 
 from mem0.client.main import AsyncMemoryClient, MemoryClient  # noqa
-from mem0.memory.main import Memory, AsyncMemory  # noqa
+from mem0.memory.main import AsyncMemory, Memory  # noqa

mem0/client/main.py

@@ -1,7 +1,7 @@
+import hashlib
 import logging
 import os
 import warnings
-import hashlib
 from functools import wraps
 from typing import Any, Dict, List, Optional, Union
 

mem0/embeddings/huggingface.py

@@ -1,16 +1,15 @@
 import logging
 from typing import Literal, Optional
 
-logging.getLogger("transformers").setLevel(logging.WARNING)
-logging.getLogger("sentence_transformers").setLevel(logging.WARNING)
-logging.getLogger("huggingface_hub").setLevel(logging.WARNING)
-
 from openai import OpenAI
 from sentence_transformers import SentenceTransformer
 
 from mem0.configs.embeddings.base import BaseEmbedderConfig
 from mem0.embeddings.base import EmbeddingBase
 
+logging.getLogger("transformers").setLevel(logging.WARNING)
+logging.getLogger("sentence_transformers").setLevel(logging.WARNING)
+logging.getLogger("huggingface_hub").setLevel(logging.WARNING)
 
 class HuggingFaceEmbedding(EmbeddingBase):
     def __init__(self, config: Optional[BaseEmbedderConfig] = None):

mem0/embeddings/langchain.py

@@ -1,4 +1,3 @@
-import os
 from typing import Literal, Optional
 
 from mem0.configs.embeddings.base import BaseEmbedderConfig

mem0/memory/memgraph_memory.py

@@ -60,9 +60,9 @@ class MemoryGraph:
         embedding_dims = self.config.embedder.config["embedding_dims"]
         create_vector_index_query = f"CREATE VECTOR INDEX memzero ON :Entity(embedding) WITH CONFIG {{'dimension': {embedding_dims}, 'capacity': 1000, 'metric': 'cos'}};"
         self.graph.query(create_vector_index_query, params={})
-        create_label_prop_index_query = f"CREATE INDEX ON :Entity(user_id);"
+        create_label_prop_index_query = "CREATE INDEX ON :Entity(user_id);"
         self.graph.query(create_label_prop_index_query, params={})
-        create_label_index_query = f"CREATE INDEX ON :Entity;"
+        create_label_index_query = "CREATE INDEX ON :Entity;"
         self.graph.query(create_label_index_query, params={})
 
     def add(self, data, filters):
@@ -269,8 +269,8 @@ class MemoryGraph:
         for node in node_list:
             n_embedding = self.embedding_model.embed(node)
 
-            cypher_query = f"""
-            MATCH (n:Entity {{user_id: $user_id}})-[r]->(m:Entity)
+            cypher_query = """
+            MATCH (n:Entity {user_id: $user_id})-[r]->(m:Entity)
             WHERE n.embedding IS NOT NULL
             WITH collect(n) AS nodes1, collect(m) AS nodes2, r
             CALL node_similarity.cosine_pairwise("embedding", nodes1, nodes2)
@@ -279,7 +279,7 @@ class MemoryGraph:
             WHERE similarity >= $threshold
             RETURN node1.user_id AS source, id(node1) AS source_id, type(r) AS relationship, id(r) AS relation_id, node2.user_id AS destination, id(node2) AS destination_id, similarity
             UNION
-            MATCH (n:Entity {{user_id: $user_id}})<-[r]-(m:Entity)
+            MATCH (n:Entity {user_id: $user_id})<-[r]-(m:Entity)
             WHERE n.embedding IS NOT NULL
             WITH collect(n) AS nodes1, collect(m) AS nodes2, r
             CALL node_similarity.cosine_pairwise("embedding", nodes1, nodes2)
@@ -481,7 +481,7 @@ class MemoryGraph:
         return entity_list
 
     def _search_source_node(self, source_embedding, user_id, threshold=0.9):
-        cypher = f"""
+        cypher = """
             CALL vector_search.search("memzero", 1, $source_embedding) 
             YIELD distance, node, similarity
             WITH node AS source_candidate, similarity
@@ -499,7 +499,7 @@ class MemoryGraph:
         return result
 
     def _search_destination_node(self, destination_embedding, user_id, threshold=0.9):
-        cypher = f"""
+        cypher = """
             CALL vector_search.search("memzero", 1, $destination_embedding) 
             YIELD distance, node, similarity
             WITH node AS destination_candidate, similarity

mem0/memory/setup.py

@@ -41,7 +41,7 @@ def get_or_create_user_id(vector_store):
         existing = vector_store.get(vector_id=VECTOR_ID)
         if existing and hasattr(existing, "payload") and existing.payload and "user_id" in existing.payload:
             return existing.payload["user_id"]
-    except:
+    except Exception:
         pass
 
     # If we get here, we need to insert the user_id
@@ -50,7 +50,7 @@ def get_or_create_user_id(vector_store):
         vector_store.insert(
             vectors=[[0.0] * dims], payloads=[{"user_id": user_id, "type": "user_identity"}], ids=[VECTOR_ID]
         )
-    except:
+    except Exception:
         pass
 
     return user_id