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Fix all lint errors (#2627)

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This commit is contained in:
Dev Khant
2025-05-06 01:16:02 +05:30
committed by GitHub
parent 725a1aa114
commit ec1d7a45d3
50 changed files with 586 additions and 570 deletions
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14
mem0/memory/memgraph_memory.py
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@@ -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