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[graph_memory]: improve delete/add graph memory (#2073)

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This commit is contained in:
Mayank
2025-01-03 22:21:05 +05:30
committed by GitHub
parent 542153ad4f
commit 78a2ef41d7
7 changed files with 439 additions and 225 deletions
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4
docs/open-source/graph_memory/overview.mdx
View File

@@ -109,6 +109,10 @@ The Mem0's graph supports the following operations:
### Add Memories
<Note>
If you are using Mem0 with Graph Memory, it is recommended to pass `user_id`. The default value of `user_id` (in case of graph memory) is `user`.
</Note>
<CodeGroup>
```python Code
m.add("I like pizza", user_id="alice")

77
mem0/graphs/tools.py
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@@ -85,7 +85,7 @@ NOOP_TOOL = {
RELATIONS_TOOL = {
"type": "function",
"function": {
"name": "establish_relations",
"name": "establish_relationships",
"description": "Establish relationships among the entities based on the provided text.",
"parameters": {
"type": "object",
@@ -99,7 +99,7 @@ RELATIONS_TOOL = {
"type": "string",
"description": "The source entity of the relationship."
},
"relation": {
"relationship": {
"type": "string",
"description": "The relationship between the source and destination entities."
},
@@ -109,9 +109,9 @@ RELATIONS_TOOL = {
},
},
"required": [
"source_entity",
"relation",
"destination_entity",
"source",
"relationship",
"destination",
],
"additionalProperties": False,
},
@@ -262,7 +262,7 @@ RELATIONS_STRUCT_TOOL = {
"type": "string",
"description": "The source entity of the relationship."
},
"relation": {
"relatationship": {
"type": "string",
"description": "The relationship between the source and destination entities."
},
@@ -273,7 +273,7 @@ RELATIONS_STRUCT_TOOL = {
},
"required": [
"source_entity",
"relation",
"relatationship",
"destination_entity",
],
"additionalProperties": False,
@@ -321,3 +321,66 @@ EXTRACT_ENTITIES_STRUCT_TOOL = {
}
}
}
DELETE_MEMORY_STRUCT_TOOL_GRAPH = {
"type": "function",
"function": {
"name": "delete_graph_memory",
"description": "Delete the relationship between two nodes. This function deletes the existing relationship.",
"strict": True,
"parameters": {
"type": "object",
"properties": {
"source": {
"type": "string",
"description": "The identifier of the source node in the relationship.",
},
"relationship": {
"type": "string",
"description": "The existing relationship between the source and destination nodes that needs to be deleted.",
},
"destination": {
"type": "string",
"description": "The identifier of the destination node in the relationship.",
}
},
"required": [
"source",
"relationship",
"destination",
],
"additionalProperties": False,
},
},
}
DELETE_MEMORY_TOOL_GRAPH = {
"type": "function",
"function": {
"name": "delete_graph_memory",
"description": "Delete the relationship between two nodes. This function deletes the existing relationship.",
"parameters": {
"type": "object",
"properties": {
"source": {
"type": "string",
"description": "The identifier of the source node in the relationship.",
},
"relationship": {
"type": "string",
"description": "The existing relationship between the source and destination nodes that needs to be deleted.",
},
"destination": {
"type": "string",
"description": "The identifier of the destination node in the relationship.",
}
},
"required": [
"source",
"relationship",
"destination",
],
"additionalProperties": False,
},
},
}

46
mem0/graphs/utils.py
View File

@@ -43,7 +43,7 @@ CUSTOM_PROMPT
Relationships:
- Use consistent, general, and timeless relationship types.
- Example: Prefer "PROFESSOR" over "BECAME_PROFESSOR."
- Example: Prefer "professor" over "became_professor."
- Relationships should only be established among the entities explicitly mentioned in the user message.
Entity Consistency:
@@ -54,15 +54,41 @@ Strive to construct a coherent and easily understandable knowledge graph by esht
Adhere strictly to these guidelines to ensure high-quality knowledge graph extraction."""
DELETE_RELATIONS_SYSTEM_PROMPT = """
You are a graph memory manager specializing in identifying, managing, and optimizing relationships within graph-based memories. Your primary task is to analyze a list of existing relationships and determine which ones should be deleted based on the new information provided.
Input:
1. Existing Graph Memories: A list of current graph memories, each containing source, relationship, and destination information.
2. New Text: The new information to be integrated into the existing graph structure.
3. Use "USER_ID" as node for any self-references (e.g., "I," "me," "my," etc.) in user messages.
def get_update_memory_prompt(existing_memories, new_memories, template):
return template.format(existing_memories=existing_memories, new_memories=new_memories)
Guidelines:
1. Identification: Use the new information to evaluate existing relationships in the memory graph.
2. Deletion Criteria: Delete a relationship only if it meets at least one of these conditions:
- Outdated or Inaccurate: The new information is more recent or accurate.
- Contradictory: The new information conflicts with or negates the existing information.
3. DO NOT DELETE if their is a possibility of same type of relationship but different destination nodes.
4. Comprehensive Analysis:
- Thoroughly examine each existing relationship against the new information and delete as necessary.
- Multiple deletions may be required based on the new information.
5. Semantic Integrity:
- Ensure that deletions maintain or improve the overall semantic structure of the graph.
- Avoid deleting relationships that are NOT contradictory/outdated to the new information.
6. Temporal Awareness: Prioritize recency when timestamps are available.
7. Necessity Principle: Only DELETE relationships that must be deleted and are contradictory/outdated to the new information to maintain an accurate and coherent memory graph.
Note: DO NOT DELETE if their is a possibility of same type of relationship but different destination nodes.
def get_update_memory_messages(existing_memories, new_memories):
return [
{
"role": "user",
"content": get_update_memory_prompt(existing_memories, new_memories, UPDATE_GRAPH_PROMPT),
},
]
For example:
Existing Memory: alice -- loves_to_eat -- pizza
New Information: Alice also loves to eat burger.
Do not delete in the above example because there is a possibility that Alice loves to eat both pizza and burger.
Memory Format:
source -- relationship -- destination
Provide a list of deletion instructions, each specifying the relationship to be deleted.
"""
def get_delete_messages(existing_memories_string, data, user_id):
return DELETE_RELATIONS_SYSTEM_PROMPT.replace("USER_ID", user_id), f"Here are the existing memories: {existing_memories_string} \n\n New Information: {data}"

512
mem0/memory/graph_memory.py
View File

@@ -13,18 +13,14 @@ except ImportError:
raise ImportError("rank_bm25 is not installed. Please install it using pip install rank-bm25")
from mem0.graphs.tools import (
ADD_MEMORY_STRUCT_TOOL_GRAPH,
ADD_MEMORY_TOOL_GRAPH,
DELETE_MEMORY_STRUCT_TOOL_GRAPH,
DELETE_MEMORY_TOOL_GRAPH,
EXTRACT_ENTITIES_STRUCT_TOOL,
EXTRACT_ENTITIES_TOOL,
NOOP_STRUCT_TOOL,
NOOP_TOOL,
RELATIONS_STRUCT_TOOL,
RELATIONS_TOOL,
UPDATE_MEMORY_STRUCT_TOOL_GRAPH,
UPDATE_MEMORY_TOOL_GRAPH,
)
from mem0.graphs.utils import EXTRACT_RELATIONS_PROMPT, get_update_memory_messages
from mem0.graphs.utils import EXTRACT_RELATIONS_PROMPT, get_delete_messages
from mem0.utils.factory import EmbedderFactory, LlmFactory
logger = logging.getLogger(__name__)
@@ -58,150 +54,17 @@ class MemoryGraph:
data (str): The data to add to the graph.
filters (dict): A dictionary containing filters to be applied during the addition.
"""
entity_type_map = self._retrieve_nodes_from_data(data, filters)
to_be_added = self._establish_nodes_relations_from_data(data, filters, entity_type_map)
search_output = self._search_graph_db(node_list=list(entity_type_map.keys()), filters=filters)
to_be_deleted = self._get_delete_entities_from_search_output(search_output, data, filters)
# retrieve the search results
search_output, entity_type_map = self._search(data, filters)
#TODO: Batch queries with APOC plugin
#TODO: Add more filter support
deleted_entities = self._delete_entities(to_be_deleted, filters["user_id"])
added_entities = self._add_entities(to_be_added, filters["user_id"], entity_type_map)
# extract relations
extracted_relations = self._extract_relations(data, filters, entity_type_map)
search_output_string = format_entities(search_output)
extracted_relations_string = format_entities(extracted_relations)
update_memory_prompt = get_update_memory_messages(search_output_string, extracted_relations_string)
_tools = [UPDATE_MEMORY_TOOL_GRAPH, ADD_MEMORY_TOOL_GRAPH, NOOP_TOOL]
if self.llm_provider in ["azure_openai_structured", "openai_structured"]:
_tools = [
UPDATE_MEMORY_STRUCT_TOOL_GRAPH,
ADD_MEMORY_STRUCT_TOOL_GRAPH,
NOOP_STRUCT_TOOL,
]
memory_updates = self.llm.generate_response(
messages=update_memory_prompt,
tools=_tools,
)
to_be_added = []
for item in memory_updates["tool_calls"]:
if item["name"] == "add_graph_memory":
to_be_added.append(item["arguments"])
elif item["name"] == "update_graph_memory":
self._update_relationship(
item["arguments"]["source"],
item["arguments"]["destination"],
item["arguments"]["relationship"],
filters,
)
elif item["name"] == "noop":
continue
returned_entities = []
for item in to_be_added:
source = item["source"].lower().replace(" ", "_")
source_type = item["source_type"].lower().replace(" ", "_")
relation = item["relationship"].lower().replace(" ", "_")
destination = item["destination"].lower().replace(" ", "_")
destination_type = item["destination_type"].lower().replace(" ", "_")
returned_entities.append({"source": source, "relationship": relation, "target": destination})
# Create embeddings
source_embedding = self.embedding_model.embed(source)
dest_embedding = self.embedding_model.embed(destination)
# Updated Cypher query to include node types and embeddings
cypher = f"""
MERGE (n:{source_type} {{name: $source_name, user_id: $user_id}})
ON CREATE SET n.created = timestamp(), n.embedding = $source_embedding
ON MATCH SET n.embedding = $source_embedding
MERGE (m:{destination_type} {{name: $dest_name, user_id: $user_id}})
ON CREATE SET m.created = timestamp(), m.embedding = $dest_embedding
ON MATCH SET m.embedding = $dest_embedding
MERGE (n)-[rel:{relation}]->(m)
ON CREATE SET rel.created = timestamp()
RETURN n, rel, m
"""
params = {
"source_name": source,
"dest_name": destination,
"source_embedding": source_embedding,
"dest_embedding": dest_embedding,
"user_id": filters["user_id"],
}
_ = self.graph.query(cypher, params=params)
logger.info(f"Added {len(to_be_added)} new memories to the graph")
return returned_entities
def _search(self, query, filters, limit=100):
_tools = [EXTRACT_ENTITIES_TOOL]
if self.llm_provider in ["azure_openai_structured", "openai_structured"]:
_tools = [EXTRACT_ENTITIES_STRUCT_TOOL]
search_results = self.llm.generate_response(
messages=[
{
"role": "system",
"content": f"You are a smart assistant who understands entities and their types in a given text. If user message contains self reference such as 'I', 'me', 'my' etc. then use {filters['user_id']} as the source entity. Extract all the entities from the text. ***DO NOT*** answer the question itself if the given text is a question.",
},
{"role": "user", "content": query},
],
tools=_tools,
)
entity_type_map = {}
try:
for item in search_results["tool_calls"][0]["arguments"]["entities"]:
entity_type_map[item["entity"]] = item["entity_type"]
except Exception as e:
logger.error(f"Error in search tool: {e}")
logger.debug(f"Entity type map: {entity_type_map}")
result_relations = []
for node in list(entity_type_map.keys()):
n_embedding = self.embedding_model.embed(node)
cypher_query = """
MATCH (n)
WHERE n.embedding IS NOT NULL AND n.user_id = $user_id
WITH n,
round(reduce(dot = 0.0, i IN range(0, size(n.embedding)-1) | dot + n.embedding[i] * $n_embedding[i]) /
(sqrt(reduce(l2 = 0.0, i IN range(0, size(n.embedding)-1) | l2 + n.embedding[i] * n.embedding[i])) *
sqrt(reduce(l2 = 0.0, i IN range(0, size($n_embedding)-1) | l2 + $n_embedding[i] * $n_embedding[i]))), 4) AS similarity
WHERE similarity >= $threshold
MATCH (n)-[r]->(m)
RETURN n.name AS source, elementId(n) AS source_id, type(r) AS relation, elementId(r) AS relation_id, m.name AS destination, elementId(m) AS destination_id, similarity
UNION
MATCH (n)
WHERE n.embedding IS NOT NULL AND n.user_id = $user_id
WITH n,
round(reduce(dot = 0.0, i IN range(0, size(n.embedding)-1) | dot + n.embedding[i] * $n_embedding[i]) /
(sqrt(reduce(l2 = 0.0, i IN range(0, size(n.embedding)-1) | l2 + n.embedding[i] * n.embedding[i])) *
sqrt(reduce(l2 = 0.0, i IN range(0, size($n_embedding)-1) | l2 + $n_embedding[i] * $n_embedding[i]))), 4) AS similarity
WHERE similarity >= $threshold
MATCH (m)-[r]->(n)
RETURN m.name AS source, elementId(m) AS source_id, type(r) AS relation, elementId(r) AS relation_id, n.name AS destination, elementId(n) AS destination_id, similarity
ORDER BY similarity DESC
LIMIT $limit
"""
params = {
"n_embedding": n_embedding,
"threshold": self.threshold,
"user_id": filters["user_id"],
"limit": limit,
}
ans = self.graph.query(cypher_query, params=params)
result_relations.extend(ans)
return result_relations, entity_type_map
return {"deleted_entities": deleted_entities, "added_entities": added_entities}
def search(self, query, filters, limit=100):
"""
@@ -217,13 +80,13 @@ class MemoryGraph:
- "contexts": List of search results from the base data store.
- "entities": List of related graph data based on the query.
"""
search_output, entity_type_map = self._search(query, filters, limit)
entity_type_map = self._retrieve_nodes_from_data(query, filters)
search_output = self._search_graph_db(node_list=list(entity_type_map.keys()), filters=filters)
if not search_output:
return []
search_outputs_sequence = [[item["source"], item["relation"], item["destination"]] for item in search_output]
search_outputs_sequence = [[item["source"], item["relatationship"], item["destination"]] for item in search_output]
bm25 = BM25Okapi(search_outputs_sequence)
tokenized_query = query.split(" ")
@@ -231,7 +94,7 @@ class MemoryGraph:
search_results = []
for item in reranked_results:
search_results.append({"source": item[0], "relationship": item[1], "target": item[2]})
search_results.append({"source": item[0], "relationship": item[1], "destination": item[2]})
logger.info(f"Returned {len(search_results)} search results")
@@ -280,8 +143,36 @@ class MemoryGraph:
return final_results
def _extract_relations(self, data, filters, entity_type_map, limit=100):
def _retrieve_nodes_from_data(self, data, filters):
"""Extracts all the entities mentioned in the query."""
_tools = [EXTRACT_ENTITIES_TOOL]
if self.llm_provider in ["azure_openai_structured", "openai_structured"]:
_tools = [EXTRACT_ENTITIES_STRUCT_TOOL]
search_results = self.llm.generate_response(
messages=[
{
"role": "system",
"content": f"You are a smart assistant who understands entities and their types in a given text. If user message contains self reference such as 'I', 'me', 'my' etc. then use {filters['user_id']} as the source entity. Extract all the entities from the text. ***DO NOT*** answer the question itself if the given text is a question.",
},
{"role": "user", "content": data},
],
tools=_tools,
)
entity_type_map = {}
try:
for item in search_results["tool_calls"][0]["arguments"]["entities"]:
entity_type_map[item["entity"]] = item["entity_type"]
except Exception as e:
logger.error(f"Error in search tool: {e}")
entity_type_map = {k.lower().replace(" ", "_"): v.lower().replace(" ", "_") for k, v in entity_type_map.items()}
logger.debug(f"Entity type map: {entity_type_map}")
return entity_type_map
def _establish_nodes_relations_from_data(self, data, filters, entity_type_map):
"""Eshtablish relations among the extracted nodes."""
if self.config.graph_store.custom_prompt:
messages = [
{
@@ -315,57 +206,292 @@ class MemoryGraph:
else:
extracted_entities = []
extracted_entities = self._remove_spaces_from_entities(extracted_entities)
logger.debug(f"Extracted entities: {extracted_entities}")
return extracted_entities
def _update_relationship(self, source, target, relationship, filters):
def _search_graph_db(self, node_list, filters, limit=100):
"""Search similar nodes among and their respective incoming and outgoing relations."""
result_relations = []
for node in node_list:
n_embedding = self.embedding_model.embed(node)
cypher_query = """
MATCH (n)
WHERE n.embedding IS NOT NULL AND n.user_id = $user_id
WITH n,
round(reduce(dot = 0.0, i IN range(0, size(n.embedding)-1) | dot + n.embedding[i] * $n_embedding[i]) /
(sqrt(reduce(l2 = 0.0, i IN range(0, size(n.embedding)-1) | l2 + n.embedding[i] * n.embedding[i])) *
sqrt(reduce(l2 = 0.0, i IN range(0, size($n_embedding)-1) | l2 + $n_embedding[i] * $n_embedding[i]))), 4) AS similarity
WHERE similarity >= $threshold
MATCH (n)-[r]->(m)
RETURN n.name AS source, elementId(n) AS source_id, type(r) AS relatationship, elementId(r) AS relation_id, m.name AS destination, elementId(m) AS destination_id, similarity
UNION
MATCH (n)
WHERE n.embedding IS NOT NULL AND n.user_id = $user_id
WITH n,
round(reduce(dot = 0.0, i IN range(0, size(n.embedding)-1) | dot + n.embedding[i] * $n_embedding[i]) /
(sqrt(reduce(l2 = 0.0, i IN range(0, size(n.embedding)-1) | l2 + n.embedding[i] * n.embedding[i])) *
sqrt(reduce(l2 = 0.0, i IN range(0, size($n_embedding)-1) | l2 + $n_embedding[i] * $n_embedding[i]))), 4) AS similarity
WHERE similarity >= $threshold
MATCH (m)-[r]->(n)
RETURN m.name AS source, elementId(m) AS source_id, type(r) AS relatationship, elementId(r) AS relation_id, n.name AS destination, elementId(n) AS destination_id, similarity
ORDER BY similarity DESC
LIMIT $limit
"""
Update or create a relationship between two nodes in the graph.
params = {
"n_embedding": n_embedding,
"threshold": self.threshold,
"user_id": filters["user_id"],
"limit": limit,
}
ans = self.graph.query(cypher_query, params=params)
result_relations.extend(ans)
Args:
source (str): The name of the source node.
target (str): The name of the target node.
relationship (str): The type of the relationship.
filters (dict): A dictionary containing filters to be applied during the update.
return result_relations
Raises:
Exception: If the operation fails.
"""
logger.info(f"Updating relationship: {source} -{relationship}-> {target}")
def _get_delete_entities_from_search_output(self, search_output, data, filters):
"""Get the entities to be deleted from the search output."""
search_output_string = format_entities(search_output)
system_prompt, user_prompt = get_delete_messages(search_output_string, data, filters["user_id"])
relationship = relationship.lower().replace(" ", "_")
_tools = [DELETE_MEMORY_TOOL_GRAPH]
if self.llm_provider in ["azure_openai_structured", "openai_structured"]:
_tools = [
DELETE_MEMORY_STRUCT_TOOL_GRAPH,
]
# Check if nodes exist and create them if they don't
check_and_create_query = """
MERGE (n1 {name: $source, user_id: $user_id})
MERGE (n2 {name: $target, user_id: $user_id})
"""
self.graph.query(
check_and_create_query,
params={"source": source, "target": target, "user_id": filters["user_id"]},
memory_updates = self.llm.generate_response(
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt},
],
tools=_tools,
)
to_be_deleted = []
for item in memory_updates["tool_calls"]:
if item["name"] == "delete_graph_memory":
to_be_deleted.append(item["arguments"])
#in case if it is not in the correct format
to_be_deleted = self._remove_spaces_from_entities(to_be_deleted)
logger.debug(f"Deleted relationships: {to_be_deleted}")
return to_be_deleted
# Delete any existing relationship between the nodes
delete_query = """
MATCH (n1 {name: $source, user_id: $user_id})-[r]->(n2 {name: $target, user_id: $user_id})
def _delete_entities(self, to_be_deleted, user_id):
"""Delete the entities from the graph."""
results = []
for item in to_be_deleted:
source = item["source"]
destination = item["destination"]
relatationship = item["relationship"]
# Delete the specific relationship between nodes
cypher = f"""
MATCH (n {{name: $source_name, user_id: $user_id}})
-[r:{relatationship}]->
(m {{name: $dest_name, user_id: $user_id}})
DELETE r
RETURN
n.name AS source,
m.name AS target,
type(r) AS relationship
"""
self.graph.query(
delete_query,
params={"source": source, "target": target, "user_id": filters["user_id"]},
)
params = {
"source_name": source,
"dest_name": destination,
"user_id": user_id,
}
result = self.graph.query(cypher, params=params)
results.append(result)
return results
# Create the new relationship
create_query = f"""
MATCH (n1 {{name: $source, user_id: $user_id}}), (n2 {{name: $target, user_id: $user_id}})
CREATE (n1)-[r:{relationship}]->(n2)
RETURN n1, r, n2
def _add_entities(self, to_be_added, user_id, entity_type_map):
"""Add the new entities to the graph. Merge the nodes if they already exist."""
results = []
for item in to_be_added:
#entities
source = item["source"]
destination = item["destination"]
relationship = item["relationship"]
#types
source_type = entity_type_map.get(source, "unknown")
destination_type = entity_type_map.get(destination, "unknown")
#embeddings
source_embedding = self.embedding_model.embed(source)
dest_embedding = self.embedding_model.embed(destination)
#search for the nodes with the closest embeddings
source_node_search_result = self._search_source_node(source_embedding, user_id, threshold=0.9)
destination_node_search_result = self._search_destination_node(dest_embedding, user_id, threshold=0.9)
#TODO: Create a cypher query and common params for all the cases
if not destination_node_search_result and source_node_search_result:
cypher = f"""
MATCH (source)
WHERE elementId(source) = $source_id
MERGE (destination:{destination_type} {{name: $destination_name, user_id: $user_id}})
ON CREATE SET
destination.created = timestamp(),
destination.embedding = $destination_embedding
MERGE (source)-[r:{relationship}]->(destination)
ON CREATE SET
r.created = timestamp()
RETURN source.name AS source, type(r) AS relationship, destination.name AS target
"""
result = self.graph.query(
create_query,
params={"source": source, "target": target, "user_id": filters["user_id"]},
)
if not result:
raise Exception(f"Failed to update or create relationship between {source} and {target}")
params = {
"source_id": source_node_search_result[0]['elementId(source_candidate)'],
"destination_name": destination,
"relationship": relationship,
"destination_type": destination_type,
"destination_embedding": dest_embedding,
"user_id": user_id,
}
resp = self.graph.query(cypher, params=params)
results.append(resp)
elif destination_node_search_result and not source_node_search_result:
cypher = f"""
MATCH (destination)
WHERE elementId(destination) = $destination_id
MERGE (source:{source_type} {{name: $source_name, user_id: $user_id}})
ON CREATE SET
source.created = timestamp(),
source.embedding = $source_embedding
MERGE (source)-[r:{relationship}]->(destination)
ON CREATE SET
r.created = timestamp()
RETURN source.name AS source, type(r) AS relationship, destination.name AS target
"""
params = {
"destination_id": destination_node_search_result[0]['elementId(destination_candidate)'],
"source_name": source,
"relationship": relationship,
"source_type": source_type,
"source_embedding": source_embedding,
"user_id": user_id,
}
resp = self.graph.query(cypher, params=params)
results.append(resp)
elif source_node_search_result and destination_node_search_result:
cypher = f"""
MATCH (source)
WHERE elementId(source) = $source_id
MATCH (destination)
WHERE elementId(destination) = $destination_id
MERGE (source)-[r:{relationship}]->(destination)
ON CREATE SET
r.created_at = timestamp(),
r.updated_at = timestamp()
RETURN source.name AS source, type(r) AS relationship, destination.name AS target
"""
params = {
"source_id": source_node_search_result[0]['elementId(source_candidate)'],
"destination_id": destination_node_search_result[0]['elementId(destination_candidate)'],
"user_id": user_id,
"relationship": relationship,
}
resp = self.graph.query(cypher, params=params)
results.append(resp)
elif not source_node_search_result and not destination_node_search_result:
cypher = f"""
MERGE (n:{source_type} {{name: $source_name, user_id: $user_id}})
ON CREATE SET n.created = timestamp(), n.embedding = $source_embedding
ON MATCH SET n.embedding = $source_embedding
MERGE (m:{destination_type} {{name: $dest_name, user_id: $user_id}})
ON CREATE SET m.created = timestamp(), m.embedding = $dest_embedding
ON MATCH SET m.embedding = $dest_embedding
MERGE (n)-[rel:{relationship}]->(m)
ON CREATE SET rel.created = timestamp()
RETURN n.name AS source, type(rel) AS relationship, m.name AS target
"""
params = {
"source_name": source,
"source_type": source_type,
"dest_name": destination,
"destination_type": destination_type,
"source_embedding": source_embedding,
"dest_embedding": dest_embedding,
"user_id": user_id,
}
resp = self.graph.query(cypher, params=params)
results.append(resp)
return results
def _remove_spaces_from_entities(self, entity_list):
for item in entity_list:
item["source"] = item["source"].lower().replace(" ", "_")
item["relationship"] = item["relationship"].lower().replace(" ", "_")
item["destination"] = item["destination"].lower().replace(" ", "_")
return entity_list
def _search_source_node(self, source_embedding, user_id, threshold=0.9):
cypher = f"""
MATCH (source_candidate)
WHERE source_candidate.embedding IS NOT NULL
AND source_candidate.user_id = $user_id
WITH source_candidate,
round(
reduce(dot = 0.0, i IN range(0, size(source_candidate.embedding)-1) |
dot + source_candidate.embedding[i] * $source_embedding[i]) /
(sqrt(reduce(l2 = 0.0, i IN range(0, size(source_candidate.embedding)-1) |
l2 + source_candidate.embedding[i] * source_candidate.embedding[i])) *
sqrt(reduce(l2 = 0.0, i IN range(0, size($source_embedding)-1) |
l2 + $source_embedding[i] * $source_embedding[i])))
, 4) AS source_similarity
WHERE source_similarity >= $threshold
WITH source_candidate, source_similarity
ORDER BY source_similarity DESC
LIMIT 1
RETURN elementId(source_candidate)
"""
params = {
"source_embedding": source_embedding,
"user_id": user_id,
"threshold": threshold,
}
result = self.graph.query(cypher, params=params)
return result
def _search_destination_node(self, destination_embedding, user_id, threshold=0.9):
cypher = f"""
MATCH (destination_candidate)
WHERE destination_candidate.embedding IS NOT NULL
AND destination_candidate.user_id = $user_id
WITH destination_candidate,
round(
reduce(dot = 0.0, i IN range(0, size(destination_candidate.embedding)-1) |
dot + destination_candidate.embedding[i] * $destination_embedding[i]) /
(sqrt(reduce(l2 = 0.0, i IN range(0, size(destination_candidate.embedding)-1) |
l2 + destination_candidate.embedding[i] * destination_candidate.embedding[i])) *
sqrt(reduce(l2 = 0.0, i IN range(0, size($destination_embedding)-1) |
l2 + $destination_embedding[i] * $destination_embedding[i])))
, 4) AS destination_similarity
WHERE destination_similarity >= $threshold
WITH destination_candidate, destination_similarity
ORDER BY destination_similarity DESC
LIMIT 1
RETURN elementId(destination_candidate)
"""
params = {
"destination_embedding": destination_embedding,
"user_id": user_id,
"threshold": threshold,
}
result = self.graph.query(cypher, params=params)
return result

11
mem0/memory/main.py
View File

@@ -240,14 +240,9 @@ class Memory(MemoryBase):
def _add_to_graph(self, messages, filters):
added_entities = []
if self.api_version == "v1.1" and self.enable_graph:
if filters["user_id"]:
self.graph.user_id = filters["user_id"]
elif filters["agent_id"]:
self.graph.agent_id = filters["agent_id"]
elif filters["run_id"]:
self.graph.run_id = filters["run_id"]
else:
self.graph.user_id = "USER"
if filters.get("user_id") is None:
filters["user_id"] = "user"
data = "\n".join([msg["content"] for msg in messages if "content" in msg and msg["role"] != "system"])
added_entities = self.graph.add(data, filters)

2
mem0/memory/utils.py
View File

@@ -24,7 +24,7 @@ def format_entities(entities):
formatted_lines = []
for entity in entities:
simplified = f"{entity['source']} -- {entity['relation'].upper()} -- {entity['destination']}"
simplified = f"{entity['source']} -- {entity['relatationship']} -- {entity['destination']}"
formatted_lines.append(simplified)
return "\n".join(formatted_lines)

2
tests/test_proxy.py
View File

@@ -94,4 +94,4 @@ def test_completions_create_with_system_message(mock_memory_client, mock_litellm
call_args = mock_litellm.completion.call_args[1]
assert call_args["messages"][0]["role"] == "system"
assert call_args["messages"][0]["content"] == MEMORY_ANSWER_PROMPT
assert call_args["messages"][0]["content"] == "You are a helpful assistant."