import logging from mem0.memory.utils import format_entities try: from langchain_neo4j import Neo4jGraph except ImportError: raise ImportError("langchain_neo4j is not installed. Please install it using pip install langchain-neo4j") try: from rank_bm25 import BM25Okapi except ImportError: raise ImportError("rank_bm25 is not installed. Please install it using pip install rank-bm25") from mem0.graphs.tools import ( DELETE_MEMORY_STRUCT_TOOL_GRAPH, DELETE_MEMORY_TOOL_GRAPH, EXTRACT_ENTITIES_STRUCT_TOOL, EXTRACT_ENTITIES_TOOL, RELATIONS_STRUCT_TOOL, RELATIONS_TOOL, ) from mem0.graphs.utils import EXTRACT_RELATIONS_PROMPT, get_delete_messages from mem0.utils.factory import EmbedderFactory, LlmFactory logger = logging.getLogger(__name__) class MemoryGraph: def __init__(self, config): self.config = config self.graph = Neo4jGraph( self.config.graph_store.config.url, self.config.graph_store.config.username, self.config.graph_store.config.password, self.config.graph_store.config.database, refresh_schema=False, driver_config={"notifications_min_severity": "OFF"}, ) self.embedding_model = EmbedderFactory.create( self.config.embedder.provider, self.config.embedder.config, self.config.vector_store.config ) self.node_label = ":`__Entity__`" if self.config.graph_store.config.base_label else "" if self.config.graph_store.config.base_label: # Safely add user_id index try: self.graph.query(f"CREATE INDEX entity_single IF NOT EXISTS FOR (n {self.node_label}) ON (n.user_id)") except Exception: pass try: # Safely try to add composite index (Enterprise only) self.graph.query( f"CREATE INDEX entity_composite IF NOT EXISTS FOR (n {self.node_label}) ON (n.name, n.user_id)" ) except Exception: pass self.llm_provider = "openai_structured" if self.config.llm.provider: self.llm_provider = self.config.llm.provider if self.config.graph_store.llm: self.llm_provider = self.config.graph_store.llm.provider self.llm = LlmFactory.create(self.llm_provider, self.config.llm.config) self.user_id = None self.threshold = 0.7 def add(self, data, filters): """ Adds data to the graph. Args: 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) # TODO: Batch queries with APOC plugin # TODO: Add more filter support deleted_entities = self._delete_entities(to_be_deleted, filters) added_entities = self._add_entities(to_be_added, filters, entity_type_map) return {"deleted_entities": deleted_entities, "added_entities": added_entities} def search(self, query, filters, limit=100): """ Search for memories and related graph data. Args: query (str): Query to search for. filters (dict): A dictionary containing filters to be applied during the search. limit (int): The maximum number of nodes and relationships to retrieve. Defaults to 100. Returns: dict: A dictionary containing: - "contexts": List of search results from the base data store. - "entities": List of related graph data based on the query. """ 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["relationship"], item["destination"]] for item in search_output ] bm25 = BM25Okapi(search_outputs_sequence) tokenized_query = query.split(" ") reranked_results = bm25.get_top_n(tokenized_query, search_outputs_sequence, n=5) search_results = [] for item in reranked_results: search_results.append({"source": item[0], "relationship": item[1], "destination": item[2]}) logger.info(f"Returned {len(search_results)} search results") return search_results def delete_all(self, filters): if filters.get("agent_id"): cypher = f""" MATCH (n {self.node_label} {{user_id: $user_id, agent_id: $agent_id}}) DETACH DELETE n """ params = {"user_id": filters["user_id"], "agent_id": filters["agent_id"]} else: cypher = f""" MATCH (n {self.node_label} {{user_id: $user_id}}) DETACH DELETE n """ params = {"user_id": filters["user_id"]} self.graph.query(cypher, params=params) def get_all(self, filters, limit=100): agent_filter = "" params = {"user_id": filters["user_id"], "limit": limit} if filters.get("agent_id"): agent_filter = "AND n.agent_id = $agent_id AND m.agent_id = $agent_id" params["agent_id"] = filters["agent_id"] query = f""" MATCH (n {self.node_label} {{user_id: $user_id}})-[r]->(m {self.node_label} {{user_id: $user_id}}) WHERE 1=1 {agent_filter} RETURN n.name AS source, type(r) AS relationship, m.name AS target LIMIT $limit """ results = self.graph.query(query, params=params) final_results = [] for result in results: final_results.append( { "source": result["source"], "relationship": result["relationship"], "target": result["target"], } ) logger.info(f"Retrieved {len(final_results)} relationships") return final_results 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 tool_call in search_results["tool_calls"]: if tool_call["name"] != "extract_entities": continue for item in tool_call["arguments"]["entities"]: entity_type_map[item["entity"]] = item["entity_type"] except Exception as e: logger.exception( f"Error in search tool: {e}, llm_provider={self.llm_provider}, search_results={search_results}" ) 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}\n search_results={search_results}") return entity_type_map def _establish_nodes_relations_from_data(self, data, filters, entity_type_map): """Establish relations among the extracted nodes.""" # Compose user identification string for prompt user_identity = f"user_id: {filters['user_id']}" if filters.get("agent_id"): user_identity += f", agent_id: {filters['agent_id']}" if self.config.graph_store.custom_prompt: system_content = EXTRACT_RELATIONS_PROMPT.replace("USER_ID", user_identity) # Add the custom prompt line if configured system_content = system_content.replace( "CUSTOM_PROMPT", f"4. {self.config.graph_store.custom_prompt}" ) messages = [ {"role": "system", "content": system_content}, {"role": "user", "content": data}, ] else: system_content = EXTRACT_RELATIONS_PROMPT.replace("USER_ID", user_identity) messages = [ {"role": "system", "content": system_content}, {"role": "user", "content": f"List of entities: {list(entity_type_map.keys())}. \n\nText: {data}"}, ] _tools = [RELATIONS_TOOL] if self.llm_provider in ["azure_openai_structured", "openai_structured"]: _tools = [RELATIONS_STRUCT_TOOL] extracted_entities = self.llm.generate_response( messages=messages, tools=_tools, ) entities = [] if extracted_entities.get("tool_calls"): entities = extracted_entities["tool_calls"][0].get("arguments", {}).get("entities", []) entities = self._remove_spaces_from_entities(entities) logger.debug(f"Extracted entities: {entities}") return entities def _search_graph_db(self, node_list, filters, limit=100): """Search similar nodes among and their respective incoming and outgoing relations.""" result_relations = [] agent_filter = "" if filters.get("agent_id"): agent_filter = "AND n.agent_id = $agent_id AND m.agent_id = $agent_id" for node in node_list: n_embedding = self.embedding_model.embed(node) cypher_query = f""" MATCH (n {self.node_label}) WHERE n.embedding IS NOT NULL AND n.user_id = $user_id {agent_filter} WITH n, round(2 * vector.similarity.cosine(n.embedding, $n_embedding) - 1, 4) AS similarity // denormalize for backward compatibility WHERE similarity >= $threshold CALL {{ MATCH (n)-[r]->(m) WHERE m.user_id = $user_id {agent_filter.replace("n.", "m.")} RETURN n.name AS source, elementId(n) AS source_id, type(r) AS relationship, elementId(r) AS relation_id, m.name AS destination, elementId(m) AS destination_id UNION MATCH (m)-[r]->(n) WHERE m.user_id = $user_id {agent_filter.replace("n.", "m.")} RETURN m.name AS source, elementId(m) AS source_id, type(r) AS relationship, elementId(r) AS relation_id, n.name AS destination, elementId(n) AS destination_id }} WITH distinct source, source_id, relationship, relation_id, destination, destination_id, similarity RETURN source, source_id, relationship, relation_id, destination, destination_id, similarity ORDER BY similarity DESC LIMIT $limit """ params = { "n_embedding": n_embedding, "threshold": self.threshold, "user_id": filters["user_id"], "limit": limit, } if filters.get("agent_id"): params["agent_id"] = filters["agent_id"] ans = self.graph.query(cypher_query, params=params) result_relations.extend(ans) return result_relations 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) # Compose user identification string for prompt user_identity = f"user_id: {filters['user_id']}" if filters.get("agent_id"): user_identity += f", agent_id: {filters['agent_id']}" system_prompt, user_prompt = get_delete_messages(search_output_string, data, user_identity) _tools = [DELETE_MEMORY_TOOL_GRAPH] if self.llm_provider in ["azure_openai_structured", "openai_structured"]: _tools = [ DELETE_MEMORY_STRUCT_TOOL_GRAPH, ] 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.get("tool_calls", []): if item.get("name") == "delete_graph_memory": to_be_deleted.append(item.get("arguments")) # Clean entities formatting to_be_deleted = self._remove_spaces_from_entities(to_be_deleted) logger.debug(f"Deleted relationships: {to_be_deleted}") return to_be_deleted def _delete_entities(self, to_be_deleted, filters): """Delete the entities from the graph.""" user_id = filters["user_id"] agent_id = filters.get("agent_id", None) results = [] for item in to_be_deleted: source = item["source"] destination = item["destination"] relationship = item["relationship"] # Build the agent filter for the query agent_filter = "" params = { "source_name": source, "dest_name": destination, "user_id": user_id, } if agent_id: agent_filter = "AND n.agent_id = $agent_id AND m.agent_id = $agent_id" params["agent_id"] = agent_id # Delete the specific relationship between nodes cypher = f""" MATCH (n {self.node_label} {{name: $source_name, user_id: $user_id}}) -[r:{relationship}]-> (m {self.node_label} {{name: $dest_name, user_id: $user_id}}) WHERE 1=1 {agent_filter} DELETE r RETURN n.name AS source, m.name AS target, type(r) AS relationship """ result = self.graph.query(cypher, params=params) results.append(result) return results def _add_entities(self, to_be_added, filters, entity_type_map): """Add the new entities to the graph. Merge the nodes if they already exist.""" user_id = filters["user_id"] agent_id = filters.get("agent_id", None) 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, "__User__") source_label = self.node_label if self.node_label else f":`{source_type}`" source_extra_set = f", source:`{source_type}`" if self.node_label else "" destination_type = entity_type_map.get(destination, "__User__") destination_label = self.node_label if self.node_label else f":`{destination_type}`" destination_extra_set = f", destination:`{destination_type}`" if self.node_label else "" # 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, filters, threshold=0.9) destination_node_search_result = self._search_destination_node(dest_embedding, filters, 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: # Build destination MERGE properties merge_props = ["name: $destination_name", "user_id: $user_id"] if agent_id: merge_props.append("agent_id: $agent_id") merge_props_str = ", ".join(merge_props) cypher = f""" MATCH (source) WHERE elementId(source) = $source_id SET source.mentions = coalesce(source.mentions, 0) + 1 WITH source MERGE (destination {destination_label} {{{merge_props_str}}}) ON CREATE SET destination.created = timestamp(), destination.mentions = 1 {destination_extra_set} ON MATCH SET destination.mentions = coalesce(destination.mentions, 0) + 1 WITH source, destination CALL db.create.setNodeVectorProperty(destination, 'embedding', $destination_embedding) WITH source, destination MERGE (source)-[r:{relationship}]->(destination) ON CREATE SET r.created = timestamp(), r.mentions = 1 ON MATCH SET r.mentions = coalesce(r.mentions, 0) + 1 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_name": destination, "destination_embedding": dest_embedding, "user_id": user_id, } if agent_id: params["agent_id"] = agent_id elif destination_node_search_result and not source_node_search_result: # Build source MERGE properties merge_props = ["name: $source_name", "user_id: $user_id"] if agent_id: merge_props.append("agent_id: $agent_id") merge_props_str = ", ".join(merge_props) cypher = f""" MATCH (destination) WHERE elementId(destination) = $destination_id SET destination.mentions = coalesce(destination.mentions, 0) + 1 WITH destination MERGE (source {source_label} {{{merge_props_str}}}) ON CREATE SET source.created = timestamp(), source.mentions = 1 {source_extra_set} ON MATCH SET source.mentions = coalesce(source.mentions, 0) + 1 WITH source, destination CALL db.create.setNodeVectorProperty(source, 'embedding', $source_embedding) WITH source, destination MERGE (source)-[r:{relationship}]->(destination) ON CREATE SET r.created = timestamp(), r.mentions = 1 ON MATCH SET r.mentions = coalesce(r.mentions, 0) + 1 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, "source_embedding": source_embedding, "user_id": user_id, } if agent_id: params["agent_id"] = agent_id elif source_node_search_result and destination_node_search_result: cypher = f""" MATCH (source) WHERE elementId(source) = $source_id SET source.mentions = coalesce(source.mentions, 0) + 1 WITH source MATCH (destination) WHERE elementId(destination) = $destination_id SET destination.mentions = coalesce(destination.mentions, 0) + 1 MERGE (source)-[r:{relationship}]->(destination) ON CREATE SET r.created_at = timestamp(), r.updated_at = timestamp(), r.mentions = 1 ON MATCH SET r.mentions = coalesce(r.mentions, 0) + 1 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, } if agent_id: params["agent_id"] = agent_id else: # Build dynamic MERGE props for both source and destination source_props = ["name: $source_name", "user_id: $user_id"] dest_props = ["name: $dest_name", "user_id: $user_id"] if agent_id: source_props.append("agent_id: $agent_id") dest_props.append("agent_id: $agent_id") source_props_str = ", ".join(source_props) dest_props_str = ", ".join(dest_props) cypher = f""" MERGE (source {source_label} {{{source_props_str}}}) ON CREATE SET source.created = timestamp(), source.mentions = 1 {source_extra_set} ON MATCH SET source.mentions = coalesce(source.mentions, 0) + 1 WITH source CALL db.create.setNodeVectorProperty(source, 'embedding', $source_embedding) WITH source MERGE (destination {destination_label} {{{dest_props_str}}}) ON CREATE SET destination.created = timestamp(), destination.mentions = 1 {destination_extra_set} ON MATCH SET destination.mentions = coalesce(destination.mentions, 0) + 1 WITH source, destination CALL db.create.setNodeVectorProperty(destination, 'embedding', $dest_embedding) WITH source, destination MERGE (source)-[rel:{relationship}]->(destination) ON CREATE SET rel.created = timestamp(), rel.mentions = 1 ON MATCH SET rel.mentions = coalesce(rel.mentions, 0) + 1 RETURN source.name AS source, type(rel) AS relationship, destination.name AS target """ params = { "source_name": source, "dest_name": destination, "source_embedding": source_embedding, "dest_embedding": dest_embedding, "user_id": user_id, } if agent_id: params["agent_id"] = agent_id result = self.graph.query(cypher, params=params) results.append(result) 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, filters, threshold=0.9): agent_filter = "" if filters.get("agent_id"): agent_filter = "AND source_candidate.agent_id = $agent_id" cypher = f""" MATCH (source_candidate {self.node_label}) WHERE source_candidate.embedding IS NOT NULL AND source_candidate.user_id = $user_id {agent_filter} WITH source_candidate, round(2 * vector.similarity.cosine(source_candidate.embedding, $source_embedding) - 1, 4) AS source_similarity // denormalize for backward compatibility 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": filters["user_id"], "threshold": threshold, } if filters.get("agent_id"): params["agent_id"] = filters["agent_id"] result = self.graph.query(cypher, params=params) return result def _search_destination_node(self, destination_embedding, filters, threshold=0.9): agent_filter = "" if filters.get("agent_id"): agent_filter = "AND destination_candidate.agent_id = $agent_id" cypher = f""" MATCH (destination_candidate {self.node_label}) WHERE destination_candidate.embedding IS NOT NULL AND destination_candidate.user_id = $user_id {agent_filter} WITH destination_candidate, round(2 * vector.similarity.cosine(destination_candidate.embedding, $destination_embedding) - 1, 4) AS destination_similarity // denormalize for backward compatibility 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": filters["user_id"], "threshold": threshold, } if filters.get("agent_id"): params["agent_id"] = filters["agent_id"] result = self.graph.query(cypher, params=params) return result