Add Memgraph integration (#2537)
This commit is contained in:
Katarina Supe
@@ -59,7 +59,10 @@ class Memory(MemoryBase):
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self.enable_graph = False
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if self.config.graph_store.config:
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from mem0.memory.graph_memory import MemoryGraph
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if self.config.graph_store.provider == "memgraph":
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from mem0.memory.memgraph_memory import MemoryGraph
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else:
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from mem0.memory.graph_memory import MemoryGraph
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self.graph = MemoryGraph(self.config)
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self.enable_graph = True
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516
mem0/memory/memgraph_memory.py
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516
mem0/memory/memgraph_memory.py
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@@ -0,0 +1,516 @@
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import logging
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from mem0.memory.utils import format_entities
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try:
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from langchain_memgraph import Memgraph
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except ImportError:
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raise ImportError(
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"langchain_memgraph is not installed. Please install it using pip install langchain-memgraph"
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)
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try:
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from rank_bm25 import BM25Okapi
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except ImportError:
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raise ImportError(
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"rank_bm25 is not installed. Please install it using pip install rank-bm25"
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)
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from mem0.graphs.tools import (
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DELETE_MEMORY_STRUCT_TOOL_GRAPH,
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DELETE_MEMORY_TOOL_GRAPH,
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EXTRACT_ENTITIES_STRUCT_TOOL,
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EXTRACT_ENTITIES_TOOL,
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RELATIONS_STRUCT_TOOL,
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RELATIONS_TOOL,
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)
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from mem0.graphs.utils import EXTRACT_RELATIONS_PROMPT, get_delete_messages
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from mem0.utils.factory import EmbedderFactory, LlmFactory
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logger = logging.getLogger(__name__)
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class MemoryGraph:
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def __init__(self, config):
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self.config = config
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self.graph = Memgraph(
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self.config.graph_store.config.url,
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self.config.graph_store.config.username,
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self.config.graph_store.config.password,
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)
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self.embedding_model = EmbedderFactory.create(
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self.config.embedder.provider,
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self.config.embedder.config,
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{"enable_embeddings": True},
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)
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self.llm_provider = "openai_structured"
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if self.config.llm.provider:
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self.llm_provider = self.config.llm.provider
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if self.config.graph_store.llm:
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self.llm_provider = self.config.graph_store.llm.provider
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self.llm = LlmFactory.create(self.llm_provider, self.config.llm.config)
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self.user_id = None
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self.threshold = 0.7
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# Setup Memgraph:
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# 1. Create vector index (created Entity label on all nodes)
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# 2. Create label property index for performance optimizations
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embedding_dims = self.config.embedder.config["embedding_dims"]
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create_vector_index_query = f"CREATE VECTOR INDEX memzero ON :Entity(embedding) WITH CONFIG {{'dimension': {embedding_dims}, 'capacity': 1000, 'metric': 'cos'}};"
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self.graph.query(create_vector_index_query, params={})
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create_label_prop_index_query = f"CREATE INDEX ON :Entity(user_id);"
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self.graph.query(create_label_prop_index_query, params={})
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create_label_index_query = f"CREATE INDEX ON :Entity;"
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self.graph.query(create_label_index_query, params={})
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def add(self, data, filters):
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"""
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Adds data to the graph.
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Args:
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data (str): The data to add to the graph.
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filters (dict): A dictionary containing filters to be applied during the addition.
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"""
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entity_type_map = self._retrieve_nodes_from_data(data, filters)
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to_be_added = self._establish_nodes_relations_from_data(
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data, filters, entity_type_map
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)
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search_output = self._search_graph_db(
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node_list=list(entity_type_map.keys()), filters=filters
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)
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to_be_deleted = self._get_delete_entities_from_search_output(
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search_output, data, filters
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)
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# TODO: Batch queries with APOC plugin
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# TODO: Add more filter support
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deleted_entities = self._delete_entities(to_be_deleted, filters["user_id"])
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added_entities = self._add_entities(
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to_be_added, filters["user_id"], entity_type_map
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)
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return {"deleted_entities": deleted_entities, "added_entities": added_entities}
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def search(self, query, filters, limit=100):
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"""
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Search for memories and related graph data.
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Args:
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query (str): Query to search for.
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filters (dict): A dictionary containing filters to be applied during the search.
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limit (int): The maximum number of nodes and relationships to retrieve. Defaults to 100.
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Returns:
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dict: A dictionary containing:
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- "contexts": List of search results from the base data store.
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- "entities": List of related graph data based on the query.
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"""
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entity_type_map = self._retrieve_nodes_from_data(query, filters)
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search_output = self._search_graph_db(
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node_list=list(entity_type_map.keys()), filters=filters
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)
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if not search_output:
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return []
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search_outputs_sequence = [
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[item["source"], item["relationship"], item["destination"]]
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for item in search_output
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]
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bm25 = BM25Okapi(search_outputs_sequence)
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tokenized_query = query.split(" ")
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reranked_results = bm25.get_top_n(tokenized_query, search_outputs_sequence, n=5)
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search_results = []
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for item in reranked_results:
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search_results.append(
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{"source": item[0], "relationship": item[1], "destination": item[2]}
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)
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logger.info(f"Returned {len(search_results)} search results")
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return search_results
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def delete_all(self, filters):
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cypher = """
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MATCH (n {user_id: $user_id})
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DETACH DELETE n
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"""
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params = {"user_id": filters["user_id"]}
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self.graph.query(cypher, params=params)
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def get_all(self, filters, limit=100):
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"""
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Retrieves all nodes and relationships from the graph database based on optional filtering criteria.
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Args:
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filters (dict): A dictionary containing filters to be applied during the retrieval.
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limit (int): The maximum number of nodes and relationships to retrieve. Defaults to 100.
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Returns:
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list: A list of dictionaries, each containing:
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- 'contexts': The base data store response for each memory.
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- 'entities': A list of strings representing the nodes and relationships
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"""
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# return all nodes and relationships
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query = """
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MATCH (n:Entity {user_id: $user_id})-[r]->(m:Entity {user_id: $user_id})
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RETURN n.name AS source, type(r) AS relationship, m.name AS target
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LIMIT $limit
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"""
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results = self.graph.query(
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query, params={"user_id": filters["user_id"], "limit": limit}
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)
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final_results = []
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for result in results:
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final_results.append(
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{
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"source": result["source"],
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"relationship": result["relationship"],
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"target": result["target"],
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}
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)
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logger.info(f"Retrieved {len(final_results)} relationships")
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return final_results
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def _retrieve_nodes_from_data(self, data, filters):
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"""Extracts all the entities mentioned in the query."""
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_tools = [EXTRACT_ENTITIES_TOOL]
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if self.llm_provider in ["azure_openai_structured", "openai_structured"]:
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_tools = [EXTRACT_ENTITIES_STRUCT_TOOL]
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search_results = self.llm.generate_response(
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messages=[
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{
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"role": "system",
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"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.",
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},
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{"role": "user", "content": data},
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],
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tools=_tools,
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)
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entity_type_map = {}
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try:
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for tool_call in search_results["tool_calls"]:
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if tool_call["name"] != "extract_entities":
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continue
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for item in tool_call["arguments"]["entities"]:
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entity_type_map[item["entity"]] = item["entity_type"]
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except Exception as e:
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logger.exception(
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f"Error in search tool: {e}, llm_provider={self.llm_provider}, search_results={search_results}"
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)
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entity_type_map = {
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k.lower().replace(" ", "_"): v.lower().replace(" ", "_")
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for k, v in entity_type_map.items()
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}
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logger.debug(
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f"Entity type map: {entity_type_map}\n search_results={search_results}"
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)
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return entity_type_map
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def _establish_nodes_relations_from_data(self, data, filters, entity_type_map):
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"""Eshtablish relations among the extracted nodes."""
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if self.config.graph_store.custom_prompt:
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messages = [
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{
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"role": "system",
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"content": EXTRACT_RELATIONS_PROMPT.replace(
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"USER_ID", filters["user_id"]
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).replace(
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"CUSTOM_PROMPT", f"4. {self.config.graph_store.custom_prompt}"
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),
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},
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{"role": "user", "content": data},
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]
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else:
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messages = [
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{
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"role": "system",
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"content": EXTRACT_RELATIONS_PROMPT.replace(
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"USER_ID", filters["user_id"]
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),
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},
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{
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"role": "user",
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"content": f"List of entities: {list(entity_type_map.keys())}. \n\nText: {data}",
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},
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]
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_tools = [RELATIONS_TOOL]
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if self.llm_provider in ["azure_openai_structured", "openai_structured"]:
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_tools = [RELATIONS_STRUCT_TOOL]
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extracted_entities = self.llm.generate_response(
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messages=messages,
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tools=_tools,
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)
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entities = []
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if extracted_entities["tool_calls"]:
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entities = extracted_entities["tool_calls"][0]["arguments"]["entities"]
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entities = self._remove_spaces_from_entities(entities)
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logger.debug(f"Extracted entities: {entities}")
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return entities
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def _search_graph_db(self, node_list, filters, limit=100):
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"""Search similar nodes among and their respective incoming and outgoing relations."""
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result_relations = []
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for node in node_list:
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n_embedding = self.embedding_model.embed(node)
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cypher_query = f"""
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MATCH (n:Entity {{user_id: $user_id}})-[r]->(m:Entity)
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WHERE n.embedding IS NOT NULL
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WITH collect(n) AS nodes1, collect(m) AS nodes2, r
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CALL node_similarity.cosine_pairwise("embedding", nodes1, nodes2)
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YIELD node1, node2, similarity
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WITH node1, node2, similarity, r
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WHERE similarity >= $threshold
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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
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UNION
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MATCH (n:Entity {{user_id: $user_id}})<-[r]-(m:Entity)
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WHERE n.embedding IS NOT NULL
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WITH collect(n) AS nodes1, collect(m) AS nodes2, r
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CALL node_similarity.cosine_pairwise("embedding", nodes1, nodes2)
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YIELD node1, node2, similarity
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WITH node1, node2, similarity, r
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WHERE similarity >= $threshold
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RETURN node2.name AS source, id(node2) AS source_id, type(r) AS relationship, id(r) AS relation_id, node1.name AS destination, id(node1) AS destination_id, similarity
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ORDER BY similarity DESC
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LIMIT $limit;
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"""
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params = {
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"n_embedding": n_embedding,
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"threshold": self.threshold,
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"user_id": filters["user_id"],
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"limit": limit,
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}
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ans = self.graph.query(cypher_query, params=params)
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result_relations.extend(ans)
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return result_relations
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def _get_delete_entities_from_search_output(self, search_output, data, filters):
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"""Get the entities to be deleted from the search output."""
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search_output_string = format_entities(search_output)
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system_prompt, user_prompt = get_delete_messages(
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search_output_string, data, filters["user_id"]
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)
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_tools = [DELETE_MEMORY_TOOL_GRAPH]
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if self.llm_provider in ["azure_openai_structured", "openai_structured"]:
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_tools = [
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DELETE_MEMORY_STRUCT_TOOL_GRAPH,
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]
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memory_updates = self.llm.generate_response(
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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],
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tools=_tools,
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)
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to_be_deleted = []
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for item in memory_updates["tool_calls"]:
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if item["name"] == "delete_graph_memory":
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to_be_deleted.append(item["arguments"])
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# in case if it is not in the correct format
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to_be_deleted = self._remove_spaces_from_entities(to_be_deleted)
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logger.debug(f"Deleted relationships: {to_be_deleted}")
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return to_be_deleted
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def _delete_entities(self, to_be_deleted, user_id):
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"""Delete the entities from the graph."""
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results = []
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for item in to_be_deleted:
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source = item["source"]
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destination = item["destination"]
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relationship = item["relationship"]
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# Delete the specific relationship between nodes
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cypher = f"""
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MATCH (n:Entity {{name: $source_name, user_id: $user_id}})
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-[r:{relationship}]->
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(m {{name: $dest_name, user_id: $user_id}})
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DELETE r
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RETURN
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n.name AS source,
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m.name AS target,
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type(r) AS relationship
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"""
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params = {
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"source_name": source,
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"dest_name": destination,
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"user_id": user_id,
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}
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result = self.graph.query(cypher, params=params)
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results.append(result)
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return results
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# added Entity label to all nodes for vector search to work
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def _add_entities(self, to_be_added, user_id, entity_type_map):
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"""Add the new entities to the graph. Merge the nodes if they already exist."""
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results = []
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for item in to_be_added:
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# entities
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source = item["source"]
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destination = item["destination"]
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relationship = item["relationship"]
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# types
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source_type = entity_type_map.get(source, "unknown")
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destination_type = entity_type_map.get(destination, "unknown")
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# embeddings
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source_embedding = self.embedding_model.embed(source)
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dest_embedding = self.embedding_model.embed(destination)
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# search for the nodes with the closest embeddings; this is basically
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# comparison of one embedding to all embeddings in a graph -> vector
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# search with cosine similarity metric
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source_node_search_result = self._search_source_node(
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source_embedding, user_id, threshold=0.9
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)
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destination_node_search_result = self._search_destination_node(
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dest_embedding, user_id, threshold=0.9
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)
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# TODO: Create a cypher query and common params for all the cases
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if not destination_node_search_result and source_node_search_result:
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cypher = f"""
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MATCH (source:Entity)
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WHERE id(source) = $source_id
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MERGE (destination:{destination_type}:Entity {{name: $destination_name, user_id: $user_id}})
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ON CREATE SET
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destination.created = timestamp(),
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destination.embedding = $destination_embedding,
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destination:Entity
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MERGE (source)-[r:{relationship}]->(destination)
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ON CREATE SET
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r.created = timestamp()
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RETURN source.name AS source, type(r) AS relationship, destination.name AS target
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"""
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params = {
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"source_id": source_node_search_result[0]["id(source_candidate)"],
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"destination_name": destination,
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"destination_embedding": dest_embedding,
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"user_id": user_id,
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}
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elif destination_node_search_result and not source_node_search_result:
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cypher = f"""
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MATCH (destination:Entity)
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WHERE id(destination) = $destination_id
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MERGE (source:{source_type}:Entity {{name: $source_name, user_id: $user_id}})
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ON CREATE SET
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source.created = timestamp(),
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source.embedding = $source_embedding,
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source:Entity
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MERGE (source)-[r:{relationship}]->(destination)
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ON CREATE SET
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r.created = timestamp()
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RETURN source.name AS source, type(r) AS relationship, destination.name AS target
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"""
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params = {
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"destination_id": destination_node_search_result[0][
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"id(destination_candidate)"
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],
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"source_name": source,
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"source_embedding": source_embedding,
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"user_id": user_id,
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}
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elif source_node_search_result and destination_node_search_result:
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cypher = f"""
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MATCH (source:Entity)
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WHERE id(source) = $source_id
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MATCH (destination:Entity)
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WHERE id(destination) = $destination_id
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MERGE (source)-[r:{relationship}]->(destination)
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ON CREATE SET
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r.created_at = timestamp(),
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r.updated_at = timestamp()
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RETURN source.name AS source, type(r) AS relationship, destination.name AS target
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"""
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params = {
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"source_id": source_node_search_result[0]["id(source_candidate)"],
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||||
"destination_id": destination_node_search_result[0][
|
||||
"id(destination_candidate)"
|
||||
],
|
||||
"user_id": user_id,
|
||||
}
|
||||
else:
|
||||
cypher = f"""
|
||||
MERGE (n:{source_type}:Entity {{name: $source_name, user_id: $user_id}})
|
||||
ON CREATE SET n.created = timestamp(), n.embedding = $source_embedding, n:Entity
|
||||
ON MATCH SET n.embedding = $source_embedding
|
||||
MERGE (m:{destination_type}:Entity {{name: $dest_name, user_id: $user_id}})
|
||||
ON CREATE SET m.created = timestamp(), m.embedding = $dest_embedding, m:Entity
|
||||
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,
|
||||
"dest_name": destination,
|
||||
"source_embedding": source_embedding,
|
||||
"dest_embedding": dest_embedding,
|
||||
"user_id": user_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, user_id, threshold=0.9):
|
||||
cypher = f"""
|
||||
CALL vector_search.search("memzero", 1, $source_embedding)
|
||||
YIELD distance, node, similarity
|
||||
WITH node AS source_candidate, similarity
|
||||
WHERE source_candidate.user_id = $user_id AND similarity >= $threshold
|
||||
RETURN id(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"""
|
||||
CALL vector_search.search("memzero", 1, $destination_embedding)
|
||||
YIELD distance, node, similarity
|
||||
WITH node AS destination_candidate, similarity
|
||||
WHERE node.user_id = $user_id AND similarity >= $threshold
|
||||
RETURN id(destination_candidate);
|
||||
"""
|
||||
params = {
|
||||
"destination_embedding": destination_embedding,
|
||||
"user_id": user_id,
|
||||
"threshold": threshold,
|
||||
}
|
||||
|
||||
result = self.graph.query(cypher, params=params)
|
||||
return result
|
||||
Reference in New Issue
Block a user