falkor-integration

cookbooks/mem0_graph_memory.py

@@ -0,0 +1,34 @@
+import os
+import sys
+from dotenv import load_dotenv
+sys.path.append(os.getcwd())
+from mem0 import Memory
+load_dotenv()
+
+config = {
+ "llm": {
+ "provider": "openai",
+ "config": {
+ "model": "gpt-4o-mini",
+ "temperature": 0
+ }
+ },
+ "graph_store": {
+ "provider": "falkordb",
+ "config": {
+ "database": "falkordb",
+ "host": os.environ['HOST'],
+ "username": os.environ['USERNAME'],
+ "password": os.environ['PASSWORD'],
+ "port": os.environ['PORT']
+ }
+ },
+ "version": "v1.1"
+}
+
+m = Memory.from_config(config_dict=config)
+
+user_id = "alice123"
+m.add("I like painting", user_id=user_id)
+m.add("I hate playing badminton", user_id=user_id)
+m.add("My friend name is john and john has a dog named tommy", user_id='gal')

docs/open-source/graph_memory/features.mdx

@@ -19,11 +19,13 @@ from mem0 import Memory
 
 config = {
  "graph_store": {
- "provider": "neo4j",
+ "provider": "falkordb",
  "config": {
- "url": "neo4j+s://xxx",
- "username": "neo4j",
- "password": "xxx"
+ "Database": "falkordb",
+ "host": "---"
+ "username": "---",
+ "password": "---",
+ "port": "---"
  },
  "custom_prompt": "Please only extract entities containing sports related relationships and nothing else.",
  },

docs/open-source/graph_memory/overview.mdx

@@ -26,7 +26,7 @@ allowfullscreen
 ## Initialize Graph Memory
 
 To initialize Graph Memory you'll need to set up your configuration with graph store providers.
-Currently, we support Neo4j as a graph store provider. You can setup [Neo4j](https://neo4j.com/) locally or use the hosted [Neo4j AuraDB](https://neo4j.com/product/auradb/). 
+Currently, we support FalkorDB and Neo4j as a graph store providers. You can setup [FalkorDB](https://www.falkordb.com/) or [Neo4j](https://neo4j.com/) locally or use the hosted [FalkorDB Cloud](https://app.falkordb.cloud/) or [Neo4j AuraDB](https://neo4j.com/product/auradb/). 
 Moreover, you also need to set the version to `v1.1` (*prior versions are not supported*). 
 
 User can also customize the LLM for Graph Memory from the [Supported LLM list](https://docs.mem0.ai/components/llms/overview) with three levels of configuration:
@@ -44,11 +44,13 @@ from mem0 import Memory
 
 config = {
  "graph_store": {
- "provider": "neo4j",
+ "provider": "falkordb",
  "config": {
- "url": "neo4j+s://xxx",
- "username": "neo4j",
- "password": "xxx"
+ "Database": "falkordb",
+ "host": "---"
+ "username": "---",
+ "password": "---",
+ "port": "---"
  }
  },
  "version": "v1.1"
@@ -70,11 +72,13 @@ config = {
  }
  },
  "graph_store": {
- "provider": "neo4j",
+ "provider": "falkordb",
  "config": {
- "url": "neo4j+s://xxx",
- "username": "neo4j",
- "password": "xxx"
+ "Database": "falkordb",
+ "host": "---"
+ "username": "---",
+ "password": "---",
+ "port": "---"
  },
  "llm" : {
  "provider": "openai",

docs/open-source/quickstart.mdx

@@ -63,11 +63,13 @@ from mem0 import Memory
 
 config = {
  "graph_store": {
- "provider": "neo4j",
+ "provider": "falkordb",
  "config": {
- "url": "neo4j+s://---",
- "username": "neo4j",
- "password": "---"
+ "Database": "falkordb",
+ "host": "---"
+ "username": "---",
+ "password": "---",
+ "port": "---"
  }
  },
  "version": "v1.1"

mem0/graphs/configs.py

@@ -22,14 +22,36 @@ def check_host_port_or_path(cls, values):
  "Please provide 'url', 'username' and 'password'."
  )
  return values
+
+class FalkorDBConfig(BaseModel):
+ database: Optional[str] = Field(None, description="Database name for the graph database")
+ host: Optional[str] = Field(None, description="Host address for the graph database")
+ username: Optional[str] = Field(None, description="Username for the graph database")
+ password: Optional[str] = Field(None, description="Password for the graph database")
+ port: Optional[int] = Field(None, description="Port for the graph database")
+
+ @model_validator(mode="before")
+ def check_host_port_or_path(cls, values):
+ database, host, username, password, port = (
+ values.get("database"),
+ values.get("host"),
+ values.get("username"),
+ values.get("password"),
+ values.get("port"),
+ )
+ if not database or not host or not username or not password or not port:
+ raise ValueError(
+ "Please provide 'host', 'username', 'password' and 'port'."
+ )
+ return values
 
 
 class GraphStoreConfig(BaseModel):
  provider: str = Field(
- description="Provider of the data store (e.g., 'neo4j')", 
- default="neo4j"
+ description="Provider of the data store (e.g., 'falkordb', 'neo4j')", 
+ default="falkordb"
  )
- config: Neo4jConfig = Field(
+ config: FalkorDBConfig = Field(
  description="Configuration for the specific data store",
  default=None
  )
@@ -47,6 +69,8 @@ def validate_config(cls, v, values):
  provider = values.data.get("provider")
  if provider == "neo4j":
  return Neo4jConfig(**v.model_dump())
+ elif provider == "falkordb":
+ return FalkorDBConfig(**v.model_dump())
  else:
  raise ValueError(f"Unsupported graph store provider: {provider}")
 

mem0/memory/graph_memory.py

@@ -1,6 +1,5 @@
 import logging
 
-from langchain_community.graphs import Neo4jGraph
 from rank_bm25 import BM25Okapi
 
 from mem0.graphs.tools import (
@@ -16,14 +15,16 @@
  SEARCH_STRUCT_TOOL
 )
 from mem0.graphs.utils import EXTRACT_ENTITIES_PROMPT, get_update_memory_messages
-from mem0.utils.factory import EmbedderFactory, LlmFactory
+from mem0.utils.factory import EmbedderFactory, LlmFactory, GraphFactory
 
 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.graph = GraphFactory.create(
+ self.config.graph_store.provider, self.config.graph_store.config
+ )
  self.embedding_model = EmbedderFactory.create(
  self.config.embedder.provider, self.config.embedder.config
  )
@@ -138,7 +139,7 @@ def add(self, data, filters):
  "user_id": filters["user_id"]
  }
 
- _ = self.graph.query(cypher, params=params)
+ _ = self.graph_query(cypher, params=params)
 
  logger.info(f"Added {len(to_be_added)} new memories to the graph")
 
@@ -202,7 +203,7 @@ def _search(self, query, filters):
  ORDER BY similarity DESC
  """
  params = {"n_embedding": n_embedding, "threshold": self.threshold, "user_id": filters["user_id"]}
- ans = self.graph.query(cypher_query, params=params)
+ ans = self.graph_query(cypher_query, params=params)
  result_relations.extend(ans)
 
  return result_relations
@@ -252,7 +253,7 @@ def delete_all(self, filters):
  DETACH DELETE n
  """
  params = {"user_id": filters["user_id"]}
- self.graph.query(cypher, params=params)
+ self.graph_query(cypher, params=params)
 
 
  def get_all(self, filters):
@@ -272,7 +273,7 @@ def get_all(self, filters):
  MATCH (n {user_id: $user_id})-[r]->(m {user_id: $user_id})
  RETURN n.name AS source, type(r) AS relationship, m.name AS target
  """
- results = self.graph.query(query, params={"user_id": filters["user_id"]})
+ results = self.graph_query(query, params={"user_id": filters["user_id"]})
 
  final_results = []
  for result in results:
@@ -309,22 +310,39 @@ def _update_relationship(self, source, target, relationship, filters):
  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"]})
+ self.graph_query(check_and_create_query, params={"source": source, "target": target, "user_id": filters["user_id"]})
 
  # 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})
  DELETE r
  """
- self.graph.query(delete_query, params={"source": source, "target": target, "user_id": filters["user_id"]})
+ self.graph_query(delete_query, params={"source": source, "target": target, "user_id": filters["user_id"]})
 
  # 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
  """
- result = self.graph.query(create_query, params={"source": source, "target": target, "user_id": filters["user_id"]})
+ 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}")
+
+ def graph_query(self, query, params):
+ """
+ Execute a Cypher query on the graph database.
+ For FalkorDB, the graph is switched based on the user_id.
+
+ Args:
+ query (str): The Cypher query to execute.
+ params (dict): A dictionary containing params to be applied during the query.
+
+ Returns:
+ list: A list of dictionaries containing the results of the query.
+ """
+ if self.config.graph_store.provider == "falkordb":
+ self.graph.switch_graph(params["user_id"])
+
+ return self.graph.query(query, params=params)

mem0/utils/factory.py

@@ -71,3 +71,20 @@ def create(cls, provider_name, config):
  return vector_store_instance(**config)
  else:
  raise ValueError(f"Unsupported VectorStore provider: {provider_name}")
+
+class GraphFactory:
+ provider_to_class = {
+ "falkordb": "langchain_community.graphs.FalkorDBGraph",
+ "neo4j": "langchain_community.graphs.Neo4jGraph",
+ }
+
+ @classmethod
+ def create(cls, provider_name, config):
+ class_type = cls.provider_to_class.get(provider_name)
+ if class_type:
+ if not isinstance(config, dict):
+ config = config.model_dump()
+ graph_instance = load_class(class_type)
+ return graph_instance(**config) 
+ else:
+ raise ValueError(f"Unsupported graph provider: {provider_name}")

pyproject.toml

@@ -25,6 +25,7 @@ sqlalchemy = "^2.0.31"
 langchain-community = "^0.2.12"
 neo4j = "^5.23.1"
 rank-bm25 = "^0.2.2"
+falkordb = "^1.0.8"
 
 [tool.poetry.group.test.dependencies]
 pytest = "^8.2.2"