falkor-integration

README.md

@@ -157,7 +157,7 @@ history = m.history(memory_id=<memory_id_1>)
 
 ### 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 set up [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*). 
 Here's how you can do it:
 
@@ -166,11 +166,12 @@ from mem0 import Memory
 
 config = {
  "graph_store": {
- "provider": "neo4j",
+ "provider": "falkordb",
  "config": {
- "url": "neo4j+s://xxx",
- "username": "neo4j",
- "password": "xxx"
+ "host": "---"
+ "username": "---",
+ "password": "---",
+ "port": "---"
  }
  },
  "version": "v1.1"

cookbooks/mem0_graph_memory.py

@@ -0,0 +1,40 @@
+# This example shows how to use graph config to use falkordb graph databese
+import os
+from mem0 import Memory
+from dotenv import load_dotenv
+
+# Loading OpenAI API Key
+load_dotenv()
+OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
+USER_ID = "test"
+
+# Creating the config dict from the environment variables
+config = {
+ "llm": { # This is the language model configuration, use your carditionals
+ "provider": "openai",
+ "config": {
+ "model": "gpt-4o-mini",
+ "temperature": 0
+ }
+ },
+ "graph_store": { # See https://app.falkordb.cloud/ for the carditionals
+ "provider": "falkordb",
+ "config": {
+ "host": os.environ['HOST'],
+ "username": os.environ['USERNAME'],# if you are using local host, the username and password will not be needed
+ "password": os.environ['PASSWORD'],
+ "port": os.environ['PORT']
+ }
+ },
+ "version": "v1.1"
+}
+
+# Create the memory class using from config
+memory = Memory.from_config(config_dict=config)
+
+# Use the Mem0 to add and search memories
+memory.add("I like painting", user_id=USER_ID)
+memory.add("I hate playing badminton", user_id=USER_ID)
+print(memory.get_all(user_id=USER_ID))
+memory.add("My friend name is john and john has a dog named tommy", user_id=USER_ID)
+print(memory.search("What I like to do", user_id=USER_ID))

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 set up [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

@@ -1,4 +1,4 @@
-from typing import Optional
+from typing import Optional, Union
 
 from pydantic import BaseModel, Field, field_validator, model_validator
 
@@ -22,14 +22,34 @@ def check_host_port_or_path(cls, values):
  "Please provide 'url', 'username' and 'password'."
  )
  return values
+
+class FalkorDBConfig(BaseModel):
+ 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")
+ # Default database name is mandatory in langchain
+ database: str = "_default_"
+
+ @model_validator(mode="before")
+ def check_host_port_or_path(cls, values):
+ host, port = (
+ values.get("host"),
+ values.get("port"),
+ )
+ if not host or not port:
+ raise ValueError(
+ "Please provide 'host' 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: Union[FalkorDBConfig, Neo4jConfig] = Field(
  description="Configuration for the specific data store",
  default=None
  )
@@ -47,6 +67,16 @@ def validate_config(cls, v, values):
  provider = values.data.get("provider")
  if provider == "neo4j":
  return Neo4jConfig(**v.model_dump())
+ elif provider == "falkordb":
+ config = v.model_dump()
+ # In case the user try to use diffrent database name
+ config["database"] = "_default_"
+
+ if config.get("host") == "localhost" or config.get("host") == None:
+ config.pop("username", None)
+ config.pop("password", None)
+
+ return FalkorDBConfig(**config)
  else:
  raise ValueError(f"Unsupported graph store provider: {provider}")
 

mem0/graphs/utils.py

@@ -54,6 +54,52 @@
 
 Adhere strictly to these guidelines to ensure high-quality knowledge graph extraction."""
 
+FALKORDB_QUERY = """
+MATCH (n)
+WHERE n.embedding IS NOT NULL AND n.user_id = $user_id
+WITH n, 
+ 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]))) AS similarity
+WHERE similarity >= $threshold
+MATCH (n)-[r]->(m)
+RETURN n.name AS source, Id(n) AS source_id, type(r) AS relation, Id(r) AS relation_id, m.name AS destination, Id(m) AS destination_id, similarity
+UNION
+MATCH (n)
+WHERE n.embedding IS NOT NULL AND n.user_id = $user_id
+WITH n, 
+ 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]))) AS similarity
+WHERE similarity >= $threshold
+MATCH (m)-[r]->(n)
+RETURN m.name AS source, Id(m) AS source_id, type(r) AS relation, Id(r) AS relation_id, n.name AS destination, Id(n) AS destination_id, similarity
+ORDER BY similarity DESC
+"""
+
+
+NEO4J_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
+"""
 
 
 def get_update_memory_prompt(existing_memories, memory, template):