FalkorDB Multi Graph ability

README.md

@@ -115,6 +115,33 @@ chat_agent = ChatAgent(
 ```
 Pass in subset of `['search', 'vision', 'locate', 'stocks']` as `include_from_defaults` for different set of default tools upon initialization.
 
+### Multi-Graph
+When using FalkorDB database, you can create multi-agents. Here is an example of how to set up personal agents for different users:
+
+```python
+# User A personal agent
+chat_agent_user_a = ChatAgent(
+    "Personal Agent",
+    memory_stream_json_user_a,
+    entity_knowledge_store_json_user_a,
+    system_persona_txt_user_a,
+    user_persona_txt_user_a,
+    past_chat_json_user_a,
+    user_id='user_a_id'
+)
+
+# User B personal agent
+chat_agent_user_b = ChatAgent(
+    "Personal Agent",
+    memory_stream_json_user_b,
+    entity_knowledge_store_json_user_b,
+    system_persona_txt_user_b,
+    user_persona_txt_user_b,
+    past_chat_json_user_b,
+    user_id='user_b_id'
+)
+```
+
 ### Adding Custom Tools
 ```python
 def multiply(a: int, b: int) -> int:

src/memary/agent/base_agent.py

@@ -51,19 +51,20 @@ class Agent(object):
 
     def __init__(
         self,
-        name,
+        agent_name,
         memory_stream_json,
         entity_knowledge_store_json,
         system_persona_txt,
         user_persona_txt,
         past_chat_json,
+        user_id='__falkor__',
         llm_model_name="llama3",
         vision_model_name="llava",
         include_from_defaults=["search", "locate", "vision", "stocks"],
         debug=True,
     ):
         load_dotenv()
-        self.name = name
+        self.name = agent_name
         self.model = llm_model_name
 
         googlemaps_api_key = os.getenv("GOOGLEMAPS_API_KEY")
@@ -83,7 +84,7 @@ def __init__(
         if self.falkordb_url is not None:
             from llama_index.graph_stores.falkordb import FalkorDBGraphStore
             # initialize FalkorDB graph resources
-            self.graph_store = FalkorDBGraphStore(self.falkordb_url, decode_responses=True)
+            self.graph_store = FalkorDBGraphStore(self.falkordb_url, database=user_id, decode_responses=True)
         else:
             from llama_index.graph_stores.neo4j import Neo4jGraphStore
             # Neo4j credentials

src/memary/agent/chat_agent.py

@@ -11,23 +11,25 @@ class ChatAgent(Agent):
 
     def __init__(
         self,
-        name,
+        agent_name,
         memory_stream_json,
         entity_knowledge_store_json,
         system_persona_txt,
         user_persona_txt,
         past_chat_json,
+        user_id='__falkor__',
         llm_model_name="llama3",
         vision_model_name="llava",
         include_from_defaults=["search", "locate", "vision", "stocks"],
     ):
         super().__init__(
-            name,
+            agent_name,
             memory_stream_json,
             entity_knowledge_store_json,
             system_persona_txt,
             user_persona_txt,
             past_chat_json,
+            user_id,
             llm_model_name,
             vision_model_name,
             include_from_defaults,

streamlit_app/app.py

@@ -23,21 +23,11 @@
 from memary.agent.chat_agent import ChatAgent
 
 load_dotenv()
-
 system_persona_txt = "data/system_persona.txt"
 user_persona_txt = "data/user_persona.txt"
 past_chat_json = "data/past_chat.json"
 memory_stream_json = "data/memory_stream.json"
 entity_knowledge_store_json = "data/entity_knowledge_store.json"
-chat_agent = ChatAgent(
-    "Personal Agent",
-    memory_stream_json,
-    entity_knowledge_store_json,
-    system_persona_txt,
-    user_persona_txt,
-    past_chat_json,
-)
-
 
 def create_graph(nodes, edges):
     g = Network(
@@ -69,7 +59,7 @@ def fill_graph(nodes, edges, cypher_query):
 
     if chat_agent.falkordb_url is not None:
         falkordb = FalkorDB.from_url(chat_agent.falkordb_url)
-        session = falkordb.select_graph('falkor')
+        session = falkordb.select_graph(user_id)
         result = session.query(cypher_query).result_set
         for record in result:
             path = record[0]
@@ -117,7 +107,18 @@ def get_models(llm_models, vision_models):
 answer = ""
 external_response = ""
 st.title("memary")
+col1, col2 = st.columns([3, 1]) 
+with col1:
+    user_id = st.text_input(
+        "Enter a user ID (Available only with FalkorDB)",
+    )
+with col2:
+    st.write("")
+    submit = st.button("Switch User ID")
 
+if not submit:
+    user_id = "__falkor__"
+
 llm_models = ["gpt-3.5-turbo"]
 vision_models = ["gpt-4-vision-preview"]
 get_models(llm_models, vision_models)
@@ -143,6 +144,7 @@ def get_models(llm_models, vision_models):
         system_persona_txt,
         user_persona_txt,
         past_chat_json,
+        user_id,
         selected_llm_model,
         selected_vision_model,
     )