FalkorDB Multi Graph ability (#61)

* multi-graph

* update-def-database

* update-app

* update-toml

* update-docs

---------

Co-authored-by: Gal Shubeli <gal@falkordb.com>

Author: galshubeli

README.md

@@ -87,8 +87,10 @@ memary will default to the locally run models unless explicitly specified. Addit
 4.  Update user persona which can be found in `streamlit_app/data/user_persona.txt` using the user persona template which can be found in `streamlit_app/data/user_persona_template.txt`. Instructions have been provided - replace the curly brackets with relevant information. 
 
 5. [Optional] Update system persona, if needed, which can be found in `streamlit_app/data/system_persona.txt`.
-   
-6. Run:
+
+6. [Optional] Multi Graphs - Users who are using FalkorDB can generate multiple graphs and switch between their IDs, which correspond to different agents. This enables seamless transitions and management of different agents' memory and knowledge contexts.
+
+7. Run:
 
 ```
 cd streamlit_app
@@ -115,6 +117,33 @@ 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:

pyproject.toml

@@ -23,7 +23,7 @@ dependencies = [
     "neo4j==5.17.0",
     "python-dotenv==1.0.1",
     "pyvis==0.3.2",
-    "streamlit==1.31.1",
+    "streamlit==1.38.0",
     "llama-index==0.11.4",
     "llama-index-agent-openai==0.3.0",
     "llama-index-core==0.11.4",

requirements.txt

@@ -2,7 +2,7 @@ FalkorDB==1.0.8
 neo4j==5.17.0
 python-dotenv==1.0.1
 pyvis==0.3.2
-streamlit==1.31.1
+streamlit==1.38.0
 llama-index==0.11.4
 llama-index-agent-openai==0.3.0
 llama-index-core==0.11.4

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 +107,23 @@ def get_models(llm_models, vision_models):
 answer = ""
 external_response = ""
 st.title("memary")
+# Create a two column layout (HTML table)
+col1, col2 = st.columns([3, 1], vertical_alignment="bottom") 
 
+# Create a text input field for user ID
+with col1:
+    user_id = st.text_input(
+        "Enter a user ID (Available only with FalkorDB)",
+    )
+# Create a button to apply the switch user ID
+with col2:
+    st.write("")
+    submit = st.button("Switch Agent ID")
+
+# If the user ID is empty, set it to "falkor"
+if len(user_id) == 0:
+    user_id = "falkor"
+    
 llm_models = ["gpt-3.5-turbo"]
 vision_models = ["gpt-4-vision-preview"]
 get_models(llm_models, vision_models)
@@ -143,6 +149,7 @@ def get_models(llm_models, vision_models):
         system_persona_txt,
         user_persona_txt,
         past_chat_json,
+        user_id,
         selected_llm_model,
         selected_vision_model,
     )