From b245d4d4df493ae9da39b1f8b5876fb203fe3df5 Mon Sep 17 00:00:00 2001
From: Jason <jtoy@grids.local>
Date: Tue, 5 Mar 2024 06:24:08 -0800
Subject: [PATCH] more tests

---
 prompts/general/talk.txt |  4 ++++
 src/agents.py            |  4 +++-
 test.py                  | 38 ++++++++++++++++++++++++++++++++++++++
 3 files changed, 45 insertions(+), 1 deletion(-)

diff --git a/prompts/general/talk.txt b/prompts/general/talk.txt
index c97c055..d557348 100644
--- a/prompts/general/talk.txt
+++ b/prompts/general/talk.txt
@@ -34,6 +34,10 @@ Before answering, consider "{{agent.meta_questions|random}}"
 
 Provide {{agent}}'s response from the first person point of view. 
 
+{% if topic %}
+Maybe you could discuss the topic of "{{topic}}"
+{% endif %}
+
 
 keep responses to under 3 sentences.
 Craft an informal spoken response.
diff --git a/src/agents.py b/src/agents.py
index a96f2f0..620856c 100644
--- a/src/agents.py
+++ b/src/agents.py
@@ -288,7 +288,8 @@ def summarize_conversation(self, timestamp):
             print_and_log(interaction, f"{self.matrix.id}:events:{self.name}")
 
         self.addMemory("conversation", interaction, timestamp, random.randint(4, 6))
-        self.matrix.add_to_logs({"step_type":"agent_set", "attribute_name": "convo", "attribute_data": {"status": "complete", "from":self.mid, "to":self.last_conversation.other_agent.mid, "convo_id":self.last_conversation.mid}})
+        if self.matrix:
+            self.matrix.add_to_logs({"step_type":"agent_set", "attribute_name": "convo", "attribute_data": {"status": "complete", "from":self.mid, "to":self.last_conversation.other_agent.mid, "convo_id":self.last_conversation.mid}})
         self.last_conversation = None
 
     def talk(self, opts={}):
@@ -363,6 +364,7 @@ def talk(self, opts={}):
             'selfContext': self.getSelfContext(),
             'relevant_memories': relevant_memories_string,
             'agent': self,
+            'topic': opts.get('topic',None),
             'convo_types': all_convo_types[:10],
             'connections': self.connections,
             'meta_questions': self.meta_questions or "",
diff --git a/test.py b/test.py
index 87cc6dd..3b612ba 100644
--- a/test.py
+++ b/test.py
@@ -254,6 +254,44 @@ def test_embeddings(self):
             print(f"Current Memory: {mem}")
             print(f"Relevance Score: {Memory.calculateRelevanceScore(mem.embedding, context_embedding)}")
         self.assertTrue(len(sorted_memory) > 0)
+
+    def test_information_spreading(self):
+        a1_data = { "name": "Viktor", "goal": "wants to throw a party next week" }
+        a2_data = { "name": "John", "description": "loves art" }
+        a3_data = { "name": "Paul", "description": "loves eating" }
+        agent1 = Agent(a1_data)
+        agent2 = Agent(a2_data)
+        agent3 = Agent(a3_data)
+        unix_time = 1704067200
+        timestamp = datetime.fromtimestamp(unix_time).strftime("%Y-%m-%d %H:%M:%S")
+        for i in range(2):
+            timestamp = datetime.fromtimestamp(unix_time).strftime("%Y-%m-%d %H:%M:%S")
+            response = agent1.talk({ "target": agent2.name, "other_agents": [agent2], "timestamp": timestamp })
+            msg = f"{agent1} said to {agent2}: {response}"
+            print(msg)
+            response = agent2.talk({ "target": agent1.name, "other_agents": [agent1], "timestamp": timestamp })
+            msg = f"{agent2} said to {agent1}: {response}"
+            print(msg)
+            unix_time = unix_time + 10
+        agent2.summarize_conversation(timestamp)
+        print("*"*20)
+        for i in range(2):
+            timestamp = datetime.fromtimestamp(unix_time).strftime("%Y-%m-%d %H:%M:%S")
+            response = agent2.talk({ "target": agent3.name, "other_agents": [agent3], "timestamp": timestamp })
+            msg = f"{agent2} said to {agent3}: {response}"
+            print(msg)
+            response = agent3.talk({ "target": agent2.name, "other_agents": [agent2], "timestamp": timestamp })
+            msg = f"{agent3} said to {agent2}: {response}"
+            print(msg)
+            unix_time = unix_time + 10
+        agent3.summarize_conversation(timestamp)
+        print(f"{agent2} memories")
+        for mem in agent2.memory:
+            print(mem)
+        print(f"{agent3} memories")
+        for mem in agent3.memory:
+            print(mem)
+
     def test_talk_stranger(self):
         agent1_data = {
             "name": "Viktor",