separate and improve conversation agent

backend/agents/conversation.py

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+import re
+import json
+import inspect
+
+from inference import LlamaMessage, replicate_llama_completion
+from agent import Agent
+
+memory_system_prompt = """
+You are an expert in simulating human-like memory for fictional characters. Given a conversation between two characters and a detailed description of one character's personality, extract the memories that this character is most likely to retain.
+The extracted memories should reflect:
+- What was emotionally significant to them
+- What aligns with their goals, fears, or desires
+- What they found surprising, confusing, or meaningful
+- What they might revisit or ruminate on later
+Do not summarize the conversation. Instead, highlight key moments or phrases that the character would mentally hold onto, framed from their perspective. Avoid information stated by yourself, you don't need to remember what you already know, instead you can identify the other's characters reactions to what your character says if it's notable or salient.
+"""
+
+memory_prompt_template = """
+# Your Character
+Name: [name]
+Personality and Role: [personality]
+
+# Output Format
+Respond only with a JSON array, where each object represents a memory, and each memory object has the following fields:
+- memory: Short description of the fact that the character will remember, phrased objectively and in third person.
+- importance: Salience score of the memory, where 5 is for facts that are definitely important for either of the characters or that may be referenced frequently, and 1 indicates facts or events of low importance or short term relevance.
+If there are no relevant memories to extract, it is ok to respond simply with an empty list.
+
+# Conversation History
+[conversation]
+"""
+
+# Used to keep track of the last chat thread it participated in
+class ThreadContext:
+    def __init__(self, data):
+        if data:
+            self.current_thread_id = data.get('current_thread_id')
+        else:
+            self.current_thread_id = None
+
+class ConversationAgent(Agent):
+    identifier = "conversation"
+    schema_fields = []
+    tools = []
+
+    def __init__(self, app, data):
+        Agent.__init__(self, app, data)
+
+        # Constant system prompt for developing purposes 
+        self.system_prompt = "You are a friendly AI having a casual conversation with the user."
+
+    async def prompt(self, content):
+        # TODO: Advanced features
+        messages = [
+            LlamaMessage("system", self.system_prompt),
+            LlamaMessage("user", content)
+        ]
+        response = await replicate_llama_completion(self.app, messages)
+        return response
+
+    async def received_message(self, message):
+
+        # TODO: before retrieving the chat context, acquire a lock for it
+
+        saved_context = ThreadContext(self.app.get_agent_resource(self.id, 'last-thread-context', message.chat_id))
+        chat_view = self.app.get_chat_view(message.chat_id)
+        print("ChatView:\n" + chat_view.debug_string())
+
+        if message.thread_id != saved_context.current_thread_id:
+            # the incoming message is not from the same thread that the agent was last aware of
+            # store details about the old thread in the agent's long term memory
+            old_thread = chat_view.get_thread(saved_context.current_thread_id)
+            await self.process_thread(old_thread)
+
+            # TODO: This is a good place to store the new context, its fine that the agent hasnt responded
+
+        current_thread = chat_view.get_thread(message.thread_id)
+
+        # REMOVE THIS. Only for testing, while the function only prints...
+        await self.process_thread(current_thread)
+
+        chat_summary = self.serialize_thread_history(current_thread)
+        messages = [
+            LlamaMessage("system", f"{self.system_prompt}\nYou are given the context of the conversation, respond with your next message in the conversation."),
+            LlamaMessage("user", f"Conversation History:\n{chat_summary}")
+        ]
+
+        response = await replicate_llama_completion(self.app, messages)
+        self.app.send_chat_message(
+            chat_id=message.chat_id,
+            sender=self.id,
+            content=response,
+            thread_id=current_thread.id,
+        )
+
+        # TODO: drop the chat context lock
+
+    # Encode a thread's messages as a string to pass to the agent
+    def serialize_thread_history(self, thread):
+        def sender_name(message):
+            sender = message.sender
+            is_digit = isinstance(sender, (int, str)) and str(sender).isdigit()
+            is_self = is_digit and str(sender) == str(self.id)
+            return self.name if is_self else sender
+        
+        return "\n".join(f"[{sender_name(message)}]: {message.content}" for message in thread.messages)
+    
+    # Compute thread summnary and concrete memories, then save the progress done
+    async def process_thread(self, thread):
+        # TODO: extract memories from the thread messages, then store them in the agent's vector memory db
+        # TODO: somewhere here it must be known if the thread was seen before: the same memory should not be stored multiple times
+        print(f"PROCESSING THREAD: {thread.id}")
+
+        chat_history = self.serialize_thread_history(thread)
+        prompt = memory_prompt_template.replace("[name]", self.name).replace("[personality]", self.system_prompt).replace("[conversation]", chat_history)
+
+        messages = [
+            LlamaMessage("system", memory_system_prompt),
+            LlamaMessage("user", prompt)
+        ]
+        response = await replicate_llama_completion(self.app, messages)
+
+        match = re.search(r'\[.*?\]', response, re.DOTALL)
+        if match:
+            json_string = match.group(0)
+            try:
+                memories = json.loads(json_string)
+                print("Extracted Memories:", memories)
+            except json.JSONDecodeError:
+                print("Bad JSON data in response...")
+                print(response)
+        else:
+            print("Memory format not found in response...")
+            print(response)
+
+        memories = [line for line in response.split('\n') if line.strip()]
+        
+        print("New agent memories:", memories)