backend: separated agent classes. frontend: chat message context menu (does nothing)

backend/agent.py

@@ -17,11 +17,45 @@ class AgentField:
 # Instances are created and destroyed constantly, agents must not depend solely on in-memory data
 # to be useful and consistent
 class Agent:
-    # Class variables:
+    # Class variables, must be defined by subclasses of Agent:
     # - identifier (for kind in creation)
     # - schema: list[AgentField{name, type: str}] (for configuration)
-    # - tools: list[ToolDescription] (actions the agent class support)
-    identifier = "basic"
+    # tools: list[ToolDescription] (custom actions supported by the class)
+
+    def __init__(self, app, data):
+        self.app = app
+
+        # These variables are set by '_set_basic_info'
+        self.id = None
+        self.name = None
+
+        # Auto populated based on the schema fields
+        agent_class = type(self)
+        self.config = {field.name: data.get(field.name, field.default) for field in getattr(agent_class, 'schema_fields', [])}
+    
+    def _set_basic_info(self, id, name):
+        self.id = id
+        self.name = name
+
+    # Receives a single string prompt, and must return a string response
+    async def prompt(self, content):
+        raise Exception("'prompt' is not implemented for this agent class")
+
+    # Receives a chat message object, agent can send messages to the chat in response
+    async def received_message(self, message):
+        raise Exception("'received_message' is not implemented for this agent class")
+    
+    # Executes a tool/action in this agent
+    async def execute_action(self, name: str, argument: dict):
+        # TODO: For security, check that the method is in the tool list
+        method = getattr(self, name)
+        result = method(**argument)
+        if inspect.isawaitable(result):
+            result = await result
+        return result
+
+class ToolUsingAgent:
+    identifier = "tool-using"
     schema_fields = [
         AgentField('system_prompt', 'string',
             "\n".join([
@@ -32,13 +66,12 @@ class Agent:
             ])
         ),
     ]
+
+    # tools: list[ToolDescription] (actions the agent class support)
     tools = []
 
-    def __init__(self, app, agent_id, data):
-        self.app = app
-        self.agent_id = agent_id
-
-        self.config = {field.name: data.get(field.name, field.default) for field in Agent.schema_fields}
+    def __init__(self, app, data):
+        Agent.__init__(self, app, data)
 
         self.tool_prompt_section = "\n".join([
             "# Commands",
@@ -106,7 +139,6 @@ class Agent:
 
         return agent_response or full_response
 
-
     # Receives a single string prompt, and must respond with a string
     async def prompt(self, content):
         response = await self._full_prompt(f"User Message", content) 
@@ -127,7 +159,7 @@ class Agent:
 
         self.app.send_chat_message(
             chat_id=message.chat_id,
-            sender=self.agent_id,
+            sender=self.id,
             content=response,
         )
 
@@ -154,17 +186,58 @@ class Agent:
                 action_result = json.dumps(action_result)
             output += f"[{action.toolset_name}.{action.tool_name}]:{action_result}\n"
         return output
-    
-    def create_from_data(app, agent_id, kind, data):
-        return Agent(app, agent_id, data)
-    
-    async def execute_action(self, name: str, argument: dict):
-        # TODO: For security, check that the method is in the tool list
-        method = getattr(self, name)
-        result = method(**argument)
-        if inspect.isawaitable(result):
-            result = await result
-        return result
+
+class Llama8bAgent(Agent):
+    identifier = "llama-8b"
+    schema_fields = [
+        AgentField('system_prompt', 'string', default="You are a helpful AI assistant."),
+    ]
+    tools = []
+
+    def __init__(self, app, data):
+        Agent.__init__(self, app, data)
+
+    async def prompt(self, content):
+        messages = [
+            LlamaMessage("system", self.config['system_prompt']),
+            LlamaMessage("user", content)
+        ]
+        response = await replicate_llama_completion(self.app, messages)
+        return response
+
+    async def received_message(self, message):
+        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
+
+        chat_context = self.app.get_chat_context(message.chat_id)
+        chat_summary = "\n".join(f"[{sender_name(message)}]: {message['content']}" for message in chat_context.messages)
+        messages = [
+            LlamaMessage("system", f"{self.config['system_prompt']}\nYou are given the context of the conversation, respond with your next message in the conversation."),
+            LlamaMessage("user", f"Recent Messages:\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,
+        )
+
+class BasicAgent(Llama8bAgent):
+    identifier = "basic"
+    schema_fields = Llama8bAgent.schema_fields
+    tools = Llama8bAgent.tools
+
+class CoderV1Agent(Llama8bAgent):
+    identifier = "coder-v1"
+    schema_fields = [
+        AgentField('system_prompt', 'string', default="You are an expert Software Engineer profficient in multiple programming languages."),
+    ]
+    # TODO: add gral functions to the tool list
+    tools = []
     
     async def gral_message(self, message_history: list, file_map: dict):
         return await gral.gral_message(self.app, self.config['system_prompt'], message_history, file_map)
@@ -173,11 +246,6 @@ class Agent:
         return await gral.gral_update_file(self.app, self.config['system_prompt'], message_history, filepath, file_content)
 
 
-class IntroAgent(Agent):
-    def __init__(self, app, agent_id, _data):
-        Agent.__init__(self, app, agent_id)
-        self.system_prompt = "You are a useful and precise AI assistant. Respond concisely."
-
 class ParsedResponse:
     def __init__(self):
         self.response = None