message edit/update functionality

backend/agent.py

@@ -208,10 +208,12 @@ class ToolUsingAgent:
             output += f"[{action.toolset_name}.{action.tool_name}]:{action_result}\n"
         return output
 
-class Llama8bAgent(Agent):
+class LlamaAgent(Agent):
     identifier = "llama-8b"
+    description = "Uses the llama 3 instruct models. Model sizes: '8b', '30b'"
     schema_fields = [
         AgentField('system_prompt', 'string', default="You are a helpful AI assistant."),
+        AgentField('model_size', 'string', default="8b"),
     ]
     tools = []
 
@@ -228,13 +230,14 @@ class Llama8bAgent(Agent):
 
     async def received_message(self, message):
         def sender_name(message):
-            sender = message['sender']
+            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)
+        chat_view = self.app.get_chat_view(message.chat_id)
+        thread_messages = chat_view.get_thread(message.thread_id).messages
+        chat_summary = "\n".join(f"[{sender_name(message)}]: {message.content}" for message in thread_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}")
@@ -248,15 +251,17 @@ class Llama8bAgent(Agent):
             thread_id=message.thread_id,
         )
 
-class BasicAgent(Llama8bAgent):
+class BasicAgent(LlamaAgent):
     identifier = "basic"
-    schema_fields = Llama8bAgent.schema_fields
-    tools = Llama8bAgent.tools
+    schema_fields = LlamaAgent.schema_fields
+    tools = LlamaAgent.tools
 
-class CoderV1Agent(Llama8bAgent):
+class CoderV1Agent(LlamaAgent):
     identifier = "coder-v1"
+    description = "Optimized for coding tasks. Based on the llama agent:\n" + LlamaAgent.description
     schema_fields = [
         AgentField('system_prompt', 'string', default="You are an expert Software Engineer profficient in multiple programming languages."),
+        AgentField('model_size', 'string', default="30b"),
     ]
     # TODO: add gral functions to the tool list
     tools = []