Introduce Expert Agent. Prompt agents from UI.

backend/src/agents/__init__.py

@@ -0,0 +1,2 @@
+from .character import CharacterAgent
+from .expert import ExpertAgent

backend/src/agents/expert.py

@@ -0,0 +1,53 @@
+import re
+import json
+import inspect
+from string import Template
+
+from inference import LlamaMessage, replicate_llama_completion, Models
+from agent import Agent, AgentField
+
+CLASS_DESCRIPTION = """
+Expert AI in any field, powered by Llama 3 70b.
+Will get guidelines, documents, and other advanced features in the future.
+""".strip()
+
+class ExpertAgent(Agent):
+    identifier = "expert"
+    description = CLASS_DESCRIPTION
+    schema_fields = [
+        AgentField('system_prompt', 'string', default="You are an expert AI assistant."),
+    ]
+
+    def __init__(self, app, data):
+        Agent.__init__(self, app, data)
+
+    async def prompt(self, instruction, input=None, **kwargs):
+        # if instruction and input are passed, instruction extends the system prompt
+        # otherwise, use the one passed as the user prompt, with the regular system prompt
+
+        # TODO: if 'history' in kwargs: interpret it as user-agent turns assuming list[str]
+
+        system = self.config['system_prompt']
+        if instruction != None and input != None:
+            system += "\n" + instruction
+
+        prompt = input if input != None else instruction
+        messages = [
+            LlamaMessage("system", system),
+            LlamaMessage("user", prompt)
+        ]
+
+        response = await replicate_llama_completion(self.app, messages, model=Models.llama_70b)
+        return response
+
+    async def received_message(self, message):
+        raise Exception("TODO: expert chat support")
+
+        messages = []
+        response = await replicate_llama_completion(self.app, messages, model=Models.llama_70b)
+        self.app.send_chat_message(
+            chat_id=message.chat_id,
+            sender=self.id,
+            content=response,
+            thread_id=message.thread_id,
+        )

backend/src/app.py

@@ -12,10 +12,10 @@ import replicate
 
 import database as db
 import agent as agent_mod
+import agents as agents_mod
 from server import start_server
 from chat import ChatView, Message, MESSAGE_PAGE_SIZE
 
-from agents.character import CharacterAgent
 from app_mixins import ChatMixin
 
 class App(ChatMixin):
@@ -37,12 +37,13 @@ class App(ChatMixin):
         # `Agent` is the kind 'basic', will be class `BasicAgent` in the future
         self.agent_classes = [
             agent_mod.BasicAgent,
-            CharacterAgent,
+            agents_mod.CharacterAgent,
+            agents_mod.ExpertAgent,
 
-            agent_mod.LlamaAgent,
-            agent_mod.CoderV1Agent,
-            agent_mod.ToolUsingAgent,
-            agent_mod.DebugAgent,
+            #agent_mod.LlamaAgent,
+            #agent_mod.CoderV1Agent,
+            #agent_mod.ToolUsingAgent,
+            #agent_mod.DebugAgent,
         ]
 
     def sql_session(self):
@@ -231,9 +232,9 @@ class App(ChatMixin):
             return fields
         return {}
 
-    async def send_agent_prompt(self, agent_id, message):
+    async def send_agent_prompt(self, agent_id, instruction, input=None, options={}):
         control = self.get_agent_control(agent_id)
-        response = await control.prompt(message)
+        response = await control.prompt(instruction, input, **options)
         return response
 
     async def call_agent_action(self, agent_id, action, args):

backend/src/inference.py

@@ -1,7 +1,6 @@
 import threading
 from stream import Stream
 
-LLAMA3_MODEL = "meta/meta-llama-3-8b-instruct"
 VALID_ROLES = ['user', 'agent', 'system']
 
 class LlamaMessage:

backend/src/server.py

@@ -224,7 +224,16 @@ def start_server(app):
                     await send({'message': "chat thread created", 'threadId': thread_id, 'nonce': nonce})
 
                 elif action == "send-agent-prompt":
-                    response = await app.send_agent_prompt(body['agentId'], body['content'])
+                    if 'instruction' not in body and 'input' not in body:
+                        await send({'error': "instruction or input required"})
+                        continue
+
+                    response = await app.send_agent_prompt(
+                        body['agentId'],
+                        body.get('instruction'),
+                        body.get('input'),
+                        body.get('options', {})
+                    )
                     await send({'message': "propmt sent", 'response': response, 'nonce': nonce})
 
                 elif action == "call-agent-action":