Merge pull request #100 from wugren/MVP

AgentAssistant

rootfs/agents/AgentAssistant/agent.toml

@@ -0,0 +1,32 @@
+instance_id = "AgentAssistant"
+fullname = "AgentAssistant"
+llm_model_name = "gpt-4-1106-preview"
+owner_env = "environment.py"
+
+[[prompt]]
+role = "system"
+content = '''
+You are an AI Agent template generation assistant that can help users better define AI Agent templates.
+AI Agent can use AI to help users complete relevant tasks based on prompt words.
+
+Please help users define AI Agent templates according to the following rules:
+New generation:
+1. Generate prompt words for users based on user questions. Prompt word suggestions can be given directly based on user input until the user confirms the prompt words.
+2. Generate instance_id and fullname based on the final prompt word content.
+3. Generate the following toml format data and return it to the user.
+```
+instance_id = "agent id"
+fullname = "agent full name"
+[[prompt]]
+role = "system"
+content = """Prompt words"""
+```
+4. After the configuration is generated, the user is supported to modify the instance_id, fullname and prompt words. After the user has modified it, toml format data needs to be regenerated.
+5. The save_agent function is called only when the user confirms the save. Do not call it when there is no input to save.
+Modify existing agent:
+1. The user enters the name of the agent that needs to be opened, and the system calls the read_agent function to read the agent configuration and return it to the user.
+2. Support users to modify the fullname and prompt words, but cannot modify the instance_id. After the user has modified it, toml format data needs to be regenerated.
+3. The save_agent function is called only when the user confirms the save. Do not call it when there is no input to save.
+
+PS: You need to communicate in the same language as the user input.
+'''

rootfs/agents/AgentAssistant/environment.py

@@ -0,0 +1,59 @@
+import logging
+from typing import Optional
+
+import toml
+
+from aios_kernel import Environment, SimpleAIFunction
+import os
+
+
+local_path = os.path.split(os.path.realpath(__file__))[0]
+
+logger = logging.getLogger(__name__)
+
+
+class AgentAssistantEnvironment(Environment):
+    def __init__(self):
+        super().__init__("agent_assistant_env")
+        self.add_ai_function(SimpleAIFunction("read_agent",
+                                              "Read the agent with the specified name",
+                                              self.read_agent,
+                                              {"agent_id": "The id of the agent to be read"}))
+        self.add_ai_function(SimpleAIFunction("save_agent",
+                                              "save the agent with the specified name",
+                                              self.save_agent,
+                                              {"agent_id": "The id of the agent to be saved",
+                                                          "agent_data": "The toml data of the agent to be saved",
+                                                          "is_new": "Whether to create a new agent, The value is true if it is created, and it is false if it is modified."}))
+
+    def _do_get_value(self, key: str) -> Optional[str]:
+        pass
+
+    async def read_agent(self, agent_id: str) -> str:
+        agent_dir = os.path.join(local_path, "..", agent_id)
+        if not os.path.exists(agent_dir):
+            return "exec failed.agent is not exists."
+
+        with open(os.path.join(agent_dir, "agent.toml"), "r", encoding='utf-8') as f:
+            agent_data = f.read()
+
+        return "exec success. agent data:\n" + agent_data
+
+    async def save_agent(self, agent_id: str, agent_data: str, is_new: str) -> str:
+        logger.info(f"save_agent: {agent_id} {agent_data}")
+
+        agent_dir = os.path.join(local_path, "..", agent_id)
+        if os.path.exists(agent_dir) and is_new == "true":
+            return "exec failed.agent already exists, please change the agent id and try again."
+
+        if not os.path.exists(agent_dir):
+            os.mkdir(agent_dir)
+
+        with open(os.path.join(agent_dir, "agent.toml"), "w", encoding='utf-8') as f:
+            f.write(agent_data)
+
+        return "exec success."
+
+
+def init():
+    return AgentAssistantEnvironment()

src/aios_kernel/agent.py

@@ -498,7 +498,7 @@ class AIAgent(BaseAIAgent):
 
 
         logger.debug(f"Agent {self.agent_id} do llm token static system:{system_prompt_len},function:{function_token_len},history:{history_token_len},input:{input_len}, totoal prompt:{system_prompt_len + function_token_len + history_token_len} ")
-        task_result = await self.do_llm_complection(prompt,msg,inner_functions=inner_functions)
+        task_result = await self.do_llm_complection(prompt,msg, env=self.owner_env,inner_functions=inner_functions)
         if task_result.result_code != ComputeTaskResultCode.OK:
             error_resp = msg.create_error_resp(task_result.error_str)
             return error_resp

src/aios_kernel/open_ai_node.py

@@ -215,7 +215,7 @@ class OpenAI_ComputeNode(ComputeNode):
                                                             messages=prompts,
                                                             response_format = response_format,
                                                             functions=llm_inner_functions,
-                                                            max_tokens=result_token,
+                                                            # max_tokens=result_token,
                                                             ) # TODO: add temperature to task params?
                 except Exception as e:
                     logger.error(f"openai run LLM_COMPLETION task error: {e}")