updated to newer function calling

autogen_functions/autogen_function_example.py

@@ -3,7 +3,7 @@
 import autogen
 
 config_list = autogen.config_list_from_json(
-    env_or_file="OAI_CONFIG_LIST",
+    env_or_file="config/OAI_CONFIG_LIST.json",
     filter_dict={
         "model": ["gpt-4"]
     },

autogen_functions/autogen_function_planner.py

@@ -1,61 +1,20 @@
 import autogen
+from typing import Annotated
 
-# config_list = autogen.config_list_from_json(
-#     env_or_file="OAI_CONFIG_LIST",
-#     filter_dict={
-#         "model": ["gpt-4"]
-#     },
-# )
-
-config_list = {
-    "model": "NULL",  # not needed
-    # NOTE: on versions of pyautogen < 0.2.0 use "api_base", and also uncomment "api_type"
-    # "api_base": "http://localhost:1234/v1",
-    # "api_type": "open_ai",
-    "base_url": "http://localhost:1234/v1",  # ex. "http://127.0.0.1:5001/v1" if you are using webui, "http://localhost:1234/v1/" if you are using LM Studio
-    "api_key": "NULL",  #  not needed
-}
-
-# print(config_list)
+config_list = autogen.config_list_from_json(
+    env_or_file="config/OAI_CONFIG_LIST.json",
+    filter_dict={
+        "model": ["gpt-3.5-turbo"]
+    },
+)
 
 llm_config = {
     "temperature": 0,
     "timeout": 300,
-    "seed": 43,
-    "config_list": config_list,
-    "functions": [
-        {
-            "name": "ask_planner",
-            "description": "ask planner to: 1. get a plan for finishing a task, 2. verify the execution result of "
-                           "the plan and potentially suggest new plan.",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "message": {
-                        "type": "string",
-                        "description": "question to ask planner. Make sure the question include enough context, "
-                                       "such as the code and the execution result. The planner does not know the "
-                                       "conversation between you and the user, unless you share the conversation "
-                                       "with the planner.",
-                    },
-                },
-                "required": ["message"],
-            },
-        },
-    ],
+    "seed": 44,
+    "config_list": config_list
 }
 
-
-def ask_planner(message):
-    planner_user.initiate_chat(planner, message=message)
-    # return the last message received from the planner
-    last_message = planner_user.last_message()["content"]
-    print("About to just get the first 10 characters of the message!")
-    print("Here is where you can do something with the message that we received.")
-    print(last_message[:10])
-    return planner_user.last_message()["content"]
-
-
 planner = autogen.AssistantAgent(
     name="planner",
     llm_config={
@@ -73,6 +32,7 @@ def ask_planner(message):
     name="planner_user",
     max_consecutive_auto_reply=0,  # terminate without auto-reply
     human_input_mode="NEVER",
+    code_execution_config={"work_dir": "planning", "use_docker": False},
 )
 
 # create an AssistantAgent instance named "assistant"
@@ -81,6 +41,12 @@ def ask_planner(message):
     llm_config=llm_config
 )
 
+# create an AssistantAgent instance named "assistant"
+assistant2 = autogen.AssistantAgent(
+    name="assistant",
+    llm_config=llm_config
+)
+
 # create a UserProxyAgent instance named "user_proxy"
 user_proxy = autogen.UserProxyAgent(
     name="user_proxy",
@@ -89,15 +55,26 @@ def ask_planner(message):
     is_termination_msg=lambda x: "content" in x and x["content"] is not None and x["content"].rstrip().endswith(
         "TERMINATE"),
     code_execution_config={"work_dir": "planning", "use_docker": False},
-    function_map={"ask_planner": ask_planner},
 )
 
-# can also register functions to an agent this way
-# user_proxy.register_function(
-#     function_map={
-#         "ask_planner": ask_planner
-#     }
-# )
+
+@user_proxy.register_for_execution()
+@assistant.register_for_llm(description="Get the first 10 characters of the message")
+def ask_planner(message: Annotated[str, "The response from the LLM"]) -> str:
+    planner_user.initiate_chat(planner, message=message)
+    # return the last message received from the planner
+    last_message = planner_user.last_message()["content"]
+    print("About to just get the first 10 characters of the message!")
+    print("Here is where you can do something with the message that we received.")
+    print(last_message[:10])
+    return planner_user.last_message()["content"]
+
+
+# @user_proxy.register_for_execution()
+# @assistant2.register_for_llm(description="Do something")
+# def do_something(message: Annotated[str, "The response from the LLM"]) -> str:
+#     return "Hello there"
+
 
 # the assistant receives a message from the user, which contains the task description
 user_proxy.initiate_chat(