updated user agents to either not execute code, or when executing, do…

…n't use Docker for update 0.2.8

ai_agency_01_workout/agents.py

@@ -7,6 +7,10 @@
     system_message=system_messages.user_proxy_message,
     human_input_mode="NEVER",
     max_consecutive_auto_reply=0,
+    code_execution_config= {
+        "work_dir": "code",
+        "use_docker": False
+    }
 )
 
 fitness_expert = autogen.AssistantAgent(

ai_agency_03_video_captions/agents.py

@@ -13,7 +13,11 @@
     name="user_proxy",
     is_termination_msg=lambda x: x.get("content", "") and x.get("content", "").rstrip().endswith("TERMINATE"),
     human_input_mode="NEVER",
-    max_consecutive_auto_reply=2
+    max_consecutive_auto_reply=2,
+    code_execution_config= {
+        "work_dir": "code",
+        "use_docker": False
+    }
 )
 
 user_proxy.register_function(

ai_agency_04_youtube_service/agents.py

@@ -19,8 +19,12 @@
     name="user_proxy",
     is_termination_msg=lambda x: x.get("content", "") and x.get("content", "").rstrip().endswith("TERMINATE"),
     human_input_mode="NEVER",
-    max_consecutive_auto_reply=0
+    max_consecutive_auto_reply=0,
+    code_execution_config={
+        "work_dir": "code",
+        "use_docker": False
+    }
 )
 
 groupchat = autogen.GroupChat(agents=[user_proxy, script_maker, description_maker], messages=[], max_round=5)
-manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=config.llm_config)
+manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=config.llm_config)

autogen_agentbuilder/OAI_CONFIG_LIST

@@ -0,0 +1,6 @@
+[
+  {
+    "model": "gpt-4-1106-preview",
+    "api_key": "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"
+  }
+]

autogen_agentbuilder/main.py

@@ -0,0 +1,63 @@
+import json
+import utility
+
+import autogen
+from autogen.agentchat.contrib.agent_builder import AgentBuilder
+
+config_file_or_env = "OAI_CONFIG_LIST"
+llm_config = {"temperature": 0}
+config_list = autogen.config_list_from_json(config_file_or_env, filter_dict={"model": ["gpt-4-1106-preview", "gpt-4"]})
+
+position_list = utility.position_list
+agent_prompt = utility.agent_sys_msg_prompt
+library_path_or_json = "./agent_library_example.json"
+
+build_manager = autogen.OpenAIWrapper(config_list=config_list)
+sys_msg_list = []
+
+
+def start_task(execution_task: str, agent_list: list):
+    group_chat = autogen.GroupChat(agents=agent_list, messages=[], max_round=12)
+    manager = autogen.GroupChatManager(groupchat=group_chat, llm_config={"config_list": config_list, **llm_config})
+    agent_list[0].initiate_chat(manager, message=execution_task)
+
+
+def generate_agents():
+    for pos in position_list:
+        resp_agent_sys_msg = (
+            build_manager.create(
+                messages=[
+                    {
+                        "role": "user",
+                        "content": agent_prompt.format(
+                            position=pos,
+                            default_sys_msg=autogen.AssistantAgent.DEFAULT_SYSTEM_MESSAGE,
+                        ),
+                    }
+                ]
+            )
+            .choices[0]
+            .message.content
+        )
+        sys_msg_list.append({"name": pos, "profile": resp_agent_sys_msg})
+
+
+generate_agents()
+
+json.dump(sys_msg_list, open(library_path_or_json, "w"), indent=4)
+
+new_builder = AgentBuilder(
+    config_file_or_env=config_file_or_env, builder_model="gpt-4-1106-preview", agent_model="gpt-4-1106-preview"
+)
+agent_list, _ = new_builder.build_from_library(utility.building_task, library_path_or_json, llm_config)
+
+# new_builder = AgentBuilder(config_file_or_env=config_file_or_env)
+# agent_list, agent_config = new_builder.load(saved_path)
+saved_path = new_builder.save("./autogen_agentbuilder")
+print(saved_path)
+start_task(
+    execution_task=utility.execution_task,
+    agent_list=agent_list,
+)
+
+new_builder.clear_all_agents()

autogen_agentbuilder/utility.py

@@ -0,0 +1,39 @@
+building_task = "Be able to create a python function with documentation on how it works."
+
+execution_task = "Create a python function that reverses a string."
+
+agent_sys_msg_prompt = """Considering the following position:
+
+POSITION: {position}
+
+What requirements should this position be satisfied?
+
+Hint:
+# Your answer should be in one sentence.
+# Your answer should be natural, starting from "As a ...".
+# People with the above position need to complete a task given by a leader or colleague.
+# People will work in a group chat, solving tasks with other people with different jobs.
+# The modified requirement should not contain the code interpreter skill.
+# Coding skill is limited to Python.
+"""
+
+position_list = [
+    "Environmental_Scientist",
+    "Astronomer",
+    "Software_Developer",
+    "Data_Analyst",
+    "Journalist",
+    "Teacher",
+    "Lawyer",
+    "Programmer",
+    "Accountant",
+    "Mathematician",
+    "Physicist",
+    "Biologist",
+    "Chemist",
+    "Statistician",
+    "IT_Specialist",
+    "Cybersecurity_Expert",
+    "Artificial_Intelligence_Engineer",
+    "Financial_Analyst",
+]

autogen_beginner_course/groupchat.py

@@ -58,10 +58,10 @@
                    "plan includes adding verifiable info such as source URL.",
     llm_config=gpt4_config,
 )
-groupchat = autogen.GroupChat(
+group_chat = autogen.GroupChat(
     agents=[user_proxy, engineer, scientist, planner, executor, critic], messages=[], max_round=50
 )
-manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=gpt4_config)
+manager = autogen.GroupChatManager(groupchat=group_chat, llm_config=gpt4_config)
 
 user_proxy.initiate_chat(
     manager,

autogen_functions/autogen_function_example.py

@@ -71,6 +71,10 @@ def currency_calculator(
     is_termination_msg=lambda x: x.get("content", "") and x.get("content", "").rstrip().endswith("TERMINATE"),
     human_input_mode="NEVER",
     max_consecutive_auto_reply=10,
+    code_execution_config={
+        "work_dir": "code",
+        "use_docker": False
+    }
 )
 
 CurrencySymbol = Literal["USD", "EUR"]

autogen_functions/autogen_function_example_decorators.py

@@ -31,6 +31,10 @@
     is_termination_msg=lambda x: x.get("content", "") and x.get("content", "").rstrip().endswith("TERMINATE"),
     human_input_mode="NEVER",
     max_consecutive_auto_reply=10,
+    code_execution_config= {
+        "work_dir": "code",
+        "use_docker": False
+    }
 )
 
 CurrencySymbol = Literal["USD", "EUR"]

autogen_functions/autogen_function_planner.py

@@ -88,7 +88,7 @@ def ask_planner(message):
     max_consecutive_auto_reply=10,
     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"},
+    code_execution_config={"work_dir": "planning", "use_docker": False},
     function_map={"ask_planner": ask_planner},
 )
 

autogen_gpt_vision/autogen_vision.py

@@ -24,7 +24,11 @@
     name="User_proxy",
     system_message="A human admin.",
     human_input_mode="NEVER",
-    max_consecutive_auto_reply=0
+    max_consecutive_auto_reply=0,
+    code_execution_config={
+        "work_dir": "code",
+        "use_docker": False
+    }
 )
 
 # Example 1

autogen_memgpt/app.py

@@ -1,6 +1,6 @@
 import autogen
 import os
-from memgpt.autogen.memgpt_agent import create_autogen_memgpt_agent
+from memgpt.autogen.memgpt_agent import create_memgpt_autogen_agent_from_config
 from dotenv import load_dotenv
 
 load_dotenv()
@@ -28,7 +28,7 @@
 user_proxy = autogen.UserProxyAgent(
     name="User_proxy",
     system_message="A human admin.",
-    code_execution_config={"last_n_messages": 2, "work_dir": "groupchat"},
+    code_execution_config={"last_n_messages": 2, "work_dir": "groupchat", "use_docker": False},
     human_input_mode="TERMINATE"
 )
 
@@ -59,14 +59,15 @@
     )
 #
 else:
-    coder = create_autogen_memgpt_agent(
+    coder = create_memgpt_autogen_agent_from_config(
         "MemGPT_coder",
-        persona_description="I am a 10x engineer, trained in Python. I was the first engineer at Uber "
-        "(which I make sure to tell everyone I work with).",
-        user_description=f"You are participating in a group chat with a user ({user_proxy.name}) "
-        f"and a product manager ({pm.name}).",
-        model=os.getenv("model"),
-        interface_kwargs=interface_kwargs
+        llm_config=llm_config,
+        system_message=f"I am a 10x engineer, trained in Python. I was the first engineer at Uber "
+                       f"(which I make sure to tell everyone I work with).\n"
+                       f"You are participating in a group chat with a user ({user_proxy.name}) "
+                       f"and a product manager ({pm.name}).",
+        interface_kwargs=interface_kwargs,
+        default_auto_reply="...",
     )
 
 # Initialize the group chat between the user and two LLM agents (PM and coder)

autogen_memgpt_lmstudio/autogen_memgpt_lmstudio.py

@@ -38,46 +38,6 @@
         },
     ]
 
-elif LLM_BACKEND == "azure":
-    # Make sure that you have access to this deployment/model on your Azure account!
-    # If you don't have access to the model, the code will fail
-    model = os.getenv("model")
-
-    azure_openai_api_key = os.getenv("AZURE_OPENAI_KEY")
-    azure_openai_version = os.getenv("AZURE_OPENAI_VERSION")
-    azure_openai_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
-    assert (
-            azure_openai_api_key is not None and azure_openai_version is not None and azure_openai_endpoint is not None
-    ), "Set all the required OpenAI Azure variables (see: https://memgpt.readthedocs.io/en/latest/endpoints/#azure)"
-
-    # This config is for AutoGen agents that are not powered by MemGPT
-    config_list = [
-        {
-            "model": model,
-            "api_type": "azure",
-            "api_key": azure_openai_api_key,
-            "api_version": azure_openai_version,
-            # NOTE: on versions of pyautogen < 0.2.0, use "api_base"
-            # "api_base": azure_openai_endpoint,
-            "base_url": azure_openai_endpoint,
-        }
-    ]
-
-    # This config is for AutoGen agents that powered by MemGPT
-    config_list_memgpt = [
-        {
-            "model": model,
-            "context_window": LLM_MAX_TOKENS[model],
-            "preset": DEFAULT_PRESET,
-            "model_wrapper": None,
-            # Azure specific
-            "model_endpoint_type": "azure",
-            "azure_key": azure_openai_api_key,
-            "azure_endpoint": azure_openai_endpoint,
-            "azure_version": azure_openai_version,
-        },
-    ]
-
 elif LLM_BACKEND == "local":
     # Example using LM Studio on a local machine
     # You will have to change the parameters based on your setup
@@ -90,7 +50,6 @@
             # "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
         },
     ]
@@ -104,7 +63,7 @@
             "context_window": 8192,  # the context window of your model (for Mistral 7B-based models, it's likely 8192)
             "model_wrapper": "airoboros-l2-70b-2.1",  # airoboros is the default wrapper and should work for most models
             "model_endpoint_type": "lmstudio",  # can use webui, ollama, llamacpp, etc.
-            "model_endpoint": "http://localhost:1234",  # the IP address of your LLM backend
+            "model_endpoint": "http://localhost:1234/v1",  # the IP address of your LLM backend
         },
     ]
 
@@ -114,7 +73,7 @@
 # If USE_MEMGPT is False, then this example will be the same as the official AutoGen repo
 # (https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat.ipynb)
 # If USE_MEMGPT is True, then we swap out the "coder" agent with a MemGPT agent
-USE_MEMGPT = True
+USE_MEMGPT = False
 
 # Set to True if you want to print MemGPT's inner workings.
 DEBUG = False
@@ -132,7 +91,7 @@
 user_proxy = autogen.UserProxyAgent(
     name="User_proxy",
     system_message="A human admin.",
-    code_execution_config={"last_n_messages": 2, "work_dir": "groupchat"},
+    code_execution_config={"last_n_messages": 2, "work_dir": "groupchat", "use_docker": False},
     human_input_mode="TERMINATE",  # needed?
     default_auto_reply="...",  # Set a default auto-reply message here (non-empty auto-reply is required for LM Studio)
 )
@@ -147,6 +106,7 @@
 
 if not USE_MEMGPT:
     # In the AutoGen example, we create an AssistantAgent to play the role of the coder
+    print("AutoGen Coder")
     coder = autogen.AssistantAgent(
         name="Coder",
         llm_config=llm_config,

autogen_transcribe_video/transcribe.py

@@ -64,7 +64,7 @@
     is_termination_msg=lambda x: x.get("content", "") and x.get("content", "").rstrip().endswith("TERMINATE"),
     human_input_mode="NEVER",
     max_consecutive_auto_reply=10,
-    code_execution_config={"work_dir": "scripts"},
+    code_execution_config={"work_dir": "scripts", "use_docker": False},
 )
 
 user_proxy.register_function(

autogen_webscraping/vision_crawl.py

@@ -20,7 +20,11 @@
     name="User_proxy",
     system_message="A human admin.",
     human_input_mode="NEVER",
-    max_consecutive_auto_reply=0
+    max_consecutive_auto_reply=0,
+    code_execution_config={
+        "work_dir": "code",
+        "use_docker": False
+    }
 )
 
 

saas_products/password/password.py

@@ -19,7 +19,7 @@
     system_message="A human admin. Interact with the planner to discuss the plan. Plan execution needs to be approved "
                    "by this admin.",
     human_input_mode="TERMINATE",
-    code_execution_config={"last_n_messages": 3, "work_dir": "programming"},
+    code_execution_config={"last_n_messages": 3, "work_dir": "programming", "use_docker": False},
 )
 
 engineer = autogen.AssistantAgent(